



"""simple docstring"""







import copy
import os
from typing import Dict, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings, logging
from ..auto import AutoConfig


__snake_case	:				List[str]  					=			logging.get_logger(__name__)


__snake_case	:				Optional[int]  					=			{
    'suno/bark-small': 'https://huggingface.co/suno/bark-small/resolve/main/config.json',
    'suno/bark': 'https://huggingface.co/suno/bark/resolve/main/config.json',
}

__snake_case	:				Union[str, Any]  					=			'\n    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model\n    according to the specified arguments, defining the model architecture. Instantiating a configuration with the\n    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)\n    architecture.\n\n    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the\n    documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        block_size (`int`, *optional*, defaults to 1024):\n            The maximum sequence length that this model might ever be used with. Typically set this to something large\n            just in case (e.g., 512 or 1024 or 2048).\n        input_vocab_size (`int`, *optional*, defaults to 10_048):\n            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the\n            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with\n            regards to the chosen sub-model.\n        output_vocab_size (`int`, *optional*, defaults to 10_048):\n            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented\n            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought\n            with regards to the chosen sub-model.\n        num_layers (`int`, *optional*, defaults to 12):\n            Number of hidden layers in the given sub-model.\n        num_heads (`int`, *optional*, defaults to 12):\n            Number of attention heads for each attention layer in the Transformer architecture.\n        hidden_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the "intermediate" (often named feed-forward) layer in the architecture.\n        dropout (`float`, *optional*, defaults to 0.0):\n            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.\n        bias (`bool`, *optional*, defaults to `True`):\n            Whether or not to use bias in the linear layers and layer norm layers.\n        initializer_range (`float`, *optional*, defaults to 0.02):\n            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n'

class       UpperCamelCase  (					a				):





     """simple docstring"""






     _lowerCamelCase					: int				   ="bark_module"
     _lowerCamelCase					: List[str]				   =["past_key_values"]

     _lowerCamelCase					: Tuple				   ={
         "num_attention_heads": "num_heads",
         "num_hidden_layers": "num_layers",
         "vocab_size": "input_vocab_size",
         "window_size": "block_size",
     }





     def __init__(					self :							Optional[int]	,      _lowerCamelCase :							Any=1_0_2_4	,      _lowerCamelCase :							Tuple=1_0_0_4_8	,      _lowerCamelCase :							Union[str, Any]=1_0_0_4_8	,      _lowerCamelCase :							Any=1_2	,      _lowerCamelCase :							Dict=1_2	,      _lowerCamelCase :							List[Any]=7_6_8	,      _lowerCamelCase :							Optional[Any]=0.0	,      _lowerCamelCase :							Tuple=True	,      _lowerCamelCase :							List[Any]=0.02	,      _lowerCamelCase :							int=True	,      **_lowerCamelCase :							Tuple	,      ):
          A__				=   block_size
          A__				=   input_vocab_size
          A__				=   output_vocab_size
          A__				=   num_layers
          A__				=   num_heads
          A__				=   hidden_size
          A__				=   dropout
          A__				=   bias
          A__				=   use_cache
          A__				=   initializer_range

          super().__init__(**_lowerCamelCase )





     @classmethod
     def 						A__  (					cls :							Tuple	,      _lowerCamelCase :							Union[str, os.PathLike]	,      _lowerCamelCase :							Optional[Union[str, os.PathLike]] = None	,      _lowerCamelCase :							bool = False	,      _lowerCamelCase :							bool = False	,      _lowerCamelCase :							Optional[Union[str, bool]] = None	,      _lowerCamelCase :							str = "main"	,      **_lowerCamelCase :							Tuple	,      ):
          A__				=   cache_dir
          A__				=   force_download
          A__				=   local_files_only
          A__				=   revision

          cls._set_token_in_kwargs(_lowerCamelCase	,      _lowerCamelCase )

          A__	,     A__				=   cls.get_config_dict(_lowerCamelCase	,      **_lowerCamelCase )

          # get the config dict if we are loading from Bark
          if config_dict.get('''model_type''' ) == "bark":
               A__				=   config_dict[F'''{cls.model_type}_config''']

          if "model_type" in config_dict and hasattr(cls	,      '''model_type''' ) and config_dict["model_type"] != cls.model_type:
               logger.warning(
                   F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
                   F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )

          return cls.from_dict(_lowerCamelCase	,      **_lowerCamelCase )


@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkSemanticConfig"      , model="BarkSemanticModel"				)      , "\n    Example:\n\n    ```python\n    >>> from transformers import BarkSemanticConfig, BarkSemanticModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkSemanticConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkSemanticModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"      , )
class       UpperCamelCase  (					a				):





     """simple docstring"""






     _lowerCamelCase					: int				   ="semantic"


@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkCoarseConfig"      , model="BarkCoarseModel"				)      , "\n    Example:\n\n    ```python\n    >>> from transformers import BarkCoarseConfig, BarkCoarseModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkCoarseConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkCoarseModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"      , )
class       UpperCamelCase  (					a				):





     """simple docstring"""






     _lowerCamelCase					: str				   ="coarse_acoustics"


@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkFineConfig"      , model="BarkFineModel"				)      , "\n        n_codes_total (`int`, *optional*, defaults to 8):\n            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.\n        n_codes_given (`int`, *optional*, defaults to 1):\n            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics\n            sub-models.\n    Example:\n\n    ```python\n    >>> from transformers import BarkFineConfig, BarkFineModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkFineConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkFineModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"      , )
class       UpperCamelCase  (					a				):





     """simple docstring"""






     _lowerCamelCase					: Optional[Any]				   ="fine_acoustics"





     def __init__(					self :							Optional[int]	,      _lowerCamelCase :							Optional[Any]=True	,      _lowerCamelCase :							List[str]=8	,      _lowerCamelCase :							Union[str, Any]=1	,      **_lowerCamelCase :							List[str] ):
          A__				=   n_codes_total
          A__				=   n_codes_given

          super().__init__(tie_word_embeddings=_lowerCamelCase	,      **_lowerCamelCase )


class       UpperCamelCase  (					a				):





     """simple docstring"""






     _lowerCamelCase					: int				   ="bark"
     _lowerCamelCase					: Dict				   =True





     def __init__(					self :							Optional[Any]	,      _lowerCamelCase :							Dict = None	,      _lowerCamelCase :							Dict = None	,      _lowerCamelCase :							Dict = None	,      _lowerCamelCase :							Dict = None	,      _lowerCamelCase :							Tuple=0.02	,      **_lowerCamelCase :							Dict	,      ):
          if semantic_config is None:
               A__				=   {}
               logger.info('''semantic_config is None. initializing the semantic model with default values.''' )

          if coarse_acoustics_config is None:
               A__				=   {}
               logger.info('''coarse_acoustics_config is None. initializing the coarse model with default values.''' )

          if fine_acoustics_config is None:
               A__				=   {}
               logger.info('''fine_acoustics_config is None. initializing the fine model with default values.''' )

          if codec_config is None:
               A__				=   {}
               logger.info('''codec_config is None. initializing the codec model with default values.''' )

          A__				=   BarkSemanticConfig(**_lowerCamelCase )
          A__				=   BarkCoarseConfig(**_lowerCamelCase )
          A__				=   BarkFineConfig(**_lowerCamelCase )
          A__				=   AutoConfig.for_model(**_lowerCamelCase )

          A__				=   initializer_range

          super().__init__(**_lowerCamelCase )





     @classmethod
     def 						A__  (					cls :							Tuple	,      _lowerCamelCase :							BarkSemanticConfig	,      _lowerCamelCase :							BarkCoarseConfig	,      _lowerCamelCase :							BarkFineConfig	,      _lowerCamelCase :							AutoConfig	,      **_lowerCamelCase :							List[str]	,      ):
          return cls(
              semantic_config=semantic_config.to_dict()	,      coarse_acoustics_config=coarse_acoustics_config.to_dict()	,      fine_acoustics_config=fine_acoustics_config.to_dict()	,      codec_config=codec_config.to_dict()	,      **_lowerCamelCase	,      )





     def 						A__  (					self :							Any ):
          A__				=   copy.deepcopy(self.__dict__ )

          A__				=   self.semantic_config.to_dict()
          A__				=   self.coarse_acoustics_config.to_dict()
          A__				=   self.fine_acoustics_config.to_dict()
          A__				=   self.codec_config.to_dict()

          A__				=   self.__class__.model_type
          return output








"""simple docstring"""







from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

__snake_case	:				Union[str, Any]  					=			{
    name: (getattr(transformers, name), getattr(transformers, name + 'Fast')) for name in SLOW_TO_FAST_CONVERTERS
}

__snake_case	:				List[Any]  					=			datasets.load_dataset('xnli', split='test+validation')

__snake_case	:				int  					=			0
__snake_case	:				int  					=			0
__snake_case	:				int  					=			0
__snake_case	:				Optional[int]  					=			0



def   a_	(			__a		,							__a		,							__a		,							__a      ):
     if spm_diff == list(reversed(__a      )      ):
          # AAA -> AA+A vs A+AA case.
          return True
     elif len(__a      ) == len(__a      ) and fast.decode(__a      ) == fast.decode(__a      ):
          # Second order OK
          # Barrich -> Barr + ich vs Bar + rich
          return True
     A__				=   slow.encode(slow.decode(__a      )      )
     A__				=   fast.encode(fast.decode(__a      )      )
     if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
          # Type 3 error.
          # Snehagatha ->
          #       Sne, h, aga, th, a
          #       Sne, ha, gat, ha
          # Encoding the wrong with sp does not even recover what spm gave us
          # It fits tokenizer however...
          return True
     return False



def   a_	(			__a		,							__a		,							__a      ):
     A__				=   fast.encode_plus(__a      )[0]
     A__				=   enc.offsets
     A__	,     A__				=   offsets[idx], offsets[idx - 1]
     if curr is not None and line[curr[0] : curr[1]] == "\u200f":
          return True
     if prev is not None and line[prev[0] : prev[1]] == "\u200f":
          return True



def   a_	(			__a		,							__a		,							__a		,							__a		,							__a      ):
     # Encoding can be the same with same result AAA -> A + AA vs AA + A
     # We can check that we use at least exactly the same number of tokens.
     for i, (spm_id, tok_id) in enumerate(zip(__a		,							__a      )      ):
          if spm_id != tok_id:
               break
     A__				=   i
     for i, (spm_id, tok_id) in enumerate(zip(reversed(__a      )		,							reversed(__a      )      )      ):
          if spm_id != tok_id:
               break
     A__				=   len(__a      ) - i

     A__				=   spm_ids[first:last]
     A__				=   tok_ids[first:last]

     if check_diff(__a		,							__a		,							__a		,							__a      ):
          return True

     if check_LTR_mark(__a		,							__a		,							__a      ):
          return True

     if last - first > 5:
          # We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
          A__				=   Counter(spm_ids[first:last]      )
          A__				=   Counter(tok_ids[first:last]      )

          A__				=   {spm_ for (spm_, si) in spms.items() if toks.get(spm_		,							0      ) == si}
          A__				=   3
          for i in range(last - first - min_width      ):
               if all(spm_ids[first + i + j] in removable_tokens for j in range(__a      )      ):
                    A__				=   [
                        k
                        for k in range(last - first - min_width      )
                        if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
                    ]
                    for j in possible_matches:
                         if check_diff(spm_ids[first : first + i]		,							tok_ids[first : first + j]		,							__a		,							__a      ) and check_details(
                             __a		,							spm_ids[first + i : last]		,							tok_ids[first + j : last]		,							__a		,							__a		,							):
                              return True

     print(f'''Spm: {[fast.decode([spm_ids[i]]      ) for i in range(__a		,							__a      )]}'''      )
     try:
          print(f'''Tok: {[fast.decode([tok_ids[i]]      ) for i in range(__a		,							__a      )]}'''      )
     except Exception:
          pass

     A__				=   fast.decode(spm_ids[:first]      )
     A__				=   fast.decode(spm_ids[last:]      )
     A__				=   fast.decode(spm_ids[first:last]      )
     print()
     print(__a      )
     return False



def   a_	(			__a		,							__a		,							__a      ):
     global perfect
     global imperfect
     global wrong
     global total

     A__				=   slow.encode(__a      )
     A__				=   fast.encode(__a      )

     A__				=   False
     total += 1

     if slow_ids != fast_ids:
          if check_details(__a		,							__a		,							__a		,							__a		,							__a      ):
               A__				=   True
               imperfect += 1
          else:
               wrong += 1
     else:
          perfect += 1

     if total % 1_0000 == 0:
          print(f'''({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})'''      )

     if skip_assert:
          return

     assert (
         slow_ids == fast_ids
     ), f'''line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(__a      )}\n{fast.tokenize(__a      )}'''



def   a_	(			__a		,							__a      ):
     global batch_total
     for i in range(len(__a      )      ):
          # premise, all languages
          for text in dataset[i]["premise"].values():
               test_string(__a		,							__a		,							__a      )

          # hypothesis, all languages
          for text in dataset[i]["hypothesis"]["translation"]:
               test_string(__a		,							__a		,							__a      )


if __name__ == "__main__":
 for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
  __snake_case	:				int  					=			list(slow_class.max_model_input_sizes.keys())
  for checkpoint in checkpoint_names:
   __snake_case	:				Union[str, Any]  					=			0
   __snake_case	:				Optional[Any]  					=			0
   __snake_case	:				List[Any]  					=			0
   __snake_case	:				List[str]  					=			0

   print(f'========================== Checking {name}: {checkpoint} ==========================')
   __snake_case	:				Union[str, Any]  					=			slow_class.from_pretrained(checkpoint, force_download=True)
   __snake_case	:				Optional[Any]  					=			fast_class.from_pretrained(checkpoint, force_download=True)
   test_tokenizer(slow, fast)
   print(f'Accuracy {perfect * 100 / total:.2f}')



