'''simple docstring'''

import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
lowercase_     		= logging.get_logger('''transformers.models.encodec''')

lowercase_     		= {
    '''quantizer.vq.layers.*._codebook.inited''': '''quantizer.layers.*.codebook.inited''',
    '''quantizer.vq.layers.*._codebook.cluster_size''': '''quantizer.layers.*.codebook.cluster_size''',
    '''quantizer.vq.layers.*._codebook.embed''': '''quantizer.layers.*.codebook.embed''',
    '''quantizer.vq.layers.*._codebook.embed_avg''': '''quantizer.layers.*.codebook.embed_avg''',
}
lowercase_     		= {
    '''encoder.model.0.conv.conv''': '''encoder.layers.0.conv''',
    '''encoder.model.1.block.1.conv.conv''': '''encoder.layers.1.block.1.conv''',
    '''encoder.model.1.block.3.conv.conv''': '''encoder.layers.1.block.3.conv''',
    '''encoder.model.1.shortcut.conv.conv''': '''encoder.layers.1.shortcut.conv''',
    '''encoder.model.3.conv.conv''': '''encoder.layers.3.conv''',
    '''encoder.model.4.block.1.conv.conv''': '''encoder.layers.4.block.1.conv''',
    '''encoder.model.4.block.3.conv.conv''': '''encoder.layers.4.block.3.conv''',
    '''encoder.model.4.shortcut.conv.conv''': '''encoder.layers.4.shortcut.conv''',
    '''encoder.model.6.conv.conv''': '''encoder.layers.6.conv''',
    '''encoder.model.7.block.1.conv.conv''': '''encoder.layers.7.block.1.conv''',
    '''encoder.model.7.block.3.conv.conv''': '''encoder.layers.7.block.3.conv''',
    '''encoder.model.7.shortcut.conv.conv''': '''encoder.layers.7.shortcut.conv''',
    '''encoder.model.9.conv.conv''': '''encoder.layers.9.conv''',
    '''encoder.model.10.block.1.conv.conv''': '''encoder.layers.10.block.1.conv''',
    '''encoder.model.10.block.3.conv.conv''': '''encoder.layers.10.block.3.conv''',
    '''encoder.model.10.shortcut.conv.conv''': '''encoder.layers.10.shortcut.conv''',
    '''encoder.model.12.conv.conv''': '''encoder.layers.12.conv''',
    '''encoder.model.13.lstm''': '''encoder.layers.13.lstm''',
    '''encoder.model.15.conv.conv''': '''encoder.layers.15.conv''',
}
lowercase_     		= {
    '''encoder.model.0.conv.norm''': '''encoder.layers.0.norm''',
    '''encoder.model.1.block.1.conv.norm''': '''encoder.layers.1.block.1.norm''',
    '''encoder.model.1.block.3.conv.norm''': '''encoder.layers.1.block.3.norm''',
    '''encoder.model.1.shortcut.conv.norm''': '''encoder.layers.1.shortcut.norm''',
    '''encoder.model.3.conv.norm''': '''encoder.layers.3.norm''',
    '''encoder.model.4.block.1.conv.norm''': '''encoder.layers.4.block.1.norm''',
    '''encoder.model.4.block.3.conv.norm''': '''encoder.layers.4.block.3.norm''',
    '''encoder.model.4.shortcut.conv.norm''': '''encoder.layers.4.shortcut.norm''',
    '''encoder.model.6.conv.norm''': '''encoder.layers.6.norm''',
    '''encoder.model.7.block.1.conv.norm''': '''encoder.layers.7.block.1.norm''',
    '''encoder.model.7.block.3.conv.norm''': '''encoder.layers.7.block.3.norm''',
    '''encoder.model.7.shortcut.conv.norm''': '''encoder.layers.7.shortcut.norm''',
    '''encoder.model.9.conv.norm''': '''encoder.layers.9.norm''',
    '''encoder.model.10.block.1.conv.norm''': '''encoder.layers.10.block.1.norm''',
    '''encoder.model.10.block.3.conv.norm''': '''encoder.layers.10.block.3.norm''',
    '''encoder.model.10.shortcut.conv.norm''': '''encoder.layers.10.shortcut.norm''',
    '''encoder.model.12.conv.norm''': '''encoder.layers.12.norm''',
    '''encoder.model.15.conv.norm''': '''encoder.layers.15.norm''',
}
lowercase_     		= {
    '''decoder.model.0.conv.conv''': '''decoder.layers.0.conv''',
    '''decoder.model.1.lstm''': '''decoder.layers.1.lstm''',
    '''decoder.model.3.convtr.convtr''': '''decoder.layers.3.conv''',
    '''decoder.model.4.block.1.conv.conv''': '''decoder.layers.4.block.1.conv''',
    '''decoder.model.4.block.3.conv.conv''': '''decoder.layers.4.block.3.conv''',
    '''decoder.model.4.shortcut.conv.conv''': '''decoder.layers.4.shortcut.conv''',
    '''decoder.model.6.convtr.convtr''': '''decoder.layers.6.conv''',
    '''decoder.model.7.block.1.conv.conv''': '''decoder.layers.7.block.1.conv''',
    '''decoder.model.7.block.3.conv.conv''': '''decoder.layers.7.block.3.conv''',
    '''decoder.model.7.shortcut.conv.conv''': '''decoder.layers.7.shortcut.conv''',
    '''decoder.model.9.convtr.convtr''': '''decoder.layers.9.conv''',
    '''decoder.model.10.block.1.conv.conv''': '''decoder.layers.10.block.1.conv''',
    '''decoder.model.10.block.3.conv.conv''': '''decoder.layers.10.block.3.conv''',
    '''decoder.model.10.shortcut.conv.conv''': '''decoder.layers.10.shortcut.conv''',
    '''decoder.model.12.convtr.convtr''': '''decoder.layers.12.conv''',
    '''decoder.model.13.block.1.conv.conv''': '''decoder.layers.13.block.1.conv''',
    '''decoder.model.13.block.3.conv.conv''': '''decoder.layers.13.block.3.conv''',
    '''decoder.model.13.shortcut.conv.conv''': '''decoder.layers.13.shortcut.conv''',
    '''decoder.model.15.conv.conv''': '''decoder.layers.15.conv''',
}
lowercase_     		= {
    '''decoder.model.0.conv.norm''': '''decoder.layers.0.norm''',
    '''decoder.model.3.convtr.norm''': '''decoder.layers.3.norm''',
    '''decoder.model.4.block.1.conv.norm''': '''decoder.layers.4.block.1.norm''',
    '''decoder.model.4.block.3.conv.norm''': '''decoder.layers.4.block.3.norm''',
    '''decoder.model.4.shortcut.conv.norm''': '''decoder.layers.4.shortcut.norm''',
    '''decoder.model.6.convtr.norm''': '''decoder.layers.6.norm''',
    '''decoder.model.7.block.1.conv.norm''': '''decoder.layers.7.block.1.norm''',
    '''decoder.model.7.block.3.conv.norm''': '''decoder.layers.7.block.3.norm''',
    '''decoder.model.7.shortcut.conv.norm''': '''decoder.layers.7.shortcut.norm''',
    '''decoder.model.9.convtr.norm''': '''decoder.layers.9.norm''',
    '''decoder.model.10.block.1.conv.norm''': '''decoder.layers.10.block.1.norm''',
    '''decoder.model.10.block.3.conv.norm''': '''decoder.layers.10.block.3.norm''',
    '''decoder.model.10.shortcut.conv.norm''': '''decoder.layers.10.shortcut.norm''',
    '''decoder.model.12.convtr.norm''': '''decoder.layers.12.norm''',
    '''decoder.model.13.block.1.conv.norm''': '''decoder.layers.13.block.1.norm''',
    '''decoder.model.13.block.3.conv.norm''': '''decoder.layers.13.block.3.norm''',
    '''decoder.model.13.shortcut.conv.norm''': '''decoder.layers.13.shortcut.norm''',
    '''decoder.model.15.conv.norm''': '''decoder.layers.15.norm''',
}
lowercase_     		= {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
lowercase_     		= {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
lowercase_     		= []
lowercase_     		= []



def 							UpperCamelCase__			(			a__			,      a__			,      a__			,      a__			,      a__      ):


						'''simple docstring'''






						for attribute in key.split('.'      ):
												_lowerCAmelCase										=getattr(a__			,      a__      )

						if weight_type is not None:
												_lowerCAmelCase										=getattr(a__			,      a__      ).shape
						else:
												_lowerCAmelCase										=hf_pointer.shape

						if hf_shape != value.shape:
												raise ValueError(
												    F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
												    F''' {value.shape} for {full_name}'''      )

						if weight_type == "weight":
												_lowerCAmelCase										=value
						elif weight_type == "weight_g":
												_lowerCAmelCase										=value
						elif weight_type == "weight_v":
												_lowerCAmelCase										=value
						elif weight_type == "bias":
												_lowerCAmelCase										=value
						elif weight_type == "running_mean":
												_lowerCAmelCase										=value
						elif weight_type == "running_var":
												_lowerCAmelCase										=value
						elif weight_type == "num_batches_tracked":
												_lowerCAmelCase										=value
						elif weight_type == "weight_ih_l0":
												_lowerCAmelCase										=value
						elif weight_type == "weight_hh_l0":
												_lowerCAmelCase										=value
						elif weight_type == "bias_ih_l0":
												_lowerCAmelCase										=value
						elif weight_type == "bias_hh_l0":
												_lowerCAmelCase										=value
						elif weight_type == "weight_ih_l1":
												_lowerCAmelCase										=value
						elif weight_type == "weight_hh_l1":
												_lowerCAmelCase										=value
						elif weight_type == "bias_ih_l1":
												_lowerCAmelCase										=value
						elif weight_type == "bias_hh_l1":
												_lowerCAmelCase										=value
						else:
												_lowerCAmelCase										=value

						logger.info(F'''{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.'''      )



def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						for key in ignore_keys:
												if key.endswith('.*'      ):
																		if name.startswith(key[:-1]      ):
																								return True
												elif ".*." in key:
																		_lowerCAmelCase						,				_lowerCAmelCase										=key.split('.*.'      )
																		if prefix in name and suffix in name:
																								return True
												elif key in name:
																		return True
						return False



def 							UpperCamelCase__			(			a__			,      a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=[]

						if model_name == "encodec_24khz" or "encodec_32khz":
												_lowerCAmelCase										=MAPPING_24K
						elif model_name == "encodec_48khz":
												_lowerCAmelCase										=MAPPING_48K
						else:
												raise ValueError(F'''Unsupported model: {model_name}'''      )

						for name, value in orig_dict.items():
												if should_ignore(a__			,      a__      ):
																		logger.info(F'''{name} was ignored'''      )
																		continue

												_lowerCAmelCase										=False
												for key, mapped_key in MAPPING.items():
																		if "*" in key:
																								_lowerCAmelCase						,				_lowerCAmelCase										=key.split('.*.'      )
																								if prefix in name and suffix in name:
																														_lowerCAmelCase										=suffix

																		if key in name:
																								# HACK otherwise .embed gets initialized with .embed_avg too
																								if key.endswith('embed'      ) and name.endswith('embed_avg'      ):
																														continue

																								_lowerCAmelCase										=True
																								if "*" in mapped_key:
																														_lowerCAmelCase										=name.split(a__      )[0].split('.'      )[-2]
																														_lowerCAmelCase										=mapped_key.replace('*'			,      a__      )
																								if "weight_g" in name:
																														_lowerCAmelCase										='weight_g'
																								elif "weight_v" in name:
																														_lowerCAmelCase										='weight_v'
																								elif "weight_ih_l0" in name:
																														_lowerCAmelCase										='weight_ih_l0'
																								elif "weight_hh_l0" in name:
																														_lowerCAmelCase										='weight_hh_l0'
																								elif "bias_ih_l0" in name:
																														_lowerCAmelCase										='bias_ih_l0'
																								elif "bias_hh_l0" in name:
																														_lowerCAmelCase										='bias_hh_l0'
																								elif "weight_ih_l1" in name:
																														_lowerCAmelCase										='weight_ih_l1'
																								elif "weight_hh_l1" in name:
																														_lowerCAmelCase										='weight_hh_l1'
																								elif "bias_ih_l1" in name:
																														_lowerCAmelCase										='bias_ih_l1'
																								elif "bias_hh_l1" in name:
																														_lowerCAmelCase										='bias_hh_l1'
																								elif "bias" in name:
																														_lowerCAmelCase										='bias'
																								elif "weight" in name:
																														_lowerCAmelCase										='weight'
																								elif "running_mean" in name:
																														_lowerCAmelCase										='running_mean'
																								elif "running_var" in name:
																														_lowerCAmelCase										='running_var'
																								elif "num_batches_tracked" in name:
																														_lowerCAmelCase										='num_batches_tracked'
																								else:
																														_lowerCAmelCase										=None
																								set_recursively(a__			,      a__			,      a__			,      a__			,      a__      )
																		continue
												if not is_used:
																		unused_weights.append(a__      )

						logger.warning(F'''Unused weights: {unused_weights}'''      )



@torch.no_grad()
def 							UpperCamelCase__			(			a__			,      a__			,      a__			,      a__=None			,      a__=None			,      ):


						'''simple docstring'''






						if config_path is not None:
												_lowerCAmelCase										=EncodecConfig.from_pretrained(a__      )
						else:
												_lowerCAmelCase										=EncodecConfig()

						if model_name == "encodec_24khz":
												pass  # config is already correct
						elif model_name == "encodec_32khz":
												_lowerCAmelCase										=[8, 5, 4, 4]
												_lowerCAmelCase										=[2.2]
												_lowerCAmelCase										=6_4
												_lowerCAmelCase										=3_2_0_0_0
												_lowerCAmelCase										=2_0_4_8
												_lowerCAmelCase										=False
												_lowerCAmelCase										=False
												_lowerCAmelCase										=False
						elif model_name == "encodec_48khz":
												_lowerCAmelCase										=[8, 5, 4, 2]
												_lowerCAmelCase										=[3.0, 6.0, 12.0, 24.0]
												_lowerCAmelCase										=4_8_0_0_0
												_lowerCAmelCase										=2
												_lowerCAmelCase										=False
												_lowerCAmelCase										='time_group_norm'
												_lowerCAmelCase										=True
												_lowerCAmelCase										=1.0
												_lowerCAmelCase										=0.01
						else:
												raise ValueError(F'''Unknown model name: {model_name}'''      )

						_lowerCAmelCase										=EncodecModel(a__      )

						_lowerCAmelCase										=EncodecFeatureExtractor(
						    feature_size=config.audio_channels			,      sampling_rate=config.sampling_rate			,      chunk_length_s=config.chunk_length_s			,      overlap=config.overlap			,      )
						feature_extractor.save_pretrained(a__      )

						_lowerCAmelCase										=torch.load(a__      )
						if "best_state" in original_checkpoint:
												# we might have a training state saved, in which case discard the yaml results and just retain the weights
												_lowerCAmelCase										=original_checkpoint['best_state']
						recursively_load_weights(a__			,      a__			,      a__      )
						model.save_pretrained(a__      )

						if repo_id:
												print('Pushing to the hub...'      )
												feature_extractor.push_to_hub(a__      )
												model.push_to_hub(a__      )


if __name__ == "__main__":
							lowercase_     		= argparse.ArgumentParser()
							parser.add_argument(
							    '''--model''',
							    default='''encodec_24khz''',
							    type=str,
							    help='''The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.''',
							)
							parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to original checkpoint''')
							parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
							)
							parser.add_argument(
							    '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
							)

							lowercase_     		= parser.parse_args()
							convert_checkpoint(
							    args.model,
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.push_to_hub,
							)






'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_     		= logging.get_logger(__name__)

lowercase_     		= {
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      List[str]       =							'data2vec-text'






		def __init__(   self		,						__A=3_0522		,						__A=768		,						__A=12		,						__A=12		,						__A=3072		,						__A="gelu"		,						__A=0.1		,						__A=0.1		,						__A=512		,						__A=2		,						__A=0.02		,						__A=1E-12		,						__A=1		,						__A=0		,						__A=2		,						__A="absolute"		,						__A=True		,						__A=None		,						**__A		,						) ->     List[Any]:
								super().__init__(pad_token_id=__A		,						bos_token_id=__A		,						eos_token_id=__A		,						**__A				)

								_lowerCAmelCase										=vocab_size
								_lowerCAmelCase										=hidden_size
								_lowerCAmelCase										=num_hidden_layers
								_lowerCAmelCase										=num_attention_heads
								_lowerCAmelCase										=hidden_act
								_lowerCAmelCase										=intermediate_size
								_lowerCAmelCase										=hidden_dropout_prob
								_lowerCAmelCase										=attention_probs_dropout_prob
								_lowerCAmelCase										=max_position_embeddings
								_lowerCAmelCase										=type_vocab_size
								_lowerCAmelCase										=initializer_range
								_lowerCAmelCase										=layer_norm_eps
								_lowerCAmelCase										=position_embedding_type
								_lowerCAmelCase										=use_cache
								_lowerCAmelCase										=classifier_dropout

class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		@property
		def 		UpperCamelCase__       (   self				) ->     Mapping[str, Mapping[int, str]]:
								if self.task == "multiple-choice":
														_lowerCAmelCase										={0: 'batch', 1: 'choice', 2: 'sequence'}
								else:
														_lowerCAmelCase										={0: 'batch', 1: 'sequence'}
								return OrderedDict(
								    [
								        ('input_ids', dynamic_axis),
								        ('attention_mask', dynamic_axis),
								    ]				)





