



'''simple docstring'''

import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
				from transformers.models.esm.modeling_esm import (
				    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    EsmEmbeddings,
				    create_position_ids_from_input_ids,
				)

class __snake_case:


						'''simple docstring'''



						def __init__(   self      ,			A_      ,			A_=13      ,			A_=7      ,			A_=False      ,			A_=True      ,			A_=False      ,			A_=True      ,			A_=33      ,			A_=32      ,			A_=5      ,			A_=4      ,			A_=37      ,			A_="gelu"      ,			A_=0.1      ,			A_=0.1      ,			A_=512      ,			A_=16      ,			A_=2      ,			A_=0.0_2      ,			A_=3      ,			A_=4      ,			A_=None      ,			)      ->      List[str]:
							lowerCAmelCase       =      parent
							lowerCAmelCase       =      batch_size
							lowerCAmelCase       =      seq_length
							lowerCAmelCase       =      is_training
							lowerCAmelCase       =      use_input_mask
							lowerCAmelCase       =      use_token_type_ids
							lowerCAmelCase       =      use_labels
							lowerCAmelCase       =      vocab_size
							lowerCAmelCase       =      hidden_size
							lowerCAmelCase       =      num_hidden_layers
							lowerCAmelCase       =      num_attention_heads
							lowerCAmelCase       =      intermediate_size
							lowerCAmelCase       =      hidden_act
							lowerCAmelCase       =      hidden_dropout_prob
							lowerCAmelCase       =      attention_probs_dropout_prob
							lowerCAmelCase       =      max_position_embeddings
							lowerCAmelCase       =      type_vocab_size
							lowerCAmelCase       =      type_sequence_label_size
							lowerCAmelCase       =      initializer_range
							lowerCAmelCase       =      num_labels
							lowerCAmelCase       =      num_choices
							lowerCAmelCase       =      scope


						def 							__snake_case       (   self )      ->      Dict:
							lowerCAmelCase       =      ids_tensor([self.batch_size, self.seq_length]      ,			self.vocab_size )

							lowerCAmelCase       =      None
							if self.use_input_mask:
								lowerCAmelCase       =      random_attention_mask([self.batch_size, self.seq_length] )

							lowerCAmelCase       =      None
							lowerCAmelCase       =      None
							lowerCAmelCase       =      None
							if self.use_labels:
								lowerCAmelCase       =      ids_tensor([self.batch_size]      ,			self.type_sequence_label_size )
								lowerCAmelCase       =      ids_tensor([self.batch_size, self.seq_length]      ,			self.num_labels )
								lowerCAmelCase       =      ids_tensor([self.batch_size]      ,			self.num_choices )

							lowerCAmelCase       =      self.get_config()

							return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels


						def 							__snake_case       (   self )      ->      List[str]:
							return EsmConfig(
							    vocab_size=self.vocab_size      ,			hidden_size=self.hidden_size      ,			pad_token_id=1      ,			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      ,			initializer_range=self.initializer_range      ,			)


						def 							__snake_case       (   self      ,			A_      ,			A_      ,			A_      ,			A_      ,			A_      ,			A_ )      ->      Tuple:
							lowerCAmelCase       =      EsmModel(config=_UpperCamelCase )
							model.to(_UpperCamelCase )
							model.eval()
							lowerCAmelCase       =      model(_UpperCamelCase      ,			attention_mask=_UpperCamelCase )
							lowerCAmelCase       =      model(_UpperCamelCase )
							lowerCAmelCase       =      model(_UpperCamelCase )

							self.parent.assertEqual(result.last_hidden_state.shape      ,			(self.batch_size, self.seq_length, self.hidden_size) )
							self.parent.assertEqual(result.pooler_output.shape      ,			(self.batch_size, self.hidden_size) )


						def 							__snake_case       (   self      ,			A_      ,			A_      ,			A_      ,			A_      ,			A_      ,			A_ )      ->      List[Any]:
							lowerCAmelCase       =      EsmForMaskedLM(config=_UpperCamelCase )
							model.to(_UpperCamelCase )
							model.eval()
							lowerCAmelCase       =      model(_UpperCamelCase      ,			attention_mask=_UpperCamelCase      ,			labels=_UpperCamelCase )
							self.parent.assertEqual(result.logits.shape      ,			(self.batch_size, self.seq_length, self.vocab_size) )


						def 							__snake_case       (   self      ,			A_      ,			A_      ,			A_      ,			A_      ,			A_      ,			A_ )      ->      Optional[Any]:
							lowerCAmelCase       =      self.num_labels
							lowerCAmelCase       =      EsmForTokenClassification(config=_UpperCamelCase )
							model.to(_UpperCamelCase )
							model.eval()
							lowerCAmelCase       =      model(_UpperCamelCase      ,			attention_mask=_UpperCamelCase      ,			labels=_UpperCamelCase )
							self.parent.assertEqual(result.logits.shape      ,			(self.batch_size, self.seq_length, self.num_labels) )




						def 							__snake_case       (   self )      ->      Optional[int]:
							lowerCAmelCase       =      self.prepare_config_and_inputs()
							(
							    (
							    lowerCAmelCase				
							),  (
							    lowerCAmelCase				
							),  (
							    lowerCAmelCase				
							),  (
							    lowerCAmelCase				
							),  (
							    lowerCAmelCase				
							),  (
							    lowerCAmelCase				
							),  				
							)       =      config_and_inputs
							lowerCAmelCase       =      {"""input_ids""": input_ids, """attention_mask""": input_mask}
							return config, inputs_dict







@require_torch
class __snake_case( _lowerCAmelCase						,				_lowerCAmelCase						,				unittest.TestCase   ):


						'''simple docstring'''



						UpperCAmelCase				:       Tuple 	=						False

						UpperCAmelCase				:       Optional[int] 	=						(
						    (
						        EsmForMaskedLM,
						        EsmModel,
						        EsmForSequenceClassification,
						        EsmForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						UpperCAmelCase				:       Optional[Any] 	=						()
						UpperCAmelCase				:       str 	=						(
						    {
						        "feature-extraction": EsmModel,
						        "fill-mask": EsmForMaskedLM,
						        "text-classification": EsmForSequenceClassification,
						        "token-classification": EsmForTokenClassification,
						        "zero-shot": EsmForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						UpperCAmelCase				:       Any 	=						True


						def 							__snake_case       (   self )      ->      Optional[int]:
							lowerCAmelCase       =      EsmModelTester(self )
							lowerCAmelCase       =      ConfigTester(self      ,			config_class=_UpperCamelCase      ,			hidden_size=37 )


						def 							__snake_case       (   self )      ->      Optional[Any]:
							self.config_tester.run_common_tests()


						def 							__snake_case       (   self )      ->      int:
							lowerCAmelCase       =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCamelCase )


						def 							__snake_case       (   self )      ->      List[str]:
							lowerCAmelCase       =      self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
								lowerCAmelCase       =      type
								self.model_tester.create_and_check_model(*_UpperCamelCase )


						def 							__snake_case       (   self )      ->      Dict:
							lowerCAmelCase       =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*_UpperCamelCase )


						def 							__snake_case       (   self )      ->      List[str]:
							lowerCAmelCase       =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase )


						@slow
						def 							__snake_case       (   self )      ->      Optional[int]:
							for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								lowerCAmelCase       =      EsmModel.from_pretrained(_UpperCamelCase )
								self.assertIsNotNone(_UpperCamelCase )


						def 							__snake_case       (   self )      ->      List[str]:
							lowerCAmelCase       =      self.model_tester.prepare_config_and_inputs()[0]
							lowerCAmelCase       =      EsmEmbeddings(config=_UpperCamelCase )

							lowerCAmelCase       =      torch.as_tensor([[12, 31, 13, model.padding_idx]] )
							lowerCAmelCase       =      torch.as_tensor(
							    [
							        [
							            0 + model.padding_idx + 1,
							            1 + model.padding_idx + 1,
							            2 + model.padding_idx + 1,
							            model.padding_idx,
							        ]
							    ] )
							lowerCAmelCase       =      create_position_ids_from_input_ids(_UpperCamelCase      ,			model.padding_idx )
							self.assertEqual(position_ids.shape      ,			expected_positions.shape )
							self.assertTrue(torch.all(torch.eq(_UpperCamelCase      ,			_UpperCamelCase ) ) )


						def 							__snake_case       (   self )      ->      Tuple:
							lowerCAmelCase       =      self.model_tester.prepare_config_and_inputs()[0]
							lowerCAmelCase       =      EsmEmbeddings(config=_UpperCamelCase )

							lowerCAmelCase       =      torch.empty(2      ,			4      ,			30 )
							lowerCAmelCase       =      [
							    0 + embeddings.padding_idx + 1,
							    1 + embeddings.padding_idx + 1,
							    2 + embeddings.padding_idx + 1,
							    3 + embeddings.padding_idx + 1,
							]
							lowerCAmelCase       =      torch.as_tensor([expected_single_positions, expected_single_positions] )
							lowerCAmelCase       =      embeddings.create_position_ids_from_inputs_embeds(_UpperCamelCase )
							self.assertEqual(position_ids.shape      ,			expected_positions.shape )
							self.assertTrue(torch.all(torch.eq(_UpperCamelCase      ,			_UpperCamelCase ) ) )


						@unittest.skip("""Esm does not support embedding resizing""" )
						def 							__snake_case       (   self )      ->      Dict:
							pass


						@unittest.skip("""Esm does not support embedding resizing""" )
						def 							__snake_case       (   self )      ->      int:
							pass




						@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
						def 							__snake_case       (   self )      ->      Optional[Any]:
							pass







@require_torch
class __snake_case( _lowerCAmelCase   ):


						'''simple docstring'''



						@slow
						def 							__snake_case       (   self )      ->      int:
							with torch.no_grad():
								lowerCAmelCase       =      EsmForMaskedLM.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
								model.eval()
								lowerCAmelCase       =      torch.tensor([[0, 1, 2, 3, 4, 5]] )
								lowerCAmelCase       =      model(_UpperCamelCase )[0]

								lowerCAmelCase       =      33

								lowerCAmelCase       =      torch.Size((1, 6, vocab_size) )
								self.assertEqual(output.shape      ,			_UpperCamelCase )

								lowerCAmelCase       =      torch.tensor(
								    [[[8.9_2_1_5, -1_0.5_8_9_8, -6.4_6_7_1], [-6.3_9_6_7, -1_3.9_1_1_4, -1.1_2_1_2], [-7.7_8_1_2, -1_3.9_5_1_6, -3.7_4_0_6]]] )
								self.assertTrue(torch.allclose(output[:, :3, :3]      ,			_UpperCamelCase      ,			atol=1e-4 ) )




						@slow
						def 							__snake_case       (   self )      ->      Union[str, Any]:
							with torch.no_grad():
								lowerCAmelCase       =      EsmModel.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
								model.eval()

								lowerCAmelCase       =      torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
								lowerCAmelCase       =      model(_UpperCamelCase )[0]
								# compare the actual values for a slice.
								lowerCAmelCase       =      torch.tensor(
								    [[[0.1_4_4_4, 0.5_4_1_3, 0.3_2_4_8], [0.3_0_3_4, 0.0_0_5_3, 0.3_1_0_8], [0.3_2_2_8, -0.2_4_9_9, 0.3_4_1_5]]] )
								self.assertTrue(torch.allclose(output[:, :3, :3]      ,			_UpperCamelCase      ,			atol=1e-4 ) )



'''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()
lowerCAmelCase						=					logging.get_logger("""transformers.models.encodec""")

lowerCAmelCase						=					{
    """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""",
}
lowerCAmelCase						=					{
    """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""",
}
lowerCAmelCase						=					{
    """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""",
}
lowerCAmelCase						=					{
    """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""",
}
lowerCAmelCase						=					{
    """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""",
}
lowerCAmelCase						=					{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
lowerCAmelCase						=					{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
lowerCAmelCase						=					[]
lowerCAmelCase						=					[]

def 					lowerCamelCase_ ( __UpperCamelCase					:       Tuple							,    __UpperCamelCase					:       Optional[Any]							,    __UpperCamelCase					:       List[Any]							,    __UpperCamelCase					:       int							,    __UpperCamelCase					:       Dict		)					->	List[Any]:







     """simple docstring"""
     for attribute in key.split('.'		):
          _A				=							getattr(__UpperCamelCase							,    __UpperCamelCase		)

     if weight_type is not None:
          _A				=							getattr(__UpperCamelCase							,    __UpperCamelCase		).shape
     else:
          _A				=							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				=							value
     elif weight_type == "weight_g":
          _A				=							value
     elif weight_type == "weight_v":
          _A				=							value
     elif weight_type == "bias":
          _A				=							value
     elif weight_type == "running_mean":
          _A				=							value
     elif weight_type == "running_var":
          _A				=							value
     elif weight_type == "num_batches_tracked":
          _A				=							value
     elif weight_type == "weight_ih_l0":
          _A				=							value
     elif weight_type == "weight_hh_l0":
          _A				=							value
     elif weight_type == "bias_ih_l0":
          _A				=							value
     elif weight_type == "bias_hh_l0":
          _A				=							value
     elif weight_type == "weight_ih_l1":
          _A				=							value
     elif weight_type == "weight_hh_l1":
          _A				=							value
     elif weight_type == "bias_ih_l1":
          _A				=							value
     elif weight_type == "bias_hh_l1":
          _A				=							value
     else:
          _A				=							value

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

def 					lowerCamelCase_ ( __UpperCamelCase					:       Optional[Any]							,    __UpperCamelCase					:       str		)					->	Dict:







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

def 					lowerCamelCase_ ( __UpperCamelCase					:       Dict							,    __UpperCamelCase					:       Optional[Any]							,    __UpperCamelCase					:       int		)					->	List[Any]:







     """simple docstring"""
     _A				=							[]

     if model_name == "encodec_24khz" or "encodec_32khz":
          _A				=							MAPPING_24K
     elif model_name == "encodec_48khz":
          _A				=							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				=							False
          for key, mapped_key in MAPPING.items():
               if "*" in key:
                    _A		,				_A				=							key.split('.*.'		)
                    if prefix in name and suffix in name:
                         _A				=							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				=							True
                    if "*" in mapped_key:
                         _A				=							name.split(__UpperCamelCase		)[0].split('.'		)[-2]
                         _A				=							mapped_key.replace('*'							,    __UpperCamelCase		)
                    if "weight_g" in name:
                         _A				=							'weight_g'
                    elif "weight_v" in name:
                         _A				=							'weight_v'
                    elif "weight_ih_l0" in name:
                         _A				=							'weight_ih_l0'
                    elif "weight_hh_l0" in name:
                         _A				=							'weight_hh_l0'
                    elif "bias_ih_l0" in name:
                         _A				=							'bias_ih_l0'
                    elif "bias_hh_l0" in name:
                         _A				=							'bias_hh_l0'
                    elif "weight_ih_l1" in name:
                         _A				=							'weight_ih_l1'
                    elif "weight_hh_l1" in name:
                         _A				=							'weight_hh_l1'
                    elif "bias_ih_l1" in name:
                         _A				=							'bias_ih_l1'
                    elif "bias_hh_l1" in name:
                         _A				=							'bias_hh_l1'
                    elif "bias" in name:
                         _A				=							'bias'
                    elif "weight" in name:
                         _A				=							'weight'
                    elif "running_mean" in name:
                         _A				=							'running_mean'
                    elif "running_var" in name:
                         _A				=							'running_var'
                    elif "num_batches_tracked" in name:
                         _A				=							'num_batches_tracked'
                    else:
                         _A				=							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					:       str							,    __UpperCamelCase					:       Optional[Any]							,    __UpperCamelCase					:       Any							,    __UpperCamelCase					:       List[str]=None							,    __UpperCamelCase					:       List[str]=None							,    )					->	List[str]:







     """simple docstring"""
     if config_path is not None:
          _A				=							EncodecConfig.from_pretrained(__UpperCamelCase		)
     else:
          _A				=							EncodecConfig()

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

     _A				=							EncodecModel(__UpperCamelCase		)

     _A				=							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				=							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				=							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__":
    lowerCAmelCase						=					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."""
    )

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





