


from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase   	=  logging.get_logger(__name__)

__lowerCamelCase   	=  {
    '''ZinengTang/tvlt-base''': '''https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json''',
}


class 	a__						(    lowerCAmelCase_     ):
       lowerCamelCase__:  List[Any]				     =  """tvlt"""

       def __init__( self   :  List[Any]   ,				lowerCamelCase_   :  Union[str, Any]=2_2_4   ,				lowerCamelCase_   :  int=2_0_4_8   ,				lowerCamelCase_   :  List[str]=1_2_8   ,				lowerCamelCase_   :  int=[1_6, 1_6]   ,				lowerCamelCase_   :  Dict=[1_6, 1_6]   ,				lowerCamelCase_   :  str=3   ,				lowerCamelCase_   :  Tuple=1   ,				lowerCamelCase_   :  Optional[Any]=8   ,				lowerCamelCase_   :  Dict=7_6_8   ,				lowerCamelCase_   :  Any=1_2   ,				lowerCamelCase_   :  Union[str, Any]=1_2   ,				lowerCamelCase_   :  str=3_0_7_2   ,				lowerCamelCase_   :  str="gelu"   ,				lowerCamelCase_   :  int=0.0   ,				lowerCamelCase_   :  List[Any]=0.0   ,				lowerCamelCase_   :  Union[str, Any]=0.0_2   ,				lowerCamelCase_   :  Optional[Any]=1E-6   ,				lowerCamelCase_   :  str=True   ,				lowerCamelCase_   :  Optional[Any]=False   ,				lowerCamelCase_   :  Optional[Any]=1_6   ,				lowerCamelCase_   :  Union[str, Any]=5_1_2   ,				lowerCamelCase_   :  Any=8   ,				lowerCamelCase_   :  List[Any]=2_0_4_8   ,				lowerCamelCase_   :  List[str]=0.7_5   ,				lowerCamelCase_   :  List[str]=0.1_5   ,				lowerCamelCase_   :  Dict="frame-level"   ,				lowerCamelCase_   :  Tuple=True   ,				lowerCamelCase_   :  Tuple=True   ,				lowerCamelCase_   :  str="classification"   ,				**lowerCamelCase_   :  Optional[Any]   ,				):
            super().__init__(**lowerCamelCase_     )

            if audio_mask_type not in ("frame-level", "patch_level"):
                 raise ValueError(
                     """audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') """
                     F'''got {audio_mask_type}'''     )

            a_     :  List[str]				      =     image_size
            a_     :  List[str]				      =     spectrogram_length
            a_     :  int				      =     frequency_length
            a_     :  List[str]				      =     image_patch_size
            a_     :  Tuple				      =     audio_patch_size
            a_     :  List[str]				      =     num_image_channels
            a_     :  Tuple				      =     num_audio_channels
            a_     :  Dict				      =     num_frames

            a_     :  Any				      =     hidden_size
            a_     :  str				      =     num_hidden_layers
            a_     :  int				      =     num_attention_heads
            a_     :  Optional[int]				      =     intermediate_size
            a_     :  Dict				      =     hidden_act
            a_     :  List[Any]				      =     hidden_dropout_prob
            a_     :  Any				      =     attention_probs_dropout_prob
            a_     :  Union[str, Any]				      =     initializer_range
            a_     :  Optional[int]				      =     layer_norm_eps
            a_     :  Any				      =     qkv_bias
            a_     :  Any				      =     use_mean_pooling

            a_     :  str				      =     decoder_num_attention_heads
            a_     :  List[Any]				      =     decoder_hidden_size
            a_     :  Any				      =     decoder_num_hidden_layers
            a_     :  Optional[Any]				      =     decoder_intermediate_size
            a_     :  List[str]				      =     pixel_mask_ratio
            a_     :  Union[str, Any]				      =     audio_mask_ratio
            a_     :  int				      =     audio_mask_type

            a_     :  Optional[int]				      =     task_matching
            a_     :  Tuple				      =     task_mae
            a_     :  Union[str, Any]				      =     loss_type






"""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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


A									=						logging.get_logger(__name__)

class        _UpperCamelCase	(						lowerCamelCase__	):


  """simple docstring"""





  snake_case_        =    ['pixel_values']



  def __init__( self					: Union[str, Any]       ,       snake_case					: bool = True       ,       snake_case					: Dict[str, int] = None       ,       snake_case					: PILImageResampling = PIL.Image.NEAREST       ,       snake_case					: bool = False       ,       snake_case					: Dict[str, int] = None       ,       snake_case					: Union[int, float] = 1 / 255       ,       snake_case					: bool = False       ,       snake_case					: bool = True       ,       snake_case					: bool = True       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: bool = True       ,       **snake_case					: Optional[Any]       ,       )	->			None:


         '''simple docstring'''





         super().__init__(**snake_case      )
         __magic_name__			:		Any        =     size if size is not None else {'''height''': 346, '''width''': 346}
         __magic_name__			:		int        =     get_size_dict(snake_case      )
         __magic_name__			:		List[str]        =     crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
         __magic_name__			:		Optional[Any]        =     get_size_dict(snake_case       ,       param_name='''crop_size'''      )

         __magic_name__			:		Any        =     do_resize
         __magic_name__			:		Tuple        =     size
         __magic_name__			:		List[Any]        =     resample
         __magic_name__			:		Tuple        =     do_center_crop
         __magic_name__			:		str        =     crop_size
         __magic_name__			:		Tuple        =     do_rescale
         __magic_name__			:		Optional[Any]        =     rescale_factor
         __magic_name__			:		Optional[Any]        =     rescale_offset
         __magic_name__			:		int        =     do_normalize
         __magic_name__			:		str        =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         __magic_name__			:		Optional[int]        =     image_std if image_std is not None else IMAGENET_STANDARD_STD
         __magic_name__			:		Optional[int]        =     include_top



  def 							_UpperCAmelCase      ( self					: List[Any]       ,       snake_case					: np.ndarray       ,       snake_case					: Dict[str, int]       ,       snake_case					: PILImageResampling = PIL.Image.NEAREST       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: Union[str, Any]       ,       )	->			np.ndarray:


         '''simple docstring'''





         __magic_name__			:		Optional[Any]        =     get_size_dict(snake_case      )
         if "height" not in size or "width" not in size:
                raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""      )
         return resize(
             snake_case       ,       size=(size['''height'''], size['''width'''])       ,       resample=snake_case       ,       data_format=snake_case       ,       **snake_case      )



  def 							_UpperCAmelCase      ( self					: Optional[Any]       ,       snake_case					: np.ndarray       ,       snake_case					: Dict[str, int]       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: str       ,       )	->			np.ndarray:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     get_size_dict(snake_case      )
         if "height" not in size or "width" not in size:
                raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""      )
         return center_crop(snake_case       ,       size=(size['''height'''], size['''width'''])       ,       data_format=snake_case       ,       **snake_case      )



  def 							_UpperCAmelCase      ( self					: List[Any]       ,       snake_case					: np.ndarray       ,       snake_case					: Union[int, float]       ,       snake_case					: bool = True       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: Dict       ,       )	->			Any:


         '''simple docstring'''





         if offset:
                __magic_name__			:		List[str]        =     (image - 127.5) * scale
                if data_format is not None:
                       __magic_name__			:		str        =     to_channel_dimension_format(snake_case       ,       snake_case      )
                __magic_name__			:		Optional[int]        =     rescaled_image.astype(np.floataa      )
         else:
                __magic_name__			:		str        =     rescale(snake_case       ,       scale=snake_case       ,       data_format=snake_case       ,       **snake_case      )
         return rescaled_image



  def 							_UpperCAmelCase      ( self					: Any       ,       snake_case					: np.ndarray       ,       snake_case					: Union[float, List[float]]       ,       snake_case					: Union[float, List[float]]       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: int       ,       )	->			np.ndarray:


         '''simple docstring'''





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




  def 							_UpperCAmelCase      ( self					: str       ,       snake_case					: ImageInput       ,       snake_case					: bool = None       ,       snake_case					: Dict[str, int] = None       ,       snake_case					: Any=None       ,       snake_case					: bool = None       ,       snake_case					: Dict[str, int] = None       ,       snake_case					: bool = None       ,       snake_case					: float = None       ,       snake_case					: bool = None       ,       snake_case					: bool = None       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: bool = None       ,       snake_case					: Optional[Union[str, TensorType]] = None       ,       snake_case					: ChannelDimension = ChannelDimension.FIRST       ,       **snake_case					: List[str]       ,       )	->			PIL.Image.Image:


         '''simple docstring'''





         __magic_name__			:		Any        =     do_resize if do_resize is not None else self.do_resize
         __magic_name__			:		Optional[Any]        =     resample if resample is not None else self.resample
         __magic_name__			:		Optional[Any]        =     do_center_crop if do_center_crop is not None else self.do_center_crop
         __magic_name__			:		Optional[int]        =     do_rescale if do_rescale is not None else self.do_rescale
         __magic_name__			:		Tuple        =     rescale_factor if rescale_factor is not None else self.rescale_factor
         __magic_name__			:		Dict        =     rescale_offset if rescale_offset is not None else self.rescale_offset
         __magic_name__			:		Union[str, Any]        =     do_normalize if do_normalize is not None else self.do_normalize
         __magic_name__			:		Tuple        =     image_mean if image_mean is not None else self.image_mean
         __magic_name__			:		Dict        =     image_std if image_std is not None else self.image_std
         __magic_name__			:		Union[str, Any]        =     include_top if include_top is not None else self.include_top

         __magic_name__			:		Any        =     size if size is not None else self.size
         __magic_name__			:		List[str]        =     get_size_dict(snake_case      )
         __magic_name__			:		List[Any]        =     crop_size if crop_size is not None else self.crop_size
         __magic_name__			:		Dict        =     get_size_dict(snake_case       ,       param_name='''crop_size'''      )

         __magic_name__			:		Optional[Any]        =     make_list_of_images(snake_case      )

         if not valid_images(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_resize and size is None or resample is None:
                raise ValueError('''Size and resample must be specified if do_resize is True.'''      )

         if do_center_crop and crop_size is None:
                raise ValueError('''Crop size must be specified if do_center_crop 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 std must be specified if do_normalize is True.'''      )

         # All transformations expect numpy arrays.
         __magic_name__			:		Union[str, Any]        =     [to_numpy_array(snake_case      ) for image in images]

         if do_resize:
                __magic_name__			:		Optional[int]        =     [self.resize(image=snake_case       ,       size=snake_case       ,       resample=snake_case      ) for image in images]

         if do_center_crop:
                __magic_name__			:		Dict        =     [self.center_crop(image=snake_case       ,       size=snake_case      ) for image in images]

         if do_rescale:
                __magic_name__			:		Any        =     [self.rescale(image=snake_case       ,       scale=snake_case       ,       offset=snake_case      ) for image in images]

         if do_normalize:
                __magic_name__			:		Union[str, Any]        =     [self.normalize(image=snake_case       ,       mean=snake_case       ,       std=snake_case      ) for image in images]

         if include_top:
                __magic_name__			:		Optional[Any]        =     [self.normalize(image=snake_case       ,       mean=[0, 0, 0]       ,       std=snake_case      ) for image in images]

         __magic_name__			:		Any        =     [to_channel_dimension_format(snake_case       ,       snake_case      ) for image in images]

         __magic_name__			:		List[str]        =     {'''pixel_values''': images}
         return BatchFeature(data=snake_case       ,       tensor_type=snake_case      )
