

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_							:					List[str]		 =   logging.get_logger(__name__)

UpperCAmelCase_							:					str		 =   {
    '''microsoft/unispeech-large-1500h-cv''': (
        '''https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'''
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Dict     					=				'''unispeech'''




	def __init__(     self						:     str					,      __A						:     Tuple=32					,      __A						:     List[str]=768					,      __A						:     Dict=12					,      __A						:     Union[str, Any]=12					,      __A						:     Tuple=3072					,      __A						:     Any="gelu"					,      __A						:     int=0.1					,      __A						:     Optional[int]=0.1					,      __A						:     List[Any]=0.1					,      __A						:     Any=0.0					,      __A						:     List[str]=0.0					,      __A						:     int=0.1					,      __A						:     List[Any]=0.1					,      __A						:     List[str]=0.0_2					,      __A						:     List[str]=1e-5					,      __A						:     List[Any]="group"					,      __A						:     int="gelu"					,      __A						:     Any=(512, 512, 512, 512, 512, 512, 512)					,      __A						:     Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)					,      __A						:     Tuple=(10, 3, 3, 3, 3, 2, 2)					,      __A						:     Optional[int]=False					,      __A						:     Any=128					,      __A						:     Union[str, Any]=16					,      __A						:     Optional[Any]=False					,      __A						:     str=True					,      __A						:     Dict=0.0_5					,      __A						:     Optional[Any]=10					,      __A						:     Dict=2					,      __A						:     int=0.0					,      __A						:     List[str]=10					,      __A						:     str=0					,      __A						:     List[str]=320					,      __A						:     List[Any]=2					,      __A						:     Tuple=0.1					,      __A						:     Optional[int]=100					,      __A						:     Any=256					,      __A						:     Dict=256					,      __A						:     Tuple=0.1					,      __A						:     List[str]="mean"					,      __A						:     int=False					,      __A						:     List[str]=False					,      __A						:     List[Any]=256					,      __A						:     str=80					,      __A						:     Tuple=0					,      __A						:     Tuple=1					,      __A						:     int=2					,      __A						:     Dict=0.5					,      **__A						:     List[Any]					,      ):
		super().__init__(**__A					,      pad_token_id=__A					,      bos_token_id=__A					,      eos_token_id=__A							)
		__A :			Dict		    =			hidden_size
		__A :			Optional[Any]		    =			feat_extract_norm
		__A :			List[Any]		    =			feat_extract_activation
		__A :			str		    =			list(__A							)
		__A :			Optional[Any]		    =			list(__A							)
		__A :			Optional[int]		    =			list(__A							)
		__A :			List[Any]		    =			conv_bias
		__A :			Optional[int]		    =			num_conv_pos_embeddings
		__A :			List[Any]		    =			num_conv_pos_embedding_groups
		__A :			int		    =			len(self.conv_dim							)
		__A :			Optional[Any]		    =			num_hidden_layers
		__A :			List[str]		    =			intermediate_size
		__A :			Union[str, Any]		    =			hidden_act
		__A :			Optional[int]		    =			num_attention_heads
		__A :			Tuple		    =			hidden_dropout
		__A :			Optional[Any]		    =			attention_dropout
		__A :			Union[str, Any]		    =			activation_dropout
		__A :			Dict		    =			feat_proj_dropout
		__A :			Optional[int]		    =			final_dropout
		__A :			Dict		    =			layerdrop
		__A :			Optional[int]		    =			layer_norm_eps
		__A :			Optional[Any]		    =			initializer_range
		__A :			Optional[int]		    =			num_ctc_classes
		__A :			Dict		    =			vocab_size
		__A :			List[str]		    =			do_stable_layer_norm
		__A :			Tuple		    =			use_weighted_layer_sum
		__A :			Any		    =			classifier_proj_size

		if (
		    (len(self.conv_stride							) != self.num_feat_extract_layers)
		    or (len(self.conv_kernel							) != self.num_feat_extract_layers)
		    or (len(self.conv_dim							) != self.num_feat_extract_layers)
		):
			raise ValueError(
			    """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
			    """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
			    F""" {len(self.conv_dim							)}`, `len(config.conv_stride) = {len(self.conv_stride							)}`,"""
			    F""" `len(config.conv_kernel) = {len(self.conv_kernel							)}`."""							)

		# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
		__A :			Tuple		    =			apply_spec_augment
		__A :			Union[str, Any]		    =			mask_time_prob
		__A :			Optional[Any]		    =			mask_time_length
		__A :			List[Any]		    =			mask_time_min_masks
		__A :			List[Any]		    =			mask_feature_prob
		__A :			Any		    =			mask_feature_length
		__A :			List[Any]		    =			mask_feature_min_masks

		# parameters for pretraining with codevector quantized representations
		__A :			Any		    =			num_codevectors_per_group
		__A :			Tuple		    =			num_codevector_groups
		__A :			List[str]		    =			contrastive_logits_temperature
		__A :			Optional[int]		    =			feat_quantizer_dropout
		__A :			int		    =			num_negatives
		__A :			List[str]		    =			codevector_dim
		__A :			int		    =			proj_codevector_dim
		__A :			Union[str, Any]		    =			diversity_loss_weight

		# ctc loss
		__A :			List[str]		    =			ctc_loss_reduction
		__A :			Any		    =			ctc_zero_infinity

		# pretraining loss
		__A :			Union[str, Any]		    =			replace_prob




	@property
	def        lowerCAmelCase_       (     self						:     int							):
		return functools.reduce(operator.mul					,      self.conv_stride					,      1							)








import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Optional[Any]			,a__    :   Union[str, Any]			,a__    :   Optional[int]       )					->				List[Any]:
	# Initialise PyTorch model
	__A :			Dict		    =			MobileBertConfig.from_json_file(a__       )
	print(f"""Building PyTorch model from configuration: {config}"""       )
	__A :			Tuple		    =			MobileBertForPreTraining(a__       )
	# Load weights from tf checkpoint
	__A :			Dict		    =			load_tf_weights_in_mobilebert(a__			,a__			,a__       )
	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
	torch.save(model.state_dict()			,a__       )


if __name__ == "__main__":
			UpperCAmelCase_							:					Any		 =   argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--mobilebert_config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained MobileBERT model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			UpperCAmelCase_							:					Tuple		 =   parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)





