



import functools
import operator

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


snake_case    :						Tuple  =						logging.get_logger(__name__)

snake_case    :						Tuple  =						{
    '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__  (	UpperCamelCase						):
					__A     :    List[str]           =						'unispeech'







					def __init__(       self      :     Tuple			,		_A      :     Any=32			,		_A      :     List[Any]=768			,		_A      :     Any=12			,		_A      :     Any=12			,		_A      :     List[str]=3072			,		_A      :     Dict="gelu"			,		_A      :     Optional[Any]=0.1			,		_A      :     Tuple=0.1			,		_A      :     str=0.1			,		_A      :     Any=0.0			,		_A      :     int=0.0			,		_A      :     str=0.1			,		_A      :     Optional[int]=0.1			,		_A      :     Union[str, Any]=0.02			,		_A      :     int=1e-5			,		_A      :     List[str]="group"			,		_A      :     List[Any]="gelu"			,		_A      :     List[Any]=(512, 512, 512, 512, 512, 512, 512)			,		_A      :     Dict=(5, 2, 2, 2, 2, 2, 2)			,		_A      :     int=(10, 3, 3, 3, 3, 2, 2)			,		_A      :     str=False			,		_A      :     Tuple=128			,		_A      :     Optional[int]=16			,		_A      :     List[Any]=False			,		_A      :     Optional[int]=True			,		_A      :     Optional[Any]=0.05			,		_A      :     List[Any]=10			,		_A      :     Union[str, Any]=2			,		_A      :     Optional[int]=0.0			,		_A      :     int=10			,		_A      :     List[str]=0			,		_A      :     List[Any]=320			,		_A      :     int=2			,		_A      :     Optional[Any]=0.1			,		_A      :     List[Any]=100			,		_A      :     Optional[int]=256			,		_A      :     Union[str, Any]=256			,		_A      :     Optional[Any]=0.1			,		_A      :     Optional[int]="mean"			,		_A      :     Optional[Any]=False			,		_A      :     Tuple=False			,		_A      :     Optional[Any]=256			,		_A      :     Union[str, Any]=80			,		_A      :     Tuple=0			,		_A      :     List[Any]=1			,		_A      :     Dict=2			,		_A      :     str=0.5			,		**_A      :     Any			,		):
											super().__init__(**_A			,		pad_token_id=_A			,		bos_token_id=_A			,		eos_token_id=_A)
											A__	:							Tuple						    = hidden_size
											A__	:							List[Any]						    = feat_extract_norm
											A__	:							Optional[int]						    = feat_extract_activation
											A__	:							int						    = list(_A)
											A__	:							str						    = list(_A)
											A__	:							List[Any]						    = list(_A)
											A__	:							str						    = conv_bias
											A__	:							Union[str, Any]						    = num_conv_pos_embeddings
											A__	:							Any						    = num_conv_pos_embedding_groups
											A__	:							Union[str, Any]						    = len(self.conv_dim)
											A__	:							Dict						    = num_hidden_layers
											A__	:							str						    = intermediate_size
											A__	:							Any						    = hidden_act
											A__	:							Optional[int]						    = num_attention_heads
											A__	:							Any						    = hidden_dropout
											A__	:							Union[str, Any]						    = attention_dropout
											A__	:							Union[str, Any]						    = activation_dropout
											A__	:							Dict						    = feat_proj_dropout
											A__	:							Tuple						    = final_dropout
											A__	:							Optional[Any]						    = layerdrop
											A__	:							Optional[int]						    = layer_norm_eps
											A__	:							Dict						    = initializer_range
											A__	:							Union[str, Any]						    = num_ctc_classes
											A__	:							Union[str, Any]						    = vocab_size
											A__	:							str						    = do_stable_layer_norm
											A__	:							Any						    = use_weighted_layer_sum
											A__	:							Optional[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__	:							Any						    = apply_spec_augment
											A__	:							Any						    = mask_time_prob
											A__	:							str						    = mask_time_length
											A__	:							Union[str, Any]						    = mask_time_min_masks
											A__	:							int						    = mask_feature_prob
											A__	:							int						    = mask_feature_length
											A__	:							Optional[Any]						    = mask_feature_min_masks

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

											# ctc loss
											A__	:							Optional[int]						    = ctc_loss_reduction
											A__	:							Optional[int]						    = ctc_zero_infinity

											# pretraining loss
											A__	:							Optional[Any]						    = replace_prob







					@property
					def  _lowercase			(       self      :     List[str]):
											return functools.reduce(operator.mul			,		self.conv_stride			,		1)




import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		List[Any]:







						"""simple docstring"""

						def get_masked_lm_array(__lowercase       ):
												A__	:							int						    = F'masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Dict						    = tf.train.load_variable(__lowercase							,     __lowercase       )

												if "kernel" in name:
																		A__	:							Any						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						def get_encoder_array(__lowercase       ):
												A__	:							Dict						    = F'encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Tuple						    = tf.train.load_variable(__lowercase							,     __lowercase       )

												if "kernel" in name:
																		A__	:							str						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						def get_encoder_layer_array(__lowercase							,     __lowercase       ):
												A__	:							int						    = F'encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Dict						    = tf.train.load_variable(__lowercase							,     __lowercase       )

												if "kernel" in name:
																		A__	:							Any						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						def get_encoder_attention_layer_array(__lowercase							,     __lowercase							,     __lowercase       ):
												A__	:							List[str]						    = F'encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							List[Any]						    = tf.train.load_variable(__lowercase							,     __lowercase       )
												A__	:							int						    = array.reshape(__lowercase       )

												if "kernel" in name:
																		A__	:							Union[str, Any]						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						print(F'Loading model based on config from {config_path}...'       )
						A__	:							Tuple						    = BertConfig.from_json_file(__lowercase       )
						A__	:							List[str]						    = BertForMaskedLM(__lowercase       )

						# Layers
						for layer_index in range(0							,     config.num_hidden_layers       ):
												A__	:							BertLayer						    = model.bert.encoder.layer[layer_index]

												# Self-attention
												A__	:							BertSelfAttention						    = layer.attention.self

												A__	:							Optional[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_query_dense/kernel"							,     self_attn.query.weight.data.shape       )
												A__	:							List[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_query_dense/bias"							,     self_attn.query.bias.data.shape       )
												A__	:							int						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_key_dense/kernel"							,     self_attn.key.weight.data.shape       )
												A__	:							Tuple						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_key_dense/bias"							,     self_attn.key.bias.data.shape       )
												A__	:							List[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_value_dense/kernel"							,     self_attn.value.weight.data.shape       )
												A__	:							Optional[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_value_dense/bias"							,     self_attn.value.bias.data.shape       )

												# Self-attention Output
												A__	:							BertSelfOutput						    = layer.attention.output

												A__	:							Tuple						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_output_dense/kernel"							,     self_output.dense.weight.data.shape       )
												A__	:							Any						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_output_dense/bias"							,     self_output.dense.bias.data.shape       )

												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_attention_layer_norm/gamma"       )
												A__	:							Optional[int]						    = get_encoder_layer_array(__lowercase							,     "_attention_layer_norm/beta"       )

												# Intermediate
												A__	:							BertIntermediate						    = layer.intermediate

												A__	:							Optional[Any]						    = get_encoder_layer_array(__lowercase							,     "_intermediate_dense/kernel"       )
												A__	:							Any						    = get_encoder_layer_array(__lowercase							,     "_intermediate_dense/bias"       )

												# Output
												A__	:							BertOutput						    = layer.output

												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_output_dense/kernel"       )
												A__	:							List[Any]						    = get_encoder_layer_array(__lowercase							,     "_output_dense/bias"       )

												A__	:							Optional[Any]						    = get_encoder_layer_array(__lowercase							,     "_output_layer_norm/gamma"       )
												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_output_layer_norm/beta"       )

						# Embeddings
						A__	:							str						    = get_encoder_array("_position_embedding_layer/embeddings"       )
						A__	:							List[str]						    = get_encoder_array("_type_embedding_layer/embeddings"       )
						A__	:							List[Any]						    = get_encoder_array("_embedding_norm_layer/gamma"       )
						A__	:							Optional[int]						    = get_encoder_array("_embedding_norm_layer/beta"       )

						# LM Head
						A__	:							Optional[Any]						    = model.cls.predictions.transform

						A__	:							int						    = get_masked_lm_array("dense/kernel"       )
						A__	:							Tuple						    = get_masked_lm_array("dense/bias"       )

						A__	:							Optional[Any]						    = get_masked_lm_array("layer_norm/gamma"       )
						A__	:							Tuple						    = get_masked_lm_array("layer_norm/beta"       )

						A__	:							Any						    = get_masked_lm_array("embedding_table"       )

						# Pooling
						A__	:							Optional[Any]						    = BertPooler(config=__lowercase       )
						A__	:							BertPooler						    = get_encoder_array("_pooler_layer/kernel"       )
						A__	:							BertPooler						    = get_encoder_array("_pooler_layer/bias"       )

						# Export final model
						model.save_pretrained(__lowercase       )

						# Integration test - should load without any errors ;)
						A__	:							Union[str, Any]						    = BertForMaskedLM.from_pretrained(__lowercase       )
						print(new_model.eval()       )

						print("Model conversion was done sucessfully!"       )


if __name__ == "__main__":
		snake_case    :						str  =						argparse.ArgumentParser()
		parser.add_argument(
		    '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow Token Dropping checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    type=str,
		    required=True,
		    help='The config json file corresponding to the BERT model. This specifies the model architecture.',
		)
		parser.add_argument(
		    '--pytorch_dump_path',
		    type=str,
		    required=True,
		    help='Path to the output PyTorch model.',
		)
		snake_case    :						Dict  =						parser.parse_args()
		convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)