


'''simple docstring'''




import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()

def       __UpperCamelCase     (     _A     :					Tuple       , _A     :					List[str]       , _A     :					List[str]       , _A     :					str       , _A     :					str					)     ->   Optional[int]:




	"""simple docstring"""





	lowerCAmelCase       :     Tuple   				=			BertConfig.from_pretrained(
	    'bert-large-cased'       , vocab_size=_A       , max_position_embeddings=5_12       , is_decoder=_A       , add_cross_attention=_A       , )
	lowerCAmelCase       :     Tuple   				=			bert_config.to_dict()
	del bert_config_dict["type_vocab_size"]
	lowerCAmelCase       :     Tuple   				=			BertGenerationConfig(**_A					)
	if is_encoder:
		lowerCAmelCase       :     Dict   				=			BertGenerationEncoder(_A					)
	else:
		lowerCAmelCase       :     List[Any]   				=			BertGenerationDecoder(_A					)
	print(F"Building PyTorch model from configuration: {config}"					)

	# Load weights from tf checkpoint
	load_tf_weights_in_bert_generation(
	    _A       , _A       , model_class='bert'       , is_encoder_named_decoder=_A       , is_encoder=_A       , )

	# Save pytorch-model
	print(F"Save PyTorch model and config to {pytorch_dump_path}"					)
	model.save_pretrained(_A					)


if __name__ == "__main__":
				_lowerCAmelCase	:		Dict		  =						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
				)
				parser.add_argument(
				    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
				)
				parser.add_argument(
				    '--is_encoder_named_decoder',
				    action='store_true',
				    help='If decoder has to be renamed to encoder in PyTorch model.',
				)
				parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
				parser.add_argument('--vocab_size', default=5_0358, type=int, help='Vocab size of model')
				_lowerCAmelCase	:		Optional[int]		  =						parser.parse_args()
				convert_tf_checkpoint_to_pytorch(
				    args.tf_hub_path,
				    args.pytorch_dump_path,
				    args.is_encoder_named_decoder,
				    args.vocab_size,
				    is_encoder=args.is_encoder,
				)




'''simple docstring'''




import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


_lowerCAmelCase	:		Optional[int]		  =						importlib.metadata.version('numpy')
_lowerCAmelCase	:		List[Any]		  =						'.'.join([str(x) for x in sys.version_info[:3]])



class 			lowerCAmelCase					(					a				):



	def 					lowercase  (       self     ):
		# lt + different version strings
		require_version_core('numpy<1000.4.5'     )
		require_version_core('numpy<1000.4'     )
		require_version_core('numpy<1000'     )

		# le
		require_version_core('numpy<=1000.4.5'     )
		require_version_core(f"numpy<={numpy_ver}"     )

		# eq
		require_version_core(f"numpy=={numpy_ver}"     )

		# ne
		require_version_core('numpy!=1000.4.5'     )

		# ge
		require_version_core('numpy>=1.0'     )
		require_version_core('numpy>=1.0.0'     )
		require_version_core(f"numpy>={numpy_ver}"     )

		# gt
		require_version_core('numpy>1.0.0'     )

		# mix
		require_version_core('numpy>1.0.0,<1000'     )

		# requirement w/o version
		require_version_core('numpy'     )

		# unmet requirements due to version conflict
		for req in ["numpy==1.0.0", "numpy>=1000.0.0", f"numpy<{numpy_ver}"]:
			try:
				require_version_core(snake_case__     )
			except ImportError as e:
				self.assertIn(f"{req} is required"			,       str(snake_case__     )     )
				self.assertIn('but found'			,       str(snake_case__     )     )

        # unmet requirements due to missing module
		for req in ["numpipypie>1", "numpipypie2"]:
			try:
				require_version_core(snake_case__     )
			except importlib.metadata.PackageNotFoundError as e:
				self.assertIn(f"The '{req}' distribution was not found and is required by this application"			,       str(snake_case__     )     )
				self.assertIn('Try: pip install transformers -U'			,       str(snake_case__     )     )

        # bogus requirements formats:
        # 1. whole thing
		for req in ["numpy??1.0.0", "numpy1.0.0"]:
			try:
				require_version_core(snake_case__     )
			except ValueError as e:
				self.assertIn('requirement needs to be in the pip package format'			,       str(snake_case__     )     )
        # 2. only operators
		for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
			try:
				require_version_core(snake_case__     )
			except ValueError as e:
				self.assertIn('need one of '			,       str(snake_case__     )     )



	def 					lowercase  (       self     ):
		# matching requirement
		require_version('python>=3.6.0'     )

		# not matching requirements
		for req in ["python>9.9.9", "python<3.0.0"]:
			try:
				require_version_core(snake_case__     )
			except ImportError as e:
				self.assertIn(f"{req} is required"			,       str(snake_case__     )     )
				self.assertIn(f"but found python=={python_ver}"			,       str(snake_case__     )     )
