






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def      a_ (      _A      ,   _A      ,   _A  )			->			Dict:







				"""simple docstring"""
				snake_case__									=     ('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

				snake_case__									=     (
				    ('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(_A  ):
								os.makedirs(_A  )

				snake_case__									=     model.state_dict()

				def to_tf_var_name(_A  ):
								for patt, repl in iter(_A  ):
												snake_case__									=     name.replace(_A      ,   _A  )
								return f'''bert/{name}'''

				def create_tf_var(_A      ,   _A      ,   _A  ):
								snake_case__									=     tf.dtypes.as_dtype(tensor.dtype  )
								snake_case__									=     tf.get_variable(dtype=_A      ,   shape=tensor.shape      ,   name=_A      ,   initializer=tf.zeros_initializer()  )
								session.run(tf.variables_initializer([tf_var]  )  )
								session.run(_A  )
								return tf_var

				tf.reset_default_graph()
				with tf.Session() as session:
								for var_name in state_dict:
												snake_case__									=     to_tf_var_name(_A  )
												snake_case__									=     state_dict[var_name].numpy()
												if any(x in var_name for x in tensors_to_transpose  ):
																snake_case__									=     torch_tensor.T
												snake_case__									=     create_tf_var(tensor=_A      ,   name=_A      ,   session=_A  )
												tf.keras.backend.set_value(_A      ,   _A  )
												snake_case__									=     session.run(_A  )
												print(f'''Successfully created {tf_name}: {np.allclose(_A      ,   _A  )}'''  )

								snake_case__									=     tf.train.Saver(tf.trainable_variables()  )
								saver.save(_A      ,   os.path.join(_A      ,   model_name.replace('-'      ,   '_'  ) + '.ckpt'  )  )
def      a_ (      _A=None  )			->			List[Any]:







				"""simple docstring"""
				snake_case__									=     argparse.ArgumentParser()
				parser.add_argument('--model_name'      ,   type=_A      ,   required=_A      ,   help='model name e.g. bert-base-uncased'  )
				parser.add_argument(
				    '--cache_dir'      ,   type=_A      ,   default=_A      ,   required=_A      ,   help='Directory containing pytorch model'  )
				parser.add_argument('--pytorch_model_path'      ,   type=_A      ,   required=_A      ,   help='/path/to/<pytorch-model-name>.bin'  )
				parser.add_argument('--tf_cache_dir'      ,   type=_A      ,   required=_A      ,   help='Directory in which to save tensorflow model'  )
				snake_case__									=     parser.parse_args(_A  )

				snake_case__									=     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=_A      ,   ckpt_dir=args.tf_cache_dir      ,   model_name=args.model_name  )


if __name__ == "__main__":
							main()












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase					:		int	  =						{"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Dict	  =						["""XGLMTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Optional[int]	  =						["""XGLMTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		List[str]	  =						[
							    """XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """XGLMForCausalLM""",
							    """XGLMModel""",
							    """XGLMPreTrainedModel""",
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Any	  =						[
							    """FlaxXGLMForCausalLM""",
							    """FlaxXGLMModel""",
							    """FlaxXGLMPreTrainedModel""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		str	  =						[
							    """TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFXGLMForCausalLM""",
							    """TFXGLMModel""",
							    """TFXGLMPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase					:		Union[str, Any]	  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure)




