






import math
from typing import Optional

import numpy as np

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


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

A__	:			int			   = {
    '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"""




				lowercase__											=       """encodec"""




				def __init__(     self						:    Any,  lowerCamelCase						:    Any=[1.5, 3.0, 6.0, 12.0, 24.0],  lowerCamelCase						:    Union[str, Any]=24_000,  lowerCamelCase						:    int=1,  lowerCamelCase						:    Optional[Any]=False,  lowerCamelCase						:    Any=None,  lowerCamelCase						:    Optional[Any]=None,  lowerCamelCase						:    Optional[Any]=128,  lowerCamelCase						:    List[str]=32,  lowerCamelCase						:    str=1,  lowerCamelCase						:    int=[8, 5, 4, 2],  lowerCamelCase						:    Any="weight_norm",  lowerCamelCase						:    str=7,  lowerCamelCase						:    Union[str, Any]=7,  lowerCamelCase						:    Optional[int]=3,  lowerCamelCase						:    Optional[Any]=2,  lowerCamelCase						:    Optional[int]=True,  lowerCamelCase						:    Dict="reflect",  lowerCamelCase						:    Optional[Any]=2,  lowerCamelCase						:    str=2,  lowerCamelCase						:    Any=1.0,  lowerCamelCase						:    Tuple=1_024,  lowerCamelCase						:    List[Any]=None,  lowerCamelCase						:    List[Any]=True,  **lowerCamelCase						:    Dict,  ):

								'''simple docstring'''
								lowercase__    =			target_bandwidths
								lowercase__    =			sampling_rate
								lowercase__    =			audio_channels
								lowercase__    =			normalize
								lowercase__    =			chunk_length_s
								lowercase__    =			overlap
								lowercase__    =			hidden_size
								lowercase__    =			num_filters
								lowercase__    =			num_residual_layers
								lowercase__    =			upsampling_ratios
								lowercase__    =			norm_type
								lowercase__    =			kernel_size
								lowercase__    =			last_kernel_size
								lowercase__    =			residual_kernel_size
								lowercase__    =			dilation_growth_rate
								lowercase__    =			use_causal_conv
								lowercase__    =			pad_mode
								lowercase__    =			compress
								lowercase__    =			num_lstm_layers
								lowercase__    =			trim_right_ratio
								lowercase__    =			codebook_size
								lowercase__    =			codebook_dim if codebook_dim is not None else hidden_size
								lowercase__    =			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 				lowercase__   (     self						:    Optional[Any]  ):

								'''simple docstring'''
								if self.chunk_length_s is None:
												return None
								else:
												return int(self.chunk_length_s * self.sampling_rate  )




				@property
				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								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 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								lowercase__    =			np.prod(self.upsampling_ratios  )
								return math.ceil(self.sampling_rate / hop_length  )




				@property
				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)  )












import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

				lowercase__    =			(
				    ('''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(lowerCamelCase_				):
								os.makedirs(lowerCamelCase_				)

				lowercase__    =			model.state_dict()

				def to_tf_var_name(lowerCamelCase_				):
								for patt, repl in iter(lowerCamelCase_				):
												lowercase__    =			name.replace(lowerCamelCase_		,   lowerCamelCase_				)
								return F"""bert/{name}"""

				def create_tf_var(lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):
								lowercase__    =			tf.dtypes.as_dtype(tensor.dtype				)
								lowercase__    =			tf.get_variable(dtype=lowerCamelCase_		,   shape=tensor.shape		,   name=lowerCamelCase_		,   initializer=tf.zeros_initializer()				)
								session.run(tf.variables_initializer([tf_var]				)				)
								session.run(lowerCamelCase_				)
								return tf_var

				tf.reset_default_graph()
				with tf.Session() as session:
								for var_name in state_dict:
												lowercase__    =			to_tf_var_name(lowerCamelCase_				)
												lowercase__    =			state_dict[var_name].numpy()
												if any(x in var_name for x in tensors_to_transpose				):
																lowercase__    =			torch_tensor.T
												lowercase__    =			create_tf_var(tensor=lowerCamelCase_		,   name=lowerCamelCase_		,   session=lowerCamelCase_				)
												tf.keras.backend.set_value(lowerCamelCase_		,   lowerCamelCase_				)
												lowercase__    =			session.run(lowerCamelCase_				)
												print(F"""Successfully created {tf_name}: {np.allclose(lowerCamelCase_		,   lowerCamelCase_				)}"""				)

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






def   a			(						lowerCamelCase_=None				):






				'''simple docstring'''
				lowercase__    =			argparse.ArgumentParser()
				parser.add_argument('''--model_name'''		,   type=lowerCamelCase_		,   required=lowerCamelCase_		,   help='''model name e.g. bert-base-uncased'''				)
				parser.add_argument(
				    '''--cache_dir'''		,   type=lowerCamelCase_		,   default=lowerCamelCase_		,   required=lowerCamelCase_		,   help='''Directory containing pytorch model'''				)
				parser.add_argument('''--pytorch_model_path'''		,   type=lowerCamelCase_		,   required=lowerCamelCase_		,   help='''/path/to/<pytorch-model-name>.bin'''				)
				parser.add_argument('''--tf_cache_dir'''		,   type=lowerCamelCase_		,   required=lowerCamelCase_		,   help='''Directory in which to save tensorflow model'''				)
				lowercase__    =			parser.parse_args(lowerCamelCase_				)

				lowercase__    =			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=lowerCamelCase_		,   ckpt_dir=args.tf_cache_dir		,   model_name=args.model_name				)


if __name__ == "__main__":
							main()




