





"""simple docstring"""


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


a__   :       Optional[Any]   						=			logging.get_logger(__name__)



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							List[str]										=       ["""pixel_values"""]

       def __init__(     self				,   __SCREAMING_SNAKE_CASE = True				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = True				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC				,   __SCREAMING_SNAKE_CASE = True				,   __SCREAMING_SNAKE_CASE = 1 / 255				,   __SCREAMING_SNAKE_CASE = True				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        super().__init__(**__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			crop_size if crop_size is not None else {"height": 256, "width": 256}
        snake_case__      							=			get_size_dict(__SCREAMING_SNAKE_CASE				,   param_name="crop_size"       )
        snake_case__      							=			size if size is not None else {"height": 224, "width": 224}
        snake_case__      							=			get_size_dict(__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			do_center_crop
        snake_case__      							=			crop_size
        snake_case__      							=			do_resize
        snake_case__      							=			size
        snake_case__      							=			resample
        snake_case__      							=			do_rescale
        snake_case__      							=			rescale_factor
        snake_case__      							=			do_normalize
        snake_case__      							=			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        snake_case__      							=			image_std if image_std is not None else IMAGENET_DEFAULT_STD

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






        '''simple docstring'''

        snake_case__      							=			self.size if size is None else size
        snake_case__      							=			get_size_dict(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			get_size_dict(__SCREAMING_SNAKE_CASE				,   param_name="crop_size"       )

        snake_case__							,				snake_case__      							=			get_image_size(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			min(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )
        snake_case__      							=			(size["height"] / crop_size["height"]) * min_dim
        snake_case__      							=			(size["width"] / crop_size["width"]) * min_dim
        return center_crop(__SCREAMING_SNAKE_CASE				,   size=(cropped_height, cropped_width)				,   data_format=__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC				,   __SCREAMING_SNAKE_CASE = None				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        snake_case__      							=			get_size_dict(__SCREAMING_SNAKE_CASE       )
        if "height" not in size or "width" not in size:
         raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"       )
        return resize(
            __SCREAMING_SNAKE_CASE				,   size=(size["height"], size["width"])				,   resample=__SCREAMING_SNAKE_CASE				,   data_format=__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE       )

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






        '''simple docstring'''

        return rescale(__SCREAMING_SNAKE_CASE				,   scale=__SCREAMING_SNAKE_CASE				,   data_format=__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE       )

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






        '''simple docstring'''

        return normalize(__SCREAMING_SNAKE_CASE				,   mean=__SCREAMING_SNAKE_CASE				,   std=__SCREAMING_SNAKE_CASE				,   data_format=__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = ChannelDimension.FIRST				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        snake_case__      							=			do_center_crop if do_center_crop is not None else self.do_center_crop
        snake_case__      							=			crop_size if crop_size is not None else self.crop_size
        snake_case__      							=			get_size_dict(__SCREAMING_SNAKE_CASE				,   param_name="crop_size"       )
        snake_case__      							=			do_resize if do_resize is not None else self.do_resize
        snake_case__      							=			size if size is not None else self.size
        snake_case__      							=			get_size_dict(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			resample if resample is not None else self.resample
        snake_case__      							=			do_rescale if do_rescale is not None else self.do_rescale
        snake_case__      							=			rescale_factor if rescale_factor is not None else self.rescale_factor
        snake_case__      							=			do_normalize if do_normalize is not None else self.do_normalize
        snake_case__      							=			image_mean if image_mean is not None else self.image_mean
        snake_case__      							=			image_std if image_std is not None else self.image_std

        snake_case__      							=			make_list_of_images(__SCREAMING_SNAKE_CASE       )

        if not valid_images(__SCREAMING_SNAKE_CASE       ):
         raise ValueError(
             "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
             "torch.Tensor, tf.Tensor or jax.ndarray."       )

        if do_center_crop and crop_size is None:
         raise ValueError("If `do_center_crop` is set to `True`, `crop_size` must be provided."       )

        if do_resize and size is None:
         raise ValueError("Size must be specified if do_resize is True."       )

        if do_rescale and rescale_factor is None:
         raise ValueError("Rescale factor must be specified if do_rescale is True."       )

        if do_normalize and (image_mean is None or image_std is None):
         raise ValueError("Image mean and image standard deviation must be specified if do_normalize is True."       )

        # All transformations expect numpy arrays.
        snake_case__      							=			[to_numpy_array(__SCREAMING_SNAKE_CASE       ) for image in images]

        if do_center_crop:
         snake_case__      							=			[self.center_crop(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   size=__SCREAMING_SNAKE_CASE       ) for image in images]

        if do_resize:
         snake_case__      							=			[self.resize(image=__SCREAMING_SNAKE_CASE				,   size=__SCREAMING_SNAKE_CASE				,   resample=__SCREAMING_SNAKE_CASE       ) for image in images]

        if do_rescale:
         snake_case__      							=			[self.rescale(image=__SCREAMING_SNAKE_CASE				,   scale=__SCREAMING_SNAKE_CASE       ) for image in images]

        if do_normalize:
         snake_case__      							=			[self.normalize(image=__SCREAMING_SNAKE_CASE				,   mean=__SCREAMING_SNAKE_CASE				,   std=__SCREAMING_SNAKE_CASE       ) for image in images]

        snake_case__      							=			[to_channel_dimension_format(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ) for image in images]

        snake_case__      							=			{"pixel_values": images}
        return BatchFeature(data=__SCREAMING_SNAKE_CASE				,   tensor_type=__SCREAMING_SNAKE_CASE       )








"""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

