


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     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							def get_masked_lm_array(__lowerCamelCase     ):
														__snake_case		:      Optional[Any]										=       F'masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'
														__snake_case		:      List[str]										=       tf.train.load_variable(UpperCAmelCase__							,     UpperCAmelCase__     )

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

														return torch.from_numpy(UpperCAmelCase__     )

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

														if "kernel" in name:
																					__snake_case		:      Optional[int]										=       array.transpose()

														return torch.from_numpy(UpperCAmelCase__     )

							def get_encoder_layer_array(__lowerCamelCase							,     __lowerCamelCase     ):
														__snake_case		:      Optional[Any]										=       F'encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'
														__snake_case		:      Dict										=       tf.train.load_variable(UpperCAmelCase__							,     UpperCAmelCase__     )

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

														return torch.from_numpy(UpperCAmelCase__     )

							def get_encoder_attention_layer_array(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
														__snake_case		:      str										=       F'encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'
														__snake_case		:      Tuple										=       tf.train.load_variable(UpperCAmelCase__							,     UpperCAmelCase__     )
														__snake_case		:      Optional[Any]										=       array.reshape(UpperCAmelCase__     )

														if "kernel" in name:
																					__snake_case		:      Optional[int]										=       array.transpose()

														return torch.from_numpy(UpperCAmelCase__     )

							print(F'Loading model based on config from {config_path}...'     )
							__snake_case		:      Optional[Any]										=       BertConfig.from_json_file(UpperCAmelCase__     )
							__snake_case		:      List[Any]										=       BertForMaskedLM(UpperCAmelCase__     )

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

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

														__snake_case		:      Union[str, Any]										=       get_encoder_attention_layer_array(
														    UpperCAmelCase__							,     "_query_dense/kernel"							,     self_attn.query.weight.data.shape     )
														__snake_case		:      str										=       get_encoder_attention_layer_array(
														    UpperCAmelCase__							,     "_query_dense/bias"							,     self_attn.query.bias.data.shape     )
														__snake_case		:      List[Any]										=       get_encoder_attention_layer_array(
														    UpperCAmelCase__							,     "_key_dense/kernel"							,     self_attn.key.weight.data.shape     )
														__snake_case		:      str										=       get_encoder_attention_layer_array(
														    UpperCAmelCase__							,     "_key_dense/bias"							,     self_attn.key.bias.data.shape     )
														__snake_case		:      Tuple										=       get_encoder_attention_layer_array(
														    UpperCAmelCase__							,     "_value_dense/kernel"							,     self_attn.value.weight.data.shape     )
														__snake_case		:      str										=       get_encoder_attention_layer_array(
														    UpperCAmelCase__							,     "_value_dense/bias"							,     self_attn.value.bias.data.shape     )

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

														__snake_case		:      List[Any]										=       get_encoder_attention_layer_array(
														    UpperCAmelCase__							,     "_output_dense/kernel"							,     self_output.dense.weight.data.shape     )
														__snake_case		:      Union[str, Any]										=       get_encoder_attention_layer_array(
														    UpperCAmelCase__							,     "_output_dense/bias"							,     self_output.dense.bias.data.shape     )

														__snake_case		:      int										=       get_encoder_layer_array(UpperCAmelCase__							,     "_attention_layer_norm/gamma"     )
														__snake_case		:      Optional[Any]										=       get_encoder_layer_array(UpperCAmelCase__							,     "_attention_layer_norm/beta"     )

														# Intermediate
														__snake_case		:      BertIntermediate										=       layer.intermediate

														__snake_case		:      List[Any]										=       get_encoder_layer_array(UpperCAmelCase__							,     "_intermediate_dense/kernel"     )
														__snake_case		:      Any										=       get_encoder_layer_array(UpperCAmelCase__							,     "_intermediate_dense/bias"     )

														# Output
														__snake_case		:      BertOutput										=       layer.output

														__snake_case		:      Optional[int]										=       get_encoder_layer_array(UpperCAmelCase__							,     "_output_dense/kernel"     )
														__snake_case		:      Optional[Any]										=       get_encoder_layer_array(UpperCAmelCase__							,     "_output_dense/bias"     )

														__snake_case		:      Dict										=       get_encoder_layer_array(UpperCAmelCase__							,     "_output_layer_norm/gamma"     )
														__snake_case		:      Any										=       get_encoder_layer_array(UpperCAmelCase__							,     "_output_layer_norm/beta"     )

							# Embeddings
							__snake_case		:      Union[str, Any]										=       get_encoder_array("_position_embedding_layer/embeddings"     )
							__snake_case		:      Any										=       get_encoder_array("_type_embedding_layer/embeddings"     )
							__snake_case		:      str										=       get_encoder_array("_embedding_norm_layer/gamma"     )
							__snake_case		:      List[Any]										=       get_encoder_array("_embedding_norm_layer/beta"     )

							# LM Head
							__snake_case		:      Optional[int]										=       model.cls.predictions.transform

							__snake_case		:      str										=       get_masked_lm_array("dense/kernel"     )
							__snake_case		:      List[Any]										=       get_masked_lm_array("dense/bias"     )

							__snake_case		:      int										=       get_masked_lm_array("layer_norm/gamma"     )
							__snake_case		:      Optional[Any]										=       get_masked_lm_array("layer_norm/beta"     )

							__snake_case		:      int										=       get_masked_lm_array("embedding_table"     )

							# Pooling
							__snake_case		:      int										=       BertPooler(config=UpperCAmelCase__     )
							__snake_case		:      BertPooler										=       get_encoder_array("_pooler_layer/kernel"     )
							__snake_case		:      BertPooler										=       get_encoder_array("_pooler_layer/bias"     )

							# Export final model
							model.save_pretrained(UpperCAmelCase__     )

							# Integration test - should load without any errors ;)
							__snake_case		:      Dict										=       BertForMaskedLM.from_pretrained(UpperCAmelCase__     )
							print(new_model.eval()     )

							print("Model conversion was done sucessfully!"     )


if __name__ == "__main__":
	_snake_case					:		Optional[int]      =    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					:		Tuple      =    parser.parse_args()
	convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





import math
from typing import Optional

import numpy as np

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


_snake_case					:		Union[str, Any]      =    logging.get_logger(__name__)

_snake_case					:		List[str]      =    {
    "facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json",
    "facebook/encodec_48khz": "https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json",
}

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Tuple					  =  "encodec"

							def __init__(							self	:  Any   ,     lowerCamelCase	:  Optional[int]=[1.5, 3.0, 6.0, 12.0, 24.0]   ,     lowerCamelCase	:  List[str]=24000   ,     lowerCamelCase	:  int=1   ,     lowerCamelCase	:  Optional[int]=False   ,     lowerCamelCase	:  Dict=None   ,     lowerCamelCase	:  Tuple=None   ,     lowerCamelCase	:  Optional[int]=128   ,     lowerCamelCase	:  Optional[int]=32   ,     lowerCamelCase	:  List[str]=1   ,     lowerCamelCase	:  str=[8, 5, 4, 2]   ,     lowerCamelCase	:  List[str]="weight_norm"   ,     lowerCamelCase	:  Any=7   ,     lowerCamelCase	:  Tuple=7   ,     lowerCamelCase	:  int=3   ,     lowerCamelCase	:  int=2   ,     lowerCamelCase	:  Union[str, Any]=True   ,     lowerCamelCase	:  List[Any]="reflect"   ,     lowerCamelCase	:  Union[str, Any]=2   ,     lowerCamelCase	:  Optional[int]=2   ,     lowerCamelCase	:  int=1.0   ,     lowerCamelCase	:  Optional[Any]=1024   ,     lowerCamelCase	:  Optional[Any]=None   ,     lowerCamelCase	:  str=True   ,     **lowerCamelCase	:  Dict   ,     )     ->   Any:
														__snake_case		:      Tuple										=       target_bandwidths
														__snake_case		:      Union[str, Any]										=       sampling_rate
														__snake_case		:      Union[str, Any]										=       audio_channels
														__snake_case		:      Dict										=       normalize
														__snake_case		:      List[Any]										=       chunk_length_s
														__snake_case		:      Tuple										=       overlap
														__snake_case		:      Optional[int]										=       hidden_size
														__snake_case		:      List[Any]										=       num_filters
														__snake_case		:      Union[str, Any]										=       num_residual_layers
														__snake_case		:      Optional[int]										=       upsampling_ratios
														__snake_case		:      List[str]										=       norm_type
														__snake_case		:      Optional[int]										=       kernel_size
														__snake_case		:      Dict										=       last_kernel_size
														__snake_case		:      Tuple										=       residual_kernel_size
														__snake_case		:      List[Any]										=       dilation_growth_rate
														__snake_case		:      Optional[int]										=       use_causal_conv
														__snake_case		:      Tuple										=       pad_mode
														__snake_case		:      Union[str, Any]										=       compress
														__snake_case		:      Union[str, Any]										=       num_lstm_layers
														__snake_case		:      int										=       trim_right_ratio
														__snake_case		:      Tuple										=       codebook_size
														__snake_case		:      Optional[Any]										=       codebook_dim if codebook_dim is not None else hidden_size
														__snake_case		:      int										=       use_conv_shortcut

														if self.norm_type not in ["weight_norm", "time_group_norm"]:
																					raise ValueError(
																					    F'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'    )

														super().__init__(**lowerCamelCase    )

							@property
							def    __snake_case (							self	:  int    )     ->   Optional[int]:
														if self.chunk_length_s is None:
																					return None
														else:
																					return int(self.chunk_length_s * self.sampling_rate    )

							@property
							def    __snake_case (							self	:  Union[str, Any]    )     ->   Optional[int]:
														if self.chunk_length_s is None or self.overlap is None:
																					return None
														else:
																					return max(1   ,     int((1.0 - self.overlap) * self.chunk_length    )    )

							@property
							def    __snake_case (							self	:  Optional[Any]    )     ->   int:
														__snake_case		:      Union[str, Any]										=       np.prod(self.upsampling_ratios    )
														return math.ceil(self.sampling_rate / hop_length    )






							@property
							def    __snake_case (							self	:  Optional[Any]    )     ->   int:
														return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)    )

