
'''simple docstring'''
import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel







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

							UpperCamelCase   			=     (
							    ('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)

							UpperCamelCase   			=     model.state_dict()

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

							def create_tf_var(A						:		np.ndarray		,     A						:		str		,     A						:		tf.Session):
														UpperCamelCase   			=     tf.dtypes.as_dtype(tensor.dtype)
														UpperCamelCase   			=     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:
																					UpperCamelCase   			=     to_tf_var_name(A)
																					UpperCamelCase   			=     state_dict[var_name].numpy()
																					if any(x in var_name for x in tensors_to_transpose):
																												UpperCamelCase   			=     torch_tensor.T
																					UpperCamelCase   			=     create_tf_var(tensor=A		,     name=A		,     session=A)
																					tf.keras.backend.set_value(A		,     A)
																					UpperCamelCase   			=     session.run(A)
																					print(f'''Successfully created {tf_name}: {np.allclose(A		,     A)}''')

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







def 		A_(     A						:		Optional[int]=None):
							UpperCamelCase   			=     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')
							UpperCamelCase   			=     parser.parse_args(A)

							UpperCamelCase   			=     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()






'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging


lowerCAmelCase					:    Optional[Any]           = logging.get_logger(__name__)

lowerCAmelCase					:    Dict           = {
    'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""mctct"""







				def __init__(       self						,				A_=8065						,				A_=1536						,				A_=36						,				A_=6144						,				A_=4						,				A_=384						,				A_=920						,				A_=1e-5						,				A_=0.3						,				A_="relu"						,				A_=0.02						,				A_=0.3						,				A_=0.3						,				A_=1						,				A_=0						,				A_=2						,				A_=1						,				A_=0.3						,				A_=1						,				A_=(7,)						,				A_=(3,)						,				A_=80						,				A_=1						,				A_=None						,				A_="sum"						,				A_=False						,				**A_						,				)->		str:







											'''simple docstring'''






											super().__init__(**A_						,				pad_token_id=A_						,				bos_token_id=A_						,				eos_token_id=A_     )
											UpperCamelCase   			=     vocab_size
											UpperCamelCase   			=     hidden_size
											UpperCamelCase   			=     num_hidden_layers
											UpperCamelCase   			=     intermediate_size
											UpperCamelCase   			=     num_attention_heads
											UpperCamelCase   			=     attention_head_dim
											UpperCamelCase   			=     max_position_embeddings
											UpperCamelCase   			=     layer_norm_eps
											UpperCamelCase   			=     layerdrop
											UpperCamelCase   			=     hidden_act
											UpperCamelCase   			=     initializer_range
											UpperCamelCase   			=     hidden_dropout_prob
											UpperCamelCase   			=     attention_probs_dropout_prob
											UpperCamelCase   			=     pad_token_id
											UpperCamelCase   			=     bos_token_id
											UpperCamelCase   			=     eos_token_id
											UpperCamelCase   			=     conv_glu_dim
											UpperCamelCase   			=     conv_dropout
											UpperCamelCase   			=     num_conv_layers
											UpperCamelCase   			=     input_feat_per_channel
											UpperCamelCase   			=     input_channels
											UpperCamelCase   			=     conv_channels
											UpperCamelCase   			=     ctc_loss_reduction
											UpperCamelCase   			=     ctc_zero_infinity

											# prevents config testing fail with exporting to json
											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     list(A_     )

											if len(self.conv_kernel     ) != self.num_conv_layers:
																		raise ValueError(
																		    'Configuration for convolutional module is incorrect. '
																		    'It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '
																		    F'''but is `len(config.conv_kernel) = {len(self.conv_kernel     )}`, '''
																		    F'''`config.num_conv_layers = {self.num_conv_layers}`.'''     )




