




'''simple docstring'''







import argparse

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


logging.set_verbosity_info()







def 	_A			(   UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase	):






   '''simple docstring'''







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

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

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


if __name__ == "__main__":
    lowerCAmelCase_    	=							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_0_3_5_8, type=int, help='''Vocab size of model''')
    lowerCAmelCase_    	=							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 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


lowerCAmelCase_    	=							logging.get_logger(__name__)

if is_tf_available():
    import tensorflow as tf

@dataclass
class   _snake_case( UpperCAmelCase			):
 __snake_case:       Optional[Any]									=				'''tf'''
 __snake_case:       Optional[str]									=				field(
     default=UpperCAmelCase					,   metadata={'''help''': '''Name of TPU'''}					,   )

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

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

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

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






 @cached_property
 def 	_UpperCamelCase   (self					:     Tuple						)					->      Tuple["tf.distribute.Strategy", int]:






    """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(a						)
          tf.tpu.experimental.initialize_tpu_system(a						)

          A__         =    tf.distribute.TPUStrategy(a						)

       elif len(a						) == 0:
          A__         =    tf.distribute.OneDeviceStrategy(device='/cpu:0'						)
       elif len(a						) == 1:
          A__         =    tf.distribute.OneDeviceStrategy(device='/gpu:0'						)
       elif len(a						) > 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 	_UpperCamelCase   (self					:     List[Any]						)					->      "tf.distribute.Strategy":






    """simple docstring"""


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






 @property
 def 	_UpperCamelCase   (self					:     Tuple						)					->      int:






    """simple docstring"""


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






 @property
 def 	_UpperCamelCase   (self					:     Optional[Any]						)					->      int:






    """simple docstring"""


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






 @property
 def 	_UpperCamelCase   (self					:     Union[str, Any]						)					->      int:






    """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 	_UpperCamelCase   (self					:     Optional[int]						)					->      int:






    """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 	_UpperCamelCase   (self					:     Optional[Any]						)					->      int:






    """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.'						,		a						,		)
    return self._setup_strategy.num_replicas_in_sync


