




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_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			def get_masked_lm_array(SCREAMING_SNAKE_CASE   ):
						SCREAMING_SNAKE_CASE           =					f"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
						SCREAMING_SNAKE_CASE           =					tf.train.load_variable(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )

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

						return torch.from_numpy(SCREAMING_SNAKE_CASE   )

			def get_encoder_array(SCREAMING_SNAKE_CASE   ):
						SCREAMING_SNAKE_CASE           =					f"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
						SCREAMING_SNAKE_CASE           =					tf.train.load_variable(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )

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

						return torch.from_numpy(SCREAMING_SNAKE_CASE   )

			def get_encoder_layer_array(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
						SCREAMING_SNAKE_CASE           =					f"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
						SCREAMING_SNAKE_CASE           =					tf.train.load_variable(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )

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

						return torch.from_numpy(SCREAMING_SNAKE_CASE   )

			def get_encoder_attention_layer_array(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
						SCREAMING_SNAKE_CASE           =					f"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
						SCREAMING_SNAKE_CASE           =					tf.train.load_variable(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
						SCREAMING_SNAKE_CASE           =					array.reshape(SCREAMING_SNAKE_CASE   )

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

						return torch.from_numpy(SCREAMING_SNAKE_CASE   )

			print(f"""Loading model based on config from {config_path}..."""   )
			SCREAMING_SNAKE_CASE           =					BertConfig.from_json_file(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					BertForMaskedLM(SCREAMING_SNAKE_CASE   )

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

						# Self-attention
						SCREAMING_SNAKE_CASE           =					layer.attention.self

						SCREAMING_SNAKE_CASE           =					get_encoder_attention_layer_array(
						    SCREAMING_SNAKE_CASE					,	"""_query_dense/kernel"""					,	self_attn.query.weight.data.shape   )
						SCREAMING_SNAKE_CASE           =					get_encoder_attention_layer_array(
						    SCREAMING_SNAKE_CASE					,	"""_query_dense/bias"""					,	self_attn.query.bias.data.shape   )
						SCREAMING_SNAKE_CASE           =					get_encoder_attention_layer_array(
						    SCREAMING_SNAKE_CASE					,	"""_key_dense/kernel"""					,	self_attn.key.weight.data.shape   )
						SCREAMING_SNAKE_CASE           =					get_encoder_attention_layer_array(
						    SCREAMING_SNAKE_CASE					,	"""_key_dense/bias"""					,	self_attn.key.bias.data.shape   )
						SCREAMING_SNAKE_CASE           =					get_encoder_attention_layer_array(
						    SCREAMING_SNAKE_CASE					,	"""_value_dense/kernel"""					,	self_attn.value.weight.data.shape   )
						SCREAMING_SNAKE_CASE           =					get_encoder_attention_layer_array(
						    SCREAMING_SNAKE_CASE					,	"""_value_dense/bias"""					,	self_attn.value.bias.data.shape   )

						# Self-attention Output
						SCREAMING_SNAKE_CASE           =					layer.attention.output

						SCREAMING_SNAKE_CASE           =					get_encoder_attention_layer_array(
						    SCREAMING_SNAKE_CASE					,	"""_output_dense/kernel"""					,	self_output.dense.weight.data.shape   )
						SCREAMING_SNAKE_CASE           =					get_encoder_attention_layer_array(
						    SCREAMING_SNAKE_CASE					,	"""_output_dense/bias"""					,	self_output.dense.bias.data.shape   )

						SCREAMING_SNAKE_CASE           =					get_encoder_layer_array(SCREAMING_SNAKE_CASE					,	"""_attention_layer_norm/gamma"""   )
						SCREAMING_SNAKE_CASE           =					get_encoder_layer_array(SCREAMING_SNAKE_CASE					,	"""_attention_layer_norm/beta"""   )

						# Intermediate
						SCREAMING_SNAKE_CASE           =					layer.intermediate

						SCREAMING_SNAKE_CASE           =					get_encoder_layer_array(SCREAMING_SNAKE_CASE					,	"""_intermediate_dense/kernel"""   )
						SCREAMING_SNAKE_CASE           =					get_encoder_layer_array(SCREAMING_SNAKE_CASE					,	"""_intermediate_dense/bias"""   )

						# Output
						SCREAMING_SNAKE_CASE           =					layer.output

						SCREAMING_SNAKE_CASE           =					get_encoder_layer_array(SCREAMING_SNAKE_CASE					,	"""_output_dense/kernel"""   )
						SCREAMING_SNAKE_CASE           =					get_encoder_layer_array(SCREAMING_SNAKE_CASE					,	"""_output_dense/bias"""   )

						SCREAMING_SNAKE_CASE           =					get_encoder_layer_array(SCREAMING_SNAKE_CASE					,	"""_output_layer_norm/gamma"""   )
						SCREAMING_SNAKE_CASE           =					get_encoder_layer_array(SCREAMING_SNAKE_CASE					,	"""_output_layer_norm/beta"""   )

			# Embeddings
			SCREAMING_SNAKE_CASE           =					get_encoder_array("""_position_embedding_layer/embeddings"""   )
			SCREAMING_SNAKE_CASE           =					get_encoder_array("""_type_embedding_layer/embeddings"""   )
			SCREAMING_SNAKE_CASE           =					get_encoder_array("""_embedding_norm_layer/gamma"""   )
			SCREAMING_SNAKE_CASE           =					get_encoder_array("""_embedding_norm_layer/beta"""   )

			# LM Head
			SCREAMING_SNAKE_CASE           =					model.cls.predictions.transform

			SCREAMING_SNAKE_CASE           =					get_masked_lm_array("""dense/kernel"""   )
			SCREAMING_SNAKE_CASE           =					get_masked_lm_array("""dense/bias"""   )

			SCREAMING_SNAKE_CASE           =					get_masked_lm_array("""layer_norm/gamma"""   )
			SCREAMING_SNAKE_CASE           =					get_masked_lm_array("""layer_norm/beta"""   )

			SCREAMING_SNAKE_CASE           =					get_masked_lm_array("""embedding_table"""   )

			# Pooling
			SCREAMING_SNAKE_CASE           =					BertPooler(config=SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					get_encoder_array("""_pooler_layer/kernel"""   )
			SCREAMING_SNAKE_CASE           =					get_encoder_array("""_pooler_layer/bias"""   )

			# Export final model
			model.save_pretrained(SCREAMING_SNAKE_CASE   )

			# Integration test - should load without any errors ;)
			SCREAMING_SNAKE_CASE           =					BertForMaskedLM.from_pretrained(SCREAMING_SNAKE_CASE   )
			print(new_model.eval()   )

			print("""Model conversion was done sucessfully!"""   )


if __name__ == "__main__":
		__A						:							List[Any]							=  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.""",
		)
		__A						:							Union[str, Any]							=  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


__A						:							List[Any]							=  logging.get_logger(__name__)

__A						:							Tuple							=  {
    """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 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     '''encodec'''





							def __init__(					self       :	Dict       ,     a       :	Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0]       ,     a       :	Union[str, Any]=24_000       ,     a       :	List[Any]=1       ,     a       :	str=False       ,     a       :	List[Any]=None       ,     a       :	List[Any]=None       ,     a       :	Optional[int]=128       ,     a       :	int=32       ,     a       :	int=1       ,     a       :	Dict=[8, 5, 4, 2]       ,     a       :	List[str]="weight_norm"       ,     a       :	str=7       ,     a       :	int=7       ,     a       :	Optional[int]=3       ,     a       :	Optional[int]=2       ,     a       :	Optional[int]=True       ,     a       :	Union[str, Any]="reflect"       ,     a       :	Dict=2       ,     a       :	Union[str, Any]=2       ,     a       :	Optional[Any]=1.0       ,     a       :	List[Any]=1_024       ,     a       :	int=None       ,     a       :	Dict=True       ,     **a       :	Tuple       ,     )			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					target_bandwidths
										SCREAMING_SNAKE_CASE           =					sampling_rate
										SCREAMING_SNAKE_CASE           =					audio_channels
										SCREAMING_SNAKE_CASE           =					normalize
										SCREAMING_SNAKE_CASE           =					chunk_length_s
										SCREAMING_SNAKE_CASE           =					overlap
										SCREAMING_SNAKE_CASE           =					hidden_size
										SCREAMING_SNAKE_CASE           =					num_filters
										SCREAMING_SNAKE_CASE           =					num_residual_layers
										SCREAMING_SNAKE_CASE           =					upsampling_ratios
										SCREAMING_SNAKE_CASE           =					norm_type
										SCREAMING_SNAKE_CASE           =					kernel_size
										SCREAMING_SNAKE_CASE           =					last_kernel_size
										SCREAMING_SNAKE_CASE           =					residual_kernel_size
										SCREAMING_SNAKE_CASE           =					dilation_growth_rate
										SCREAMING_SNAKE_CASE           =					use_causal_conv
										SCREAMING_SNAKE_CASE           =					pad_mode
										SCREAMING_SNAKE_CASE           =					compress
										SCREAMING_SNAKE_CASE           =					num_lstm_layers
										SCREAMING_SNAKE_CASE           =					trim_right_ratio
										SCREAMING_SNAKE_CASE           =					codebook_size
										SCREAMING_SNAKE_CASE           =					codebook_dim if codebook_dim is not None else hidden_size
										SCREAMING_SNAKE_CASE           =					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__(**a			)





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





							@property
							def    _UpperCAmelCase     (					self       :	Optional[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    _UpperCAmelCase     (					self       :	List[Any]			)			->					int:
										SCREAMING_SNAKE_CASE           =					np.prod(self.upsampling_ratios			)
										return math.ceil(self.sampling_rate / hop_length			)







							@property
							def    _UpperCAmelCase     (					self       :	Any			)			->					int:
										return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)			)






