'''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


UpperCamelCase       :     str			=    logging.get_logger(__name__)



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    A     :    List[str]				=			["pixel_values"]





    def __init__(					self     :						Dict							,							UpperCAmelCase_     :						bool = True							,							UpperCAmelCase_     :						Dict[str, int] = None							,							UpperCAmelCase_     :						bool = True							,							UpperCAmelCase_     :						Dict[str, int] = None							,							UpperCAmelCase_     :						PILImageResampling = PILImageResampling.BICUBIC							,							UpperCAmelCase_     :						bool = True							,							UpperCAmelCase_     :						Union[int, float] = 1 / 2_5_5							,							UpperCAmelCase_     :						bool = True							,							UpperCAmelCase_     :						Optional[Union[float, List[float]]] = None							,							UpperCAmelCase_     :						Optional[Union[float, List[float]]] = None							,							**UpperCAmelCase_     :						Union[str, Any]							,							):


         """simple docstring"""







         super().__init__(**UpperCAmelCase_)
         a					:							str							= crop_size if crop_size is not None else {'height': 2_5_6, 'width': 2_5_6}
         a					:							Dict							= get_size_dict(UpperCAmelCase_							,							param_name='crop_size')
         a					:							Dict							= size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
         a					:							Tuple							= get_size_dict(UpperCAmelCase_)

         a					:							List[str]							= do_center_crop
         a					:							List[Any]							= crop_size
         a					:							List[str]							= do_resize
         a					:							Dict							= size
         a					:							Any							= resample
         a					:							Tuple							= do_rescale
         a					:							List[str]							= rescale_factor
         a					:							int							= do_normalize
         a					:							Optional[Any]							= image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         a					:							Union[str, Any]							= image_std if image_std is not None else IMAGENET_DEFAULT_STD





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[Any]							,							UpperCAmelCase_     :						np.ndarray							,							UpperCAmelCase_     :						Dict[str, int]							,							UpperCAmelCase_     :						Optional[int] = None							,							UpperCAmelCase_     :						Optional[Union[str, ChannelDimension]] = None							,							**UpperCAmelCase_     :						str							,							):


         """simple docstring"""







         a					:							Any							= self.size if size is None else size
         a					:							Union[str, Any]							= get_size_dict(UpperCAmelCase_)
         a					:							List[Any]							= get_size_dict(UpperCAmelCase_							,							param_name='crop_size')

         a							,			a					:							int							= get_image_size(UpperCAmelCase_)
         a					:							Dict							= min(UpperCAmelCase_							,							UpperCAmelCase_)
         a					:							Dict							= (size['height'] / crop_size['height']) * min_dim
         a					:							Tuple							= (size['width'] / crop_size['width']) * min_dim
         return center_crop(UpperCAmelCase_							,							size=(cropped_height, cropped_width)							,							data_format=UpperCAmelCase_							,							**UpperCAmelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						int							,							UpperCAmelCase_     :						np.ndarray							,							UpperCAmelCase_     :						Dict[str, int]							,							UpperCAmelCase_     :						PILImageResampling = PIL.Image.BICUBIC							,							UpperCAmelCase_     :						Optional[Union[str, ChannelDimension]] = None							,							**UpperCAmelCase_     :						List[str]							,							):


         """simple docstring"""







         a					:							int							= get_size_dict(UpperCAmelCase_)
         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(
             UpperCAmelCase_							,							size=(size['height'], size['width'])							,							resample=UpperCAmelCase_							,							data_format=UpperCAmelCase_							,							**UpperCAmelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						int							,							UpperCAmelCase_     :						np.ndarray							,							UpperCAmelCase_     :						Union[int, float]							,							UpperCAmelCase_     :						Optional[Union[str, ChannelDimension]] = None							,							**UpperCAmelCase_     :						Optional[Any]							,							):


         """simple docstring"""







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





    def    SCREAMING_SNAKE_CASE_     (					self     :						int							,							UpperCAmelCase_     :						np.ndarray							,							UpperCAmelCase_     :						Union[float, List[float]]							,							UpperCAmelCase_     :						Union[float, List[float]]							,							UpperCAmelCase_     :						Optional[Union[str, ChannelDimension]] = None							,							**UpperCAmelCase_     :						Tuple							,							):


         """simple docstring"""







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







    def    SCREAMING_SNAKE_CASE_     (					self     :						List[Any]							,							UpperCAmelCase_     :						ImageInput							,							UpperCAmelCase_     :						Optional[bool] = None							,							UpperCAmelCase_     :						Optional[Dict[str, int]] = None							,							UpperCAmelCase_     :						Optional[bool] = None							,							UpperCAmelCase_     :						Optional[Dict[str, int]] = None							,							UpperCAmelCase_     :						PILImageResampling = None							,							UpperCAmelCase_     :						Optional[bool] = None							,							UpperCAmelCase_     :						Optional[float] = None							,							UpperCAmelCase_     :						Optional[bool] = None							,							UpperCAmelCase_     :						Optional[Union[float, List[float]]] = None							,							UpperCAmelCase_     :						Optional[Union[float, List[float]]] = None							,							UpperCAmelCase_     :						Optional[Union[str, TensorType]] = None							,							UpperCAmelCase_     :						ChannelDimension = ChannelDimension.FIRST							,							**UpperCAmelCase_     :						Optional[Any]							,							):


         """simple docstring"""







         a					:							Union[str, Any]							= do_center_crop if do_center_crop is not None else self.do_center_crop
         a					:							Tuple							= crop_size if crop_size is not None else self.crop_size
         a					:							Tuple							= get_size_dict(UpperCAmelCase_							,							param_name='crop_size')
         a					:							str							= do_resize if do_resize is not None else self.do_resize
         a					:							Union[str, Any]							= size if size is not None else self.size
         a					:							int							= get_size_dict(UpperCAmelCase_)
         a					:							List[Any]							= resample if resample is not None else self.resample
         a					:							int							= do_rescale if do_rescale is not None else self.do_rescale
         a					:							Optional[int]							= rescale_factor if rescale_factor is not None else self.rescale_factor
         a					:							List[Any]							= do_normalize if do_normalize is not None else self.do_normalize
         a					:							Union[str, Any]							= image_mean if image_mean is not None else self.image_mean
         a					:							Optional[Any]							= image_std if image_std is not None else self.image_std

         a					:							List[str]							= make_list_of_images(UpperCAmelCase_)

         if not valid_images(UpperCAmelCase_):
              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.
         a					:							List[Any]							= [to_numpy_array(UpperCAmelCase_) for image in images]

         if do_center_crop:
              a					:							Optional[Any]							= [self.center_crop(UpperCAmelCase_							,							UpperCAmelCase_							,							size=UpperCAmelCase_) for image in images]

         if do_resize:
              a					:							str							= [self.resize(image=UpperCAmelCase_							,							size=UpperCAmelCase_							,							resample=UpperCAmelCase_) for image in images]

         if do_rescale:
              a					:							str							= [self.rescale(image=UpperCAmelCase_							,							scale=UpperCAmelCase_) for image in images]

         if do_normalize:
              a					:							List[Any]							= [self.normalize(image=UpperCAmelCase_							,							mean=UpperCAmelCase_							,							std=UpperCAmelCase_) for image in images]

         a					:							str							= [to_channel_dimension_format(UpperCAmelCase_							,							UpperCAmelCase_) for image in images]

         a					:							List[Any]							= {'pixel_values': images}
         return BatchFeature(data=UpperCAmelCase_							,							tensor_type=UpperCAmelCase_)


'''simple docstring'''





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


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

UpperCamelCase       :     Union[str, Any]			=    {
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    A     :    int				=			"tvlt"





    def __init__(					self     :						List[str]							,							UpperCAmelCase_     :						Any=2_2_4							,							UpperCAmelCase_     :						Optional[Any]=2_0_4_8							,							UpperCAmelCase_     :						Dict=1_2_8							,							UpperCAmelCase_     :						str=[1_6, 1_6]							,							UpperCAmelCase_     :						int=[1_6, 1_6]							,							UpperCAmelCase_     :						int=3							,							UpperCAmelCase_     :						Optional[int]=1							,							UpperCAmelCase_     :						List[str]=8							,							UpperCAmelCase_     :						List[Any]=7_6_8							,							UpperCAmelCase_     :						int=1_2							,							UpperCAmelCase_     :						List[Any]=1_2							,							UpperCAmelCase_     :						Optional[Any]=3_0_7_2							,							UpperCAmelCase_     :						List[str]="gelu"							,							UpperCAmelCase_     :						Tuple=0.0							,							UpperCAmelCase_     :						Optional[Any]=0.0							,							UpperCAmelCase_     :						Tuple=0.02							,							UpperCAmelCase_     :						Any=1e-6							,							UpperCAmelCase_     :						Dict=True							,							UpperCAmelCase_     :						Union[str, Any]=False							,							UpperCAmelCase_     :						Tuple=1_6							,							UpperCAmelCase_     :						str=5_1_2							,							UpperCAmelCase_     :						List[str]=8							,							UpperCAmelCase_     :						Union[str, Any]=2_0_4_8							,							UpperCAmelCase_     :						Tuple=0.75							,							UpperCAmelCase_     :						Optional[Any]=0.15							,							UpperCAmelCase_     :						Union[str, Any]="frame-level"							,							UpperCAmelCase_     :						List[str]=True							,							UpperCAmelCase_     :						Any=True							,							UpperCAmelCase_     :						Optional[int]="classification"							,							**UpperCAmelCase_     :						Any							,							):


         """simple docstring"""







         super().__init__(**UpperCAmelCase_)

         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					:							str							= image_size
         a					:							List[Any]							= spectrogram_length
         a					:							Union[str, Any]							= frequency_length
         a					:							str							= image_patch_size
         a					:							Dict							= audio_patch_size
         a					:							Dict							= num_image_channels
         a					:							Tuple							= num_audio_channels
         a					:							Optional[int]							= num_frames

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

         a					:							List[Any]							= decoder_num_attention_heads
         a					:							Any							= decoder_hidden_size
         a					:							int							= decoder_num_hidden_layers
         a					:							Tuple							= decoder_intermediate_size
         a					:							Tuple							= pixel_mask_ratio
         a					:							List[Any]							= audio_mask_ratio
         a					:							Optional[Any]							= audio_mask_type

         a					:							Any							= task_matching
         a					:							Optional[Any]							= task_mae
         a					:							List[str]							= loss_type

