






'''simple docstring'''

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 YolosImageProcessor




class     A    (      unittest.TestCase						):






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase=7			,     __lowerCAmelCase=3			,     __lowerCAmelCase=30			,     __lowerCAmelCase=4_00			,     __lowerCAmelCase=True			,     __lowerCAmelCase=None			,     __lowerCAmelCase=True			,     __lowerCAmelCase=[0.5, 0.5, 0.5]			,     __lowerCAmelCase=[0.5, 0.5, 0.5]			,     __lowerCAmelCase=True			,     __lowerCAmelCase=1 / 2_55			,     __lowerCAmelCase=True			,     ):
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       UpperCamelCase_				:   Optional[Any]					    =							size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
       UpperCamelCase_				:   Union[str, Any]					    =							parent
       UpperCamelCase_				:   Dict					    =							batch_size
       UpperCamelCase_				:   Optional[int]					    =							num_channels
       UpperCamelCase_				:   int					    =							min_resolution
       UpperCamelCase_				:   Union[str, Any]					    =							max_resolution
       UpperCamelCase_				:   Union[str, Any]					    =							do_resize
       UpperCamelCase_				:   List[str]					    =							size
       UpperCamelCase_				:   Optional[int]					    =							do_normalize
       UpperCamelCase_				:   List[Any]					    =							image_mean
       UpperCamelCase_				:   Dict					    =							image_std
       UpperCamelCase_				:   Union[str, Any]					    =							do_rescale
       UpperCamelCase_				:   Any					    =							rescale_factor
       UpperCamelCase_				:   List[str]					    =							do_pad
 def   _UpperCAmelCase       (							self ):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_pad": self.do_pad,
       }





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase=False ):
       if not batched:
             UpperCamelCase_				:   Optional[int]					    =							image_inputs[0]
             if isinstance(snake_case_			,     Image.Image ):
                   UpperCamelCase_    ,			UpperCamelCase_				:   Tuple					    =							image.size
             else:
                   UpperCamelCase_    ,			UpperCamelCase_				:   Tuple					    =							image.shape[1], image.shape[2]
             if w < h:
                   UpperCamelCase_				:   Tuple					    =							int(self.size["""shortest_edge"""] * h / w )
                   UpperCamelCase_				:   Optional[int]					    =							self.size["""shortest_edge"""]
             elif w > h:
                   UpperCamelCase_				:   List[Any]					    =							self.size["""shortest_edge"""]
                   UpperCamelCase_				:   int					    =							int(self.size["""shortest_edge"""] * w / h )
             else:
                   UpperCamelCase_				:   Optional[int]					    =							self.size["""shortest_edge"""]
                   UpperCamelCase_				:   Any					    =							self.size["""shortest_edge"""]

       else:
             UpperCamelCase_				:   Optional[int]					    =							[]
             for image in image_inputs:
                   UpperCamelCase_    ,			UpperCamelCase_				:   Dict					    =							self.get_expected_values([image] )
                   expected_values.append((expected_height, expected_width) )
             UpperCamelCase_				:   Dict					    =							max(snake_case_			,     key=lambda __lowerCAmelCase : item[0] )[0]
             UpperCamelCase_				:   Union[str, Any]					    =							max(snake_case_			,     key=lambda __lowerCAmelCase : item[1] )[1]

       return expected_height, expected_width






@require_torch
@require_vision
class     A    (      _a,   unittest.TestCase						):






 """simple docstring"""





 __a			:						Tuple      =						YolosImageProcessor if is_vision_available() else None
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   str					    =							YolosImageProcessingTester(self )
 @property
 def   _UpperCAmelCase       (							self ):
       return self.image_processor_tester.prepare_image_processor_dict()
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(snake_case_			,     """image_mean""" ) )
       self.assertTrue(hasattr(snake_case_			,     """image_std""" ) )
       self.assertTrue(hasattr(snake_case_			,     """do_normalize""" ) )
       self.assertTrue(hasattr(snake_case_			,     """do_resize""" ) )
       self.assertTrue(hasattr(snake_case_			,     """size""" ) )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   int					    =							self.image_processing_class.from_dict(self.image_processor_dict )
       self.assertEqual(image_processor.size			,     {"""shortest_edge""": 18, """longest_edge""": 13_33} )
       self.assertEqual(image_processor.do_pad			,     snake_case_ )

       UpperCamelCase_				:   Any					    =							self.image_processing_class.from_dict(
           self.image_processor_dict			,     size=42			,     max_size=84			,     pad_and_return_pixel_mask=snake_case_ )
       self.assertEqual(image_processor.size			,     {"""shortest_edge""": 42, """longest_edge""": 84} )
       self.assertEqual(image_processor.do_pad			,     snake_case_ )
 def   _UpperCAmelCase       (							self ):
       pass
 def   _UpperCAmelCase       (							self ):
       # Initialize image_processing
       UpperCamelCase_				:   str					    =							self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       UpperCamelCase_				:   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
       UpperCamelCase_				:   Tuple					    =							image_processing(image_inputs[0]			,     return_tensors="""pt""" ).pixel_values

       UpperCamelCase_    ,			UpperCamelCase_				:   List[Any]					    =							self.image_processor_tester.get_expected_values(snake_case_ )

       self.assertEqual(
           encoded_images.shape			,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,     )

       # Test batched
       UpperCamelCase_    ,			UpperCamelCase_				:   Union[str, Any]					    =							self.image_processor_tester.get_expected_values(snake_case_			,     batched=snake_case_ )

       UpperCamelCase_				:   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,
               expected_height,
               expected_width,
           )			,     )
 def   _UpperCAmelCase       (							self ):
       # Initialize image_processing
       UpperCamelCase_				:   str					    =							self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       UpperCamelCase_				:   Any					    =							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
       UpperCamelCase_				:   Dict					    =							image_processing(image_inputs[0]			,     return_tensors="""pt""" ).pixel_values

       UpperCamelCase_    ,			UpperCamelCase_				:   int					    =							self.image_processor_tester.get_expected_values(snake_case_ )

       self.assertEqual(
           encoded_images.shape			,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,     )

       # Test batched
       UpperCamelCase_				:   Optional[int]					    =							image_processing(snake_case_			,     return_tensors="""pt""" ).pixel_values

       UpperCamelCase_    ,			UpperCamelCase_				:   Optional[int]					    =							self.image_processor_tester.get_expected_values(snake_case_			,     batched=snake_case_ )

       self.assertEqual(
           encoded_images.shape			,     (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )			,     )
 def   _UpperCAmelCase       (							self ):
       # Initialize image_processing
       UpperCamelCase_				:   List[Any]					    =							self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       UpperCamelCase_				:   Dict					    =							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
       UpperCamelCase_				:   Union[str, Any]					    =							image_processing(image_inputs[0]			,     return_tensors="""pt""" ).pixel_values

       UpperCamelCase_    ,			UpperCamelCase_				:   str					    =							self.image_processor_tester.get_expected_values(snake_case_ )

       self.assertEqual(
           encoded_images.shape			,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,     )

       # Test batched
       UpperCamelCase_				:   List[Any]					    =							image_processing(snake_case_			,     return_tensors="""pt""" ).pixel_values

       UpperCamelCase_    ,			UpperCamelCase_				:   Optional[Any]					    =							self.image_processor_tester.get_expected_values(snake_case_			,     batched=snake_case_ )

       self.assertEqual(
           encoded_images.shape			,     (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )			,     )
 def   _UpperCAmelCase       (							self ):
       # Initialize image_processings
       UpperCamelCase_				:   str					    =							self.image_processing_class(**self.image_processor_dict )
       UpperCamelCase_				:   Optional[Any]					    =							self.image_processing_class(do_resize=snake_case_			,     do_normalize=snake_case_			,     do_rescale=snake_case_ )
       # create random PyTorch tensors
       UpperCamelCase_				:   List[Any]					    =							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 whether the method "pad" and calling the image processor return the same tensors
       UpperCamelCase_				:   List[Any]					    =							image_processing_a.pad(snake_case_			,     return_tensors="""pt""" )
       UpperCamelCase_				:   Optional[Any]					    =							image_processing_a(snake_case_			,     return_tensors="""pt""" )

       self.assertTrue(
           torch.allclose(encoded_images_with_method["""pixel_values"""]			,     encoded_images["""pixel_values"""]			,     atol=1E-4 ) )
 @slow
 def   _UpperCAmelCase       (							self ):
       # prepare image and target
       UpperCamelCase_				:   Union[str, Any]					    =							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
       with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""			,     """r""" ) as f:
             UpperCamelCase_				:   Optional[int]					    =							json.loads(f.read() )

       UpperCamelCase_				:   int					    =							{"""image_id""": 3_97_69, """annotations""": target}

       # encode them
       UpperCamelCase_				:   Dict					    =							YolosImageProcessor.from_pretrained("""hustvl/yolos-small""" )
       UpperCamelCase_				:   Tuple					    =							image_processing(images=snake_case_			,     annotations=snake_case_			,     return_tensors="""pt""" )

       # verify pixel values
       UpperCamelCase_				:   Dict					    =							torch.Size([1, 3, 8_00, 10_66] )
       self.assertEqual(encoding["""pixel_values"""].shape			,     snake_case_ )

       UpperCamelCase_				:   List[Any]					    =							torch.tensor([0.27_96, 0.31_38, 0.34_81] )
       self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]			,     snake_case_			,     atol=1E-4 ) )

       # verify area
       UpperCamelCase_				:   Dict					    =							torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38] )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]			,     snake_case_ ) )
       # verify boxes
       UpperCamelCase_				:   Union[str, Any]					    =							torch.Size([6, 4] )
       self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape			,     snake_case_ )
       UpperCamelCase_				:   Optional[Any]					    =							torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15] )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]			,     snake_case_			,     atol=1E-3 ) )
       # verify image_id
       UpperCamelCase_				:   Union[str, Any]					    =							torch.tensor([3_97_69] )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]			,     snake_case_ ) )
       # verify is_crowd
       UpperCamelCase_				:   List[str]					    =							torch.tensor([0, 0, 0, 0, 0, 0] )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]			,     snake_case_ ) )
       # verify class_labels
       UpperCamelCase_				:   Optional[int]					    =							torch.tensor([75, 75, 63, 65, 17, 17] )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]			,     snake_case_ ) )
       # verify orig_size
       UpperCamelCase_				:   Tuple					    =							torch.tensor([4_80, 6_40] )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]			,     snake_case_ ) )
       # verify size
       UpperCamelCase_				:   Dict					    =							torch.tensor([8_00, 10_66] )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]			,     snake_case_ ) )





 @slow
 def   _UpperCAmelCase       (							self ):
       # prepare image, target and masks_path
       UpperCamelCase_				:   Any					    =							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
       with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""			,     """r""" ) as f:
             UpperCamelCase_				:   str					    =							json.loads(f.read() )

       UpperCamelCase_				:   List[str]					    =							{"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

       UpperCamelCase_				:   int					    =							pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )

       # encode them
       UpperCamelCase_				:   List[Any]					    =							YolosImageProcessor(format="""coco_panoptic""" )
       UpperCamelCase_				:   Optional[Any]					    =							image_processing(images=snake_case_			,     annotations=snake_case_			,     masks_path=snake_case_			,     return_tensors="""pt""" )

       # verify pixel values
       UpperCamelCase_				:   Tuple					    =							torch.Size([1, 3, 8_00, 10_66] )
       self.assertEqual(encoding["""pixel_values"""].shape			,     snake_case_ )

       UpperCamelCase_				:   List[str]					    =							torch.tensor([0.27_96, 0.31_38, 0.34_81] )
       self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]			,     snake_case_			,     atol=1E-4 ) )

       # verify area
       UpperCamelCase_				:   Any					    =							torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47] )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]			,     snake_case_ ) )
       # verify boxes
       UpperCamelCase_				:   str					    =							torch.Size([6, 4] )
       self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape			,     snake_case_ )
       UpperCamelCase_				:   List[str]					    =							torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25] )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]			,     snake_case_			,     atol=1E-3 ) )
       # verify image_id
       UpperCamelCase_				:   str					    =							torch.tensor([3_97_69] )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]			,     snake_case_ ) )
       # verify is_crowd
       UpperCamelCase_				:   List[str]					    =							torch.tensor([0, 0, 0, 0, 0, 0] )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]			,     snake_case_ ) )
       # verify class_labels
       UpperCamelCase_				:   Dict					    =							torch.tensor([17, 17, 63, 75, 75, 93] )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]			,     snake_case_ ) )
       # verify masks
       UpperCamelCase_				:   List[Any]					    =							82_28_73
       self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()			,     snake_case_ )
       # verify orig_size
       UpperCamelCase_				:   str					    =							torch.tensor([4_80, 6_40] )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]			,     snake_case_ ) )
       # verify size
       UpperCamelCase_				:   List[str]					    =							torch.tensor([8_00, 10_66] )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]			,     snake_case_ ) )














'''simple docstring'''

from __future__ import annotations

import os
from typing import Any

import requests

UpperCamelCase							="https://api.github.com"

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

# https://github.com/settings/tokens
UpperCamelCase							=os.environ.get("USER_TOKEN", "")



def 							snake_case					(     a_							: str	)       ->				dict[Any, Any]:





      """simple docstring"""

      UpperCamelCase_				:   Tuple					    =							{
          """Authorization""": f"token {auth_token}",
          """Accept""": """application/vnd.github.v3+json""",
      }
      return requests.get(a_    ,   headers=a_	).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.")






