
"""simple docstring"""

import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		IFInpaintingSuperResolutionPipeline
     lowercase		     =		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
     lowercase		     =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}    )
     lowercase		     =		PipelineTesterMixin.required_optional_params - {"latents"}





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self._get_superresolution_dummy_components()





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase=0   ):

      '''simple docstring'''




      if str(__UpperCAmelCase   ).startswith('mps'   ):
       __UpperCamelCase       						= torch.manual_seed(__UpperCAmelCase   )
      else:
       __UpperCamelCase       						= torch.Generator(device=__UpperCAmelCase   ).manual_seed(__UpperCAmelCase   )

      __UpperCamelCase       						= floats_tensor((1, 3, 16, 16)			,  rng=random.Random(__UpperCAmelCase   )   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= floats_tensor((1, 3, 32, 32)			,  rng=random.Random(__UpperCAmelCase   )   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= floats_tensor((1, 3, 32, 32)			,  rng=random.Random(__UpperCAmelCase   )   ).to(__UpperCAmelCase   )

      __UpperCamelCase       						= {
          'prompt': 'A painting of a squirrel eating a burger',
          'image': image,
          'original_image': original_image,
          'mask_image': mask_image,
          'generator': generator,
          'num_inference_steps': 2,
          'output_type': 'numpy',
      }

      return inputs





     @unittest.skipIf(
         torch_device != 'cuda' or not is_xformers_available()			,  reason='XFormers attention is only available with CUDA and `xformers` installed'			,  )
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      self._test_save_load_optional_components()





     @unittest.skipIf(torch_device != 'cuda'			,  reason='float16 requires CUDA'   )
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      super().test_save_load_floataa(expected_max_diff=1E-1   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      self._test_save_load_local()







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2			,  )





"""simple docstring"""

from __future__ import annotations

import os
from typing import Any

import requests

UpperCamelCase			:       Union[str, Any]									=				"https://api.github.com"

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

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






def 							A		(       snake_case   :str	)   ->			dict[Any, Any]:
 __UpperCamelCase       						= {
     'Authorization': f'token {auth_token}',
     'Accept': 'application/vnd.github.v3+json',
 }
 return requests.get(snake_case       ,      headers=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.")



