


import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class     UpperCAmelCase__       (					A_   ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Dict      =  GPTaTokenizer
    UpperCAmelCase__						:		Any      =  GPTaTokenizerFast
    UpperCAmelCase__						:		Tuple      =  True
    UpperCAmelCase__						:		int      =  {"add_prefix_space": True}
    UpperCAmelCase__						:		Any      =  False






    def _a					(			self    )  ->       Optional[int]:
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         __UpperCamelCase    							=[
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             '\u0120',
             '\u0120l',
             '\u0120n',
             '\u0120lo',
             '\u0120low',
             'er',
             '\u0120lowest',
             '\u0120newer',
             '\u0120wider',
             '<unk>',
             '<|endoftext|>',
         ]
         __UpperCamelCase    							=dict(zip(A_					,   range(len(A_    )    )    )    )
         __UpperCamelCase    							=['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
         __UpperCamelCase    							={'unk_token': '<unk>'}

         __UpperCamelCase    							=os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES['vocab_file']    )
         __UpperCamelCase    							=os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES['merges_file']    )
         with open(self.vocab_file					,   'w'					,   encoding='utf-8'    ) as fp:
              fp.write(json.dumps(A_    ) + '\n'    )
         with open(self.merges_file					,   'w'					,   encoding='utf-8'    ) as fp:
              fp.write('\n'.join(A_    )    )






    def _a					(			self					,   **A_    )  ->       str:
         kwargs.update(self.special_tokens_map    )
         return GPTaTokenizer.from_pretrained(self.tmpdirname					,   **A_    )






    def _a					(			self					,   **A_    )  ->       Optional[Any]:
         kwargs.update(self.special_tokens_map    )
         return GPTaTokenizerFast.from_pretrained(self.tmpdirname					,   **A_    )






    def _a					(			self					,   A_    )  ->       Tuple:
         __UpperCamelCase    							='lower newer'
         __UpperCamelCase    							='lower newer'
         return input_text, output_text






    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase    							=GPTaTokenizer(self.vocab_file					,   self.merges_file					,   **self.special_tokens_map    )
         __UpperCamelCase    							='lower newer'
         __UpperCamelCase    							=['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
         __UpperCamelCase    							=tokenizer.tokenize(A_					,   add_prefix_space=A_    )
         self.assertListEqual(A_					,   A_    )

         __UpperCamelCase    							=tokens + [tokenizer.unk_token]
         __UpperCamelCase    							=[14, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(A_    )					,   A_    )






    def _a					(			self    )  ->       int:
         if not self.test_rust_tokenizer:
              return

         __UpperCamelCase    							=self.get_tokenizer()
         __UpperCamelCase    							=self.get_rust_tokenizer(add_prefix_space=A_    )

         __UpperCamelCase    							='lower newer'

         # Testing tokenization
         __UpperCamelCase    							=tokenizer.tokenize(A_					,   add_prefix_space=A_    )
         __UpperCamelCase    							=rust_tokenizer.tokenize(A_    )
         self.assertListEqual(A_					,   A_    )

         # Testing conversion to ids without special tokens
         __UpperCamelCase    							=tokenizer.encode(A_					,   add_special_tokens=A_					,   add_prefix_space=A_    )
         __UpperCamelCase    							=rust_tokenizer.encode(A_					,   add_special_tokens=A_    )
         self.assertListEqual(A_					,   A_    )

         # Testing conversion to ids with special tokens
         __UpperCamelCase    							=self.get_rust_tokenizer(add_prefix_space=A_    )
         __UpperCamelCase    							=tokenizer.encode(A_					,   add_prefix_space=A_    )
         __UpperCamelCase    							=rust_tokenizer.encode(A_    )
         self.assertListEqual(A_					,   A_    )

         # Testing the unknown token
         __UpperCamelCase    							=tokens + [rust_tokenizer.unk_token]
         __UpperCamelCase    							=[14, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(A_    )					,   A_    )






    def _a					(			self					,   *A_					,   **A_    )  ->       Optional[int]:
         # It's very difficult to mix/test pretokenization with byte-level
         # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
         pass






    def _a					(			self					,   A_=15    )  ->       List[str]:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'    ):
                   __UpperCamelCase    							=self.rust_tokenizer_class.from_pretrained(A_					,   **A_    )

                   # Simple input
                   __UpperCamelCase    							='This is a simple input'
                   __UpperCamelCase    							=['This is a simple input 1', 'This is a simple input 2']
                   __UpperCamelCase    							=('This is a simple input', 'This is a pair')
                   __UpperCamelCase    							=[
                       ('This is a simple input 1', 'This is a simple input 2'),
                       ('This is a simple pair 1', 'This is a simple pair 2'),
                   ]

                   # Simple input tests
                   self.assertRaises(A_					,   tokenizer_r.encode					,   A_					,   max_length=A_					,   padding='max_length'    )

                   # Simple input
                   self.assertRaises(A_					,   tokenizer_r.encode_plus					,   A_					,   max_length=A_					,   padding='max_length'    )

                   # Simple input
                   self.assertRaises(
                       A_					,   tokenizer_r.batch_encode_plus					,   A_					,   max_length=A_					,   padding='max_length'					,   )

                   # Pair input
                   self.assertRaises(A_					,   tokenizer_r.encode					,   A_					,   max_length=A_					,   padding='max_length'    )

                   # Pair input
                   self.assertRaises(A_					,   tokenizer_r.encode_plus					,   A_					,   max_length=A_					,   padding='max_length'    )

                   # Pair input
                   self.assertRaises(
                       A_					,   tokenizer_r.batch_encode_plus					,   A_					,   max_length=A_					,   padding='max_length'					,   )






    def _a					(			self    )  ->       int:
         __UpperCamelCase    							=GPTaTokenizer.from_pretrained(self.tmpdirname					,   pad_token='<pad>'    )

         # Simple input
         __UpperCamelCase    							='This is a simple input'
         __UpperCamelCase    							=['This is a simple input looooooooong', 'This is a simple input']
         __UpperCamelCase    							=('This is a simple input', 'This is a pair')
         __UpperCamelCase    							=[
             ('This is a simple input loooooong', 'This is a simple input'),
             ('This is a simple pair loooooong', 'This is a simple pair'),
         ]

         __UpperCamelCase    							=tokenizer.pad_token_id

         __UpperCamelCase    							=tokenizer(A_					,   padding='max_length'					,   max_length=30					,   return_tensors='np'    )
         __UpperCamelCase    							=tokenizer(A_					,   padding=A_					,   truncate=A_					,   return_tensors='np'    )
         __UpperCamelCase    							=tokenizer(*A_					,   padding='max_length'					,   max_length=60					,   return_tensors='np'    )
         __UpperCamelCase    							=tokenizer(A_					,   padding=A_					,   truncate=A_					,   return_tensors='np'    )

         # s
         # test single string max_length padding
         self.assertEqual(out_s['input_ids'].shape[-1]					,   30    )
         self.assertTrue(pad_token_id in out_s['input_ids']    )
         self.assertTrue(0 in out_s['attention_mask']    )

         # s2
         # test automatic padding
         self.assertEqual(out_sa['input_ids'].shape[-1]					,   33    )
         # long slice doesn't have padding
         self.assertFalse(pad_token_id in out_sa['input_ids'][0]    )
         self.assertFalse(0 in out_sa['attention_mask'][0]    )
         # short slice does have padding
         self.assertTrue(pad_token_id in out_sa['input_ids'][1]    )
         self.assertTrue(0 in out_sa['attention_mask'][1]    )

         # p
         # test single pair max_length padding
         self.assertEqual(out_p['input_ids'].shape[-1]					,   60    )
         self.assertTrue(pad_token_id in out_p['input_ids']    )
         self.assertTrue(0 in out_p['attention_mask']    )

         # p2
         # test automatic padding pair
         self.assertEqual(out_pa['input_ids'].shape[-1]					,   52    )
         # long slice pair doesn't have padding
         self.assertFalse(pad_token_id in out_pa['input_ids'][0]    )
         self.assertFalse(0 in out_pa['attention_mask'][0]    )
         # short slice pair does have padding
         self.assertTrue(pad_token_id in out_pa['input_ids'][1]    )
         self.assertTrue(0 in out_pa['attention_mask'][1]    )






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							='$$$'
         __UpperCamelCase    							=GPTaTokenizer.from_pretrained(self.tmpdirname					,   bos_token=A_					,   add_bos_token=A_    )

         __UpperCamelCase    							='This is a simple input'
         __UpperCamelCase    							=['This is a simple input 1', 'This is a simple input 2']

         __UpperCamelCase    							=tokenizer.bos_token_id

         __UpperCamelCase    							=tokenizer(A_    )
         __UpperCamelCase    							=tokenizer(A_    )

         self.assertEqual(out_s.input_ids[0]					,   A_    )
         self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids    )    )

         __UpperCamelCase    							=tokenizer.decode(out_s.input_ids    )
         __UpperCamelCase    							=tokenizer.batch_decode(out_sa.input_ids    )

         self.assertEqual(decode_s.split()[0]					,   A_    )
         self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa    )    )






    def _a					(			self    )  ->       Optional[int]:
         pass






    def _a					(			self    )  ->       Any:
         # TODO: change to self.get_tokenizers() when the fast version is implemented
         __UpperCamelCase    							=[self.get_tokenizer(do_lower_case=A_					,   add_bos_token=A_    )]
         for tokenizer in tokenizers:
              with self.subTest(f'{tokenizer.__class__.__name__}'    ):
                   __UpperCamelCase    							='Encode this.'
                   __UpperCamelCase    							='This one too please.'
                   __UpperCamelCase    							=tokenizer.encode(A_					,   add_special_tokens=A_    )
                   encoded_sequence += tokenizer.encode(A_					,   add_special_tokens=A_    )
                   __UpperCamelCase    							=tokenizer.encode_plus(
                       A_					,   A_					,   add_special_tokens=A_					,   return_special_tokens_mask=A_					,   )
                   __UpperCamelCase    							=encoded_sequence_dict['input_ids']
                   __UpperCamelCase    							=encoded_sequence_dict['special_tokens_mask']
                   self.assertEqual(len(A_    )					,   len(A_    )    )

                   __UpperCamelCase    							=[
                       (x if not special_tokens_mask[i] else None) for i, x in enumerate(A_    )
                   ]
                   __UpperCamelCase    							=[x for x in filtered_sequence if x is not None]
                   self.assertEqual(A_					,   A_    )






@require_tokenizers
class     UpperCAmelCase__       (					unittest.TestCase		):



    """simple docstring"""






    def _a					(			self    )  ->       Optional[Any]:
         # More context:
         # https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
         # https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
         # https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

         __UpperCamelCase    							=AutoTokenizer.from_pretrained('facebook/opt-350m'					,   from_slow=A_    )
         __UpperCamelCase    							='A photo of a cat'

         __UpperCamelCase    							=tokenizer.encode(
             A_					,   )
         self.assertEqual(A_					,   [2, 250, 1345, 9, 10, 4758]    )
         tokenizer.save_pretrained('test_opt'    )

         __UpperCamelCase    							=AutoTokenizer.from_pretrained('./test_opt'    )
         __UpperCamelCase    							=tokenizer.encode(
             A_					,   )
         self.assertEqual(A_					,   [2, 250, 1345, 9, 10, 4758]    )






    def _a					(			self    )  ->       Dict:
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('facebook/opt-350m'					,   use_slow=A_    )
         __UpperCamelCase    							='A photo of a cat'

         __UpperCamelCase    							=tokenizer.encode(
             A_					,   )
         # Same as above
         self.assertEqual(A_					,   [2, 250, 1345, 9, 10, 4758]    )






    @unittest.skip('This test is failing because of a bug in the fast tokenizer'    )
    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('facebook/opt-350m'					,   from_slow=A_    )

         __UpperCamelCase    							='bos'
         __UpperCamelCase    							=tokenizer.get_vocab()['bos']

         __UpperCamelCase    							='A photo of a cat'
         __UpperCamelCase    							=tokenizer.encode(
             A_					,   )
         # We changed the bos token
         self.assertEqual(A_					,   [31957, 250, 1345, 9, 10, 4758]    )
         tokenizer.save_pretrained('./tok'    )
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('./tok'    )
         self.assertTrue(tokenizer.is_fast    )
         __UpperCamelCase    							=tokenizer.encode(
             A_					,   )
         self.assertEqual(A_					,   [31957, 250, 1345, 9, 10, 4758]    )






"""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,
 )


