





"""simple docstring"""


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							List[Any]      							=       ["""image_processor""", """tokenizer"""]
       snake_case__			:							Union[str, Any]      							=       """AutoImageProcessor"""
       snake_case__			:							List[str]      							=       """AutoTokenizer"""

       def __init__(     self				,   __SCREAMING_SNAKE_CASE=None				,   __SCREAMING_SNAKE_CASE=None				,   **__SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			None
        if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."				,   __SCREAMING_SNAKE_CASE				,   )
         snake_case__      							=			kwargs.pop("feature_extractor"       )

        snake_case__      							=			image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."       )
        if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."       )

        super().__init__(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )
        snake_case__      							=			self.image_processor
        snake_case__      							=			False

       def __call__(     self				,   *__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        if self._in_target_context_manager:
         return self.current_processor(*__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			kwargs.pop("images"				,   __SCREAMING_SNAKE_CASE       )
        snake_case__      							=			kwargs.pop("text"				,   __SCREAMING_SNAKE_CASE       )
        if len(__SCREAMING_SNAKE_CASE       ) > 0:
         snake_case__      							=			args[0]
         snake_case__      							=			args[1:]

        if images is None and text is None:
         raise ValueError("You need to specify either an `images` or `text` input to process."       )

        if images is not None:
         snake_case__      							=			self.image_processor(__SCREAMING_SNAKE_CASE				,   *__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE       )
        if text is not None:
         snake_case__      							=			self.tokenizer(__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE       )

        if text is None:
         return inputs
        elif images is None:
         return encodings
        else:
         snake_case__      							=			encodings["input_ids"]
         return inputs

       def 						_SCREAMING_SNAKE_CASE(     self				,   *__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   *__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE       )

       @contextmanager
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        warnings.warn(
            "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
            "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
            "your images inputs, or in a separate call."       )
        snake_case__      							=			True
        snake_case__      							=			self.tokenizer
        yield
        snake_case__      							=			self.image_processor
        snake_case__      							=			False

       @property
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        warnings.warn(
            "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,   __SCREAMING_SNAKE_CASE				,   )
        return self.image_processor_class

       @property
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        warnings.warn(
            "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,   __SCREAMING_SNAKE_CASE				,   )
        return self.image_processor








"""simple docstring"""


import copy
from typing import Dict

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


a__   :       str   						=			logging.get_logger(__name__)



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							Dict										=       """semantic"""

       def __init__(     self				,   __SCREAMING_SNAKE_CASE=1_0000				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=768				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=0.7				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=1_0048				,   __SCREAMING_SNAKE_CASE=12_9595				,   __SCREAMING_SNAKE_CASE=12_9599				,   __SCREAMING_SNAKE_CASE=1_0000				,   __SCREAMING_SNAKE_CASE=256				,   __SCREAMING_SNAKE_CASE=49.9				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        super().__init__(
            temperature=__SCREAMING_SNAKE_CASE				,   do_sample=__SCREAMING_SNAKE_CASE				,   eos_token_id=__SCREAMING_SNAKE_CASE				,   renormalize_logits=__SCREAMING_SNAKE_CASE				,   max_new_tokens=__SCREAMING_SNAKE_CASE				,   output_scores=__SCREAMING_SNAKE_CASE				,   return_dict_in_generate=__SCREAMING_SNAKE_CASE				,   output_hidden_states=__SCREAMING_SNAKE_CASE				,   output_attentions=__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE				,   )

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



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							Union[str, Any]										=       """coarse_acoustics"""

       def __init__(     self				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=0.7				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=1_2048				,   __SCREAMING_SNAKE_CASE=75				,   __SCREAMING_SNAKE_CASE=2				,   __SCREAMING_SNAKE_CASE=1_2050				,   __SCREAMING_SNAKE_CASE=256				,   __SCREAMING_SNAKE_CASE = 630				,   __SCREAMING_SNAKE_CASE = 60				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        super().__init__(
            temperature=__SCREAMING_SNAKE_CASE				,   do_sample=__SCREAMING_SNAKE_CASE				,   renormalize_logits=__SCREAMING_SNAKE_CASE				,   output_scores=__SCREAMING_SNAKE_CASE				,   return_dict_in_generate=__SCREAMING_SNAKE_CASE				,   output_hidden_states=__SCREAMING_SNAKE_CASE				,   output_attentions=__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE				,   )

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



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							Union[str, Any]										=       """fine_acoustics"""

       def __init__(     self				,   __SCREAMING_SNAKE_CASE=0.5				,   __SCREAMING_SNAKE_CASE=512				,   __SCREAMING_SNAKE_CASE=1024				,   __SCREAMING_SNAKE_CASE=8				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        super().__init__(temperature=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			max_fine_history_length
        snake_case__      							=			max_fine_input_length
        snake_case__      							=			n_fine_codebooks



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							List[str]										=       """bark"""
       snake_case__			:							Dict										=       True

       def __init__(     self				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE=2_4000				,   __SCREAMING_SNAKE_CASE=1024				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        if semantic_config is None:
         snake_case__      							=			{}
         logger.info("semantic_config is None. initializing the semantic model with default values."       )

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

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

        snake_case__      							=			BarkSemanticGenerationConfig(**__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			BarkCoarseGenerationConfig(**__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			BarkFineGenerationConfig(**__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			sample_rate
        snake_case__      							=			codebook_size

       @classmethod
       def 						_SCREAMING_SNAKE_CASE(     cls				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        return cls(
            semantic_config=semantic_config.to_dict()				,   coarse_acoustics_config=coarse_acoustics_config.to_dict()				,   fine_acoustics_config=fine_acoustics_config.to_dict()				,   **__SCREAMING_SNAKE_CASE				,   )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			copy.deepcopy(self.__dict__       )

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

        snake_case__      							=			self.__class__.model_type
        return output

