



'''simple docstring'''







import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


lowercase__  :	int            =      logging.get_logger(__name__)

class    lowerCamelCase_				(						lowerCAmelCase_	):






   '''simple docstring'''

   A			:  Tuple					=						"semantic"


   def __init__(	self  ,    __UpperCamelCase=1_00_00  ,    __UpperCamelCase=True  ,    __UpperCamelCase=7_68  ,    __UpperCamelCase=False  ,    __UpperCamelCase=False  ,    __UpperCamelCase=False  ,    __UpperCamelCase=False  ,    __UpperCamelCase=0.7  ,    __UpperCamelCase=True  ,    __UpperCamelCase=1_00_48  ,    __UpperCamelCase=12_95_95  ,    __UpperCamelCase=12_95_99  ,    __UpperCamelCase=1_00_00  ,    __UpperCamelCase=2_56  ,    __UpperCamelCase=49.9  ,    **__UpperCamelCase  ,    ):
       super().__init__(
           temperature=__UpperCamelCase  ,    do_sample=__UpperCamelCase  ,    eos_token_id=__UpperCamelCase  ,    renormalize_logits=__UpperCamelCase  ,    max_new_tokens=__UpperCamelCase  ,    output_scores=__UpperCamelCase  ,    return_dict_in_generate=__UpperCamelCase  ,    output_hidden_states=__UpperCamelCase  ,    output_attentions=__UpperCamelCase  ,    **__UpperCamelCase  ,    )

       UpperCamelCase__	     =      text_encoding_offset
       UpperCamelCase__	     =      text_pad_token
       UpperCamelCase__	     =      eos_token_id
       UpperCamelCase__	     =      semantic_infer_token
       UpperCamelCase__	     =      semantic_vocab_size
       UpperCamelCase__	     =      max_input_semantic_length
       UpperCamelCase__	     =      semantic_rate_hz

class    lowerCamelCase_				(						lowerCAmelCase_	):






   '''simple docstring'''

   A			:  Optional[Any]					=						"coarse_acoustics"


   def __init__(	self  ,    __UpperCamelCase=True  ,    __UpperCamelCase=False  ,    __UpperCamelCase=False  ,    __UpperCamelCase=False  ,    __UpperCamelCase=False  ,    __UpperCamelCase=0.7  ,    __UpperCamelCase=True  ,    __UpperCamelCase=1_20_48  ,    __UpperCamelCase=75  ,    __UpperCamelCase=2  ,    __UpperCamelCase=1_20_50  ,    __UpperCamelCase=2_56  ,    __UpperCamelCase = 6_30  ,    __UpperCamelCase = 60  ,    **__UpperCamelCase  ,    ):
       super().__init__(
           temperature=__UpperCamelCase  ,    do_sample=__UpperCamelCase  ,    renormalize_logits=__UpperCamelCase  ,    output_scores=__UpperCamelCase  ,    return_dict_in_generate=__UpperCamelCase  ,    output_hidden_states=__UpperCamelCase  ,    output_attentions=__UpperCamelCase  ,    **__UpperCamelCase  ,    )

       UpperCamelCase__	     =      coarse_semantic_pad_token
       UpperCamelCase__	     =      coarse_rate_hz
       UpperCamelCase__	     =      n_coarse_codebooks
       UpperCamelCase__	     =      coarse_infer_token
       UpperCamelCase__	     =      max_coarse_input_length
       UpperCamelCase__	     =      max_coarse_history
       UpperCamelCase__	     =      sliding_window_len

class    lowerCamelCase_				(						lowerCAmelCase_	):






   '''simple docstring'''

   A			:  Tuple					=						"fine_acoustics"


   def __init__(	self  ,    __UpperCamelCase=0.5  ,    __UpperCamelCase=5_12  ,    __UpperCamelCase=10_24  ,    __UpperCamelCase=8  ,    **__UpperCamelCase  ,    ):
       super().__init__(temperature=__UpperCamelCase			)

       UpperCamelCase__	     =      max_fine_history_length
       UpperCamelCase__	     =      max_fine_input_length
       UpperCamelCase__	     =      n_fine_codebooks

class    lowerCamelCase_				(						lowerCAmelCase_	):






   '''simple docstring'''

   A			:  str					=						"bark"
   A			:  Optional[int]					=						True


   def __init__(	self  ,    __UpperCamelCase = None  ,    __UpperCamelCase = None  ,    __UpperCamelCase = None  ,    __UpperCamelCase=2_40_00  ,    __UpperCamelCase=10_24  ,    **__UpperCamelCase  ,    ):
       if semantic_config is None:
           UpperCamelCase__	     =      {}
           logger.info("semantic_config is None. initializing the semantic model with default values."			)

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

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

       UpperCamelCase__	     =      BarkSemanticGenerationConfig(**__UpperCamelCase			)
       UpperCamelCase__	     =      BarkCoarseGenerationConfig(**__UpperCamelCase			)
       UpperCamelCase__	     =      BarkFineGenerationConfig(**__UpperCamelCase			)

       UpperCamelCase__	     =      sample_rate
       UpperCamelCase__	     =      codebook_size


   @classmethod
   def 						_UpperCamelCase							(	cls  ,    __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()  ,    **__UpperCamelCase  ,    )







   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      copy.deepcopy(self.__dict__			)

       UpperCamelCase__	     =      self.semantic_config.to_dict()
       UpperCamelCase__	     =      self.coarse_acoustics_config.to_dict()
       UpperCamelCase__	     =      self.fine_acoustics_config.to_dict()

       UpperCamelCase__	     =      self.__class__.model_type
       return output









'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__  :	Optional[Any]            =      {
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :	List[Any]            =      ['MarianTokenizer']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :	List[str]            =      [
          'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
          'MarianForCausalLM',
          'MarianModel',
          'MarianMTModel',
          'MarianPreTrainedModel',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :	Optional[Any]            =      ['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :	Any            =      ['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
      from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_marian import MarianTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_marian import (
                MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
                MarianForCausalLM,
                MarianModel,
                MarianMTModel,
                MarianPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
      import sys

      lowercase__  :	Any            =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




