
"""simple docstring"""


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def    _snake_case					(		snake_case__ :  BertModel						,   snake_case__ :  str						,   snake_case__ :  str  ):
	A     = ('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

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

	A     = model.state_dict()

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

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

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

		A     = tf.train.Saver(tf.trainable_variables()  )
		saver.save(snake_case__						,   os.path.join(snake_case__						,   model_name.replace('-'						,   '_'  ) + '.ckpt'  )  )






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

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


if __name__ == "__main__":
		main()

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_    							=					{
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """NllbMoeForConditionalGeneration""",
						    """NllbMoeModel""",
						    """NllbMoePreTrainedModel""",
						    """NllbMoeTop2Router""",
						    """NllbMoeSparseMLP""",
						]


if TYPE_CHECKING:
						from .configuration_nllb_moe import (
						    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    NllbMoeConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nllb_moe import (
												    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NllbMoeForConditionalGeneration,
												    NllbMoeModel,
												    NllbMoePreTrainedModel,
												    NllbMoeSparseMLP,
												    NllbMoeTopaRouter,
												)


else:
						import sys

						A_    							=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



