



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__       =			logging.get_logger(__name__)





class    __lowerCAmelCase					(	lowerCamelCase__			):
				__lowerCamelCase					=							['''pixel_values''']




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



									"""simple docstring"""





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

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




				def 				snake_case  (				self		,							_snake_case		,							_snake_case		,							_snake_case = None		,							_snake_case = None		,							**_snake_case		,							):



									"""simple docstring"""





									_lowerCAmelCase         =       self.size if size is None else size
									_lowerCAmelCase         =       get_size_dict(_UpperCAmelCase						)
									_lowerCAmelCase         =       get_size_dict(_UpperCAmelCase		,							param_name="""crop_size"""						)

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




				def 				snake_case  (				self		,							_snake_case		,							_snake_case		,							_snake_case = PIL.Image.BICUBIC		,							_snake_case = None		,							**_snake_case		,							):



									"""simple docstring"""





									_lowerCAmelCase         =       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 				snake_case  (				self		,							_snake_case		,							_snake_case		,							_snake_case = None		,							**_snake_case		,							):



									"""simple docstring"""





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




				def 				snake_case  (				self		,							_snake_case		,							_snake_case		,							_snake_case		,							_snake_case = None		,							**_snake_case		,							):



									"""simple docstring"""





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




				def 				snake_case  (				self		,							_snake_case		,							_snake_case = None		,							_snake_case = None		,							_snake_case = None		,							_snake_case = None		,							_snake_case = None		,							_snake_case = None		,							_snake_case = None		,							_snake_case = None		,							_snake_case = None		,							_snake_case = None		,							_snake_case = None		,							_snake_case = ChannelDimension.FIRST		,							**_snake_case		,							):



									"""simple docstring"""





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

									_lowerCAmelCase         =       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.
									_lowerCAmelCase         =       [to_numpy_array(_UpperCAmelCase						) for image in images]

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

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

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

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

									_lowerCAmelCase         =       [to_channel_dimension_format(_UpperCAmelCase		,							_UpperCAmelCase						) for image in images]

									_lowerCAmelCase         =       {"""pixel_values""": images}
									return BatchFeature(data=_UpperCAmelCase		,							tensor_type=_UpperCAmelCase						)











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


_UpperCAmelCase					:						Any         =     logging.get_logger(__name__)

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


class 		lowerCAmelCase_				(  UpperCamelCase ):
     __lowerCamelCase       :    Any 	=     '''tvlt'''





     def __init__(	self							, _UpperCAmelCase=224							, _UpperCAmelCase=2048							, _UpperCAmelCase=128							, _UpperCAmelCase=[16, 16]							, _UpperCAmelCase=[16, 16]							, _UpperCAmelCase=3							, _UpperCAmelCase=1							, _UpperCAmelCase=8							, _UpperCAmelCase=768							, _UpperCAmelCase=12							, _UpperCAmelCase=12							, _UpperCAmelCase=3072							, _UpperCAmelCase="gelu"							, _UpperCAmelCase=0.0							, _UpperCAmelCase=0.0							, _UpperCAmelCase=0.02							, _UpperCAmelCase=1E-6							, _UpperCAmelCase=True							, _UpperCAmelCase=False							, _UpperCAmelCase=16							, _UpperCAmelCase=512							, _UpperCAmelCase=8							, _UpperCAmelCase=2048							, _UpperCAmelCase=0.75							, _UpperCAmelCase=0.15							, _UpperCAmelCase="frame-level"							, _UpperCAmelCase=True							, _UpperCAmelCase=True							, _UpperCAmelCase="classification"							, **_UpperCAmelCase							, )		->				List[Any]:
        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}'''  )

        lowerCamelCase									=image_size
        lowerCamelCase									=spectrogram_length
        lowerCamelCase									=frequency_length
        lowerCamelCase									=image_patch_size
        lowerCamelCase									=audio_patch_size
        lowerCamelCase									=num_image_channels
        lowerCamelCase									=num_audio_channels
        lowerCamelCase									=num_frames

        lowerCamelCase									=hidden_size
        lowerCamelCase									=num_hidden_layers
        lowerCamelCase									=num_attention_heads
        lowerCamelCase									=intermediate_size
        lowerCamelCase									=hidden_act
        lowerCamelCase									=hidden_dropout_prob
        lowerCamelCase									=attention_probs_dropout_prob
        lowerCamelCase									=initializer_range
        lowerCamelCase									=layer_norm_eps
        lowerCamelCase									=qkv_bias
        lowerCamelCase									=use_mean_pooling

        lowerCamelCase									=decoder_num_attention_heads
        lowerCamelCase									=decoder_hidden_size
        lowerCamelCase									=decoder_num_hidden_layers
        lowerCamelCase									=decoder_intermediate_size
        lowerCamelCase									=pixel_mask_ratio
        lowerCamelCase									=audio_mask_ratio
        lowerCamelCase									=audio_mask_type

        lowerCamelCase									=task_matching
        lowerCamelCase									=task_mae
        lowerCamelCase									=loss_type