"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TFDebertaVaForMaskedLM,
     TFDebertaVaForQuestionAnswering,
     TFDebertaVaForSequenceClassification,
     TFDebertaVaForTokenClassification,
     TFDebertaVaModel,
 )




class    __snake_case       :



      def __init__(						self    ,			lowercase    ,			lowercase=13    ,			lowercase=7    ,			lowercase=True    ,			lowercase=True    ,			lowercase=True    ,			lowercase=True    ,			lowercase=99    ,			lowercase=32    ,			lowercase=2    ,			lowercase=4    ,			lowercase=37    ,			lowercase="gelu"    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=5_12    ,			lowercase=16    ,			lowercase=2    ,			lowercase=0.02    ,			lowercase=False    ,			lowercase=True    ,			lowercase="None"    ,			lowercase=3    ,			lowercase=4    ,			lowercase=None    ,			)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     Union[str, Any]										=	parent
        a__:     str										=	batch_size
        a__:     List[str]										=	seq_length
        a__:     int										=	is_training
        a__:     Union[str, Any]										=	use_input_mask
        a__:     str										=	use_token_type_ids
        a__:     Union[str, Any]										=	use_labels
        a__:     Any										=	vocab_size
        a__:     str										=	hidden_size
        a__:     Tuple										=	num_hidden_layers
        a__:     Optional[Any]										=	num_attention_heads
        a__:     Dict										=	intermediate_size
        a__:     Tuple										=	hidden_act
        a__:     List[Any]										=	hidden_dropout_prob
        a__:     Tuple										=	attention_probs_dropout_prob
        a__:     int										=	max_position_embeddings
        a__:     int										=	type_vocab_size
        a__:     Any										=	type_sequence_label_size
        a__:     Optional[int]										=	initializer_range
        a__:     List[Any]										=	num_labels
        a__:     Optional[Any]										=	num_choices
        a__:     Union[str, Any]										=	relative_attention
        a__:     Optional[int]										=	position_biased_input
        a__:     Tuple										=	pos_att_type
        a__:     Union[str, Any]										=	scope



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Dict										=	ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size)

        a__:     str										=	None
        if self.use_input_mask:
          a__:     List[str]										=	random_attention_mask([self.batch_size, self.seq_length])

        a__:     List[Any]										=	None
        if self.use_token_type_ids:
          a__:     Optional[Any]										=	ids_tensor([self.batch_size, self.seq_length]    ,			self.type_vocab_size)

        a__:     str										=	None
        a__:     List[str]										=	None
        a__:     List[Any]										=	None
        if self.use_labels:
          a__:     List[Any]										=	ids_tensor([self.batch_size]    ,			self.type_sequence_label_size)
          a__:     Tuple										=	ids_tensor([self.batch_size, self.seq_length]    ,			self.num_labels)

        a__:     List[Any]										=	DebertaVaConfig(
            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    ,			relative_attention=self.relative_attention    ,			position_biased_input=self.position_biased_input    ,			initializer_range=self.initializer_range    ,			return_dict=lowercase    ,			)

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



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			Dict:





        '''simple docstring'''





        a__:     Union[str, Any]										=	TFDebertaVaModel(config=lowercase)
        a__:     Dict										=	{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

        a__:     Optional[Any]										=	[input_ids, input_mask]
        a__:     Optional[Any]										=	model(lowercase)

        a__:     int										=	model(lowercase)

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



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			List[Any]:





        '''simple docstring'''





        a__:     Optional[Any]										=	TFDebertaVaForMaskedLM(config=lowercase)
        a__:     Optional[int]										=	{
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }
        a__:     Any										=	model(lowercase)
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.seq_length, self.vocab_size))



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			Optional[int]:





        '''simple docstring'''





        a__:     Dict										=	self.num_labels
        a__:     Union[str, Any]										=	TFDebertaVaForSequenceClassification(config=lowercase)
        a__:     Optional[int]										=	{
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }

        a__:     List[Any]										=	model(lowercase)
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.num_labels))



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			List[Any]:





        '''simple docstring'''





        a__:     int										=	self.num_labels
        a__:     int										=	TFDebertaVaForTokenClassification(config=lowercase)
        a__:     Optional[int]										=	{
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }
        a__:     Any										=	model(lowercase)
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.seq_length, self.num_labels))



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			List[str]:





        '''simple docstring'''





        a__:     Dict										=	TFDebertaVaForQuestionAnswering(config=lowercase)
        a__:     str										=	{
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }

        a__:     List[str]										=	model(lowercase)
        self.parent.assertEqual(result.start_logits.shape    ,			(self.batch_size, self.seq_length))
        self.parent.assertEqual(result.end_logits.shape    ,			(self.batch_size, self.seq_length))



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        a__:     List[Any]										=	self.prepare_config_and_inputs()
        (
            (
            a__
        )							,    (
            a__
        )							,    (
            a__
        )							,    (
            a__
        )							,    (
            a__
        )							,    (
            a__
        )							,    (
            a__
        )							,    
        ):     Tuple										=	config_and_inputs
        a__:     Optional[int]										=	{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
        return config, inputs_dict







@require_tf
class    __snake_case       (     __lowerCAmelCase       ,      __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		(
          (
              TFDebertaVaModel,
              TFDebertaVaForMaskedLM,
              TFDebertaVaForQuestionAnswering,
              TFDebertaVaForSequenceClassification,
              TFDebertaVaForTokenClassification,
          )
          if is_tf_available()
          else ()
      )
      a__        =		(
          {
              """feature-extraction""": TFDebertaVaModel,
              """fill-mask""": TFDebertaVaForMaskedLM,
              """question-answering""": TFDebertaVaForQuestionAnswering,
              """text-classification""": TFDebertaVaForSequenceClassification,
              """token-classification""": TFDebertaVaForTokenClassification,
              """zero-shot""": TFDebertaVaForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )

      a__        =		False
      a__        =		False



      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     str										=	TFDebertaVaModelTester(self)
        a__:     Any										=	ConfigTester(self    ,			config_class=lowercase    ,			hidden_size=37)



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        self.config_tester.run_common_tests()



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     str										=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase)



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Any										=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*lowercase)



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        a__:     Dict										=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowercase)



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     List[Any]										=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowercase)



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        a__:     Any										=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowercase)



      @slow
      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        a__:     Optional[int]										=	TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge')
        self.assertIsNotNone(lowercase)







@require_tf
class    __snake_case       (     unittest.TestCase				):



      @unittest.skip(reason='Model not available yet')
      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        pass



      @slow
      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        a__:     Tuple										=	TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge')
        a__:     Optional[int]										=	tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]])
        a__:     Any										=	tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
        a__:     Tuple										=	model(lowercase    ,			attention_mask=lowercase)[0]

        a__:     Optional[int]										=	tf.constant(
            [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]])
        tf.debugging.assert_near(output[:, 1:4, 1:4]    ,			lowercase    ,			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()
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 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->int:
  for attribute in key.split('.'   ):
    a__:     str										=	getattr(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

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

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







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Dict:
  for key in ignore_keys:
    if key.endswith('.*'   ):
      if name.startswith(key[:-1]   ):
        return True
    elif ".*." in key:
      a__							,    a__:     Optional[Any]										=	key.split('.*.'   )
      if prefix in name and suffix in name:
        return True
    elif key in name:
      return True
  return False







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->List[str]:
  a__:     List[Any]										=	[]

  if model_name == "encodec_24khz" or "encodec_32khz":
    a__:     Optional[int]										=	MAPPING_24K
  elif model_name == "encodec_48khz":
    a__:     List[Any]										=	MAPPING_48K
  else:
    raise ValueError(F'Unsupported model: {model_name}'   )

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

    a__:     int										=	False
    for key, mapped_key in MAPPING.items():
      if "*" in key:
        a__							,    a__:     str										=	key.split('.*.'   )
        if prefix in name and suffix in name:
          a__:     List[str]										=	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[str]										=	True
        if "*" in mapped_key:
          a__:     List[str]										=	name.split(_SCREAMING_SNAKE_CASE   )[0].split('.'   )[-2]
          a__:     str										=	mapped_key.replace('*'		,     _SCREAMING_SNAKE_CASE   )
        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__:     int										=	'weight_ih_l0'
        elif "weight_hh_l0" in name:
          a__:     Union[str, Any]										=	'weight_hh_l0'
        elif "bias_ih_l0" in name:
          a__:     Optional[Any]										=	'bias_ih_l0'
        elif "bias_hh_l0" in name:
          a__:     Optional[int]										=	'bias_hh_l0'
        elif "weight_ih_l1" in name:
          a__:     Dict										=	'weight_ih_l1'
        elif "weight_hh_l1" in name:
          a__:     Optional[Any]										=	'weight_hh_l1'
        elif "bias_ih_l1" in name:
          a__:     List[str]										=	'bias_ih_l1'
        elif "bias_hh_l1" in name:
          a__:     Optional[Any]										=	'bias_hh_l1'
        elif "bias" in name:
          a__:     List[str]										=	'bias'
        elif "weight" in name:
          a__:     Any										=	'weight'
        elif "running_mean" in name:
          a__:     Dict										=	'running_mean'
        elif "running_var" in name:
          a__:     Dict										=	'running_var'
        elif "num_batches_tracked" in name:
          a__:     Dict										=	'num_batches_tracked'
        else:
          a__:     List[str]										=	None
        set_recursively(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
      continue
    if not is_used:
      unused_weights.append(_SCREAMING_SNAKE_CASE   )

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







@torch.no_grad()
def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=None		,     _SCREAMING_SNAKE_CASE=None		,     )	->int:
  if config_path is not None:
    a__:     Dict										=	EncodecConfig.from_pretrained(_SCREAMING_SNAKE_CASE   )
  else:
    a__:     Tuple										=	EncodecConfig()

  if model_name == "encodec_24khz":
    pass  # config is already correct
  elif model_name == "encodec_32khz":
    a__:     Any										=	[8, 5, 4, 4]
    a__:     List[str]										=	[2.2]
    a__:     List[Any]										=	64
    a__:     Dict										=	32000
    a__:     Union[str, Any]										=	2048
    a__:     Union[str, Any]										=	False
    a__:     Any										=	False
    a__:     Optional[Any]										=	False
  elif model_name == "encodec_48khz":
    a__:     Optional[int]										=	[8, 5, 4, 2]
    a__:     Union[str, Any]										=	[3.0, 6.0, 12.0, 24.0]
    a__:     List[str]										=	48000
    a__:     Tuple										=	2
    a__:     Optional[Any]										=	False
    a__:     Optional[int]										=	'time_group_norm'
    a__:     Union[str, Any]										=	True
    a__:     Dict										=	1.0
    a__:     str										=	0.01
  else:
    raise ValueError(F'Unknown model name: {model_name}'   )

  a__:     Optional[int]										=	EncodecModel(_SCREAMING_SNAKE_CASE   )

  a__:     List[str]										=	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(_SCREAMING_SNAKE_CASE   )

  a__:     int										=	torch.load(_SCREAMING_SNAKE_CASE   )
  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__:     str										=	original_checkpoint['best_state']
  recursively_load_weights(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
  model.save_pretrained(_SCREAMING_SNAKE_CASE   )

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


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,
 )


