



import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


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

if is_tf_available():
	import tensorflow as tf
@dataclass
class lowerCAmelCase_       (   A_			):







	'''simple docstring'''




	A_       :     List[str]									=		'tf'
	A_       :     Optional[str]									=		field(
	    default=A_ ,metadata={'help': 'Name of TPU'} ,)

	A_       :     Optional[str]									=		field(
	    default=A_ ,metadata={'help': 'Zone of TPU'} ,)

	A_       :     Optional[str]									=		field(
	    default=A_ ,metadata={'help': 'Name of Cloud TPU-enabled project'} ,)

	A_       :     float									=		field(
	    default=1.0 ,metadata={'help': 'Power for the Polynomial decay LR scheduler.'} ,)

	A_       :     bool									=		field(default=A_ ,metadata={'help': 'Whether to activate the XLA compilation or not'}			)

	@cached_property
	def 				_A			(	self       ):



				'''simple docstring'''


				requires_backends(self   ,       ["""tf"""]       )
				logger.info("""Tensorflow: setting up strategy"""       )

				a__					=  tf.config.list_physical_devices("""GPU"""       )

				# Set to float16 at first
				if self.fpaa:
							tf.keras.mixed_precision.set_global_policy("""mixed_float16"""       )

				if self.no_cuda:
							a__					=  tf.distribute.OneDeviceStrategy(device="""/cpu:0"""       )
				else:
							try:
										if self.tpu_name:
													a__					=  tf.distribute.cluster_resolver.TPUClusterResolver(
													    self.tpu_name   ,       zone=self.tpu_zone   ,       project=self.gcp_project       )
										else:
													a__					=  tf.distribute.cluster_resolver.TPUClusterResolver()
							except ValueError:
										if self.tpu_name:
													raise RuntimeError(f'Couldn\'t connect to TPU {self.tpu_name}!'       )
										else:
													a__					=  None

							if tpu:
										# Set to bfloat16 in case of TPU
										if self.fpaa:
													tf.keras.mixed_precision.set_global_policy("""mixed_bfloat16"""       )

										tf.config.experimental_connect_to_cluster(lowerCamelCase       )
										tf.tpu.experimental.initialize_tpu_system(lowerCamelCase       )

										a__					=  tf.distribute.TPUStrategy(lowerCamelCase       )

							elif len(lowerCamelCase       ) == 0:
										a__					=  tf.distribute.OneDeviceStrategy(device="""/cpu:0"""       )
							elif len(lowerCamelCase       ) == 1:
										a__					=  tf.distribute.OneDeviceStrategy(device="""/gpu:0"""       )
							elif len(lowerCamelCase       ) > 1:
										# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
										a__					=  tf.distribute.MirroredStrategy()
							else:
										raise ValueError("""Cannot find the proper strategy, please check your environment properties."""       )

				return strategy

	@property
	def 				_A			(	self       ):



				'''simple docstring'''


				requires_backends(self   ,       ["""tf"""]       )
				return self._setup_strategy

	@property
	def 				_A			(	self       ):



				'''simple docstring'''


				requires_backends(self   ,       ["""tf"""]       )
				return self._setup_strategy.num_replicas_in_sync

	@property
	def 				_A			(	self       ):



				'''simple docstring'''


				return False  # TF Logging is handled by Keras not the Trainer

	@property
	def 				_A			(	self       ):



				'''simple docstring'''


				if self.per_gpu_train_batch_size:
							logger.warning(
							    """Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future """
							    """version. Using `--per_device_train_batch_size` is preferred."""       )
				a__					=  self.per_gpu_train_batch_size or self.per_device_train_batch_size
				return per_device_batch_size * self.n_replicas

	@property
	def 				_A			(	self       ):



				'''simple docstring'''


				if self.per_gpu_eval_batch_size:
							logger.warning(
							    """Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future """
							    """version. Using `--per_device_eval_batch_size` is preferred."""       )
				a__					=  self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
				return per_device_batch_size * self.n_replicas







	@property
	def 				_A			(	self       ):



				'''simple docstring'''


				requires_backends(self   ,       ["""tf"""]       )
				warnings.warn(
				    """The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead."""   ,       lowerCamelCase   ,       )
				return self._setup_strategy.num_replicas_in_sync






import argparse

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


logging.set_verbosity_info()




def    UpperCAmelCase					(  lowercase__						:					int				,					lowercase__						:					str				,					lowercase__						:					Union[str, Any]				,					lowercase__						:					Optional[int]				,					lowercase__						:					Optional[Any]	):



			'''simple docstring'''





			a__					=  BertConfig.from_pretrained(
			    """bert-large-cased"""				,					vocab_size=lowercase__				,					max_position_embeddings=512				,					is_decoder=lowercase__				,					add_cross_attention=lowercase__				,					)
			a__					=  bert_config.to_dict()
			del bert_config_dict["type_vocab_size"]
			a__					=  BertGenerationConfig(**lowercase__	)
			if is_encoder:
						a__					=  BertGenerationEncoder(lowercase__	)
			else:
						a__					=  BertGenerationDecoder(lowercase__	)
			print(f'Building PyTorch model from configuration: {config}'	)

			# Load weights from tf checkpoint
			load_tf_weights_in_bert_generation(
			    lowercase__				,					lowercase__				,					model_class="""bert"""				,					is_encoder_named_decoder=lowercase__				,					is_encoder=lowercase__				,					)

			# Save pytorch-model
			print(f'Save PyTorch model and config to {pytorch_dump_path}'	)
			model.save_pretrained(lowercase__	)


if __name__ == "__main__":
	_lowercase				:					str     					=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_03_58, type=int, help="""Vocab size of model""")
	_lowercase				:					List[str]     					=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,
	)

