





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

import requests





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__=None )  ->				List[Any]:







			'''simple docstring'''

			UpperCAmelCase   		=				None
			if token is not None:
						UpperCAmelCase   		=				{'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"""Bearer {token}"""}

			UpperCAmelCase   		=				F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
			UpperCAmelCase   		=				requests.get(UpperCamelCase__			,			headers=UpperCamelCase__ ).json()
			UpperCAmelCase   		=				{}

			try:
						job_links.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']} )
						UpperCAmelCase   		=				math.ceil((result['''total_count'''] - 100) / 100 )

						for i in range(UpperCamelCase__ ):
									UpperCAmelCase   		=				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 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__=None )  ->				Tuple:







			'''simple docstring'''

			UpperCAmelCase   		=				None
			if token is not None:
						UpperCAmelCase   		=				{'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"""Bearer {token}"""}

			UpperCAmelCase   		=				F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"""
			UpperCAmelCase   		=				requests.get(UpperCamelCase__			,			headers=UpperCamelCase__ ).json()
			UpperCAmelCase   		=				{}

			try:
						artifacts.update({artifact['''name''']: artifact['''archive_download_url'''] for artifact in result['''artifacts''']} )
						UpperCAmelCase   		=				math.ceil((result['''total_count'''] - 100) / 100 )

						for i in range(UpperCamelCase__ ):
									UpperCAmelCase   		=				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 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				Tuple:







			'''simple docstring'''

			UpperCAmelCase   		=				None
			if token is not None:
						UpperCAmelCase   		=				{'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"""Bearer {token}"""}

			UpperCAmelCase   		=				requests.get(UpperCamelCase__			,			headers=UpperCamelCase__			,			allow_redirects=UpperCamelCase__ )
			UpperCAmelCase   		=				result.headers['''Location''']
			UpperCAmelCase   		=				requests.get(UpperCamelCase__			,			allow_redirects=UpperCamelCase__ )
			UpperCAmelCase   		=				os.path.join(UpperCamelCase__			,			F"""{artifact_name}.zip""" )
			with open(UpperCamelCase__			,			'''wb''' ) as fp:
						fp.write(response.content )





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__=None )  ->				int:







			'''simple docstring'''

			UpperCAmelCase   		=				[]
			UpperCAmelCase   		=				[]
			UpperCAmelCase   		=				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   		=				line.decode('''UTF-8''' ).strip()
																					if filename == "failures_line.txt":
																								try:
																											# `error_line` is the place where `error` occurs
																											UpperCAmelCase   		=				line[: line.index(''': ''' )]
																											UpperCAmelCase   		=				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   		=				line[len('''FAILED ''' ) :]
																								failed_tests.append(UpperCamelCase__ )
																					elif filename == "job_name.txt":
																								UpperCAmelCase   		=				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   		=				None
			if job_name and job_links:
						UpperCAmelCase   		=				job_links.get(UpperCamelCase__			,			UpperCamelCase__ )

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

			return result





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__=None )  ->				int:







			'''simple docstring'''

			UpperCAmelCase   		=				[]

			UpperCAmelCase   		=				[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 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__=None )  ->				str:







			'''simple docstring'''

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

			UpperCAmelCase   		=				dict(sorted(r.items()			,			key=lambda UpperCamelCase__ : item[1]["count"]			,			reverse=UpperCamelCase__ ) )
			return r





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				Union[str, Any]:







			'''simple docstring'''

			UpperCAmelCase   		=				test.split('''::''' )[0]
			if test.startswith('''tests/models/''' ):
						UpperCAmelCase   		=				test.split('''/''' )[2]
			else:
						UpperCAmelCase   		=				None

			return test





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__=None )  ->				List[str]:







			'''simple docstring'''

			UpperCAmelCase   		=				[(x[0], x[1], get_model(x[2] )) for x in logs]
			UpperCAmelCase   		=				[x for x in logs if x[2] is not None]
			UpperCAmelCase   		=				{x[2] for x in logs}

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

			UpperCAmelCase   		=				dict(sorted(r.items()			,			key=lambda UpperCamelCase__ : item[1]["count"]			,			reverse=UpperCamelCase__ ) )
			return r





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				List[str]:







			'''simple docstring'''

			UpperCAmelCase   		=				'''| no. | error | status |'''
			UpperCAmelCase   		=				'''|-:|:-|:-|'''
			UpperCAmelCase   		=				[header, sep]
			for error in reduced_by_error:
						UpperCAmelCase   		=				reduced_by_error[error]['''count''']
						UpperCAmelCase   		=				F"""| {count} | {error[:100]} |  |"""
						lines.append(UpperCamelCase__ )

			return "\n".join(UpperCamelCase__ )





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				str:







			'''simple docstring'''

			UpperCAmelCase   		=				'''| model | no. of errors | major error | count |'''
			UpperCAmelCase   		=				'''|-:|-:|-:|-:|'''
			UpperCAmelCase   		=				[header, sep]
			for model in reduced_by_model:
						UpperCAmelCase   		=				reduced_by_model[model]['''count''']
						UpperCAmelCase		,			UpperCAmelCase   		=				list(reduced_by_model[model]['''errors'''].items() )[0]
						UpperCAmelCase   		=				F"""| {model} | {count} | {error[:60]} | {_count} |"""
						lines.append(UpperCamelCase__ )

			return "\n".join(UpperCamelCase__ )


if __name__ == "__main__":
				__A				:    int       =   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				:    Union[str, Any]       =   parser.parse_args()

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

				__A				:    Optional[int]       =   get_job_links(args.workflow_run_id, token=args.token)
				__A				:    Optional[Any]       =   {}
				# 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				:    List[Any]       =   k.find(" / ")
																__A				:    str       =   k[index + len(" / ") :]
												__A				:    Optional[int]       =   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				:    List[str]       =   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				:    Dict       =   get_all_errors(args.output_dir, job_links=job_links)

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

				# print the top 30 most common test errors
				__A				:    Optional[Any]       =   counter.most_common(30)
				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				:    int       =   reduce_by_error(errors)
				__A				:    List[Any]       =   reduce_by_model(errors)

				__A				:    int       =   make_github_table(reduced_by_error)
				__A				:    int       =   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)










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A				:    Dict       =   {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    Union[str, Any]       =   ["RoFormerTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    str       =   [
				    "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "RoFormerForCausalLM",
				    "RoFormerForMaskedLM",
				    "RoFormerForMultipleChoice",
				    "RoFormerForQuestionAnswering",
				    "RoFormerForSequenceClassification",
				    "RoFormerForTokenClassification",
				    "RoFormerLayer",
				    "RoFormerModel",
				    "RoFormerPreTrainedModel",
				    "load_tf_weights_in_roformer",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    int       =   [
				    "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFRoFormerForCausalLM",
				    "TFRoFormerForMaskedLM",
				    "TFRoFormerForMultipleChoice",
				    "TFRoFormerForQuestionAnswering",
				    "TFRoFormerForSequenceClassification",
				    "TFRoFormerForTokenClassification",
				    "TFRoFormerLayer",
				    "TFRoFormerModel",
				    "TFRoFormerPreTrainedModel",
				]


try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    Any       =   [
				    "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "FlaxRoFormerForMaskedLM",
				    "FlaxRoFormerForMultipleChoice",
				    "FlaxRoFormerForQuestionAnswering",
				    "FlaxRoFormerForSequenceClassification",
				    "FlaxRoFormerForTokenClassification",
				    "FlaxRoFormerModel",
				    "FlaxRoFormerPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
				from .tokenization_roformer import RoFormerTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_roformer_fast import RoFormerTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_roformer import (
								    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RoFormerForCausalLM,
								    RoFormerForMaskedLM,
								    RoFormerForMultipleChoice,
								    RoFormerForQuestionAnswering,
								    RoFormerForSequenceClassification,
								    RoFormerForTokenClassification,
								    RoFormerLayer,
								    RoFormerModel,
								    RoFormerPreTrainedModel,
								    load_tf_weights_in_roformer,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_roformer import (
								    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRoFormerForCausalLM,
								    TFRoFormerForMaskedLM,
								    TFRoFormerForMultipleChoice,
								    TFRoFormerForQuestionAnswering,
								    TFRoFormerForSequenceClassification,
								    TFRoFormerForTokenClassification,
								    TFRoFormerLayer,
								    TFRoFormerModel,
								    TFRoFormerPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_roformer import (
								    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FlaxRoFormerForMaskedLM,
								    FlaxRoFormerForMultipleChoice,
								    FlaxRoFormerForQuestionAnswering,
								    FlaxRoFormerForSequenceClassification,
								    FlaxRoFormerForTokenClassification,
								    FlaxRoFormerModel,
								    FlaxRoFormerPreTrainedModel,
								)


else:
				import sys

				__A				:    int       =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



