



"""simple docstring"""






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def 			snake_case_    ( A_   :						BertModel,							A_   :						str,							A_   :						str		):


       '''simple docstring'''





       _lowerCamelCase		:							Optional[int]            =					('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

       _lowerCamelCase		:							List[Any]            =					(
           ('''layer.''', '''layer_'''),
           ('''word_embeddings.weight''', '''word_embeddings'''),
           ('''position_embeddings.weight''', '''position_embeddings'''),
           ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
           ('''.''', '''/'''),
           ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
           ('''LayerNorm/bias''', '''LayerNorm/beta'''),
           ('''weight''', '''kernel'''),
       )

       if not os.path.isdir(SCREAMING_SNAKE_CASE_		):
              os.makedirs(SCREAMING_SNAKE_CASE_		)

       _lowerCamelCase		:							Union[str, Any]            =					model.state_dict()

       def to_tf_var_name(A_   :						str		):
              for patt, repl in iter(SCREAMING_SNAKE_CASE_		):
                     _lowerCamelCase		:							Tuple            =					name.replace(SCREAMING_SNAKE_CASE_,							SCREAMING_SNAKE_CASE_		)
              return F'''bert/{name}'''

       def create_tf_var(A_   :						np.ndarray,							A_   :						str,							A_   :						tf.Session		):
              _lowerCamelCase		:							List[Any]            =					tf.dtypes.as_dtype(tensor.dtype		)
              _lowerCamelCase		:							Optional[int]            =					tf.get_variable(dtype=SCREAMING_SNAKE_CASE_,							shape=tensor.shape,							name=SCREAMING_SNAKE_CASE_,							initializer=tf.zeros_initializer()		)
              session.run(tf.variables_initializer([tf_var]		)		)
              session.run(SCREAMING_SNAKE_CASE_		)
              return tf_var

       tf.reset_default_graph()
       with tf.Session() as session:
              for var_name in state_dict:
                     _lowerCamelCase		:							str            =					to_tf_var_name(SCREAMING_SNAKE_CASE_		)
                     _lowerCamelCase		:							Union[str, Any]            =					state_dict[var_name].numpy()
                     if any(x in var_name for x in tensors_to_transpose		):
                            _lowerCamelCase		:							Dict            =					torch_tensor.T
                     _lowerCamelCase		:							Tuple            =					create_tf_var(tensor=SCREAMING_SNAKE_CASE_,							name=SCREAMING_SNAKE_CASE_,							session=SCREAMING_SNAKE_CASE_		)
                     tf.keras.backend.set_value(SCREAMING_SNAKE_CASE_,							SCREAMING_SNAKE_CASE_		)
                     _lowerCamelCase		:							List[str]            =					session.run(SCREAMING_SNAKE_CASE_		)
                     print(F'''Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE_,							SCREAMING_SNAKE_CASE_		)}'''		)

              _lowerCamelCase		:							Any            =					tf.train.Saver(tf.trainable_variables()		)
              saver.save(SCREAMING_SNAKE_CASE_,							os.path.join(SCREAMING_SNAKE_CASE_,							model_name.replace('''-''',							'''_'''		) + '''.ckpt'''		)		)






def 			snake_case_    ( A_   :						Any=None		):


       '''simple docstring'''





       _lowerCamelCase		:							Optional[int]            =					argparse.ArgumentParser()
       parser.add_argument('''--model_name''',							type=SCREAMING_SNAKE_CASE_,							required=SCREAMING_SNAKE_CASE_,							help='''model name e.g. bert-base-uncased'''		)
       parser.add_argument(
           '''--cache_dir''',							type=SCREAMING_SNAKE_CASE_,							default=SCREAMING_SNAKE_CASE_,							required=SCREAMING_SNAKE_CASE_,							help='''Directory containing pytorch model'''		)
       parser.add_argument('''--pytorch_model_path''',							type=SCREAMING_SNAKE_CASE_,							required=SCREAMING_SNAKE_CASE_,							help='''/path/to/<pytorch-model-name>.bin'''		)
       parser.add_argument('''--tf_cache_dir''',							type=SCREAMING_SNAKE_CASE_,							required=SCREAMING_SNAKE_CASE_,							help='''Directory in which to save tensorflow model'''		)
       _lowerCamelCase		:							Any            =					parser.parse_args(SCREAMING_SNAKE_CASE_		)

       _lowerCamelCase		:							Union[str, Any]            =					BertModel.from_pretrained(
           pretrained_model_name_or_path=args.model_name,							state_dict=torch.load(args.pytorch_model_path		),							cache_dir=args.cache_dir,							)

       convert_pytorch_checkpoint_to_tf(model=SCREAMING_SNAKE_CASE_,							ckpt_dir=args.tf_cache_dir,							model_name=args.model_name		)


if __name__ == "__main__":
   main()




from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     Optional[int]:
						return {key.lstrip("""-"""       ): value for key, value in zip(unknown_args[::2]		,   unknown_args[1::2]       )}




def 	UpperCamelCase__  (     )  ->     Dict:
						_lowercase					       =					ArgumentParser(
						    """HuggingFace Datasets CLI tool"""		,   usage="""datasets-cli <command> [<args>]"""		,   allow_abbrev=SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					parser.add_subparsers(help="""datasets-cli command helpers"""       )
						set_verbosity_info()

						# Register commands
						ConvertCommand.register_subcommand(SCREAMING_SNAKE_CASE_       )
						EnvironmentCommand.register_subcommand(SCREAMING_SNAKE_CASE_       )
						TestCommand.register_subcommand(SCREAMING_SNAKE_CASE_       )
						RunBeamCommand.register_subcommand(SCREAMING_SNAKE_CASE_       )
						DummyDataCommand.register_subcommand(SCREAMING_SNAKE_CASE_       )

						# Parse args
						_lowercase       ,						_lowercase					       =					parser.parse_known_args()
						if not hasattr(SCREAMING_SNAKE_CASE_		,   """func"""       ):
												parser.print_help()
												exit(1       )
						_lowercase					       =					parse_unknown_args(SCREAMING_SNAKE_CASE_       )

						# Run
						_lowercase					       =					args.func(SCREAMING_SNAKE_CASE_		,   **SCREAMING_SNAKE_CASE_       )
						service.run()


if __name__ == "__main__":
		main()