'''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,
    make_list_of_images,
    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__)




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					['''pixel_values''']







	def __init__(    self ,				_lowerCAmelCase = True ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = PILImageResampling.BILINEAR ,				_lowerCAmelCase = True ,				_lowerCAmelCase = 1 / 255 ,				_lowerCAmelCase = True ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				**_lowerCAmelCase ,				)    ->    None:
							'''simple docstring'''






							super().__init__(**UpperCamelCase_						)
							lowercase        =							size if size is not None else {'shortest_edge': 384}
							lowercase        =							get_size_dict(UpperCamelCase_ ,				default_to_square=UpperCamelCase_						)

							lowercase        =							do_resize
							lowercase        =							size
							# Default value set here for backwards compatibility where the value in config is None
							lowercase        =							crop_pct if crop_pct is not None else 224 / 256
							lowercase        =							resample
							lowercase        =							do_rescale
							lowercase        =							rescale_factor
							lowercase        =							do_normalize
							lowercase        =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowercase        =							image_std if image_std is not None else IMAGENET_STANDARD_STD







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase = PILImageResampling.BICUBIC ,				_lowerCAmelCase = None ,				**_lowerCAmelCase ,				)    ->    np.ndarray:
							'''simple docstring'''






							lowercase        =							get_size_dict(UpperCamelCase_ ,				default_to_square=UpperCamelCase_						)
							if "shortest_edge" not in size:
													raise ValueError(F"""Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}"""						)
							lowercase        =							size['shortest_edge']

							if shortest_edge < 384:
													# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
													lowercase        =							int(shortest_edge / crop_pct						)
													lowercase        =							get_resize_output_image_size(UpperCamelCase_ ,				size=UpperCamelCase_ ,				default_to_square=UpperCamelCase_						)
													lowercase        =							resize(image=UpperCamelCase_ ,				size=UpperCamelCase_ ,				resample=UpperCamelCase_ ,				data_format=UpperCamelCase_ ,				**UpperCamelCase_						)
													# then crop to (shortest_edge, shortest_edge)
													return center_crop(image=UpperCamelCase_ ,				size=(shortest_edge, shortest_edge) ,				data_format=UpperCamelCase_ ,				**UpperCamelCase_						)
							else:
													# warping (no cropping) when evaluated at 384 or larger
													return resize(
													    UpperCamelCase_ ,				size=(shortest_edge, shortest_edge) ,				resample=UpperCamelCase_ ,				data_format=UpperCamelCase_ ,				**UpperCamelCase_						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase = None ,				**_lowerCAmelCase ,				)    ->    Dict:
							'''simple docstring'''






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







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase = None ,				**_lowerCAmelCase ,				)    ->    np.ndarray:
							'''simple docstring'''






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







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = ChannelDimension.FIRST ,				**_lowerCAmelCase ,				)    ->    PIL.Image.Image:
							'''simple docstring'''






							lowercase        =							do_resize if do_resize is not None else self.do_resize
							lowercase        =							crop_pct if crop_pct is not None else self.crop_pct
							lowercase        =							resample if resample is not None else self.resample
							lowercase        =							do_rescale if do_rescale is not None else self.do_rescale
							lowercase        =							rescale_factor if rescale_factor is not None else self.rescale_factor
							lowercase        =							do_normalize if do_normalize is not None else self.do_normalize
							lowercase        =							image_mean if image_mean is not None else self.image_mean
							lowercase        =							image_std if image_std is not None else self.image_std

							lowercase        =							size if size is not None else self.size
							lowercase        =							get_size_dict(UpperCamelCase_ ,				default_to_square=UpperCamelCase_						)

							lowercase        =							make_list_of_images(UpperCamelCase_						)

							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."""						)

							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_resize and size["shortest_edge"] < 384 and crop_pct is None:
													raise ValueError("""crop_pct must be specified if size < 384."""						)

							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.
							lowercase        =							[to_numpy_array(UpperCamelCase_						) for image in images]

							if do_resize:
													lowercase        =							[self.resize(image=UpperCamelCase_ ,				size=UpperCamelCase_ ,				crop_pct=UpperCamelCase_ ,				resample=UpperCamelCase_						) for image in images]

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

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

							lowercase        =							[to_channel_dimension_format(UpperCamelCase_ ,				UpperCamelCase_						) for image in images]

							lowercase        =							{'pixel_values': images}
							return BatchFeature(data=UpperCamelCase_ ,				tensor_type=UpperCamelCase_						)







'''simple docstring'''


from __future__ import annotations

import os
from typing import Any

import requests

lowercase_      :				List[str]   					=    '''https://api.github.com'''

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
lowercase_      :				Any   					=    BASE_URL + '''/user'''

# https://github.com/settings/tokens
lowercase_      :				Union[str, Any]   					=    os.environ.get('''USER_TOKEN''', '''''')







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str						):
						lowercase        =							{
						    """Authorization""": F"""token {auth_token}""",
						    """Accept""": """application/vnd.github.v3+json""",
						}
						return requests.get(lowercase_ ,  headers=lowercase_						).json()


if __name__ == "__main__":  # pragma: no cover
						if USER_TOKEN:
												for key, value in fetch_github_info(USER_TOKEN).items():
																		print(f'''{key}: {value}''')
						else:
												raise ValueError('''\'USER_TOKEN\' field cannot be empty.''')


