


'''simple docstring'''


from __future__ import annotations

import os
from typing import Any

import requests

UpperCAmelCase_		:   List[Any]							   =		'https://api.github.com'

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

# https://github.com/settings/tokens
UpperCAmelCase_		:   str							   =		os.environ.get('USER_TOKEN', '')







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      {
				    """Authorization""": f"""token {auth_token}""",
				    """Accept""": """application/vnd.github.v3+json""",
				}
				return requests.get(SCREAMING_SNAKE_CASE__							,      headers=SCREAMING_SNAKE_CASE__     ).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.')








'''simple docstring'''


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import MobileNetVaImageProcessor





class     lowercase__  (						unittest.TestCase					):





							'''simple docstring'''


							def __init__(    self    ,					__snake_case    ,					__snake_case=7    ,					__snake_case=3    ,					__snake_case=18    ,					__snake_case=30    ,					__snake_case=400    ,					__snake_case=True    ,					__snake_case=None    ,					__snake_case=True    ,					__snake_case=None    ,					):
											_SCREAMING_SNAKE_CASE :       Any     		=      size if size is not None else {"""shortest_edge""": 20}
											_SCREAMING_SNAKE_CASE :       int     		=      crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
											_SCREAMING_SNAKE_CASE :       Dict     		=      parent
											_SCREAMING_SNAKE_CASE :       Any     		=      batch_size
											_SCREAMING_SNAKE_CASE :       Any     		=      num_channels
											_SCREAMING_SNAKE_CASE :       Dict     		=      image_size
											_SCREAMING_SNAKE_CASE :       Any     		=      min_resolution
											_SCREAMING_SNAKE_CASE :       Any     		=      max_resolution
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      do_resize
											_SCREAMING_SNAKE_CASE :       Tuple     		=      size
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      do_center_crop
											_SCREAMING_SNAKE_CASE :       str     		=      crop_size






							def UpperCAmelCase_	(    self  ):
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											}





@require_torch
@require_vision
class     lowercase__  (						_snake_case     ,     unittest.TestCase					):





							'''simple docstring'''


							A_							:      Union[str, Any]      	=  MobileNetVaImageProcessor if is_vision_available() else None






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       int     		=      MobileNetVaImageProcessingTester(self  )






							@property
							def UpperCAmelCase_	(    self  ):
											return self.image_processor_tester.prepare_image_processor_dict()






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       str     		=      self.image_processing_class(**self.image_processor_dict  )
											self.assertTrue(hasattr(__snake_case    ,					"""do_resize"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""size"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""do_center_crop"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""crop_size"""  )  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       str     		=      self.image_processing_class.from_dict(self.image_processor_dict  )
											self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 20}  )
											self.assertEqual(image_processor.crop_size    ,					{"""height""": 18, """width""": 18}  )

											_SCREAMING_SNAKE_CASE :       Dict     		=      self.image_processing_class.from_dict(self.image_processor_dict    ,					size=42    ,					crop_size=84  )
											self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 42}  )
											self.assertEqual(image_processor.crop_size    ,					{"""height""": 84, """width""": 84}  )






							def UpperCAmelCase_	(    self  ):
											pass






							def UpperCAmelCase_	(    self  ):
											# Initialize image_processing
											_SCREAMING_SNAKE_CASE :       str     		=      self.image_processing_class(**self.image_processor_dict  )
											# create random PIL images
											_SCREAMING_SNAKE_CASE :       List[str]     		=      prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=__snake_case  )
											for image in image_inputs:
															self.assertIsInstance(__snake_case    ,					Image.Image  )

											# Test not batched input
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values
											self.assertEqual(
											    encoded_images.shape    ,					(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )    ,					)

											# Test batched
											_SCREAMING_SNAKE_CASE :       int     		=      image_processing(__snake_case    ,					return_tensors="""pt"""  ).pixel_values
											self.assertEqual(
											    encoded_images.shape    ,					(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )    ,					)






							def UpperCAmelCase_	(    self  ):
											# Initialize image_processing
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.image_processing_class(**self.image_processor_dict  )
											# create random numpy tensors
											_SCREAMING_SNAKE_CASE :       Tuple     		=      prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=__snake_case    ,					numpify=__snake_case  )
											for image in image_inputs:
															self.assertIsInstance(__snake_case    ,					np.ndarray  )

											# Test not batched input
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values
											self.assertEqual(
											    encoded_images.shape    ,					(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )    ,					)

											# Test batched
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      image_processing(__snake_case    ,					return_tensors="""pt"""  ).pixel_values
											self.assertEqual(
											    encoded_images.shape    ,					(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )    ,					)






							def UpperCAmelCase_	(    self  ):
											# Initialize image_processing
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      self.image_processing_class(**self.image_processor_dict  )
											# create random PyTorch tensors
											_SCREAMING_SNAKE_CASE :       str     		=      prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=__snake_case    ,					torchify=__snake_case  )
											for image in image_inputs:
															self.assertIsInstance(__snake_case    ,					torch.Tensor  )

											# Test not batched input
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values
											self.assertEqual(
											    encoded_images.shape    ,					(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )    ,					)

											# Test batched
											_SCREAMING_SNAKE_CASE :       str     		=      image_processing(__snake_case    ,					return_tensors="""pt"""  ).pixel_values
											self.assertEqual(
											    encoded_images.shape    ,					(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )    ,					)




