






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


A__      :     Optional[int]       =					logging.get_logger(__name__)






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     set()
				lowercase__  					=     []

				def parse_line(lowerCamelCase_							):
								for line in fp:
												if isinstance(lowerCamelCase_						,  lowerCamelCase_							):
																lowercase__  					=     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(lowerCamelCase_							) > 0:
																				lowercase__  					=     '''\n'''.join(lowerCamelCase_							)
																				# Only keep the warnings specified in `targets`
																				if any(F""": {x}: """ in warning for x in targets							):
																								selected_warnings.add(lowerCamelCase_							)
																				buffer.clear()
																continue
												else:
																lowercase__  					=     line.strip()
																buffer.append(lowerCamelCase_							)

				if from_gh:
								for filename in os.listdir(lowerCamelCase_							):
												lowercase__  					=     os.path.join(lowerCamelCase_						,  lowerCamelCase_							)
												if not os.path.isdir(lowerCamelCase_							):
																# read the file
																if filename != "warnings.txt":
																				continue
																with open(lowerCamelCase_							) as fp:
																				parse_line(lowerCamelCase_							)
				else:
								try:
												with zipfile.ZipFile(lowerCamelCase_							) as z:
																for filename in z.namelist():
																				if not os.path.isdir(lowerCamelCase_							):
																								# read the file
																								if filename != "warnings.txt":
																												continue
																								with z.open(lowerCamelCase_							) as fp:
																												parse_line(lowerCamelCase_							)
								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     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     set()

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

				return selected_warnings


if __name__ == "__main__":






							def     a     (		lowerCamelCase_							):






											'''simple docstring'''
											return values.split(''','''							)

							A__      :     str       =					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.',
							)

							A__      :     Dict       =					parser.parse_args()

							A__      :     Dict       =					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
														A__      :     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
							A__      :     Dict       =					extract_warnings(args.output_dir, args.targets)
							A__      :     List[str]       =					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)












import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
							import jax.numpy as jnp

							from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
							    FlaxRobertaPreLayerNormForCausalLM,
							    FlaxRobertaPreLayerNormForMaskedLM,
							    FlaxRobertaPreLayerNormForMultipleChoice,
							    FlaxRobertaPreLayerNormForQuestionAnswering,
							    FlaxRobertaPreLayerNormForSequenceClassification,
							    FlaxRobertaPreLayerNormForTokenClassification,
							    FlaxRobertaPreLayerNormModel,
							)
class  _UpperCAmelCase   (			unittest.TestCase      ):

				"""simple docstring"""




				def __init__(	self		:       List[Any],    lowerCamelCase		:       Dict,    lowerCamelCase		:       Dict=13,    lowerCamelCase		:       Optional[int]=7,    lowerCamelCase		:       Optional[Any]=True,    lowerCamelCase		:       Union[str, Any]=True,    lowerCamelCase		:       Optional[Any]=True,    lowerCamelCase		:       Optional[Any]=True,    lowerCamelCase		:       List[Any]=99,    lowerCamelCase		:       Any=32,    lowerCamelCase		:       List[str]=5,    lowerCamelCase		:       Union[str, Any]=4,    lowerCamelCase		:       Union[str, Any]=37,    lowerCamelCase		:       Optional[int]="gelu",    lowerCamelCase		:       Tuple=0.1,    lowerCamelCase		:       List[str]=0.1,    lowerCamelCase		:       Optional[Any]=512,    lowerCamelCase		:       int=16,    lowerCamelCase		:       str=2,    lowerCamelCase		:       int=0.02,    lowerCamelCase		:       int=4,    ):

								'''simple docstring'''
								lowercase__  					=     parent
								lowercase__  					=     batch_size
								lowercase__  					=     seq_length
								lowercase__  					=     is_training
								lowercase__  					=     use_attention_mask
								lowercase__  					=     use_token_type_ids
								lowercase__  					=     use_labels
								lowercase__  					=     vocab_size
								lowercase__  					=     hidden_size
								lowercase__  					=     num_hidden_layers
								lowercase__  					=     num_attention_heads
								lowercase__  					=     intermediate_size
								lowercase__  					=     hidden_act
								lowercase__  					=     hidden_dropout_prob
								lowercase__  					=     attention_probs_dropout_prob
								lowercase__  					=     max_position_embeddings
								lowercase__  					=     type_vocab_size
								lowercase__  					=     type_sequence_label_size
								lowercase__  					=     initializer_range
								lowercase__  					=     num_choices




				def 							lowercase__  (	self		:       Dict    ):

								'''simple docstring'''
								lowercase__  					=     ids_tensor([self.batch_size, self.seq_length],    self.vocab_size    )

								lowercase__  					=     None
								if self.use_attention_mask:
												lowercase__  					=     random_attention_mask([self.batch_size, self.seq_length]    )

								lowercase__  					=     None
								if self.use_token_type_ids:
												lowercase__  					=     ids_tensor([self.batch_size, self.seq_length],    self.type_vocab_size    )

								lowercase__  					=     RobertaPreLayerNormConfig(
								    vocab_size=self.vocab_size,    hidden_size=self.hidden_size,    num_hidden_layers=self.num_hidden_layers,    num_attention_heads=self.num_attention_heads,    intermediate_size=self.intermediate_size,    hidden_act=self.hidden_act,    hidden_dropout_prob=self.hidden_dropout_prob,    attention_probs_dropout_prob=self.attention_probs_dropout_prob,    max_position_embeddings=self.max_position_embeddings,    type_vocab_size=self.type_vocab_size,    is_decoder=lowerCamelCase,    initializer_range=self.initializer_range,    )

								return config, input_ids, token_type_ids, attention_mask




				def 							lowercase__  (	self		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     self.prepare_config_and_inputs()
								lowercase__					,		lowercase__					,		lowercase__					,		lowercase__  					=     config_and_inputs
								lowercase__  					=     {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
								return config, inputs_dict




				def 							lowercase__  (	self		:       Optional[int]    ):

								'''simple docstring'''
								lowercase__  					=     self.prepare_config_and_inputs()
								lowercase__					,		lowercase__					,		lowercase__					,		lowercase__  					=     config_and_inputs

								lowercase__  					=     True
								lowercase__  					=     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
								lowercase__  					=     ids_tensor([self.batch_size, self.seq_length],    vocab_size=2    )

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)



@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class  _UpperCAmelCase   (			A__						,unittest.TestCase      ):

				"""simple docstring"""




				lowercase__											=			True

				lowercase__											=			(
				    (
				        FlaxRobertaPreLayerNormModel,
				        FlaxRobertaPreLayerNormForCausalLM,
				        FlaxRobertaPreLayerNormForMaskedLM,
				        FlaxRobertaPreLayerNormForSequenceClassification,
				        FlaxRobertaPreLayerNormForTokenClassification,
				        FlaxRobertaPreLayerNormForMultipleChoice,
				        FlaxRobertaPreLayerNormForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)




				def 							lowercase__  (	self		:       int    ):

								'''simple docstring'''
								lowercase__  					=     FlaxRobertaPreLayerNormModelTester(self    )




				@slow
				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								for model_class_name in self.all_model_classes:
												lowercase__  					=     model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''',    from_pt=lowerCamelCase    )
												lowercase__  					=     model(np.ones((1, 1)    )    )
												self.assertIsNotNone(lowerCamelCase    )



@require_flax
class  _UpperCAmelCase   (			unittest.TestCase      ):

				"""simple docstring"""




				@slow
				def 							lowercase__  (	self		:       Union[str, Any]    ):

								'''simple docstring'''
								lowercase__  					=     FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''',    from_pt=lowerCamelCase    )

								lowercase__  					=     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]],    dtype=jnp.intaa    )
								lowercase__  					=     model(lowerCamelCase    )[0]
								lowercase__  					=     [1, 11, 50_265]
								self.assertEqual(list(output.shape    ),    lowerCamelCase    )
								# compare the actual values for a slice.
								lowercase__  					=     np.array(
								    [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]],    dtype=np.floataa    )
								self.assertTrue(np.allclose(output[:, :3, :3],    lowerCamelCase,    atol=1E-4    )    )




				@slow
				def 							lowercase__  (	self		:       Any    ):

								'''simple docstring'''
								lowercase__  					=     FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''',    from_pt=lowerCamelCase    )

								lowercase__  					=     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]],    dtype=jnp.intaa    )
								lowercase__  					=     model(lowerCamelCase    )[0]
								# compare the actual values for a slice.
								lowercase__  					=     np.array(
								    [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]],    dtype=np.floataa    )
								self.assertTrue(np.allclose(output[:, :3, :3],    lowerCamelCase,    atol=1E-4    )    )




