



import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   int													=					("""dense.weight""", """attention.self.query""", """attention.self.key""", """attention.self.value""")

				lowercase__					:   Optional[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(lowercase__						):
								os.makedirs(lowercase__						)

				lowercase__					:   List[str]													=					model.state_dict()

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

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

				tf.reset_default_graph()
				with tf.Session() as session:
								for var_name in state_dict:
												lowercase__					:   List[Any]													=					to_tf_var_name(lowercase__						)
												lowercase__					:   List[Any]													=					state_dict[var_name].numpy()
												if any(x in var_name for x in tensors_to_transpose						):
																lowercase__					:   List[Any]													=					torch_tensor.T
												lowercase__					:   Any													=					create_tf_var(tensor=lowercase__					,			name=lowercase__					,			session=lowercase__						)
												tf.keras.backend.set_value(lowercase__					,			lowercase__						)
												lowercase__					:   Dict													=					session.run(lowercase__						)
												print(F"""Successfully created {tf_name}: {np.allclose(lowercase__					,			lowercase__						)}"""						)

								lowercase__					:   Optional[Any]													=					tf.train.Saver(tf.trainable_variables()						)
								saver.save(lowercase__					,			os.path.join(lowercase__					,			model_name.replace("-"					,			"_"						) + ".ckpt"						)						)


def  __lowerCamelCase (       lowerCamelCase__=None						):

				"""simple docstring"""


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

				lowercase__					:   List[str]													=					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=lowercase__					,			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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__					  =  {
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  ['''DebertaTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  [
				    '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''DebertaForMaskedLM''',
				    '''DebertaForQuestionAnswering''',
				    '''DebertaForSequenceClassification''',
				    '''DebertaForTokenClassification''',
				    '''DebertaModel''',
				    '''DebertaPreTrainedModel''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  [
				    '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFDebertaForMaskedLM''',
				    '''TFDebertaForQuestionAnswering''',
				    '''TFDebertaForSequenceClassification''',
				    '''TFDebertaForTokenClassification''',
				    '''TFDebertaModel''',
				    '''TFDebertaPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
				from .tokenization_deberta import DebertaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_deberta_fast import DebertaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deberta import (
								    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DebertaForMaskedLM,
								    DebertaForQuestionAnswering,
								    DebertaForSequenceClassification,
								    DebertaForTokenClassification,
								    DebertaModel,
								    DebertaPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_deberta import (
								    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFDebertaForMaskedLM,
								    TFDebertaForQuestionAnswering,
								    TFDebertaForSequenceClassification,
								    TFDebertaForTokenClassification,
								    TFDebertaModel,
								    TFDebertaPreTrainedModel,
								)


else:
				import sys

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




