

'''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


A											=     logging.get_logger(__name__)





def a(lowercase__	):





	'''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 SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  ["""pixel_values"""]


			def __init__(    self ,				__UpperCamelCase = True ,				__UpperCamelCase = None ,				__UpperCamelCase = PILImageResampling.BILINEAR ,				__UpperCamelCase = True ,				__UpperCamelCase = None ,				__UpperCamelCase = True ,				__UpperCamelCase = 1 / 2_55 ,				__UpperCamelCase = True ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				**__UpperCamelCase ,				):

				"""simple docstring"""


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

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


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase = PILImageResampling.BILINEAR ,				__UpperCamelCase = None ,				**__UpperCamelCase ,				):

				"""simple docstring"""


				snake_case_      		= get_size_dict(__UpperCamelCase ,				default_to_square=__UpperCamelCase							)
				if "shortest_edge" in size:
					snake_case_      		= get_resize_output_image_size(__UpperCamelCase ,				size['shortest_edge'] ,				default_to_square=__UpperCamelCase							)
				elif "height" in size and "width" in size:
					snake_case_      		= (size['height'], size['width'])
				else:
					raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""							)
				return resize(__UpperCamelCase ,				size=__UpperCamelCase ,				resample=__UpperCamelCase ,				data_format=__UpperCamelCase ,				**__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase = None ,				**__UpperCamelCase ,				):

				"""simple docstring"""


				snake_case_      		= get_size_dict(__UpperCamelCase							)
				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(__UpperCamelCase ,				size=(size['height'], size['width']) ,				data_format=__UpperCamelCase ,				**__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase = None ,				**__UpperCamelCase ,				):

				"""simple docstring"""


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


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase = None ,				**__UpperCamelCase ,				):

				"""simple docstring"""


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


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = 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.'							)

				# All transformations expect numpy arrays.
				snake_case_      		= to_numpy_array(__UpperCamelCase							)

				if do_resize:
					snake_case_      		= self.resize(image=__UpperCamelCase ,				size=__UpperCamelCase ,				resample=__UpperCamelCase							)

				if do_center_crop:
					snake_case_      		= self.center_crop(__UpperCamelCase ,				size=__UpperCamelCase							)

				if do_rescale:
					snake_case_      		= self.rescale(image=__UpperCamelCase ,				scale=__UpperCamelCase							)

				if do_normalize:
					snake_case_      		= self.normalize(image=__UpperCamelCase ,				mean=__UpperCamelCase ,				std=__UpperCamelCase							)

				snake_case_      		= to_channel_dimension_format(__UpperCamelCase ,				__UpperCamelCase							)
				return image







			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = ChannelDimension.FIRST ,				**__UpperCamelCase ,				):

				"""simple docstring"""


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

				snake_case_      		= size if size is not None else self.size
				snake_case_      		= get_size_dict(__UpperCamelCase ,				default_to_square=__UpperCamelCase							)
				snake_case_      		= crop_size if crop_size is not None else self.crop_size
				snake_case_      		= get_size_dict(__UpperCamelCase ,				param_name='crop_size'							)

				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.'							)

				snake_case_      		= make_batched(__UpperCamelCase							)

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

				snake_case_      		= {'pixel_values': videos}
				return BatchFeature(data=__UpperCamelCase ,				tensor_type=__UpperCamelCase							)



# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


A											=     '3'

print('Python version:', sys.version)

print('OS platform:', platform.platform())
print('OS architecture:', platform.machine())

try:
		import torch

		print('Torch version:', torch.__version__)
		print('Cuda available:', torch.cuda.is_available())
		print('Cuda version:', torch.version.cuda)
		print('CuDNN version:', torch.backends.cudnn.version())
		print('Number of GPUs available:', torch.cuda.device_count())
except ImportError:
		print('Torch version:', None)

try:
		import transformers

		print('transformers version:', transformers.__version__)
except ImportError:
		print('transformers version:', None)
