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

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
 from transformers import (
     TFAutoModel,
     TFAutoModelForCausalLM,
     TFAutoModelForMaskedLM,
     TFAutoModelForPreTraining,
     TFAutoModelForQuestionAnswering,
     TFAutoModelForSeqaSeqLM,
     TFAutoModelForSequenceClassification,
     TFAutoModelForTableQuestionAnswering,
     TFAutoModelForTokenClassification,
     TFAutoModelWithLMHead,
     TFBertForMaskedLM,
     TFBertForPreTraining,
     TFBertForQuestionAnswering,
     TFBertForSequenceClassification,
     TFBertModel,
     TFFunnelBaseModel,
     TFFunnelModel,
     TFGPTaLMHeadModel,
     TFRobertaForMaskedLM,
     TFTaForConditionalGeneration,
     TFTapasForQuestionAnswering,
 )
 from transformers.models.auto.modeling_tf_auto import (
     TF_MODEL_FOR_CAUSAL_LM_MAPPING,
     TF_MODEL_FOR_MASKED_LM_MAPPING,
     TF_MODEL_FOR_PRETRAINING_MAPPING,
     TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
     TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
     TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
     TF_MODEL_MAPPING,
 )
 from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
 from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
 from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
 from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		"""new-model"""


if is_tf_available():




 class    __snake_case       (     __lowerCAmelCase				):
       a__        =		NewModelConfig




@require_tf
class    __snake_case       (     unittest.TestCase				):



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





        '''simple docstring'''





        a__:     Dict										=	'bert-base-cased'
        a__:     List[str]										=	AutoConfig.from_pretrained(lowercase)
        self.assertIsNotNone(lowercase)
        self.assertIsInstance(lowercase    ,			lowercase)

        a__:     List[str]										=	TFAutoModel.from_pretrained(lowercase)
        self.assertIsNotNone(lowercase)
        self.assertIsInstance(lowercase    ,			lowercase)



      @slow
      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Union[str, Any]										=	'bert-base-cased'
        a__:     Optional[Any]										=	AutoConfig.from_pretrained(lowercase)
        self.assertIsNotNone(lowercase)
        self.assertIsInstance(lowercase    ,			lowercase)

        a__:     List[Any]										=	TFAutoModelForPreTraining.from_pretrained(lowercase)
        self.assertIsNotNone(lowercase)
        self.assertIsInstance(lowercase    ,			lowercase)



      @slow
      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__:     List[Any]										=	AutoConfig.from_pretrained(lowercase)
          self.assertIsNotNone(lowercase)
          self.assertIsInstance(lowercase    ,			lowercase)

          a__:     List[Any]										=	TFAutoModelForCausalLM.from_pretrained(lowercase)
          a__							,    a__:     Union[str, Any]										=	TFAutoModelForCausalLM.from_pretrained(lowercase    ,			output_loading_info=lowercase)
          self.assertIsNotNone(lowercase)
          self.assertIsInstance(lowercase    ,			lowercase)



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





        '''simple docstring'''





        for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__:     Any										=	AutoConfig.from_pretrained(lowercase)
          self.assertIsNotNone(lowercase)
          self.assertIsInstance(lowercase    ,			lowercase)

          a__:     Optional[int]										=	TFAutoModelWithLMHead.from_pretrained(lowercase)
          self.assertIsNotNone(lowercase)
          self.assertIsInstance(lowercase    ,			lowercase)



      @slow
      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__:     Optional[Any]										=	AutoConfig.from_pretrained(lowercase)
          self.assertIsNotNone(lowercase)
          self.assertIsInstance(lowercase    ,			lowercase)

          a__:     Tuple										=	TFAutoModelForMaskedLM.from_pretrained(lowercase)
          a__							,    a__:     Dict										=	TFAutoModelForMaskedLM.from_pretrained(lowercase    ,			output_loading_info=lowercase)
          self.assertIsNotNone(lowercase)
          self.assertIsInstance(lowercase    ,			lowercase)



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





        '''simple docstring'''





        for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__:     str										=	AutoConfig.from_pretrained(lowercase)
          self.assertIsNotNone(lowercase)
          self.assertIsInstance(lowercase    ,			lowercase)

          a__:     int										=	TFAutoModelForSeqaSeqLM.from_pretrained(lowercase)
          a__							,    a__:     str										=	TFAutoModelForSeqaSeqLM.from_pretrained(lowercase    ,			output_loading_info=lowercase)
          self.assertIsNotNone(lowercase)
          self.assertIsInstance(lowercase    ,			lowercase)



      @slow
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        for model_name in ["bert-base-uncased"]:
          a__:     Any										=	AutoConfig.from_pretrained(lowercase)
          self.assertIsNotNone(lowercase)
          self.assertIsInstance(lowercase    ,			lowercase)

          a__:     Optional[Any]										=	TFAutoModelForSequenceClassification.from_pretrained(lowercase)
          self.assertIsNotNone(lowercase)
          self.assertIsInstance(lowercase    ,			lowercase)



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





        '''simple docstring'''





        for model_name in ["bert-base-uncased"]:
          a__:     Optional[Any]										=	AutoConfig.from_pretrained(lowercase)
          self.assertIsNotNone(lowercase)
          self.assertIsInstance(lowercase    ,			lowercase)

          a__:     Dict										=	TFAutoModelForQuestionAnswering.from_pretrained(lowercase)
          self.assertIsNotNone(lowercase)
          self.assertIsInstance(lowercase    ,			lowercase)



      @slow
      @require_tensorflow_probability
      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
          a__:     Any										=	AutoConfig.from_pretrained(lowercase)
          self.assertIsNotNone(lowercase)
          self.assertIsInstance(lowercase    ,			lowercase)

          a__:     Optional[Any]										=	TFAutoModelForTableQuestionAnswering.from_pretrained(lowercase)
          a__							,    a__:     Union[str, Any]										=	TFAutoModelForTableQuestionAnswering.from_pretrained(
              lowercase    ,			output_loading_info=lowercase)
          self.assertIsNotNone(lowercase)
          self.assertIsInstance(lowercase    ,			lowercase)



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





        '''simple docstring'''





        a__:     Any										=	TFAutoModelWithLMHead.from_pretrained(lowercase)
        self.assertIsInstance(lowercase    ,			lowercase)
        self.assertEqual(model.num_parameters()    ,			1_44_10)
        self.assertEqual(model.num_parameters(only_trainable=lowercase)    ,			1_44_10)



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





        '''simple docstring'''





        a__:     List[Any]										=	TFAutoModelWithLMHead.from_pretrained(lowercase)
        self.assertIsInstance(lowercase    ,			lowercase)
        self.assertEqual(model.num_parameters()    ,			1_44_10)
        self.assertEqual(model.num_parameters(only_trainable=lowercase)    ,			1_44_10)



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





        '''simple docstring'''





        a__:     Union[str, Any]										=	TFAutoModel.from_pretrained('sgugger/funnel-random-tiny')
        self.assertIsInstance(lowercase    ,			lowercase)

        a__:     Any										=	copy.deepcopy(model.config)
        a__:     Any										=	['FunnelBaseModel']
        a__:     Union[str, Any]										=	TFAutoModel.from_config(lowercase)
        self.assertIsInstance(lowercase    ,			lowercase)

        with tempfile.TemporaryDirectory() as tmp_dir:
          model.save_pretrained(lowercase)
          a__:     Union[str, Any]										=	TFAutoModel.from_pretrained(lowercase)
          self.assertIsInstance(lowercase    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        try:
          AutoConfig.register('new-model'    ,			lowercase)

          a__:     Optional[Any]										=	[
              TFAutoModel,
              TFAutoModelForCausalLM,
              TFAutoModelForMaskedLM,
              TFAutoModelForPreTraining,
              TFAutoModelForQuestionAnswering,
              TFAutoModelForSequenceClassification,
              TFAutoModelForTokenClassification,
          ]

          for auto_class in auto_classes:
            with self.subTest(auto_class.__name__):
              # Wrong config class will raise an error
              with self.assertRaises(lowercase):
                auto_class.register(lowercase    ,			lowercase)
              auto_class.register(lowercase    ,			lowercase)
              # Trying to register something existing in the Transformers library will raise an error
              with self.assertRaises(lowercase):
                auto_class.register(lowercase    ,			lowercase)

              # Now that the config is registered, it can be used as any other config with the auto-API
              a__:     Optional[int]										=	BertModelTester(self).get_config()
              a__:     Tuple										=	NewModelConfig(**tiny_config.to_dict())
              a__:     int										=	auto_class.from_config(lowercase)
              self.assertIsInstance(lowercase    ,			lowercase)

              with tempfile.TemporaryDirectory() as tmp_dir:
                model.save_pretrained(lowercase)
                a__:     Dict										=	auto_class.from_pretrained(lowercase)
                self.assertIsInstance(lowercase    ,			lowercase)

        finally:
          if "new-model" in CONFIG_MAPPING._extra_content:
            del CONFIG_MAPPING._extra_content["new-model"]
          for mapping in (
              TF_MODEL_MAPPING,
              TF_MODEL_FOR_PRETRAINING_MAPPING,
              TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
              TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
              TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
              TF_MODEL_FOR_CAUSAL_LM_MAPPING,
              TF_MODEL_FOR_MASKED_LM_MAPPING,
          ):
            if NewModelConfig in mapping._extra_content:
              del mapping._extra_content[NewModelConfig]



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





        '''simple docstring'''





        with self.assertRaisesRegex(
            lowercase    ,			'bert-base is not a local folder and is not a valid model identifier'):
          a__:     str										=	TFAutoModel.from_pretrained('bert-base')



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





        '''simple docstring'''





        with self.assertRaisesRegex(
            lowercase    ,			r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
          a__:     List[str]										=	TFAutoModel.from_pretrained(lowercase    ,			revision='aaaaaa')



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





        '''simple docstring'''





        with self.assertRaisesRegex(
            lowercase    ,			'hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin'    ,			):
          a__:     List[str]										=	TFAutoModel.from_pretrained('hf-internal-testing/config-no-model')



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





        '''simple docstring'''





        with self.assertRaisesRegex(lowercase    ,			'Use `from_pt=True` to load this model'):
          a__:     Union[str, Any]										=	TFAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only')



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





        '''simple docstring'''





        a__:     Optional[int]										=	TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert')
        with RequestCounter() as counter:
          a__:     Optional[int]										=	TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert')
          self.assertEqual(counter.get_request_count    ,			0)
          self.assertEqual(counter.head_request_count    ,			1)
          self.assertEqual(counter.other_request_count    ,			0)

        # With a sharded checkpoint
        a__:     Optional[int]										=	TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded')
        with RequestCounter() as counter:
          a__:     List[str]										=	TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded')
          self.assertEqual(counter.get_request_count    ,			0)
          self.assertEqual(counter.head_request_count    ,			1)
          self.assertEqual(counter.other_request_count    ,			0)



"""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,
 )


