
import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       ):
				lowerCamelCase_         =     ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

				lowerCamelCase_         =     (
				    ("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(lowerCamelCase__       ):
								os.makedirs(lowerCamelCase__       )

				lowerCamelCase_         =     model.state_dict()

				def to_tf_var_name(lowerCamelCase__       ):
								for patt, repl in iter(lowerCamelCase__       ):
												lowerCamelCase_         =     name.replace(lowerCamelCase__      ,					lowerCamelCase__       )
								return F'bert/{name}'

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

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

								lowerCamelCase_         =     tf.train.Saver(tf.trainable_variables()       )
								saver.save(lowerCamelCase__      ,					os.path.join(lowerCamelCase__      ,					model_name.replace("-"      ,					"_"       ) + ".ckpt"       )       )





def      lowerCamelCase_					(							lowerCamelCase__=None       ):
				lowerCamelCase_         =     argparse.ArgumentParser()
				parser.add_argument("--model_name"      ,					type=lowerCamelCase__      ,					required=lowerCamelCase__      ,					help="model name e.g. bert-base-uncased"       )
				parser.add_argument(
				    "--cache_dir"      ,					type=lowerCamelCase__      ,					default=lowerCamelCase__      ,					required=lowerCamelCase__      ,					help="Directory containing pytorch model"       )
				parser.add_argument("--pytorch_model_path"      ,					type=lowerCamelCase__      ,					required=lowerCamelCase__      ,					help="/path/to/<pytorch-model-name>.bin"       )
				parser.add_argument("--tf_cache_dir"      ,					type=lowerCamelCase__      ,					required=lowerCamelCase__      ,					help="Directory in which to save tensorflow model"       )
				lowerCamelCase_         =     parser.parse_args(lowerCamelCase__       )

				lowerCamelCase_         =     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=lowerCamelCase__      ,					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_torch_available


__A							={
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A							=[
			    '''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''GPTNeoForCausalLM''',
			    '''GPTNeoForQuestionAnswering''',
			    '''GPTNeoForSequenceClassification''',
			    '''GPTNeoForTokenClassification''',
			    '''GPTNeoModel''',
			    '''GPTNeoPreTrainedModel''',
			    '''load_tf_weights_in_gpt_neo''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A							=[
			    '''FlaxGPTNeoForCausalLM''',
			    '''FlaxGPTNeoModel''',
			    '''FlaxGPTNeoPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_neo import (
						    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTNeoForCausalLM,
						    GPTNeoForQuestionAnswering,
						    GPTNeoForSequenceClassification,
						    GPTNeoForTokenClassification,
						    GPTNeoModel,
						    GPTNeoPreTrainedModel,
						    load_tf_weights_in_gpt_neo,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
			import sys

			__A							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


