


'''simple docstring'''
import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


__SCREAMING_SNAKE_CASE					:				List[Any]									=		logging.get_logger(__name__)







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		set()
							A_			    =		[]

							def parse_line(UpperCamelCase_ ):
														for line in fp:
																					if isinstance(UpperCamelCase_						,  UpperCamelCase_ ):
																												A_			    =		line.decode("UTF-8" )
																					if "warnings summary (final)" in line:
																												continue
																					# This means we are outside the body of a warning
																					elif not line.startswith(" " ):
																												# process a single warning and move it to `selected_warnings`.
																												if len(UpperCamelCase_ ) > 0:
																																			A_			    =		"\n".join(UpperCamelCase_ )
																																			# Only keep the warnings specified in `targets`
																																			if any(f": {x}: " in warning for x in targets ):
																																										selected_warnings.add(UpperCamelCase_ )
																																			buffer.clear()
																												continue
																					else:
																												A_			    =		line.strip()
																												buffer.append(UpperCamelCase_ )

							if from_gh:
														for filename in os.listdir(UpperCamelCase_ ):
																					A_			    =		os.path.join(UpperCamelCase_						,  UpperCamelCase_ )
																					if not os.path.isdir(UpperCamelCase_ ):
																												# read the file
																												if filename != "warnings.txt":
																																			continue
																												with open(UpperCamelCase_ ) as fp:
																																			parse_line(UpperCamelCase_ )
							else:
														try:
																					with zipfile.ZipFile(UpperCamelCase_ ) as z:
																												for filename in z.namelist():
																																			if not os.path.isdir(UpperCamelCase_ ):
																																										# read the file
																																										if filename != "warnings.txt":
																																																	continue
																																										with z.open(UpperCamelCase_ ) as fp:
																																																	parse_line(UpperCamelCase_ )
														except Exception:
																					logger.warning(
																					    f"{artifact_path} is either an invalid zip file or something else wrong. This file is skipped." )

							return selected_warnings







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		set()

							A_			    =		[os.path.join(UpperCamelCase_						,  UpperCamelCase_ ) for p in os.listdir(UpperCamelCase_ ) if (p.endswith(".zip" ) or from_gh)]
							for p in paths:
														selected_warnings.update(extract_warnings_from_single_artifact(UpperCamelCase_						,  UpperCamelCase_ ) )

							return selected_warnings


if __name__ == "__main__":







							def       a_				(	UpperCamelCase_ ):
														return values.split("," )

							__SCREAMING_SNAKE_CASE					:				List[Any]									=		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.''')
							# optional parameters
							parser.add_argument(
							    '''--targets''',
							    default='''DeprecationWarning,UserWarning,FutureWarning''',
							    type=list_str,
							    help='''Comma-separated list of target warning(s) which we want to extract.''',
							)
							parser.add_argument(
							    '''--from_gh''',
							    action='''store_true''',
							    help='''If running from a GitHub action workflow and collecting warnings from its artifacts.''',
							)

							__SCREAMING_SNAKE_CASE					:				Optional[int]									=		parser.parse_args()

							__SCREAMING_SNAKE_CASE					:				int									=		args.from_gh
							if from_gh:
														# The artifacts have to be downloaded using `actions/download-artifact@v3`
														pass
							else:
														os.makedirs(args.output_dir, exist_ok=True)

														# get download links
														__SCREAMING_SNAKE_CASE					:				List[Any]									=		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)

														# download artifacts
														for idx, (name, url) in enumerate(artifacts.items()):
																					print(name)
																					print(url)
																					print('''=''' * 80)
																					download_artifact(name, url, args.output_dir, args.token)
																					# Be gentle to GitHub
																					time.sleep(1)

    # extract warnings from artifacts
							__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		extract_warnings(args.output_dir, args.targets)
							__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		sorted(selected_warnings)
							with open(os.path.join(args.output_dir, '''selected_warnings.json'''), '''w''', encoding='''UTF-8''') as fp:
														json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)









'''simple docstring'''
import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
							import jax

							from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
							from transformers.models.bert.modeling_flax_bert import FlaxBertModel
							from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel






@require_flax
class 					__lowerCAmelCase   (   unittest.TestCase						):

						"""simple docstring"""






						@slow
						def 				_UpperCAmelCase       (  self							:      Dict	):
													for model_name in ["bert-base-cased", "bert-large-uncased"]:
																				with self.subTest(lowerCAmelCase	):
																											A_			    =		AutoConfig.from_pretrained(lowerCAmelCase	)
																											self.assertIsNotNone(lowerCAmelCase	)
																											self.assertIsInstance(lowerCAmelCase ,  lowerCAmelCase	)

																											A_			    =		FlaxAutoModel.from_pretrained(lowerCAmelCase	)
																											self.assertIsNotNone(lowerCAmelCase	)
																											self.assertIsInstance(lowerCAmelCase ,  lowerCAmelCase	)






						@slow
						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													for model_name in ["roberta-base", "roberta-large"]:
																				with self.subTest(lowerCAmelCase	):
																											A_			    =		AutoConfig.from_pretrained(lowerCAmelCase	)
																											self.assertIsNotNone(lowerCAmelCase	)
																											self.assertIsInstance(lowerCAmelCase ,  lowerCAmelCase	)

																											A_			    =		FlaxAutoModel.from_pretrained(lowerCAmelCase	)
																											self.assertIsNotNone(lowerCAmelCase	)
																											self.assertIsInstance(lowerCAmelCase ,  lowerCAmelCase	)






						@slow
						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													for model_name in ["bert-base-cased", "bert-large-uncased"]:
																				A_			    =		AutoTokenizer.from_pretrained(lowerCAmelCase	)
																				A_			    =		FlaxBertModel.from_pretrained(lowerCAmelCase	)
																				A_			    =		tokenizer("Do you support jax jitted function?" ,  return_tensors=TensorType.JAX	)

																				@jax.jit
																				def eval(**lowerCAmelCase							:      Tuple	):
																											return model(**lowerCAmelCase	)

																				eval(**lowerCAmelCase	).block_until_ready()






						@slow
						def 				_UpperCAmelCase       (  self							:      str	):
													for model_name in ["roberta-base", "roberta-large"]:
																				A_			    =		AutoTokenizer.from_pretrained(lowerCAmelCase	)
																				A_			    =		FlaxRobertaModel.from_pretrained(lowerCAmelCase	)
																				A_			    =		tokenizer("Do you support jax jitted function?" ,  return_tensors=TensorType.JAX	)

																				@jax.jit
																				def eval(**lowerCAmelCase							:      Any	):
																											return model(**lowerCAmelCase	)

																				eval(**lowerCAmelCase	).block_until_ready()






						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													with self.assertRaisesRegex(
													    lowerCAmelCase ,  "bert-base is not a local folder and is not a valid model identifier"	):
																				A_			    =		FlaxAutoModel.from_pretrained("bert-base"	)






						def 				_UpperCAmelCase       (  self							:      Any	):
													with self.assertRaisesRegex(
													    lowerCAmelCase ,  r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"	):
																				A_			    =		FlaxAutoModel.from_pretrained(lowerCAmelCase ,  revision="aaaaaa"	)






						def 				_UpperCAmelCase       (  self							:      Dict	):
													with self.assertRaisesRegex(
													    lowerCAmelCase ,  "hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack" ,  ):
																				A_			    =		FlaxAutoModel.from_pretrained("hf-internal-testing/config-no-model"	)






						def 				_UpperCAmelCase       (  self							:      Any	):
													with self.assertRaisesRegex(lowerCAmelCase ,  "Use `from_pt=True` to load this model"	):
																				A_			    =		FlaxAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only"	)





