Kids Tâmpa U18 vs U Craiova U18

General Expert Overview

The upcoming match between Kids Tâmpa U18 and U Craiova U18 is a highly anticipated encounter in the youth football circuit. Both teams have shown promising form recently, with Kids Tâmpa demonstrating strong offensive capabilities while U Craiova has been solid defensively. Historically, these teams have had a balanced head-to-head record, making this matchup intriguing for bettors. Key factors influencing the game include the recent form of both teams, individual player performances, and tactical setups by their respective coaches.

Kids Tâmpa has been particularly effective in attacking transitions, often capitalizing on quick counter-attacks. Their ability to convert chances into goals will be crucial against a resilient U Craiova defense. On the other hand, U Craiova’s disciplined backline has been instrumental in keeping clean sheets, which could pose challenges for Kids Tâmpa’s forwards.

Match Result Analysis

1X2 Market

In the 1X2 market, Kids Tâmpa is slightly favored due to their recent scoring prowess. However, given U Craiova’s defensive strength, an outright win for either side is not guaranteed. A draw could be a plausible outcome if both teams play to their strengths without overcommitting offensively.

Both Teams to Score (BTTS)

The BTTS market suggests a high probability of both teams finding the net. With Kids Tâmpa’s attacking threat and U Craiova’s occasional lapses in defense, it is reasonable to expect goals from both sides. The prediction leans towards ‘Yes’ for BTTS at approximately 65% confidence.

Under/Over Goals

Analyzing the over/under goals market reveals that betting on over 1.5 and over 2.5 goals aligns with recent trends of high-scoring games involving these teams. The statistical evidence supports an expectation of at least two goals being scored during the match.

Goals Market Assessment

Total Goals Over/Under

The total goals market favors an over bet based on historical data indicating that matches between these two teams often result in more than one goal being scored. Given the offensive capabilities of Kids Tâmpa and occasional defensive vulnerabilities of U Craiova, betting on over 1.5 and over 2.5 goals seems prudent.

First Goal Scorer

Predicting the first goal scorer involves analyzing key players who are likely to influence early proceedings. For Kids Tâmpa, their leading striker has been pivotal in recent matches and could be a prime candidate for scoring first. Conversely, U Craiova’s most creative midfielder might exploit any gaps in defense to score early.

Kids Tâmpa Potential Scorers

Kids Tâmpa’s top strikers have been consistent in front of goal, making them reliable candidates for scoring first. Their ability to press high up the pitch increases their chances of breaking through early defenses.

U Craiova Potential Scorers

U Craiova’s midfield maestro has shown flair in penetrating defenses and creating scoring opportunities for himself or his teammates. His vision and passing accuracy make him a significant threat early in the game.

Overall predictions suggest a dynamic match with potential for multiple goals from both sides, emphasizing bets on over 1.5 and over 2.5 goals as well as BTTS ‘Yes’. Confidence levels remain high due to statistical backing and current team forms.

***** Tag Data *****
ID: 6
description: Detailed analysis with nested headings providing expert insights into
various betting markets including Match Result Analysis and Goals Market Assessment.
start line: 138
end line: 184
dependencies:
– type: Other
name: HTML structure rules
start line: 11
end line: 15
– type: Other
name: Content structure requirements
start line: 16
end line: 23
context description: This snippet covers detailed betting analysis using nested headings,
providing expert insights into different markets such as Match Result Analysis,
Goals Market Assessment.
algorithmic depth: 4
algorithmic depth external: N
obscurity: 4
advanced coding concepts: 4
interesting for students: 5
self contained: Y

************
## Challenging aspects

### Challenging aspects in above code

1. **Hierarchical Structure**: Maintaining strict adherence to heading hierarchy (`

` → `

` → `

`). Skipping levels or improper nesting can lead to semantic errors.

2. **Content Requirements**: Each section must contain expert insights rather than just listing market data points or outcomes.

3. **Analytical Depth**: The content must go beyond surface-level analysis by integrating statistical evidence and contextual factors.

4. **Data-Driven Insights**: Recommendations should be backed by data-driven insights rather than mere speculation.

5. **Confidence Levels**: Predictions must include specific confidence levels along with reasoning behind them.

### Extension

To extend this exercise:

1. **Dynamic Content Generation**: Implement logic that dynamically generates sections based on varying inputs like different sports events or different types of analyses (e.g., player performance).

2. **Interactivity**: Introduce elements where users can interact with certain parts of the content (e.g., toggling additional details).

3. **Integration with External Data Sources**: Pull real-time data from APIs related to sports statistics or betting odds.

4. **Error Handling**: Incorporate robust error handling mechanisms for cases where data might be incomplete or malformed.

## Exercise

### Task Description

You are required to create an advanced HTML-based betting analysis page using Python that dynamically generates content based on provided inputs about sports matches.

#### Requirements:

1. **Input Parsing**:
– Accept input JSON containing details about multiple sports matches.
– Each match should include details like team names, form history, head-to-head stats, key players’ stats etc.

2. **Dynamic Section Generation**:
– Generate sections dynamically based on input data.
– Use `

` for main sections like “General Expert Overview” and “Betting Analysis”.
– Use `

` within each main section for specific categories like “Match Result Analysis”, “Goals Market Assessment”.
– Use `

` within subcategories if needed (e.g., under “Goals Market Assessment” you may have “Total Goals Over/Under”).

3. **Content Requirements**:
– Ensure each section contains expert insights backed by statistical evidence.
– Provide detailed reasoning behind each recommendation.
– Include specific predictions with confidence levels.

4. **Error Handling**:
– Handle cases where input data might be incomplete or malformed gracefully.

5. **Output**:
– Render HTML content adhering strictly to hierarchical structure rules.

6.Bonus Points:
– Integrate real-time data fetching from an external API related to sports statistics or betting odds.
– Add interactivity elements allowing users to toggle additional details within sections.

### Example Input JSON

json
[
{
“match”: {
“teamA”: “Team A”,
“teamB”: “Team B”,
“date”: “2023-10-05”,
“time”: “15:00”
},
“formHistory”: {
“teamA”: [“Win”, “Draw”, “Loss”],
“teamB”: [“Draw”, “Win”, “Win”]
},
…
}
]

### Solution

python
import json

def generate_html(input_data):
html_content = “”

# General Expert Overview Section
html_content += “

General Expert Overview

”
html_content += “

This section provides comprehensive match context…

”

# Dynamic Section Generation Based on Input Data

# Betting Analysis Section
html_content += “

Betting Analysis

”

# Iterate through each match input
for match_data in input_data:
html_content += f”

{match_data[‘match’][‘teamA’]} vs {match_data[‘match’][‘teamB’]}

”

# Match Result Analysis
html_content += “

Match Result Analysis

”

# Example Sub-sections
html_content += “

1X2 Market

”
html_content += “

”
html_content += f”
• {match_data[‘match’][‘teamA’]} favored due to recent wins…

”
html_content += “

”

# More sub-sections…

# Goals Market Assessment
html_content += “

Goals Market Assessment

”

# Total Goals Over/Under Sub-section
html_content += “

Total Goals Over/Under

”

# Example Content
form_history_a = ‘, ‘.join(match_data.get(‘formHistory’, {}).get(‘teamA’, []))
form_history_b = ‘, ‘.join(match_data.get(‘formHistory’, {}).get(‘teamB’, []))

html_content += f”

”
html_content += f”
• {match_data[‘match’][‘teamA’]} has shown consistent performance with results {form_history_a}…

”
html_content += f”

• {match_data[‘match’][‘teamB’]} has displayed strong form recently with results {form_history_b}…

”

# More detailed insights…

html_content += “

”

# Closing HTML Tags
html_content += “”

return html_content

# Sample Input JSON Data
input_json = ”’
[
{
“match”: {
“teamA”: “Team A”,
“teamB”: “Team B”,
“date”: “2023-10-05”,
“time”: “15:00″
},
…
// Additional necessary fields…
}
]
”’

input_data = json.loads(input_json)
output_html = generate_html(input_data)

# Save output HTML content into a file (for demonstration purposes)
with open(“betting_analysis.html”, ‘w’) as file:
file.write(output_html)

## Follow-up exercise

### Task Description

Extend your solution by adding interactivity:

1) Implement JavaScript functions that allow users to toggle additional details within sections (e.g., showing/hiding detailed player stats).

#### Requirements:

1) Use JavaScript embedded within your HTML output.

#### Solution Outline:

function toggleDetails(sectionId) {
var x = document.getElementById(sectionId);
if (x.style.display === ‘none’) {
x.style.display = ‘block’;
} else {
x.style.display = ‘none’;
}
}

Embed this script within your generated HTML content where needed:

python
html_script = “””

function toggleDetails(sectionId) {
var x = document.getElementById(sectionId);
if (x.style.display === ‘none’) {
x.style.display = ‘block’;
} else {
x.style.display = ‘none’;
}
}

“””

html_toggle_button_example = “””
Toggle Team A Details

“””

Integrate `html_script` at appropriate locations within your `generate_html` function output.

***** Tag Data *****
ID: 7
description: Comprehensive expert opinion formatted using nested headings detailing
various aspects such as team forms and key players’ performances.
start line: 205
end line:249
dependencies:
– type: Other name=HTML structure rules’
start line :11 end line :15′
– type : Other name=’Content structure requirements’
start line :16 end line :23′
context description : This snippet provides an analytical overview structured using nested headings,
relying heavily on statistical evidence and contextual factors across various sections,
including general overview , team forms , head-to-head records , key players’ performances,
and final predictions .
algorithmic depth :4 algorithmic depth external :N algorithmic depth obscurity :4 advanced coding concepts :4 interesting students :5 self contained :Y

*************
## Suggestions for complexity

1. **Dynamic Heading Levels:** Allow dynamic adjustment of heading levels based on user preferences while maintaining proper hierarchy integrity throughout nested structures.

2. **Conditional Content Insertion:** Implement conditional logic so certain sections only appear if specific criteria are met (e.g., only show `Key Players’ Performances` if there are notable performances).

3. **Customizable Statistical Evidence Integration:** Allow embedding customizable statistical charts/graphs inline within specific subsections using third-party libraries like D3.js directly through HTML templates.

4. **Real-Time Data Fetching:** Integrate real-time data fetching from APIs directly into predefined placeholders within the text blocks before rendering them into HTML format.

5. **Advanced Error Handling:** Develop sophisticated error handling mechanisms that log errors contextually when invalid heading hierarchies or missing mandatory content are detected during processing.

## Conversation

I need help with this code [SNIPPET] can we add dynamic heading levels? User can choose level

Sure! We can create a function that adjusts heading levels dynamically based on user input while ensuring hierarchy integrity remains intact.

Here’s how you can modify your code snippet:

python
def generate_heading(level):
return f'<{" ".join(["” for i in range(level)])}>’

def close_heading(level):
return f'</{" ".join(["” for i in range(level)])}>’

# Example usage inside your text block generation logic:

user_defined_level_1 = int(input(“Enter level for main title/headline (default=H2): “) or ‘2’)
user_defined_level_2 = int(input(“Enter level for top-level sections (default=H3): “) or ‘3’)

text_block_with_dynamic_headings.append(generate_heading(user_defined_level_1))
text_block_with_dynamic_headings.append(“Your Main Title Here”)
text_block_with_dynamic_headings.append(close_heading(user_defined_level_1))

text_block_with_dynamic_headings.append(generate_heading(user_defined_level_2))
text_block_with_dynamic_headings.append(“Top-Level Section”)
text_block_with_dynamic_headings.append(close_heading(user_defined_level_2))

# Continue similarly…

This way you ensure flexibility while maintaining structural integrity.

can we make it conditional? like show key players only if there are notable performances?

Certainly! You can add conditional logic around specific sections based on certain criteria such as notable performances being present or not.

Here’s how you can modify your code snippet:

python
notable_performances_present = True # This would typically come from some logical check or data condition

if notable_performances_present:
text_block_conditional_sections.append(““)
text_block_conditional_sections.append(“Key Players’ Performances”)
text_block_conditional_sections.append(““)
text_block_conditional_sections.append(“
“)
text_block_conditional_sections.append(“
“)
text_block_conditional_sections.append(“Detailed analysis of key players…”)
else:
text_block_conditional_sections.append(““)
text_block_conditional_sections.append(“No notable performances recorded.”)
text_block_conditional_sections.append(““)

In this example, we append different content depending upon whether `notable_performances_present` is `True` or `False`. You’d replace `”Detailed analysis of key players…”` with actual performance analysis.

how about embedding charts inline? like d3.js graphs?

Embedding D3.js graphs inline requires integrating JavaScript directly into your HTML template alongside your Python-generated text blocks.

Here’s how you could embed D3.js charts inline:

python
detailed_analysis_section_start_marker_index_in_text_blocks_array = len(text_blocks_array)
text_blocks_array.extend([
“”,
“

“,
“”,
“””
document.addEventListener(‘DOMContentLoaded’, function() {
const svgWidth = window.innerWidth;
const svgHeight = window.innerHeight;

const svg = d3.select(“#chart”).append(“svg”)
.attr(“width”, svgWidth)
.attr(“height”, svgHeight);

// Example D3 chart setup here…
const dataset = [/* Your dataset here */];

svg.selectAll(“circle”)
.data(dataset)
.enter()
.append(“circle”)
.attr(“cx”, function(d) { return d.x; })
.attr(“cy”, function(d) { return d.y; })
.attr(“r”, function(d) { return d.r; });
});
“””,
“”,
])
detailed_analysis_section_end_marker_index_in_text_blocks_array=len(text_blocks_array)

# Continue appending other parts…

print(text_blocks_array[detailed_analysis_section_start_marker_index_in_text_blocks_array:detailed_analysis_section_end_marker_index_in_text_blocks_array])

This example sets up an SVG element using D3.js inside your generated text block array which will render once the DOM is fully loaded.

[0]: import numpy as np
[1]: import matplotlib.pyplot as plt
[2]: import pandas as pd
[3]: import os

[4]: def load_mat_file(matfile):
[5]: “””
[6]: Load MATLAB mat-file.

[7]: Parameters
[8]: ———-
[9]: matfile : str
[10]: Path/filename

[11]: Returns
[12]: ——-
[13]: dict
[14]: Dictionary containing variables stored in mat-file.

[15]: “””

[16]: import scipy.io

[17]: mat_dict=scipy.io.loadmat(matfile)

[18]: return mat_dict

***** Tag Data *****
ID: N/A
description: No advanced coding techniques identified within this snippet itself,
but it serves as a utility function loading MATLAB files which could integrate into larger,
more complex workflows involving scientific computing tasks.
start line:0
end line18
dependencies:
– type Function Definition/Declaration/Signature/Prototype/Implementation Body/Closure Scope/etc..
name | load_mat_file | start line | description | end lines |
—- | ————- | ———- | ———– | ——— |
load_mat_file | load_mat_file definition starts at line | Utility function designed to load MATLAB (.mat) files using scipy.io.loadmat(). It returns a dictionary containing variables stored within those files.| load_mat_file definition ends at |
context description: While not complex itself, understanding how this utility integrates into broader scientific computing tasks can provide insight into its application scope.
algorithmic depth fourchitecture obscurity score explanation rationale integration utility purpose broader context scientific computing tasks insight application scope potential advanced implementations leveraging loaded MATLAB files deeper integration numpy pandas matplotlib etc.

*************
## Suggestions for complexity

1.) Implement asynchronous loading of multiple `.mat` files concurrently while managing memory efficiently since `.mat` files can be large datasets commonly used in scientific computing tasks.

Example request might be something like implementing async functionality using asyncio library combined with concurrent.futures.ThreadPoolExecutor tailored specifically around loading multiple `.mat` files simultaneously without running out of memory issues due to large datasets common in scientific contexts.

*** Exercise ***
complexity and obscurity The nuances involved here include understanding how MATLAB.mat-files work,
comparison between different libraries available for loading such files (.mat), differences/similarities between scipy.io.loadmat() versus other alternatives such as h5py/matplotlib etc., understanding what kind of objects/matrices/scalars get returned when dealing with different types/formats found inside matlab.mat-files etc.,
final answer suggestions [{‘final answer’: ‘`scipy.io.loadmat()` returns dictionaries where keys correspond variable names defined inside MATLAB.mat-files.’, ‘sc feasible’: ‘`scipy.io.loadmat()` returns what kind of object?’, ‘ textbook info’: u’This fact about `scipy.io.loadmat()` returning dictionaries where keys correspond variable names defined inside MATLAB.mat-files is covered extensively across many advanced texts discussing interoperability between Python & Matlab environments.’, ‘ elusive’: u’This question requires nuanced understanding about how scipy handles matlab.mat-file objects.’, ‘unique’: u’This fact isn’t common knowledge unless one has worked extensively with Matlab & Python interoperability.’}, {‘final answer’: ‘`scipy.io.loadmat()` cannot read all types/formats found inside matlab.mat-files.’,
‘SC feasible’: What limitation does `scipy.io.loadmat()` have regarding reading matlab.mat-files?’,
‘textbook info’: u’Many textbooks dealing specifically w/ Matlab-Python interoperability mention limitations associated w/
reading matlab.mat-files via scipy.’,
elusive:’This question requires deep knowledge about what types/formats/scenarios cause problems when reading matlab.mat-files via scipy.’,
unique:’This limitation isn’t widely known unless one works extensively w/
Matlab-Python interoperability.’}, {‘final answer’: ‘`scipy.io.loadmat()` loads all variables stored inside given MATLAB.mat-file into dictionary format.’,
‘SC feasible’: How does `scipy.io.loadmat()` store variables from given MATLAB.mat-file?
‘textbook info’:u’This fact about storing all variables from given matlab.mat-file into dictionary format via scipy is covered extensively across many advanced texts discussing interoperability between Python & Matlab environments.’,
elusive:’This question requires nuanced understanding about how scipy handles matlab.mat-file objects’,
unique:’This fact isn’t common knowledge unless one has worked extensively w/
Matlab & Python interoperability.’}]
question suggestions [{‘question’:’What kind object does `scipy.io.loadmat()` return?’, ‘Is self contained’:’Y’, ‘
??textbook info’:’Y’, ‘
??elusive’:’N’, ‘
??unique’:’N’}, {‘question’:’What limitation does `scipy.io.loadmat()` have regarding reading matlab.mat-files?’,
Is self contained’:’Y’,
??textbook info’:’Y’,
??elusive:’Y’,
??unique:’N’}, {‘question’:’How does `scipy.io.loadmat()` store variables from given MATLAB.mat-file?’,
Is self contained’:’Y’,
??textbook info’:’Y’,
??elusive:’N’,
??unique:’N’}]
final answer:`scipy.io.loadmat()` returns dictionaries where keys correspond variable names defined inside MATLAB.mat-files.’
question:”What kind object does `scipy.io.loadmat()` return?”
Is self contained:”Y”
Is spoonfeeding:”N”
revised question:”What kind object does `scipy.io.loadmat()` return?”
misleading choices:”numpy array”,”list”,”tuple”,”NoneType”
detailed solution:”The correct answer is that `scipy.io.loadmat()` returns dictionaries where keys correspond variable names defined inside MATLAB.mat-files.nnWhen working with `.mat` files using SciPy’s module `loadmat`, it reads all variables stored inside these files and stores them as dictionary entries where each entry’s key corresponds directly to variable names found inside `.MATLAB` environment.nnUnderstanding this behavior helps when handling large datasets saved originally via MatLab software because accessing elements becomes straightforward once they’re loaded correctly.”
*** Excerpt ***

*** Revision 0 ***

## Plan

To create an exercise that demands an advanced level of comprehension along with additional factual knowledge beyond what is presented explicitly in the excerpt itself would require several steps during its design phase:

1) The excerpt needs enhancement so it encompasses intricate details that demand background knowledge—this could involve integrating complex themes from science, philosophy, history among others which aren’t immediately apparent without prior study or research.

2) Incorporate deductive reasoning elements by presenting scenarios or statements whose conclusions aren’t directly stated but must be inferred through logical steps derived from the information provided plus external factual knowledge.

3) Embedding nested counterfactuals (“if X had not happened then Y would be true”) and conditionals (“if X then Y; if Y then Z”) increases complexity significantly because it requires tracking multiple hypothetical scenarios simultaneously alongside actual facts mentioned explicitly or implicitly within the excerpt.

## Rewritten Excerpt

In a parallel universe wherein Newtonian physics was supplanted by Aristotelian principles governing motion—wherein objects naturally move towards their designated places according to their inherent qualities—and Einstein never proposed his theory of relativity because time travel was discovered centuries earlier thanks to Archimedes’ lost treatise “On Temporal Displacement,” society evolved differently around energy consumption norms and space exploration endeavors became paramount much sooner than our timeline witnessed NASA’s moon landing initiative postulated after World War II ended abruptly due to Nikola Tesla successfully demonstrating wireless energy transmission globally nullifying economic incentives fueling warfare technology advancements thereafter.

## Suggested Exercise

In a parallel universe described above where Aristotelian physics prevails over Newtonian physics due primarily because time travel was discovered centuries earlier thanks largely attributed efforts by Archimedes’ lost treatise “On Temporal Displacement,” leading subsequently Einstein never proposing his theory of relativity; considering Nikola Tesla’s successful demonstration globally transmitting wireless energy negated economic incentives fueling warfare technology advancements henceforth causing World War II’s abrupt end—how did these historical deviations impact societal evolution particularly concerning energy consumption norms?

A) Society remained reliant on fossil fuels longer due delayed technological innovation stemming from diminished warfare technology advancements driving energy sector evolution forward post-WWII era observed historically speaking Earth timeline contrastingly speaking because Tesla’s demonstration eliminated economic incentives traditionally pushing boundaries advancing new energy technologies including nuclear power development seen prominently mid-20th century Earth timeline hence fossil fuels maintained dominance longer delaying renewable energy adoption significantly compared observed historically Earth timeline evolution pace-wise speaking.

B) Society evolved quicker towards renewable energy sources despite Tesla’s global wireless energy transmission success because economic incentives driving warfare technology advancements were nullified leading nations focusing resources towards space exploration endeavors becoming paramount much sooner than our timeline witnessed NASA’s moon landing initiative postulated after WWII ended abruptly hence accelerating societal shift away from traditional fossil fuels towards exploring sustainable energy options including solar power harnessing technologies inspired by space exploration needs uniquely characteristic this alternate universe scenario outlined above relative Earth historical timeline progression pace-wise speaking concerning renewable energy adoption rates normatively speaking societal evolution patterns observable historically speaking Earth timeline comparison basis-wise speaking fundamentally speaking.

*** Revision 1 ***

check requirements:
– req_no: 1
discussion: The draft lacks explicit reliance on external academic facts outside
those implied directly by its own narrative construction.
score: 0
– req_no: 2
discussion: Understanding subtleties relies mostly on internal narrative logic rather-than-drawing-on-extensive-pre-existing-knowledge-or-facts-
external-to-the-excerpt-itself-as-required-by-this-criterion-
score:-0-
correct choice:-Society evolved quicker towards renewable energy sources despite Tesla’s global wireless energy transmission success because economic incentives driving warfare technology advancements were nullified leading nations focusing resources towards space exploration endeavors becoming paramount much sooner than our timeline witnessed NASA’s moon landing initiative postulated after WWII ended abruptly hence accelerating societal shift away from traditional fossil fuels towards exploring sustainable energy options including solar power harnessing technologies inspired by space exploration needs uniquely characteristic this alternate universe scenario outlined above relative Earth historical timeline progression pace-wise speaking concerning renewable energy adoption rates normatively speaking societal evolution patterns observable historically speaking Earth timeline comparison basis-wise speaking fundamentally speaking-
revised exercise:-Considering the alternate history presented above wherein Aristotelian physics replaces Newtonian principles following early discoveries akin-to-Archimedes’ hypothetical treatise leading Einstein never proposing his theory due-to-time-travel advances-and-Tesla achieving global wireless power transmission ending WWII prematurely-how would these developments likely influence global priorities regarding technological innovation especially concerning alternative energies compared against our historical trajectory?
incorrect choices:
– Society remained reliant on fossil fuels longer due delayed technological innovation stemming-from-diminished-warfare technology advancements-driving-energy-sector-evolution-forward-post-WWII-era observed-historically-speaking-Earth-timeline contrastingly-speaking-because-Tesla’s-demonstration eliminated-economic-incentives-traditionally-pushing-boundaries advancing-new-energy-technologies-including-nuclear-power-development-seen-prominently-mid-20th-century-Earth-timeline hence-fossil-fuels-maintained-dominance-longer-delaying-renewable-energy-adoption-significantly-compared observed-historically-Earth-timeline-evolution pace-wise-speaking-
<>I’m trying out some tensor operations involving einsum notation but I’m getting stuck somewhere along the way! Here’s my code snippet which uses PyTorch tensors:

python

import torch

qkv_same_dim_bias_maybe_masked_weight_bias_hump_projection_matrix_multiplication_addition_tensor_einsum_notation_qkvpma =
torch.einsum(
“b n s e , b n s e -> b n s e”,
qkv_same_dim_bias_maybe_masked_weight_bias_hump_projection_matrix_multiplication_addition_tensor_einsum_notation_qkvpma.view(
bsz * num_attention_heads * tgt_len * src_len,
head_dim),
qkv_same_dim_bias_maybe_masked_weight_bias_hump_projection_matrix_multiplication_addition_tensor_einsum_notation_qkvpma.view(
bsz * num_attention_heads * tgt_len * src_len,
head_dim),
optimize=True)

qkv_same_dim_bias_maybe_masked_weight_bias_hump_projection_matrix_multiplication_addition_tensor_einsum_notation_qkvpma =
torch.einsum(
“b n s e , b n s d -> b n s d”,
qkv_same_dim_bias_maybe_masked_weight_bias_hump_projection_matrix_multiplication_addition_tensor_einsum_notation_qkvpma.view(
bsz * num_attention_heads * tgt_len * src_len,
head_dim),
torch.ones((bsz * num_attention_heads * tgt_len,), dtype=torch.long),
optimize=True)

qkv_same_dim_bias_maybe_masked_weight_bias_hump_projection_matrix_multiplication_addition_tensor_einsum_notation_qkvpma =
torch.einsum(
“b n s e , b n t e -> b n s t”,
qkv_same_dim_bias_maybe_masked_weight_bias_hump_projection_matrix_multiplication_addition_tensor_einsum_notation_qkvpma.view(
bsz * num_attention_heads * tgt_len * src_len,
head_dim),
qkv_same_dim_bias_maybe_masked_weight_bias_hump_projection_matrix_multiplication_addition_tensor_einsum_notation_qkvpma.view(
bsz * num_attention_heads * tgt_len * src_len,
head_dim),
optimize=True)

qkv_same_dim_bias_maybe_masked_weight_bias_hump_projection_matrix_multiplication_addition_tensor_einsum_notation_qkvpma =
torch.einsum(
“b n s e , b n t d -> b n s t”,
qkv_same_dim_bias_maybe_masked_weight_bias_hump_projection_matrix_multiplication_addition_tensor_einsum_notation_qkvpma.view(
bsz * num_attention_heads * tgt_len * src_len,
head_dim),
torch.ones((bsz * num_attention_heads * tgt_len,), dtype=torch.long),
optimize=True)

And here’s the traceback I’m seeing:

plaintext

Traceback (most recent call last):
File “/path/to/script.py”, line X, in
result_aaa =
torch.einsum(
File “/path/to/torch/__init__.py”, line Y, in einsum

raise RuntimeError(f”Einstein sum subscript ‘{subscripts}’ includes output subscript ‘{output_subscript}’ ”
RuntimeError:Einstein sum subscript ‘bnse,bnse->bnse’ includes output subscript ‘bnse’ repeated exactly once on lhs but not rhs

File “/path/to/script.py”, line Z, in
result_bbb =
torch.einsum(

RuntimeError:Einstein sum subscript ‘bnse,bnsd->bnsd’ includes output subscript ‘bnsd’ repeated exactly once on lhs but not rhs

File “/path/to/script.py”, line W,in .
result_ccc =

RuntimeError:Einstein sum subscript ”bnse,bnte->bnst” includes output subscript ”bnst” repeated exactly once on lhs but not rhs

File “/path/to/script.py”,line V,in .
result_ddd =

RuntimeError:Einstein sum subscript ”bnse,bntd->bnst” includes output subscript ”bnst” repeated exactly onceon lhs but not rhs

I think I’ve got something wrong with my einstein summations here – they seem pretty repetitive! Can anyone help me figure out why I’m getting these errors? I thought I set up everything correctly but maybe I’m missing something obvious… Thanks!
<> Certainly! It looks like you’re encountering issues because your einstein summations don’t specify any indices being summed over properly when moving from left-hand side (lhs) tensors to right-hand side (rhs). In einstein summation notation (`einsum`), when you specify indices without any operation symbol (`+`, `-`, etc.), those indices need either duplication across lhs terms or need explicit summation instructions (`->`) specifying how they should collapse together.

Here are some suggestions based on your code snippets:

### Problematic Lines & Solutions:

1. **First EinSum Operation**
python
torch.einsum(
“b n s e , b n s e -> b n s e”,
tensor_a.view(…),
tensor_b.view(…)
)

This operation doesn’t change dimensions since no indices are summed over (`e`). If you want element-wise multiplication without changing dimensions:
python
torch.einsum(
“…,…->…”,
tensor_a.view(…),
tensor_b.view(…)
)

2. **Second EinSum Operation**
python
torch.einsum(
“b n s e , b n s d -> b n s d”,
tensor_a.view(…),
ones_vector.unsqueeze(-1).expand(…)
)

Here you’re multiplying along dimensions incorrectly specified; ensure dimensions align properly before multiplication.

3 & 4 Similar Issue:

For operations involving tensors reshaped similarly (`view(…)`), ensure matching dimensions across operands except those meant explicitly summed over (`einsteinsum`). For instance,

python
torch.einsum(
“…,…->…”,
qtensor_view,
ktensor_view.expand_as(qtensor_view)
)

### General Tips:

– Ensure dimensionality alignment before performing operations unless explicitly intended otherwise via broadcasting rules.

– Use ellipsis (`…`) when possible—it automatically handles batch dimensions simplifying expressions without specifying every single dimension manually unless necessary.

Try adjusting according these tips! Let me know if further clarification is needed! 😊🔍📚✨