
import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def __A						(   a_ :							List[Any]     )->							Any:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Dict				= [
		    '''decoder.version''',
		    '''decoder.output_projection.weight''',
		    '''_float_tensor''',
		    '''decoder.embed_positions._float_tensor''',
		]
		for k in ignore_keys:
				state_dict.pop(a_		,		a_     )





def __A						(   a_ :							Optional[Any]     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= emb.weight.shape
		SCREAMING_SNAKE_CASE       :     int				= nn.Linear(a_		,		a_		,		bias=a_     )
		SCREAMING_SNAKE_CASE       :     Any				= emb.weight.data
		return lin_layer





def __A						(   a_ :							List[Any]     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= torch.load(a_		,		map_location='''cpu'''     )
		SCREAMING_SNAKE_CASE       :     Any				= Namespace(**checkpoint['''cfg''']['''model''']     )
		SCREAMING_SNAKE_CASE       :     List[Any]				= checkpoint['''model''']
		remove_ignore_keys_(a_     )
		SCREAMING_SNAKE_CASE       :     Tuple				= state_dict['''decoder.embed_tokens.weight'''].shape[0]

		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= {key.replace('''decoder'''		,		'''model'''     ): val for key, val in state_dict.items()}

		SCREAMING_SNAKE_CASE       :     Tuple				= XGLMConfig(
		    vocab_size=a_		,		max_position_embeddings=args.max_target_positions		,		num_layers=args.decoder_layers		,		attention_heads=args.decoder_attention_heads		,		ffn_dim=args.decoder_ffn_embed_dim		,		d_model=args.decoder_embed_dim		,		layerdrop=args.decoder_layerdrop		,		dropout=args.dropout		,		attention_dropout=args.attention_dropout		,		activation_dropout=args.activation_dropout		,		activation_function='''gelu'''		,		scale_embedding=not args.no_scale_embedding		,		tie_word_embeddings=args.share_decoder_input_output_embed		,		)

		SCREAMING_SNAKE_CASE       :     Optional[int]				= XGLMForCausalLM(a_     )
		SCREAMING_SNAKE_CASE       :     str				= model.load_state_dict(a_		,		strict=a_     )
		print(a_     )
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= make_linear_from_emb(model.model.embed_tokens     )

		return model


if __name__ == "__main__":
					lowerCamelCase__ :       str						     =			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
					parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
					lowerCamelCase__ :       Optional[int]						     =			parser.parse_args()
					lowerCamelCase__ :       int						     =			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
					model.save_pretrained(args.pytorch_dump_folder_path)







"""simple docstring"""






import math
from typing import Optional

import numpy as np

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


lowerCamelCase__ :       Tuple						     =			logging.get_logger(__name__)

lowerCamelCase__ :       Any						     =			{
    "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 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """encodec"""





	def __init__(						self    :List[str]		, lowerCamelCase_    :Tuple=[1.5, 3.0, 6.0, 1_2.0, 2_4.0]		, lowerCamelCase_    :str=2_40_00		, lowerCamelCase_    :Any=1		, lowerCamelCase_    :List[Any]=False		, lowerCamelCase_    :Optional[int]=None		, lowerCamelCase_    :Optional[Any]=None		, lowerCamelCase_    :str=1_28		, lowerCamelCase_    :Any=32		, lowerCamelCase_    :int=1		, lowerCamelCase_    :Dict=[8, 5, 4, 2]		, lowerCamelCase_    :List[Any]="weight_norm"		, lowerCamelCase_    :Optional[int]=7		, lowerCamelCase_    :Tuple=7		, lowerCamelCase_    :Optional[Any]=3		, lowerCamelCase_    :int=2		, lowerCamelCase_    :Dict=True		, lowerCamelCase_    :Optional[int]="reflect"		, lowerCamelCase_    :Optional[int]=2		, lowerCamelCase_    :Union[str, Any]=2		, lowerCamelCase_    :Dict=1.0		, lowerCamelCase_    :Any=10_24		, lowerCamelCase_    :str=None		, lowerCamelCase_    :Union[str, Any]=True		, **lowerCamelCase_    :Optional[int]		, )							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= target_bandwidths
			SCREAMING_SNAKE_CASE       :     List[str]				= sampling_rate
			SCREAMING_SNAKE_CASE       :     Tuple				= audio_channels
			SCREAMING_SNAKE_CASE       :     Tuple				= normalize
			SCREAMING_SNAKE_CASE       :     str				= chunk_length_s
			SCREAMING_SNAKE_CASE       :     List[str]				= overlap
			SCREAMING_SNAKE_CASE       :     int				= hidden_size
			SCREAMING_SNAKE_CASE       :     Optional[int]				= num_filters
			SCREAMING_SNAKE_CASE       :     Tuple				= num_residual_layers
			SCREAMING_SNAKE_CASE       :     List[Any]				= upsampling_ratios
			SCREAMING_SNAKE_CASE       :     Optional[int]				= norm_type
			SCREAMING_SNAKE_CASE       :     Any				= kernel_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= last_kernel_size
			SCREAMING_SNAKE_CASE       :     Tuple				= residual_kernel_size
			SCREAMING_SNAKE_CASE       :     Any				= dilation_growth_rate
			SCREAMING_SNAKE_CASE       :     Optional[int]				= use_causal_conv
			SCREAMING_SNAKE_CASE       :     str				= pad_mode
			SCREAMING_SNAKE_CASE       :     List[Any]				= compress
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= num_lstm_layers
			SCREAMING_SNAKE_CASE       :     Dict				= trim_right_ratio
			SCREAMING_SNAKE_CASE       :     List[Any]				= codebook_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= codebook_dim if codebook_dim is not None else hidden_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= 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   __lowerCAmelCase (						self    :Optional[Any]					)							->			Optional[int]:







			'''simple docstring'''




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





	@property
	def   __lowerCAmelCase (						self    :List[str]					)							->			Optional[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   __lowerCAmelCase (						self    :Dict					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= np.prod(self.upsampling_ratios					)
			return math.ceil(self.sampling_rate / hop_length					)







	@property
	def   __lowerCAmelCase (						self    :Dict					)							->			int:







			'''simple docstring'''




			return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)					)





