



import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
				import faiss

@require_faiss
class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			def  snake_case__     (   self							: Optional[int] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Any													=	tempfile.mkdtemp()
					__UpperCAmelCase    :     Optional[int]													=	8

					# DPR tok
					__UpperCAmelCase    :     str													=	[
					    '''[UNK]''',
					    '''[CLS]''',
					    '''[SEP]''',
					    '''[PAD]''',
					    '''[MASK]''',
					    '''want''',
					    '''##want''',
					    '''##ed''',
					    '''wa''',
					    '''un''',
					    '''runn''',
					    '''##ing''',
					    ''',''',
					    '''low''',
					    '''lowest''',
					]
					__UpperCAmelCase    :     Optional[Any]													=	os.path.join(self.tmpdirname       ,       '''dpr_tokenizer''' )
					os.makedirs(a_       ,       exist_ok=a_ )
					__UpperCAmelCase    :     Optional[Any]													=	os.path.join(a_       ,       DPR_VOCAB_FILES_NAMES['''vocab_file'''] )
					with open(self.vocab_file       ,       '''w'''       ,       encoding='''utf-8''' ) as vocab_writer:
							vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )

					# BART tok
					__UpperCAmelCase    :     Dict													=	[
					    '''l''',
					    '''o''',
					    '''w''',
					    '''e''',
					    '''r''',
					    '''s''',
					    '''t''',
					    '''i''',
					    '''d''',
					    '''n''',
					    '''\u0120''',
					    '''\u0120l''',
					    '''\u0120n''',
					    '''\u0120lo''',
					    '''\u0120low''',
					    '''er''',
					    '''\u0120lowest''',
					    '''\u0120newer''',
					    '''\u0120wider''',
					    '''<unk>''',
					]
					__UpperCAmelCase    :     Dict													=	dict(zip(a_       ,       range(len(a_ ) ) ) )
					__UpperCAmelCase    :     List[str]													=	['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
					__UpperCAmelCase    :     Any													=	{'''unk_token''': '''<unk>'''}

					__UpperCAmelCase    :     Union[str, Any]													=	os.path.join(self.tmpdirname       ,       '''bart_tokenizer''' )
					os.makedirs(a_       ,       exist_ok=a_ )
					__UpperCAmelCase    :     Dict													=	os.path.join(a_       ,       BART_VOCAB_FILES_NAMES['''vocab_file'''] )
					__UpperCAmelCase    :     List[Any]													=	os.path.join(a_       ,       BART_VOCAB_FILES_NAMES['''merges_file'''] )
					with open(self.vocab_file       ,       '''w'''       ,       encoding='''utf-8''' ) as fp:
							fp.write(json.dumps(a_ ) + '''\n''' )
					with open(self.merges_file       ,       '''w'''       ,       encoding='''utf-8''' ) as fp:
							fp.write('''\n'''.join(a_ ) )




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,       '''dpr_tokenizer''' ) )




			def  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





					return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,       '''dpr_tokenizer''' ) )




			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,       '''bart_tokenizer''' ) )




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					shutil.rmtree(self.tmpdirname )




			def  snake_case__     (   self							: int ):
					'''simple docstring'''





					__UpperCAmelCase    :     int													=	Dataset.from_dict(
					    {
					        '''id''': ['''0''', '''1'''],
					        '''text''': ['''foo''', '''bar'''],
					        '''title''': ['''Foo''', '''Bar'''],
					        '''embeddings''': [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
					    } )
					dataset.add_faiss_index('''embeddings'''       ,       string_factory='''Flat'''       ,       metric_type=faiss.METRIC_INNER_PRODUCT )
					return dataset




			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[int]													=	self.get_dummy_dataset()
					__UpperCAmelCase    :     Optional[Any]													=	RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size       ,       question_encoder=DPRConfig().to_dict()       ,       generator=BartConfig().to_dict()       ,       )
					with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
							__UpperCAmelCase    :     Any													=	dataset
							__UpperCAmelCase    :     Tuple													=	RagRetriever(
							    a_       ,       question_encoder_tokenizer=self.get_dpr_tokenizer()       ,       generator_tokenizer=self.get_bart_tokenizer()       ,       )
					return retriever




			def  snake_case__     (   self							: Optional[int]       ,       a_							: bool ):
					'''simple docstring'''





					__UpperCAmelCase    :     Union[str, Any]													=	self.get_dummy_dataset()
					__UpperCAmelCase    :     Union[str, Any]													=	RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size       ,       question_encoder=DPRConfig().to_dict()       ,       generator=BartConfig().to_dict()       ,       index_name='''custom'''       ,       )
					if from_disk:
							__UpperCAmelCase    :     Tuple													=	os.path.join(self.tmpdirname       ,       '''dataset''' )
							__UpperCAmelCase    :     int													=	os.path.join(self.tmpdirname       ,       '''index.faiss''' )
							dataset.get_index('''embeddings''' ).save(os.path.join(self.tmpdirname       ,       '''index.faiss''' ) )
							dataset.drop_index('''embeddings''' )
							dataset.save_to_disk(os.path.join(self.tmpdirname       ,       '''dataset''' ) )
							del dataset
							__UpperCAmelCase    :     Dict													=	RagRetriever(
							    a_       ,       question_encoder_tokenizer=self.get_dpr_tokenizer()       ,       generator_tokenizer=self.get_bart_tokenizer()       ,       )
					else:
							__UpperCAmelCase    :     Optional[Any]													=	RagRetriever(
							    a_       ,       question_encoder_tokenizer=self.get_dpr_tokenizer()       ,       generator_tokenizer=self.get_bart_tokenizer()       ,       index=CustomHFIndex(config.retrieval_vector_size       ,       a_ )       ,       )
					return retriever




			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Union[str, Any]													=	Dataset.from_dict(
					    {
					        '''id''': ['''0''', '''1'''],
					        '''text''': ['''foo''', '''bar'''],
					        '''title''': ['''Foo''', '''Bar'''],
					        '''embeddings''': [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
					    } )
					dataset.add_faiss_index('''embeddings'''       ,       string_factory='''Flat'''       ,       metric_type=faiss.METRIC_INNER_PRODUCT )

					__UpperCAmelCase    :     Optional[Any]													=	os.path.join(self.tmpdirname       ,       '''hf_bert_base.hnswSQ8_correct_phi_128.c_index''' )
					dataset.save_faiss_index('''embeddings'''       ,       index_file_name + '''.index.dpr''' )
					pickle.dump(dataset['''id''']       ,       open(index_file_name + '''.index_meta.dpr'''       ,       '''wb''' ) )

					__UpperCAmelCase    :     int													=	os.path.join(self.tmpdirname       ,       '''psgs_w100.tsv.pkl''' )
					__UpperCAmelCase    :     Any													=	{sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
					pickle.dump(a_       ,       open(a_       ,       '''wb''' ) )

					__UpperCAmelCase    :     List[str]													=	RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size       ,       question_encoder=DPRConfig().to_dict()       ,       generator=BartConfig().to_dict()       ,       index_name='''legacy'''       ,       index_path=self.tmpdirname       ,       )
					__UpperCAmelCase    :     Optional[Any]													=	RagRetriever(
					    a_       ,       question_encoder_tokenizer=self.get_dpr_tokenizer()       ,       generator_tokenizer=self.get_bart_tokenizer() )
					return retriever




			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	1
					__UpperCAmelCase    :     Any													=	self.get_dummy_canonical_hf_index_retriever()
					__UpperCAmelCase    :     str													=	np.array(
					    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]       ,       dtype=np.floataa )
					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     Optional[int]													=	retriever.retrieve(a_       ,       n_docs=a_ )
					self.assertEqual(retrieved_doc_embeds.shape       ,       (2, n_docs, self.retrieval_vector_size) )
					self.assertEqual(len(a_ )       ,       2 )
					self.assertEqual(sorted(doc_dicts[0] )       ,       ['''embeddings''', '''id''', '''text''', '''title'''] )
					self.assertEqual(len(doc_dicts[0]['''id'''] )       ,       a_ )
					self.assertEqual(doc_dicts[0]['''id'''][0]       ,       '''1''' )  # max inner product is reached with second doc
					self.assertEqual(doc_dicts[1]['''id'''][0]       ,       '''0''' )  # max inner product is reached with first doc
					self.assertListEqual(doc_ids.tolist()       ,       [[1], [0]] )




			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[int]													=	self.get_dummy_canonical_hf_index_retriever()
					with tempfile.TemporaryDirectory() as tmp_dirname:
							with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
									__UpperCAmelCase    :     List[str]													=	self.get_dummy_dataset()
									retriever.save_pretrained(a_ )
									__UpperCAmelCase    :     Dict													=	RagRetriever.from_pretrained(a_ )
									self.assertIsInstance(a_       ,       a_ )
									__UpperCAmelCase    :     Union[str, Any]													=	np.array(
									    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]       ,       dtype=np.floataa )
									__UpperCAmelCase    :     Union[str, Any]													=	retriever.retrieve(a_       ,       n_docs=1 )
									self.assertTrue(out is not None )




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	1
					__UpperCAmelCase    :     Optional[Any]													=	self.get_dummy_custom_hf_index_retriever(from_disk=a_ )
					__UpperCAmelCase    :     Dict													=	np.array(
					    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]       ,       dtype=np.floataa )
					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     Tuple													=	retriever.retrieve(a_       ,       n_docs=a_ )
					self.assertEqual(retrieved_doc_embeds.shape       ,       (2, n_docs, self.retrieval_vector_size) )
					self.assertEqual(len(a_ )       ,       2 )
					self.assertEqual(sorted(doc_dicts[0] )       ,       ['''embeddings''', '''id''', '''text''', '''title'''] )
					self.assertEqual(len(doc_dicts[0]['''id'''] )       ,       a_ )
					self.assertEqual(doc_dicts[0]['''id'''][0]       ,       '''1''' )  # max inner product is reached with second doc
					self.assertEqual(doc_dicts[1]['''id'''][0]       ,       '''0''' )  # max inner product is reached with first doc
					self.assertListEqual(doc_ids.tolist()       ,       [[1], [0]] )




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Union[str, Any]													=	self.get_dummy_custom_hf_index_retriever(from_disk=a_ )
					with tempfile.TemporaryDirectory() as tmp_dirname:
							retriever.save_pretrained(a_ )
							__UpperCAmelCase    :     Tuple													=	RagRetriever.from_pretrained(a_ )
							self.assertIsInstance(a_       ,       a_ )
							__UpperCAmelCase    :     Union[str, Any]													=	np.array(
							    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]       ,       dtype=np.floataa )
							__UpperCAmelCase    :     Any													=	retriever.retrieve(a_       ,       n_docs=1 )
							self.assertTrue(out is not None )




			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[str]													=	1
					__UpperCAmelCase    :     int													=	self.get_dummy_custom_hf_index_retriever(from_disk=a_ )
					__UpperCAmelCase    :     str													=	np.array(
					    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]       ,       dtype=np.floataa )
					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     List[str]													=	retriever.retrieve(a_       ,       n_docs=a_ )
					self.assertEqual(retrieved_doc_embeds.shape       ,       (2, n_docs, self.retrieval_vector_size) )
					self.assertEqual(len(a_ )       ,       2 )
					self.assertEqual(sorted(doc_dicts[0] )       ,       ['''embeddings''', '''id''', '''text''', '''title'''] )
					self.assertEqual(len(doc_dicts[0]['''id'''] )       ,       a_ )
					self.assertEqual(doc_dicts[0]['''id'''][0]       ,       '''1''' )  # max inner product is reached with second doc
					self.assertEqual(doc_dicts[1]['''id'''][0]       ,       '''0''' )  # max inner product is reached with first doc
					self.assertListEqual(doc_ids.tolist()       ,       [[1], [0]] )




			def  snake_case__     (   self							: Optional[int] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	self.get_dummy_custom_hf_index_retriever(from_disk=a_ )
					with tempfile.TemporaryDirectory() as tmp_dirname:
							retriever.save_pretrained(a_ )
							__UpperCAmelCase    :     Dict													=	RagRetriever.from_pretrained(a_ )
							self.assertIsInstance(a_       ,       a_ )
							__UpperCAmelCase    :     List[str]													=	np.array(
							    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]       ,       dtype=np.floataa )
							__UpperCAmelCase    :     List[Any]													=	retriever.retrieve(a_       ,       n_docs=1 )
							self.assertTrue(out is not None )




			def  snake_case__     (   self							: str ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	1
					__UpperCAmelCase    :     Optional[Any]													=	self.get_dummy_legacy_index_retriever()
					__UpperCAmelCase    :     Any													=	np.array(
					    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]       ,       dtype=np.floataa )
					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     Any													=	retriever.retrieve(a_       ,       n_docs=a_ )
					self.assertEqual(retrieved_doc_embeds.shape       ,       (2, n_docs, self.retrieval_vector_size) )
					self.assertEqual(len(a_ )       ,       2 )
					self.assertEqual(sorted(doc_dicts[0] )       ,       ['''text''', '''title'''] )
					self.assertEqual(len(doc_dicts[0]['''text'''] )       ,       a_ )
					self.assertEqual(doc_dicts[0]['''text'''][0]       ,       '''bar''' )  # max inner product is reached with second doc
					self.assertEqual(doc_dicts[1]['''text'''][0]       ,       '''foo''' )  # max inner product is reached with first doc
					self.assertListEqual(doc_ids.tolist()       ,       [[1], [0]] )




			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	self.get_dummy_legacy_index_retriever()
					with tempfile.TemporaryDirectory() as tmp_dirname:
							retriever.save_pretrained(a_ )
							__UpperCAmelCase    :     Tuple													=	RagRetriever.from_pretrained(a_ )
							self.assertIsInstance(a_       ,       a_ )
							__UpperCAmelCase    :     int													=	np.array(
							    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]       ,       dtype=np.floataa )
							__UpperCAmelCase    :     Optional[Any]													=	retriever.retrieve(a_       ,       n_docs=1 )
							self.assertTrue(out is not None )




			@require_torch
			@require_tokenizers
			@require_sentencepiece
			def  snake_case__     (   self							: str ):
					'''simple docstring'''





					import torch

					__UpperCAmelCase    :     str													=	1
					__UpperCAmelCase    :     Any													=	self.get_dummy_canonical_hf_index_retriever()
					__UpperCAmelCase    :     str													=	[[5, 7], [10, 11]]
					__UpperCAmelCase    :     Optional[Any]													=	np.array(
					    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]       ,       dtype=np.floataa )
					__UpperCAmelCase    :     Tuple													=	retriever(a_       ,       a_       ,       prefix=retriever.config.generator.prefix       ,       n_docs=a_ )
					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     Union[str, Any]													=	(
					    out['''context_input_ids'''],
					    out['''context_attention_mask'''],
					    out['''retrieved_doc_embeds'''],
					)
					self.assertEqual(retrieved_doc_embeds.shape       ,       (2, n_docs, self.retrieval_vector_size) )
					self.assertIsInstance(a_       ,       a_ )
					self.assertIsInstance(a_       ,       a_ )
					self.assertIsInstance(a_       ,       np.ndarray )

					__UpperCAmelCase    :     Optional[Any]													=	retriever(
					    a_       ,       a_       ,       prefix=retriever.config.generator.prefix       ,       n_docs=a_       ,       return_tensors='''pt'''       ,       )
					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     Any													=	(  # noqa: F841
					    out['''context_input_ids'''],
					    out['''context_attention_mask'''],
					    out['''retrieved_doc_embeds'''],
					    out['''doc_ids'''],
					)
					self.assertEqual(retrieved_doc_embeds.shape       ,       (2, n_docs, self.retrieval_vector_size) )
					self.assertIsInstance(a_       ,       torch.Tensor )
					self.assertIsInstance(a_       ,       torch.Tensor )
					self.assertIsInstance(a_       ,       torch.Tensor )




			@require_torch
			@require_tokenizers
			@require_sentencepiece
			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[int]													=	self.get_dpr_ctx_encoder_tokenizer()
					__UpperCAmelCase    :     Any													=	1
					__UpperCAmelCase    :     List[str]													=	self.get_dummy_custom_hf_index_retriever(from_disk=a_ )
					retriever.set_ctx_encoder_tokenizer(a_ )

					__UpperCAmelCase    :     Dict													=	[[5, 7], [10, 11]]
					__UpperCAmelCase    :     List[Any]													=	np.array(
					    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]       ,       dtype=np.floataa )
					__UpperCAmelCase    :     Union[str, Any]													=	retriever(a_       ,       a_       ,       prefix=retriever.config.generator.prefix       ,       n_docs=a_ )

					self.assertEqual(
					    len(a_ )       ,       6 )  # check whether the retriever output consist of 6 attributes including tokenized docs
					self.assertEqual(
					    all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''') )       ,       a_ )  # check for doc token related keys in dictionary.










import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests

def        a						(      _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					Any=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[int]													=	None
		if token is not None:
				__UpperCAmelCase    :     Optional[Any]													=	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'Bearer {token}'}

		__UpperCAmelCase    :     List[str]													=	f'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
		__UpperCAmelCase    :     Union[str, Any]													=	requests.get(_UpperCAmelCase ,   headers=_UpperCAmelCase						).json()
		__UpperCAmelCase    :     Union[str, Any]													=	{}

		try:
				job_links.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']}						)
				__UpperCAmelCase    :     List[Any]													=	math.ceil((result['''total_count'''] - 1_00) / 1_00						)

				for i in range(_UpperCAmelCase						):
						__UpperCAmelCase    :     Optional[Any]													=	requests.get(url + f'&page={i + 2}' ,   headers=_UpperCAmelCase						).json()
						job_links.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']}						)

				return job_links
		except Exception:
				print(f'Unknown error, could not fetch links:\n{traceback.format_exc()}'						)

		return {}

def        a						(      _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					List[str]=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[Any]													=	None
		if token is not None:
				__UpperCAmelCase    :     Tuple													=	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'Bearer {token}'}

		__UpperCAmelCase    :     int													=	f'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
		__UpperCAmelCase    :     str													=	requests.get(_UpperCAmelCase ,   headers=_UpperCAmelCase						).json()
		__UpperCAmelCase    :     List[Any]													=	{}

		try:
				artifacts.update({artifact['''name''']: artifact['''archive_download_url'''] for artifact in result['''artifacts''']}						)
				__UpperCAmelCase    :     str													=	math.ceil((result['''total_count'''] - 1_00) / 1_00						)

				for i in range(_UpperCAmelCase						):
						__UpperCAmelCase    :     Dict													=	requests.get(url + f'&page={i + 2}' ,   headers=_UpperCAmelCase						).json()
						artifacts.update({artifact['''name''']: artifact['''archive_download_url'''] for artifact in result['''artifacts''']}						)

				return artifacts
		except Exception:
				print(f'Unknown error, could not fetch links:\n{traceback.format_exc()}'						)

		return {}

def        a						(      _UpperCAmelCase							:					Optional[Any] ,   _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					int						):







		'''simple docstring'''





		__UpperCAmelCase    :     str													=	None
		if token is not None:
				__UpperCAmelCase    :     str													=	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'Bearer {token}'}

		__UpperCAmelCase    :     int													=	requests.get(_UpperCAmelCase ,   headers=_UpperCAmelCase ,   allow_redirects=_UpperCAmelCase						)
		__UpperCAmelCase    :     Optional[int]													=	result.headers['''Location''']
		__UpperCAmelCase    :     Optional[int]													=	requests.get(_UpperCAmelCase ,   allow_redirects=_UpperCAmelCase						)
		__UpperCAmelCase    :     int													=	os.path.join(_UpperCAmelCase ,   f'{artifact_name}.zip'						)
		with open(_UpperCAmelCase ,   '''wb'''						) as fp:
				fp.write(response.content						)

def        a						(      _UpperCAmelCase							:					Dict ,   _UpperCAmelCase							:					List[str]=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	[]
		__UpperCAmelCase    :     int													=	[]
		__UpperCAmelCase    :     List[Any]													=	None

		with zipfile.ZipFile(_UpperCAmelCase						) as z:
				for filename in z.namelist():
						if not os.path.isdir(_UpperCAmelCase						):
								# read the file
								if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
										with z.open(_UpperCAmelCase						) as f:
												for line in f:
														__UpperCAmelCase    :     Tuple													=	line.decode('''UTF-8'''						).strip()
														if filename == "failures_line.txt":
																try:
																		# `error_line` is the place where `error` occurs
																		__UpperCAmelCase    :     int													=	line[: line.index(''': '''						)]
																		__UpperCAmelCase    :     Any													=	line[line.index(''': '''						) + len(''': '''						) :]
																		errors.append([error_line, error]						)
																except Exception:
																		# skip un-related lines
																		pass
														elif filename == "summary_short.txt" and line.startswith('''FAILED '''						):
																# `test` is the test method that failed
																__UpperCAmelCase    :     str													=	line[len('''FAILED '''						) :]
																failed_tests.append(_UpperCAmelCase						)
														elif filename == "job_name.txt":
																__UpperCAmelCase    :     Tuple													=	line

		if len(_UpperCAmelCase						) != len(_UpperCAmelCase						):
				raise ValueError(
				    f'`errors` and `failed_tests` should have the same number of elements. Got {len(_UpperCAmelCase						)} for `errors` '
				    f'and {len(_UpperCAmelCase						)} for `failed_tests` instead. The test reports in {artifact_zip_path} have some'
				    ''' problem.'''						)

		__UpperCAmelCase    :     str													=	None
		if job_name and job_links:
				__UpperCAmelCase    :     Any													=	job_links.get(_UpperCAmelCase ,   _UpperCAmelCase						)

		# A list with elements of the form (line of error, error, failed test)
		__UpperCAmelCase    :     Dict													=	[x + [y] + [job_link] for x, y in zip(_UpperCAmelCase ,   _UpperCAmelCase						)]

		return result

def        a						(      _UpperCAmelCase							:					Dict ,   _UpperCAmelCase							:					Tuple=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     str													=	[]

		__UpperCAmelCase    :     str													=	[os.path.join(_UpperCAmelCase ,   _UpperCAmelCase						) for p in os.listdir(_UpperCAmelCase						) if p.endswith('''.zip'''						)]
		for p in paths:
				errors.extend(get_errors_from_single_artifact(_UpperCAmelCase ,   job_links=_UpperCAmelCase						)						)

		return errors

def        a						(      _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					Optional[Any]=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	Counter()
		counter.update([x[1] for x in logs]						)
		__UpperCAmelCase    :     Dict													=	counter.most_common()
		__UpperCAmelCase    :     int													=	{}
		for error, count in counts:
				if error_filter is None or error not in error_filter:
						__UpperCAmelCase    :     Tuple													=	{'''count''': count, '''failed_tests''': [(x[2], x[0]) for x in logs if x[1] == error]}

		__UpperCAmelCase    :     Union[str, Any]													=	dict(sorted(r.items() ,   key=lambda _UpperCAmelCase						: item[1]["count"] ,   reverse=_UpperCAmelCase						)						)
		return r

def        a						(      _UpperCAmelCase							:					Dict						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[str]													=	test.split('''::'''						)[0]
		if test.startswith('''tests/models/'''						):
				__UpperCAmelCase    :     Optional[int]													=	test.split('''/'''						)[2]
		else:
				__UpperCAmelCase    :     Optional[Any]													=	None

		return test

def        a						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					int=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	[(x[0], x[1], get_model(x[2]						)) for x in logs]
		__UpperCAmelCase    :     int													=	[x for x in logs if x[2] is not None]
		__UpperCAmelCase    :     Tuple													=	{x[2] for x in logs}

		__UpperCAmelCase    :     List[str]													=	{}
		for test in tests:
				__UpperCAmelCase    :     List[str]													=	Counter()
				# count by errors in `test`
				counter.update([x[1] for x in logs if x[2] == test]						)
				__UpperCAmelCase    :     Tuple													=	counter.most_common()
				__UpperCAmelCase    :     Any													=	{error: count for error, count in counts if (error_filter is None or error not in error_filter)}
				__UpperCAmelCase    :     List[Any]													=	sum(error_counts.values()						)
				if n_errors > 0:
						__UpperCAmelCase    :     Tuple													=	{'''count''': n_errors, '''errors''': error_counts}

		__UpperCAmelCase    :     Optional[int]													=	dict(sorted(r.items() ,   key=lambda _UpperCAmelCase						: item[1]["count"] ,   reverse=_UpperCAmelCase						)						)
		return r

def        a						(      _UpperCAmelCase							:					Union[str, Any]						):







		'''simple docstring'''





		__UpperCAmelCase    :     Any													=	'''| no. | error | status |'''
		__UpperCAmelCase    :     List[str]													=	'''|-:|:-|:-|'''
		__UpperCAmelCase    :     Union[str, Any]													=	[header, sep]
		for error in reduced_by_error:
				__UpperCAmelCase    :     Tuple													=	reduced_by_error[error]['''count''']
				__UpperCAmelCase    :     int													=	f'| {count} | {error[:1_00]} |  |'
				lines.append(_UpperCAmelCase						)

		return "\n".join(_UpperCAmelCase						)

def        a						(      _UpperCAmelCase							:					Dict						):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	'''| model | no. of errors | major error | count |'''
		__UpperCAmelCase    :     List[Any]													=	'''|-:|-:|-:|-:|'''
		__UpperCAmelCase    :     Tuple													=	[header, sep]
		for model in reduced_by_model:
				__UpperCAmelCase    :     List[Any]													=	reduced_by_model[model]['''count''']
				__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	list(reduced_by_model[model]['''errors'''].items()						)[0]
				__UpperCAmelCase    :     Dict													=	f'| {model} | {count} | {error[:60]} | {_count} |'
				lines.append(_UpperCAmelCase						)

		return "\n".join(_UpperCAmelCase						)


if __name__ == "__main__":
				__A   			=argparse.ArgumentParser()
				# Required parameters
				parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
				parser.add_argument(
				    "--output_dir",
				    type=str,
				    required=True,
				    help="Where to store the downloaded artifacts and other result files.",
				)
				parser.add_argument("--token", default=None, type=str, help="A token that has actions:read permission.")
				__A   			=parser.parse_args()

				os.makedirs(args.output_dir, exist_ok=True)

				__A   			=get_job_links(args.workflow_run_id, token=args.token)
				__A   			={}
				# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
				# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
				if _job_links:
								for k, v in _job_links.items():
												# This is how GitHub actions combine job names.
												if " / " in k:
																__A   			=k.find(" / ")
																__A   			=k[index + len(" / ") :]
												__A   			=v
				with open(os.path.join(args.output_dir, "job_links.json"), "w", encoding="UTF-8") as fp:
								json.dump(job_links, fp, ensure_ascii=False, indent=4)

				__A   			=get_artifacts_links(args.workflow_run_id, token=args.token)
				with open(os.path.join(args.output_dir, "artifacts.json"), "w", encoding="UTF-8") as fp:
								json.dump(artifacts, fp, ensure_ascii=False, indent=4)

				for idx, (name, url) in enumerate(artifacts.items()):
								download_artifact(name, url, args.output_dir, args.token)
								# Be gentle to GitHub
								time.sleep(1)

				__A   			=get_all_errors(args.output_dir, job_links=job_links)

				# `e[1]` is the error
				__A   			=Counter()
				counter.update([e[1] for e in errors])

				# print the top 30 most common test errors
				__A   			=counter.most_common(3_0)
				for item in most_common:
								print(item)

				with open(os.path.join(args.output_dir, "errors.json"), "w", encoding="UTF-8") as fp:
								json.dump(errors, fp, ensure_ascii=False, indent=4)

				__A   			=reduce_by_error(errors)
				__A   			=reduce_by_model(errors)

				__A   			=make_github_table(reduced_by_error)
				__A   			=make_github_table_per_model(reduced_by_model)

				with open(os.path.join(args.output_dir, "reduced_by_error.txt"), "w", encoding="UTF-8") as fp:
								fp.write(sa)
				with open(os.path.join(args.output_dir, "reduced_by_model.txt"), "w", encoding="UTF-8") as fp:
								fp.write(sa)





