
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		  =   logging.get_logger(__name__)


_snake_case		  =   {
    "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		  =   "\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'''







      a		:Any						 =      'bark_module'
      a		:Tuple						 =      ['past_key_values']

      a		:int						 =      {
          'num_attention_heads': 'num_heads',
          'num_hidden_layers': 'num_layers',
          'vocab_size': 'input_vocab_size',
          'window_size': 'block_size',
      }







      def __init__( self					,					_UpperCAmelCase=1_024					,					_UpperCAmelCase=10_048					,					_UpperCAmelCase=10_048					,					_UpperCAmelCase=12					,					_UpperCAmelCase=12					,					_UpperCAmelCase=768					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=True					,					_UpperCAmelCase=0.02					,					_UpperCAmelCase=True					,					**_UpperCAmelCase					,					):
            lowerCAmelCase_						=      block_size
            lowerCAmelCase_						=      input_vocab_size
            lowerCAmelCase_						=      output_vocab_size
            lowerCAmelCase_						=      num_layers
            lowerCAmelCase_						=      num_heads
            lowerCAmelCase_						=      hidden_size
            lowerCAmelCase_						=      dropout
            lowerCAmelCase_						=      bias
            lowerCAmelCase_						=      use_cache
            lowerCAmelCase_						=      initializer_range

            super().__init__(**SCREAMING_SNAKE_CASE_)







      @classmethod
      def lowercase__		( cls					,					_UpperCAmelCase					,					_UpperCAmelCase = None					,					_UpperCAmelCase = False					,					_UpperCAmelCase = False					,					_UpperCAmelCase = None					,					_UpperCAmelCase = "main"					,					**_UpperCAmelCase					,					):
            lowerCAmelCase_						=      cache_dir
            lowerCAmelCase_						=      force_download
            lowerCAmelCase_						=      local_files_only
            lowerCAmelCase_						=      revision

            cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_)

            lowerCAmelCase_			,		lowerCAmelCase_						=      cls.get_config_dict(SCREAMING_SNAKE_CASE_					,					**SCREAMING_SNAKE_CASE_)

            # get the config dict if we are loading from Bark
            if config_dict.get('''model_type''') == "bark":
                  lowerCAmelCase_						=      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(SCREAMING_SNAKE_CASE_					,					**SCREAMING_SNAKE_CASE_)


@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'''







      a		:List[str]						 =      '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'''







      a		:Dict						 =      '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'''







      a		:Dict						 =      'fine_acoustics'







      def __init__( self					,					_UpperCAmelCase=True					,					_UpperCAmelCase=8					,					_UpperCAmelCase=1					,					**_UpperCAmelCase):
            lowerCAmelCase_						=      n_codes_total
            lowerCAmelCase_						=      n_codes_given

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


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      a		:str						 =      'bark'
      a		:Dict						 =      True







      def __init__( self					,					_UpperCAmelCase = None					,					_UpperCAmelCase = None					,					_UpperCAmelCase = None					,					_UpperCAmelCase = None					,					_UpperCAmelCase=0.02					,					**_UpperCAmelCase					,					):
            if semantic_config is None:
                  lowerCAmelCase_						=      {}
                  logger.info('''semantic_config is None. initializing the semantic model with default values.''')

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

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

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

            lowerCAmelCase_						=      BarkSemanticConfig(**SCREAMING_SNAKE_CASE_)
            lowerCAmelCase_						=      BarkCoarseConfig(**SCREAMING_SNAKE_CASE_)
            lowerCAmelCase_						=      BarkFineConfig(**SCREAMING_SNAKE_CASE_)
            lowerCAmelCase_						=      AutoConfig.for_model(**SCREAMING_SNAKE_CASE_)

            lowerCAmelCase_						=      initializer_range

            super().__init__(**SCREAMING_SNAKE_CASE_)







      @classmethod
      def lowercase__		( cls					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					**_UpperCAmelCase					,					):
            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()					,					**SCREAMING_SNAKE_CASE_					,					)







      def lowercase__		( self):
            lowerCAmelCase_						=      copy.deepcopy(self.__dict__)

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

            lowerCAmelCase_						=      self.__class__.model_type
            return output





import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class  _lowerCamelCase			(lowerCamelCase							,					unittest.TestCase					):
     lowercase__				  =			HerbertTokenizer
     lowercase__				  =			HerbertTokenizerFast
     lowercase__				  =			True

     def 						__lowerCamelCase				(  self    ):
            super().setUp()

            # Use a simpler test file without japanese/chinese characters
            with open(f'''{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'''							,       encoding='utf-8'    ) as f_data:
                   __snake_case						       =   f_data.read().replace('\n\n'							,       '\n'    ).strip()

            __snake_case						       =   [
                '<s>',
                '</s>',
                'l',
                'o',
                'w',
                'e',
                'r',
                's',
                't',
                'i',
                'd',
                'n',
                'w</w>',
                'r</w>',
                't</w>',
                'lo',
                'low',
                'er</w>',
                'low</w>',
                'lowest</w>',
                'newer</w>',
                'wider</w>',
                ',</w>',
                '<unk>',
            ]
            __snake_case						       =   dict(zip(SCREAMING_SNAKE_CASE_							,       range(len(SCREAMING_SNAKE_CASE_    )    )    )    )
            __snake_case						       =   ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

            __snake_case						       =   os.path.join(self.tmpdirname							,       VOCAB_FILES_NAMES['vocab_file']    )
            __snake_case						       =   os.path.join(self.tmpdirname							,       VOCAB_FILES_NAMES['merges_file']    )
            with open(self.vocab_file							,       'w'    ) as fp:
                   fp.write(json.dumps(SCREAMING_SNAKE_CASE_    )    )
            with open(self.merges_file							,       'w'    ) as fp:
                   fp.write('\n'.join(SCREAMING_SNAKE_CASE_    )    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   'lower newer'
            __snake_case						       =   'lower newer'
            return input_text, output_text

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.tokenizer_class(vocab_file=self.vocab_file							,       merges_file=self.merges_file    )

            __snake_case						       =   'lower'
            __snake_case						       =   ['low', 'er</w>']
            __snake_case						       =   tokenizer.tokenize(SCREAMING_SNAKE_CASE_    )
            self.assertListEqual(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   tokens + ['<unk>']
            __snake_case						       =   [16, 17, 23]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_    )							,       SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self    ):
            if not self.test_rust_tokenizer:
                   return

            __snake_case						       =   self.get_tokenizer()
            __snake_case						       =   self.get_rust_tokenizer()

            __snake_case						       =   'lower,newer'

            __snake_case						       =   tokenizer.tokenize(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_    )
            self.assertListEqual(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   tokenizer.encode(SCREAMING_SNAKE_CASE_							,       add_special_tokens=SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   rust_tokenizer.encode(SCREAMING_SNAKE_CASE_							,       add_special_tokens=SCREAMING_SNAKE_CASE_    )
            self.assertListEqual(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   self.get_rust_tokenizer()
            __snake_case						       =   tokenizer.encode(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   rust_tokenizer.encode(SCREAMING_SNAKE_CASE_    )
            self.assertListEqual(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

     @slow
     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.tokenizer_class.from_pretrained('allegro/herbert-base-cased'    )

            __snake_case						       =   tokenizer.encode('konstruowanie sekwencji'							,       add_special_tokens=SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   tokenizer.encode('konstruowanie wielu sekwencji'							,       add_special_tokens=SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

            assert encoded_sentence == [0] + text + [2]
            assert encoded_pair == [0] + text + [2] + text_a + [2]
