




from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
		import tensorflow as tf

		from transformers import TFBlipTextModel
		from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST

class 						__lowerCAmelCase      :
							def __init__(		self		:Optional[int]						, __magic_name__		:str						, __magic_name__		:int=12						, __magic_name__		:Optional[Any]=7						, __magic_name__		:Any=True						, __magic_name__		:Optional[int]=True						, __magic_name__		:List[Any]=True						, __magic_name__		:List[Any]=99						, __magic_name__		:Optional[int]=32						, __magic_name__		:List[Any]=32						, __magic_name__		:List[Any]=2						, __magic_name__		:Dict=4						, __magic_name__		:Optional[Any]=37						, __magic_name__		:int=0.1						, __magic_name__		:str=0.1						, __magic_name__		:Tuple=512						, __magic_name__		:Optional[Any]=0.02						, __magic_name__		:Dict=0						, __magic_name__		:List[str]=None						, ):







								'''simple docstring'''







								a									=				parent
								a									=				batch_size
								a									=				seq_length
								a									=				is_training
								a									=				use_input_mask
								a									=				use_labels
								a									=				vocab_size
								a									=				hidden_size
								a									=				projection_dim
								a									=				num_hidden_layers
								a									=				num_attention_heads
								a									=				intermediate_size
								a									=				dropout
								a									=				attention_dropout
								a									=				max_position_embeddings
								a									=				initializer_range
								a									=				scope
								a									=				bos_token_id
							def  lowerCamelCase__				(		self		:Optional[Any]							):







								'''simple docstring'''







								a									=				ids_tensor([self.batch_size, self.seq_length]						, self.vocab_size							)

								a									=				None
								if self.use_input_mask:
									a									=				random_attention_mask([self.batch_size, self.seq_length]							)

								if input_mask is not None:
									a									=				input_mask.numpy()
									a									=				input_mask.shape
									a									=				np.random.randint(1						, seq_length - 1						, size=(batch_size,)							)
									for batch_idx, start_index in enumerate(_lowercase							):
										a									=				1
										a									=				0

								a									=				self.get_config()

								return config, input_ids, tf.convert_to_tensor(_lowercase							)
							def  lowerCamelCase__				(		self		:int							):







								'''simple docstring'''







								return BlipTextConfig(
								    vocab_size=self.vocab_size						, hidden_size=self.hidden_size						, projection_dim=self.projection_dim						, num_hidden_layers=self.num_hidden_layers						, num_attention_heads=self.num_attention_heads						, intermediate_size=self.intermediate_size						, dropout=self.dropout						, attention_dropout=self.attention_dropout						, max_position_embeddings=self.max_position_embeddings						, initializer_range=self.initializer_range						, bos_token_id=self.bos_token_id						, )
							def  lowerCamelCase__				(		self		:Dict						, __magic_name__		:Optional[Any]						, __magic_name__		:Optional[int]						, __magic_name__		:List[str]							):







								'''simple docstring'''







								a									=				TFBlipTextModel(config=_lowercase							)
								a									=				model(_lowercase						, attention_mask=_lowercase						, training=_lowercase							)
								a									=				model(_lowercase						, training=_lowercase							)
								self.parent.assertEqual(result.last_hidden_state.shape						, (self.batch_size, self.seq_length, self.hidden_size)							)
								self.parent.assertEqual(result.pooler_output.shape						, (self.batch_size, self.hidden_size)							)





							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								a									=				self.prepare_config_and_inputs()
								a									=				config_and_inputs
								a									=				{"input_ids": input_ids, "attention_mask": input_mask}
								return config, inputs_dict







@require_tf
class 						__lowerCAmelCase      (      SCREAMING_SNAKE_CASE__ ,			unittest.TestCase					):
							UpperCamelCase__   	=    (TFBlipTextModel,) if is_tf_available() else ()
							UpperCamelCase__   	=    False
							UpperCamelCase__   	=    False
							UpperCamelCase__   	=    False
							def  lowerCamelCase__				(		self		:Any							):







								'''simple docstring'''







								a									=				BlipTextModelTester(self							)
								a									=				ConfigTester(self						, config_class=_lowercase						, hidden_size=37							)
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								self.config_tester.run_common_tests()
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								a									=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowercase							)
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								pass
							def  lowerCamelCase__				(		self		:int							):







								'''simple docstring'''







								pass
							@unittest.skip(reason="""Blip does not use inputs_embeds"""							)
							def  lowerCamelCase__				(		self		:Dict							):







								'''simple docstring'''







								pass
							@unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""							)
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								pass
							@unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""							)
							def  lowerCamelCase__				(		self		:Tuple							):







								'''simple docstring'''







								pass
							@slow
							def  lowerCamelCase__				(		self		:Dict							):







								'''simple docstring'''







								for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									a									=				TFBlipTextModel.from_pretrained(_lowercase							)
									self.assertIsNotNone(_lowercase							)





							def  lowerCamelCase__				(		self		:Dict						, __magic_name__		:Optional[int]=True							):







								'''simple docstring'''







								super().test_pt_tf_model_equivalence(allow_missing_keys=_lowercase							)






"""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


A		=						logging.get_logger(__name__)

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							List[Any] ):
						"""simple docstring"""






						snake_case   :			Optional[Any]						    = set()
						snake_case   :			Optional[int]						    = []

						def parse_line(lowerCamelCase_:							Union[str, Any] ):
												for line in fp:
																		if isinstance(lowerCamelCase_     ,		lowerCamelCase_ ):
																								snake_case   :			Optional[Any]						    = 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:
																														snake_case   :			str						    = "\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:
																								snake_case   :			Dict						    = line.strip()
																								buffer.append(lowerCamelCase_ )

						if from_gh:
												for filename in os.listdir(lowerCamelCase_ ):
																		snake_case   :			Tuple						    = 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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Union[str, Any]     ,		lowerCamelCase_:							Any ):
						"""simple docstring"""






						snake_case   :			Dict						    = set()

						snake_case   :			str						    = [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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Dict ):
									"""simple docstring"""






									return values.split("," )

			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.')
			# 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		=						parser.parse_args()

			A		=						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		=						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('=' * 8_0)
									download_artifact(name, url, args.output_dir, args.token)
									# Be gentle to GitHub
									time.sleep(1)

    # extract warnings from artifacts
			A		=						extract_warnings(args.output_dir, args.targets)
			A		=						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)



