


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()
snake_case							:       List[Any]										=	logging.get_logger('''transformers.models.encodec''')

snake_case							:       int										=	{
    '''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''',
}
snake_case							:       Any										=	{
    '''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''',
}
snake_case							:       Union[str, Any]										=	{
    '''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''',
}
snake_case							:       Dict										=	{
    '''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''',
}
snake_case							:       str										=	{
    '''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''',
}
snake_case							:       Union[str, Any]										=	{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
snake_case							:       Union[str, Any]										=	{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
snake_case							:       List[str]										=	[]
snake_case							:       Any										=	[]
def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						Union[str, Any]					,    UpperCAmelCase_		:						List[Any]					,    UpperCAmelCase_		:						Dict					,    UpperCAmelCase_		:						Tuple		):




      """simple docstring"""





      for attribute in key.split('''.'''		):
            a		:Any									=			getattr(UpperCAmelCase_					,    UpperCAmelCase_		)

      if weight_type is not None:
            a		:Dict									=			getattr(UpperCAmelCase_					,    UpperCAmelCase_		).shape
      else:
            a		:Tuple									=			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":
            a		:Optional[int]									=			value
      elif weight_type == "weight_g":
            a		:List[str]									=			value
      elif weight_type == "weight_v":
            a		:List[Any]									=			value
      elif weight_type == "bias":
            a		:Optional[int]									=			value
      elif weight_type == "running_mean":
            a		:int									=			value
      elif weight_type == "running_var":
            a		:List[Any]									=			value
      elif weight_type == "num_batches_tracked":
            a		:Union[str, Any]									=			value
      elif weight_type == "weight_ih_l0":
            a		:Optional[Any]									=			value
      elif weight_type == "weight_hh_l0":
            a		:List[str]									=			value
      elif weight_type == "bias_ih_l0":
            a		:List[str]									=			value
      elif weight_type == "bias_hh_l0":
            a		:Any									=			value
      elif weight_type == "weight_ih_l1":
            a		:Union[str, Any]									=			value
      elif weight_type == "weight_hh_l1":
            a		:List[Any]									=			value
      elif weight_type == "bias_ih_l1":
            a		:Dict									=			value
      elif weight_type == "bias_hh_l1":
            a		:Any									=			value
      else:
            a		:Optional[int]									=			value

      logger.info(F'''{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.'''		)
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Any					,    UpperCAmelCase_		:						str		):




      """simple docstring"""





      for key in ignore_keys:
            if key.endswith('''.*'''		):
                  if name.startswith(key[:-1]		):
                        return True
            elif ".*." in key:
                  a				,							a		:int									=			key.split('''.*.'''		)
                  if prefix in name and suffix in name:
                        return True
            elif key in name:
                  return True
      return False
def 							__lowerCamelCase		(					UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						Union[str, Any]					,    UpperCAmelCase_		:						Optional[Any]		):




      """simple docstring"""





      a		:Optional[Any]									=			[]

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

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

            a		:Optional[int]									=			False
            for key, mapped_key in MAPPING.items():
                  if "*" in key:
                        a				,							a		:List[str]									=			key.split('''.*.'''		)
                        if prefix in name and suffix in name:
                              a		:Optional[Any]									=			suffix

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

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

      logger.warning(F'''Unused weights: {unused_weights}'''		)
@torch.no_grad()
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Dict					,    UpperCAmelCase_		:						Tuple					,    UpperCAmelCase_		:						List[Any]					,    UpperCAmelCase_		:						List[Any]=None					,    UpperCAmelCase_		:						Any=None					,    ):




      """simple docstring"""





      if config_path is not None:
            a		:Tuple									=			EncodecConfig.from_pretrained(UpperCAmelCase_		)
      else:
            a		:Dict									=			EncodecConfig()

      if model_name == "encodec_24khz":
            pass  # config is already correct
      elif model_name == "encodec_32khz":
            a		:Union[str, Any]									=			[8, 5, 4, 4]
            a		:Optional[int]									=			[2.2]
            a		:List[str]									=			64
            a		:Dict									=			3_2000
            a		:str									=			2048
            a		:int									=			False
            a		:List[str]									=			False
            a		:str									=			False
      elif model_name == "encodec_48khz":
            a		:List[Any]									=			[8, 5, 4, 2]
            a		:str									=			[3.0, 6.0, 12.0, 24.0]
            a		:Tuple									=			4_8000
            a		:Union[str, Any]									=			2
            a		:List[str]									=			False
            a		:Any									=			'''time_group_norm'''
            a		:Dict									=			True
            a		:Optional[int]									=			1.0
            a		:Dict									=			0.01
      else:
            raise ValueError(F'''Unknown model name: {model_name}'''		)

      a		:int									=			EncodecModel(UpperCAmelCase_		)

      a		:Optional[Any]									=			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(UpperCAmelCase_		)

      a		:Union[str, Any]									=			torch.load(UpperCAmelCase_		)
      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
            a		:Tuple									=			original_checkpoint['''best_state''']
      recursively_load_weights(UpperCAmelCase_					,    UpperCAmelCase_					,    UpperCAmelCase_		)
      model.save_pretrained(UpperCAmelCase_		)

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


if __name__ == "__main__":
     snake_case							:       Any										=	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.'''
     )

     snake_case							:       List[Any]										=	parser.parse_args()
     convert_checkpoint(
         args.model,
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.push_to_hub,
     )












'''simple docstring'''




import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
			from transformers.models.roberta.modeling_flax_roberta import (
			    FlaxRobertaForCausalLM,
			    FlaxRobertaForMaskedLM,
			    FlaxRobertaForMultipleChoice,
			    FlaxRobertaForQuestionAnswering,
			    FlaxRobertaForSequenceClassification,
			    FlaxRobertaForTokenClassification,
			    FlaxRobertaModel,
			)






class 				__snake_case     (					unittest.TestCase							):





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_=13      ,       UpperCamelCase_=7      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=99      ,       UpperCamelCase_=32      ,       UpperCamelCase_=5      ,       UpperCamelCase_=4      ,       UpperCamelCase_=37      ,       UpperCamelCase_="gelu"      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=512      ,       UpperCamelCase_=16      ,       UpperCamelCase_=2      ,       UpperCamelCase_=0.0_2      ,       UpperCamelCase_=4      ,       )      ->		List[Any]:
		snake_case__	       =   parent
		snake_case__	       =   batch_size
		snake_case__	       =   seq_length
		snake_case__	       =   is_training
		snake_case__	       =   use_attention_mask
		snake_case__	       =   use_token_type_ids
		snake_case__	       =   use_labels
		snake_case__	       =   vocab_size
		snake_case__	       =   hidden_size
		snake_case__	       =   num_hidden_layers
		snake_case__	       =   num_attention_heads
		snake_case__	       =   intermediate_size
		snake_case__	       =   hidden_act
		snake_case__	       =   hidden_dropout_prob
		snake_case__	       =   attention_probs_dropout_prob
		snake_case__	       =   max_position_embeddings
		snake_case__	       =   type_vocab_size
		snake_case__	       =   type_sequence_label_size
		snake_case__	       =   initializer_range
		snake_case__	       =   num_choices





	def   _snake_case   (		self		)      ->		Any:
		snake_case__	       =   ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size		)

		snake_case__	       =   None
		if self.use_attention_mask:
			snake_case__	       =   random_attention_mask([self.batch_size, self.seq_length]		)

		snake_case__	       =   None
		if self.use_token_type_ids:
			snake_case__	       =   ids_tensor([self.batch_size, self.seq_length]      ,       self.type_vocab_size		)

		snake_case__	       =   RobertaConfig(
		    vocab_size=self.vocab_size      ,       hidden_size=self.hidden_size      ,       num_hidden_layers=self.num_hidden_layers      ,       num_attention_heads=self.num_attention_heads      ,       intermediate_size=self.intermediate_size      ,       hidden_act=self.hidden_act      ,       hidden_dropout_prob=self.hidden_dropout_prob      ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,       max_position_embeddings=self.max_position_embeddings      ,       type_vocab_size=self.type_vocab_size      ,       is_decoder=UpperCamelCase_      ,       initializer_range=self.initializer_range      ,       )

		return config, input_ids, token_type_ids, attention_mask





	def   _snake_case   (		self		)      ->		Tuple:
		snake_case__	       =   self.prepare_config_and_inputs()
		snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__	       =   config_and_inputs
		snake_case__	       =   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
		return config, inputs_dict





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   self.prepare_config_and_inputs()
		snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__	       =   config_and_inputs

		snake_case__	       =   True
		snake_case__	       =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
		snake_case__	       =   ids_tensor([self.batch_size, self.seq_length]      ,       vocab_size=2		)

		return (
		    config,
		    input_ids,
		    token_type_ids,
		    encoder_hidden_states,
		    encoder_attention_mask,
		)






@require_flax
class 				__snake_case     (					__magic_name__       ,       unittest.TestCase							):
	__lowerCAmelCase       						=  True

	__lowerCAmelCase       						=  (
	    (
	        FlaxRobertaModel,
	        FlaxRobertaForCausalLM,
	        FlaxRobertaForMaskedLM,
	        FlaxRobertaForSequenceClassification,
	        FlaxRobertaForTokenClassification,
	        FlaxRobertaForMultipleChoice,
	        FlaxRobertaForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)





	def   _snake_case   (		self		)      ->		List[Any]:
		snake_case__	       =   FlaxRobertaModelTester(self		)





	@slow
	def   _snake_case   (		self		)      ->		Tuple:
		for model_class_name in self.all_model_classes:
			snake_case__	       =   model_class_name.from_pretrained('roberta-base'      ,       from_pt=UpperCamelCase_		)
			snake_case__	       =   model(np.ones((1, 1)		)		)
			self.assertIsNotNone(UpperCamelCase_		)



