






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel







def 	_UpperCamelCase   (    UpperCamelCase_       :					BertModel    ,       UpperCamelCase_       :					str    ,       UpperCamelCase_       :					str		)  ->   Dict:

						"""simple docstring"""



						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(_SCREAMING_SNAKE_CASE		):
												os.makedirs(_SCREAMING_SNAKE_CASE		)

						lowerCAmelCase__    						=						model.state_dict()

						def to_tf_var_name(UpperCamelCase_       :					str		):
												for patt, repl in iter(_SCREAMING_SNAKE_CASE		):
																		lowerCAmelCase__    						=						name.replace(_SCREAMING_SNAKE_CASE    ,       _SCREAMING_SNAKE_CASE		)
												return F"bert/{name}"

						def create_tf_var(UpperCamelCase_       :					np.ndarray    ,       UpperCamelCase_       :					str    ,       UpperCamelCase_       :					tf.Session		):
												lowerCAmelCase__    						=						tf.dtypes.as_dtype(tensor.dtype		)
												lowerCAmelCase__    						=						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__    						=						to_tf_var_name(_SCREAMING_SNAKE_CASE		)
																		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=_SCREAMING_SNAKE_CASE    ,       name=_SCREAMING_SNAKE_CASE    ,       session=_SCREAMING_SNAKE_CASE		)
																		tf.keras.backend.set_value(_SCREAMING_SNAKE_CASE    ,       _SCREAMING_SNAKE_CASE		)
																		lowerCAmelCase__    						=						session.run(_SCREAMING_SNAKE_CASE		)
																		print(F"Successfully created {tf_name}: {np.allclose(_SCREAMING_SNAKE_CASE    ,       _SCREAMING_SNAKE_CASE		)}"		)

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







def 	_UpperCamelCase   (    UpperCamelCase_       :					List[Any]=None		)  ->   Any:

						"""simple docstring"""



						lowerCAmelCase__    						=						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__    						=						parser.parse_args(_SCREAMING_SNAKE_CASE		)

						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=_SCREAMING_SNAKE_CASE    ,       ckpt_dir=args.tf_cache_dir    ,       model_name=args.model_name		)


if __name__ == "__main__":
							main()













# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

__snake_case    :    Optional[int]         =     """facebook/wmt19-en-de"""
__snake_case    :    Optional[Any]         =     FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__snake_case    :    List[str]         =     FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

__snake_case    :    Optional[Any]         =     FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
__snake_case    :    List[Any]         =     tokenizer(["""Making tiny model"""], return_tensors="""pt""")
__snake_case    :    Any         =     tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
__snake_case    :    Any         =     """tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-de





