



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

SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)


def A    (							__UpperCamelCase       )    ->   List[List[ImageInput]]:
			if isinstance(__UpperCamelCase			,						(list, tuple)       ) and isinstance(videos[0]			,						(list, tuple)       ) and is_valid_image(videos[0][0]       ):
						return videos

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

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

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






class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					A__					:		Any      							=     ["pixel_values"]



					def __init__(  self       :							int					,       _snake_case       :							bool = True					,       _snake_case       :							Dict[str, int] = None					,       _snake_case       :							PILImageResampling = PILImageResampling.BILINEAR					,       _snake_case       :							bool = True					,       _snake_case       :							Dict[str, int] = None					,       _snake_case       :							bool = True					,       _snake_case       :							Union[int, float] = 1 / 2_55					,       _snake_case       :							bool = True					,       _snake_case       :							bool = True					,       _snake_case       :							Optional[Union[float, List[float]]] = None					,       _snake_case       :							Optional[Union[float, List[float]]] = None					,       **_snake_case       :							Tuple					,       ):






								"""simple docstring"""


								super().__init__(**_snake_case       )
								A__	  =     size if size is not None else {'shortest_edge': 2_56}
								A__	  =     get_size_dict(_snake_case					,       default_to_square=_snake_case       )
								A__	  =     crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
								A__	  =     get_size_dict(_snake_case					,       param_name='crop_size'       )

								A__	  =     do_resize
								A__	  =     size
								A__	  =     do_center_crop
								A__	  =     crop_size
								A__	  =     resample
								A__	  =     do_rescale
								A__	  =     rescale_factor
								A__	  =     offset
								A__	  =     do_normalize
								A__	  =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								A__	  =     image_std if image_std is not None else IMAGENET_STANDARD_STD



					def      _a				(  self       :							Dict					,       _snake_case       :							np.ndarray					,       _snake_case       :							Dict[str, int]					,       _snake_case       :							PILImageResampling = PILImageResampling.BILINEAR					,       _snake_case       :							Optional[Union[str, ChannelDimension]] = None					,       **_snake_case       :							int					,       ):






								"""simple docstring"""


								A__	  =     get_size_dict(_snake_case					,       default_to_square=_snake_case       )
								if "shortest_edge" in size:
											A__	  =     get_resize_output_image_size(_snake_case					,       size['shortest_edge']					,       default_to_square=_snake_case       )
								elif "height" in size and "width" in size:
											A__	  =     (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       :							Any					,       _snake_case       :							np.ndarray					,       _snake_case       :							Dict[str, int]					,       _snake_case       :							Optional[Union[str, ChannelDimension]] = None					,       **_snake_case       :							str					,       ):






								"""simple docstring"""


								A__	  =     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       :							Optional[Any]					,       _snake_case       :							np.ndarray					,       _snake_case       :							Union[int, float]					,       _snake_case       :							bool = True					,       _snake_case       :							Optional[Union[str, ChannelDimension]] = None					,       **_snake_case       :							str					,       ):






								"""simple docstring"""


								A__	  =     image.astype(np.floataa       )
								if offset:
											A__	  =     image - (scale / 2)
								return rescale(_snake_case					,       scale=_snake_case					,       data_format=_snake_case					,       **_snake_case       )



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






								"""simple docstring"""


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



					def      _a				(  self       :							List[Any]					,       _snake_case       :							ImageInput					,       _snake_case       :							bool = None					,       _snake_case       :							Dict[str, int] = None					,       _snake_case       :							PILImageResampling = None					,       _snake_case       :							bool = None					,       _snake_case       :							Dict[str, int] = None					,       _snake_case       :							bool = None					,       _snake_case       :							float = None					,       _snake_case       :							bool = None					,       _snake_case       :							bool = None					,       _snake_case       :							Optional[Union[float, List[float]]] = None					,       _snake_case       :							Optional[Union[float, List[float]]] = None					,       _snake_case       :							Optional[ChannelDimension] = 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.
								A__	  =     to_numpy_array(_snake_case       )

								if do_resize:
											A__	  =     self.resize(image=_snake_case					,       size=_snake_case					,       resample=_snake_case       )

								if do_center_crop:
											A__	  =     self.center_crop(_snake_case					,       size=_snake_case       )

								if do_rescale:
											A__	  =     self.rescale(image=_snake_case					,       scale=_snake_case					,       offset=_snake_case       )

								if do_normalize:
											A__	  =     self.normalize(image=_snake_case					,       mean=_snake_case					,       std=_snake_case       )

								A__	  =     to_channel_dimension_format(_snake_case					,       _snake_case       )
								return image



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






								"""simple docstring"""


								A__	  =     do_resize if do_resize is not None else self.do_resize
								A__	  =     resample if resample is not None else self.resample
								A__	  =     do_center_crop if do_center_crop is not None else self.do_center_crop
								A__	  =     do_rescale if do_rescale is not None else self.do_rescale
								A__	  =     rescale_factor if rescale_factor is not None else self.rescale_factor
								A__	  =     offset if offset is not None else self.offset
								A__	  =     do_normalize if do_normalize is not None else self.do_normalize
								A__	  =     image_mean if image_mean is not None else self.image_mean
								A__	  =     image_std if image_std is not None else self.image_std

								A__	  =     size if size is not None else self.size
								A__	  =     get_size_dict(_snake_case					,       default_to_square=_snake_case       )
								A__	  =     crop_size if crop_size is not None else self.crop_size
								A__	  =     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.'       )

								A__	  =     make_batched(_snake_case       )

								A__	  =     [
								    [
								        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
								]

								A__	  =     {'pixel_values': videos}
								return BatchFeature(data=_snake_case					,       tensor_type=_snake_case       )








import os
import sys

lowerCamelCase__							:						Optional[int]							     =   os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowerCamelCase__							:						Optional[int]							     =   [
    """torch""",
    """numpy""",
    """tokenizers""",
    """filelock""",
    """requests""",
    """tqdm""",
    """regex""",
    """sentencepiece""",
    """sacremoses""",
    """importlib_metadata""",
    """huggingface_hub""",
]







@add_start_docstrings(AutoConfig.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Any:
  return AutoConfig.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoTokenizer.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				List[str]:
  return AutoTokenizer.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModel.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Tuple:
  return AutoModel.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForCausalLM.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Union[str, Any]:
  return AutoModelForCausalLM.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForMaskedLM.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				List[Any]:
  return AutoModelForMaskedLM.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForSequenceClassification.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				List[str]:
  return AutoModelForSequenceClassification.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Union[str, Any]:
  return AutoModelForQuestionAnswering.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)

