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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


lowerCAmelCase__					=				logging.get_logger(__name__)


if is_vision_available():
    import PIL
class        a_				(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 UpperCAmelCase_							=      ['pixel_values']




 def __init__(    self      :						Optional[int]    ,					lowercase__      :						bool = True    ,					lowercase__      :						Dict[str, int] = None    ,					lowercase__      :						PILImageResampling = PILImageResampling.BILINEAR    ,					lowercase__      :						bool = True    ,					lowercase__      :						bool = False    ,					lowercase__      :						bool = True    ,					lowercase__      :						bool = True    ,					lowercase__      :						Union[int, float] = 1 / 255    ,					lowercase__      :						bool = True    ,					lowercase__      :						Optional[Union[float, List[float]]] = None    ,					lowercase__      :						Optional[Union[float, List[float]]] = None    ,					**lowercase__      :						Union[str, Any]    ,					):







        '''simple docstring'''



        super().__init__(**lowercase__)

        lowerCAmelCase__  			=      size if size is not None else {'height': 2_560, 'width': 1_920}
        if isinstance(lowercase__    ,					(tuple, list)):
               # The previous feature extractor size parameter was in (width, height) format
               lowerCAmelCase__  			=      size[::-1]
        lowerCAmelCase__  			=      get_size_dict(lowercase__)

        lowerCAmelCase__  			=      do_resize
        lowerCAmelCase__  			=      size
        lowerCAmelCase__  			=      resample
        lowerCAmelCase__  			=      do_thumbnail
        lowerCAmelCase__  			=      do_align_long_axis
        lowerCAmelCase__  			=      do_pad
        lowerCAmelCase__  			=      do_rescale
        lowerCAmelCase__  			=      rescale_factor
        lowerCAmelCase__  			=      do_normalize
        lowerCAmelCase__  			=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        lowerCAmelCase__  			=      image_std if image_std is not None else IMAGENET_STANDARD_STD




 def 	__snake_case		(    self      :						int    ,					lowercase__      :						np.ndarray    ,					lowercase__      :						Dict[str, int]    ,					lowercase__      :						Optional[Union[str, ChannelDimension]] = None):







        '''simple docstring'''



        lowerCAmelCase__			,      lowerCAmelCase__  			=      get_image_size(lowercase__)
        lowerCAmelCase__			,      lowerCAmelCase__  			=      size['height'], size['width']

        if (output_width < output_height and input_width > input_height) or (
            output_width > output_height and input_width < input_height
        ):
               lowerCAmelCase__  			=      np.rotaa(lowercase__    ,					3)

        if data_format is not None:
               lowerCAmelCase__  			=      to_channel_dimension_format(lowercase__    ,					lowercase__)

        return image




 def 	__snake_case		(    self      :						int    ,					lowercase__      :						np.ndarray    ,					lowercase__      :						Dict[str, int]    ,					lowercase__      :						bool = False    ,					lowercase__      :						Optional[Union[str, ChannelDimension]] = None    ,					):







        '''simple docstring'''



        lowerCAmelCase__			,      lowerCAmelCase__  			=      size['height'], size['width']
        lowerCAmelCase__			,      lowerCAmelCase__  			=      get_image_size(lowercase__)

        lowerCAmelCase__  			=      output_width - input_width
        lowerCAmelCase__  			=      output_height - input_height

        if random_padding:
               lowerCAmelCase__  			=      np.random.randint(low=0    ,					high=delta_height + 1)
               lowerCAmelCase__  			=      np.random.randint(low=0    ,					high=delta_width + 1)
        else:
               lowerCAmelCase__  			=      delta_height // 2
               lowerCAmelCase__  			=      delta_width // 2

        lowerCAmelCase__  			=      delta_height - pad_top
        lowerCAmelCase__  			=      delta_width - pad_left

        lowerCAmelCase__  			=      ((pad_top, pad_bottom), (pad_left, pad_right))
        return pad(lowercase__    ,					lowercase__    ,					data_format=lowercase__)




 def 	__snake_case		(    self      :						Dict    ,					*lowercase__      :						List[str]    ,					**lowercase__      :						str):







        '''simple docstring'''



        logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.')
        return self.pad_image(*lowercase__    ,					**lowercase__)




 def 	__snake_case		(    self      :						str    ,					lowercase__      :						np.ndarray    ,					lowercase__      :						Dict[str, int]    ,					lowercase__      :						PILImageResampling = PILImageResampling.BICUBIC    ,					lowercase__      :						Optional[Union[str, ChannelDimension]] = None    ,					**lowercase__      :						Dict    ,					):







        '''simple docstring'''



        lowerCAmelCase__			,      lowerCAmelCase__  			=      get_image_size(lowercase__)
        lowerCAmelCase__			,      lowerCAmelCase__  			=      size['height'], size['width']

        # We always resize to the smallest of either the input or output size.
        lowerCAmelCase__  			=      min(lowercase__    ,					lowercase__)
        lowerCAmelCase__  			=      min(lowercase__    ,					lowercase__)

        if height == input_height and width == input_width:
               return image

        if input_height > input_width:
               lowerCAmelCase__  			=      int(input_width * height / input_height)
        elif input_width > input_height:
               lowerCAmelCase__  			=      int(input_height * width / input_width)

        return resize(
            lowercase__    ,					size=(height, width)    ,					resample=lowercase__    ,					reducing_gap=2.0    ,					data_format=lowercase__    ,					**lowercase__)




 def 	__snake_case		(    self      :						Union[str, Any]    ,					lowercase__      :						np.ndarray    ,					lowercase__      :						Dict[str, int]    ,					lowercase__      :						PILImageResampling = PILImageResampling.BICUBIC    ,					lowercase__      :						Optional[Union[str, ChannelDimension]] = None    ,					**lowercase__      :						Dict    ,					):







        '''simple docstring'''



        lowerCAmelCase__  			=      get_size_dict(lowercase__)
        lowerCAmelCase__  			=      min(size['height']    ,					size['width'])
        lowerCAmelCase__  			=      get_resize_output_image_size(lowercase__    ,					size=lowercase__    ,					default_to_square=lowercase__)
        lowerCAmelCase__  			=      resize(lowercase__    ,					size=lowercase__    ,					resample=lowercase__    ,					data_format=lowercase__    ,					**lowercase__)
        return resized_image




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







        '''simple docstring'''



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




 def 	__snake_case		(    self      :						List[str]    ,					lowercase__      :						np.ndarray    ,					lowercase__      :						Union[float, List[float]]    ,					lowercase__      :						Union[float, List[float]]    ,					lowercase__      :						Optional[Union[str, ChannelDimension]] = None    ,					**lowercase__      :						Dict    ,					):







        '''simple docstring'''



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




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







        '''simple docstring'''



        lowerCAmelCase__  			=      do_resize if do_resize is not None else self.do_resize
        lowerCAmelCase__  			=      size if size is not None else self.size
        if isinstance(lowercase__    ,					(tuple, list)):
               # Previous feature extractor had size in (width, height) format
               lowerCAmelCase__  			=      size[::-1]
        lowerCAmelCase__  			=      get_size_dict(lowercase__)
        lowerCAmelCase__  			=      resample if resample is not None else self.resample
        lowerCAmelCase__  			=      do_thumbnail if do_thumbnail is not None else self.do_thumbnail
        lowerCAmelCase__  			=      do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
        lowerCAmelCase__  			=      do_pad if do_pad is not None else self.do_pad
        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(lowercase__)

        if not valid_images(lowercase__):
               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:
               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_pad and size is None:
               raise ValueError('Size must be specified if do_pad 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.
        lowerCAmelCase__  			=      [to_numpy_array(lowercase__) for image in images]

        if do_align_long_axis:
               lowerCAmelCase__  			=      [self.align_long_axis(lowercase__    ,					size=lowercase__) for image in images]

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

        if do_thumbnail:
               lowerCAmelCase__  			=      [self.thumbnail(image=lowercase__    ,					size=lowercase__) for image in images]

        if do_pad:
               lowerCAmelCase__  			=      [self.pad_image(image=lowercase__    ,					size=lowercase__    ,					random_padding=lowercase__) for image in images]

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

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

        lowerCAmelCase__  			=      [to_channel_dimension_format(lowercase__    ,					lowercase__) for image in images]

        lowerCAmelCase__  			=      {'pixel_values': images}
        return BatchFeature(data=lowercase__    ,					tensor_type=lowercase__)

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_				(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 UpperCAmelCase_							=      'tvlt'




 def __init__(    self      :						Dict    ,					lowercase__      :						str=224    ,					lowercase__      :						int=2_048    ,					lowercase__      :						int=128    ,					lowercase__      :						Optional[Any]=[16, 16]    ,					lowercase__      :						List[str]=[16, 16]    ,					lowercase__      :						Optional[int]=3    ,					lowercase__      :						Tuple=1    ,					lowercase__      :						Union[str, Any]=8    ,					lowercase__      :						int=768    ,					lowercase__      :						Tuple=12    ,					lowercase__      :						List[str]=12    ,					lowercase__      :						Union[str, Any]=3_072    ,					lowercase__      :						Union[str, Any]="gelu"    ,					lowercase__      :						Dict=0.0    ,					lowercase__      :						Tuple=0.0    ,					lowercase__      :						Union[str, Any]=0.02    ,					lowercase__      :						Any=1e-6    ,					lowercase__      :						Dict=True    ,					lowercase__      :						int=False    ,					lowercase__      :						Tuple=16    ,					lowercase__      :						Optional[int]=512    ,					lowercase__      :						int=8    ,					lowercase__      :						List[Any]=2_048    ,					lowercase__      :						Optional[int]=0.75    ,					lowercase__      :						Union[str, Any]=0.15    ,					lowercase__      :						List[str]="frame-level"    ,					lowercase__      :						Dict=True    ,					lowercase__      :						Tuple=True    ,					lowercase__      :						Any="classification"    ,					**lowercase__      :						Union[str, Any]    ,					):







        '''simple docstring'''



        super().__init__(**lowercase__)

        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
