


'''simple docstring'''







from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
       import PIL

lowerCAmelCase    :     Optional[int]					=						logging.get_logger(__name__)






def 			lowercase				(_A					):







       """simple docstring"""







       if isinstance(_A   ,						(list, tuple)					) and isinstance(videos[0]   ,						(list, tuple)					) and is_valid_image(videos[0][0]					):
              return videos

       elif isinstance(_A   ,						(list, tuple)					) and is_valid_image(videos[0]					):
              return [videos]

       elif is_valid_image(_A					):
              return [[videos]]

       raise ValueError(f'Could not make batched video from {videos}'					)
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    ["pixel_values"]







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

            '''simple docstring'''

            super().__init__(**snake_case__   )
            _lowerCAmelCase    :							Optional[Any]   			=       size if size is not None else {'shortest_edge': 256}
            _lowerCAmelCase    :							str   			=       get_size_dict(snake_case__      ,	default_to_square=snake_case__   )
            _lowerCAmelCase    :							Union[str, Any]   			=       crop_size if crop_size is not None else {'height': 224, 'width': 224}
            _lowerCAmelCase    :							Any   			=       get_size_dict(snake_case__      ,	param_name='crop_size'   )

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







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__ = PILImageResampling.BILINEAR      ,	snake_case__ = None      ,	**snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       get_size_dict(snake_case__      ,	default_to_square=snake_case__   )
            if "shortest_edge" in size:
                   _lowerCAmelCase    :							List[Any]   			=       get_resize_output_image_size(snake_case__      ,	size['shortest_edge']      ,	default_to_square=snake_case__   )
            elif "height" in size and "width" in size:
                   _lowerCAmelCase    :							Union[str, Any]   			=       (size['height'], size['width'])
            else:
                   raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'   )
            return resize(snake_case__      ,	size=snake_case__      ,	resample=snake_case__      ,	data_format=snake_case__      ,	**snake_case__   )







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__ = None      ,	**snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       get_size_dict(snake_case__   )
            if "height" not in size or "width" not in size:
                   raise ValueError(F'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'   )
            return center_crop(snake_case__      ,	size=(size['height'], size['width'])      ,	data_format=snake_case__      ,	**snake_case__   )







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__ = True      ,	snake_case__ = None      ,	**snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       image.astype(np.floataa   )
            if offset:
                   _lowerCAmelCase    :							Union[str, Any]   			=       image - (scale / 2)
            return rescale(snake_case__      ,	scale=snake_case__      ,	data_format=snake_case__      ,	**snake_case__   )







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__ = None      ,	**snake_case__      ,	):

            '''simple docstring'''

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







     def a	( 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      ,	):

            '''simple docstring'''

            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.'   )

            if offset and not do_rescale:
                   raise ValueError('For offset, do_rescale must also be set to True.'   )

            # All transformations expect numpy arrays.
            _lowerCAmelCase    :							Dict   			=       to_numpy_array(snake_case__   )

            if do_resize:
                   _lowerCAmelCase    :							Dict   			=       self.resize(image=snake_case__      ,	size=snake_case__      ,	resample=snake_case__   )

            if do_center_crop:
                   _lowerCAmelCase    :							Union[str, Any]   			=       self.center_crop(snake_case__      ,	size=snake_case__   )

            if do_rescale:
                   _lowerCAmelCase    :							Union[str, Any]   			=       self.rescale(image=snake_case__      ,	scale=snake_case__      ,	offset=snake_case__   )

            if do_normalize:
                   _lowerCAmelCase    :							int   			=       self.normalize(image=snake_case__      ,	mean=snake_case__      ,	std=snake_case__   )

            _lowerCAmelCase    :							List[str]   			=       to_channel_dimension_format(snake_case__      ,	snake_case__   )
            return image







     def a	( 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__ = None      ,	snake_case__ = ChannelDimension.FIRST      ,	**snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       do_resize if do_resize is not None else self.do_resize
            _lowerCAmelCase    :							Any   			=       resample if resample is not None else self.resample
            _lowerCAmelCase    :							Optional[int]   			=       do_center_crop if do_center_crop is not None else self.do_center_crop
            _lowerCAmelCase    :							str   			=       do_rescale if do_rescale is not None else self.do_rescale
            _lowerCAmelCase    :							List[Any]   			=       rescale_factor if rescale_factor is not None else self.rescale_factor
            _lowerCAmelCase    :							str   			=       offset if offset is not None else self.offset
            _lowerCAmelCase    :							Any   			=       do_normalize if do_normalize is not None else self.do_normalize
            _lowerCAmelCase    :							Union[str, Any]   			=       image_mean if image_mean is not None else self.image_mean
            _lowerCAmelCase    :							List[str]   			=       image_std if image_std is not None else self.image_std

            _lowerCAmelCase    :							Tuple   			=       size if size is not None else self.size
            _lowerCAmelCase    :							Union[str, Any]   			=       get_size_dict(snake_case__      ,	default_to_square=snake_case__   )
            _lowerCAmelCase    :							str   			=       crop_size if crop_size is not None else self.crop_size
            _lowerCAmelCase    :							List[str]   			=       get_size_dict(snake_case__      ,	param_name='crop_size'   )

            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.'   )

            _lowerCAmelCase    :							int   			=       make_batched(snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       [
                [
                    self._preprocess_image(
                        image=snake_case__      ,	do_resize=snake_case__      ,	size=snake_case__      ,	resample=snake_case__      ,	do_center_crop=snake_case__      ,	crop_size=snake_case__      ,	do_rescale=snake_case__      ,	rescale_factor=snake_case__      ,	offset=snake_case__      ,	do_normalize=snake_case__      ,	image_mean=snake_case__      ,	image_std=snake_case__      ,	data_format=snake_case__      ,	)
                    for img in video
                ]
                for video in videos
            ]

            _lowerCAmelCase    :							Any   			=       {'pixel_values': videos}
            return BatchFeature(data=snake_case__      ,	tensor_type=snake_case__   )





'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


lowerCAmelCase    :     int					=						{
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase    :     Optional[Any]					=						[
           """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TrOCRForCausalLM""",
           """TrOCRPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
       from .processing_trocr import TrOCRProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
       import sys

       lowerCAmelCase    :     Any					=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

