



"""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,
    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 TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


_lowercase		:				List[str]      		=logging.get_logger(__name__)

def      lowerCAmelCase_					(  _lowercase    :       int)							->	List[List[ImageInput]]:







			"""simple docstring"""
			if isinstance(_lowercase						,       (list, tuple)) and isinstance(videos[0]						,       (list, tuple)) and is_valid_image(videos[0][0]):
						return videos

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

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

			raise ValueError(F'''Could not make batched video from {videos}''')





class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:str      =					["pixel_values"]

						def __init__(       self ,       __lowercase = True ,       __lowercase = None ,       __lowercase = PILImageResampling.BILINEAR ,       __lowercase = True ,       __lowercase = None ,       __lowercase = True ,       __lowercase = 1 / 2_5_5 ,       __lowercase = True ,       __lowercase = None ,       __lowercase = None ,       **__lowercase ,       ) ->				None:
									"""simple docstring"""




									super().__init__(**__lowercase  )
									a__       :						str							=  size if size is not None else {"""shortest_edge""": 2_2_4}
									a__       :						Dict							=  get_size_dict(__lowercase ,       default_to_square=__lowercase  )
									a__       :						Tuple							=  crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
									a__       :						List[Any]							=  get_size_dict(__lowercase ,       param_name="""crop_size"""  )

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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase = PILImageResampling.BILINEAR ,       __lowercase = None ,       **__lowercase ,       ) ->				np.ndarray:
									"""simple docstring"""




									a__       :						Dict							=  get_size_dict(__lowercase ,       default_to_square=__lowercase  )
									if "shortest_edge" in size:
												a__       :						Optional[int]							=  get_resize_output_image_size(__lowercase ,       size["""shortest_edge"""] ,       default_to_square=__lowercase  )
									elif "height" in size and "width" in size:
												a__       :						List[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(__lowercase ,       size=__lowercase ,       resample=__lowercase ,       data_format=__lowercase ,       **__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase = None ,       **__lowercase ,       ) ->				np.ndarray:
									"""simple docstring"""




									a__       :						Dict							=  get_size_dict(__lowercase  )
									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(__lowercase ,       size=(size["""height"""], size["""width"""]) ,       data_format=__lowercase ,       **__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase = None ,       **__lowercase ,       ) ->				Optional[Any]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase = None ,       **__lowercase ,       ) ->				np.ndarray:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       __lowercase = ChannelDimension.FIRST ,       ) ->				np.ndarray:
									"""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."""  )

									# All transformations expect numpy arrays.
									a__       :						str							=  to_numpy_array(__lowercase  )

									if do_resize:
												a__       :						str							=  self.resize(image=__lowercase ,       size=__lowercase ,       resample=__lowercase  )

									if do_center_crop:
												a__       :						List[Any]							=  self.center_crop(__lowercase ,       size=__lowercase  )

									if do_rescale:
												a__       :						List[Any]							=  self.rescale(image=__lowercase ,       scale=__lowercase  )

									if do_normalize:
												a__       :						Any							=  self.normalize(image=__lowercase ,       mean=__lowercase ,       std=__lowercase  )

									a__       :						int							=  to_channel_dimension_format(__lowercase ,       __lowercase  )
									return image

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       __lowercase = ChannelDimension.FIRST ,       **__lowercase ,       ) ->				PIL.Image.Image:
									"""simple docstring"""




									a__       :						Optional[int]							=  do_resize if do_resize is not None else self.do_resize
									a__       :						Optional[Any]							=  resample if resample is not None else self.resample
									a__       :						Optional[Any]							=  do_center_crop if do_center_crop is not None else self.do_center_crop
									a__       :						Dict							=  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__       :						int							=  do_normalize if do_normalize is not None else self.do_normalize
									a__       :						Optional[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__       :						Union[str, Any]							=  size if size is not None else self.size
									a__       :						Union[str, Any]							=  get_size_dict(__lowercase ,       default_to_square=__lowercase  )
									a__       :						Tuple							=  crop_size if crop_size is not None else self.crop_size
									a__       :						Optional[Any]							=  get_size_dict(__lowercase ,       param_name="""crop_size"""  )

									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."""  )

									a__       :						List[Any]							=  make_batched(__lowercase  )

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

									a__       :						str							=  {"""pixel_values""": videos}
									return BatchFeature(data=__lowercase ,       tensor_type=__lowercase  )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_lowercase		:				int      		={
    "configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:				Optional[Any]      		=[
		    "LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "LongT5EncoderModel",
		    "LongT5ForConditionalGeneration",
		    "LongT5Model",
		    "LongT5PreTrainedModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:				List[Any]      		=[
		    "FlaxLongT5ForConditionalGeneration",
		    "FlaxLongT5Model",
		    "FlaxLongT5PreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_longta import (
				    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LongTaEncoderModel,
				    LongTaForConditionalGeneration,
				    LongTaModel,
				    LongTaPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_longta import (
				    FlaxLongTaForConditionalGeneration,
				    FlaxLongTaModel,
				    FlaxLongTaPreTrainedModel,
				)


else:
		import sys

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




