






'''simple docstring'''

import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class     A    (      unittest.TestCase						):






 """simple docstring"""





 @property
 def   _UpperCAmelCase       (							self ):
       torch.manual_seed(0 )
       UpperCamelCase_				:   Tuple					    =							UNetaDModel(
           block_out_channels=(32, 64)			,     layers_per_block=2			,     sample_size=32			,     in_channels=3			,     out_channels=3			,     down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")			,     up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")			,     )
       return model
 @property
 def   _UpperCAmelCase       (							self ):
       torch.manual_seed(0 )
       UpperCamelCase_				:   Optional[int]					    =							VQModel(
           block_out_channels=[32, 64]			,     in_channels=3			,     out_channels=3			,     down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]			,     up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]			,     latent_channels=3			,     )
       return model
 @property
 def   _UpperCAmelCase       (							self ):
       torch.manual_seed(0 )
       UpperCamelCase_				:   int					    =							CLIPTextConfig(
           bos_token_id=0			,     eos_token_id=2			,     hidden_size=32			,     intermediate_size=37			,     layer_norm_eps=1E-05			,     num_attention_heads=4			,     num_hidden_layers=5			,     pad_token_id=1			,     vocab_size=10_00			,     )
       return CLIPTextModel(__lowerCAmelCase )





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[Any]					    =							self.dummy_uncond_unet
       UpperCamelCase_				:   Union[str, Any]					    =							DDIMScheduler()
       UpperCamelCase_				:   List[str]					    =							self.dummy_vq_model

       UpperCamelCase_				:   Union[str, Any]					    =							LDMPipeline(unet=__lowerCAmelCase			,     vqvae=__lowerCAmelCase			,     scheduler=__lowerCAmelCase )
       ldm.to(__lowerCAmelCase )
       ldm.set_progress_bar_config(disable=__lowerCAmelCase )

       UpperCamelCase_				:   str					    =							torch.manual_seed(0 )
       UpperCamelCase_				:   Tuple					    =							ldm(generator=__lowerCAmelCase			,     num_inference_steps=2			,     output_type="""numpy""" ).images

       UpperCamelCase_				:   Dict					    =							torch.manual_seed(0 )
       UpperCamelCase_				:   Optional[int]					    =							ldm(generator=__lowerCAmelCase			,     num_inference_steps=2			,     output_type="""numpy"""			,     return_dict=__lowerCAmelCase )[0]

       UpperCamelCase_				:   List[str]					    =							image[0, -3:, -3:, -1]
       UpperCamelCase_				:   Optional[int]					    =							image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       UpperCamelCase_				:   List[Any]					    =							np.array([0.85_12, 0.8_18, 0.64_11, 0.68_08, 0.44_65, 0.56_18, 0.46, 0.62_31, 0.51_72] )
       UpperCamelCase_				:   Optional[int]					    =							1E-2 if torch_device != """mps""" else 3E-2

       assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance






@slow
@require_torch
class     A    (      unittest.TestCase						):






 """simple docstring"""





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Tuple					    =							LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256""" )
       ldm.to(__lowerCAmelCase )
       ldm.set_progress_bar_config(disable=__lowerCAmelCase )

       UpperCamelCase_				:   List[str]					    =							torch.manual_seed(0 )
       UpperCamelCase_				:   Union[str, Any]					    =							ldm(generator=__lowerCAmelCase			,     num_inference_steps=5			,     output_type="""numpy""" ).images

       UpperCamelCase_				:   str					    =							image[0, -3:, -3:, -1]

       assert image.shape == (1, 2_56, 2_56, 3)
       UpperCamelCase_				:   Tuple					    =							np.array([0.43_99, 0.4_49_75, 0.4_68_25, 0.4_74, 0.43_59, 0.45_81, 0.4_50_95, 0.43_41, 0.44_47] )
       UpperCamelCase_				:   Optional[int]					    =							1E-2 if torch_device != """mps""" else 3E-2

       assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance














'''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.")






