





import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class A_					(			_lowerCamelCase ,      unittest.TestCase   ):
   lowerCAmelCase__        = DDIMPipeline
   lowerCAmelCase__        = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
   lowerCAmelCase__        = PipelineTesterMixin.required_optional_params - {
       """num_images_per_prompt""",
       """latents""",
       """callback""",
       """callback_steps""",
   }
   lowerCAmelCase__        = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
   lowerCAmelCase__        = False




   def _lowerCAmelCase      (self	:List[Any] )->					Optional[int]:
         torch.manual_seed(0 )
         __A          =				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''')    ,   )
         __A          =				DDIMScheduler()
         __A          =				{'''unet''': unet, '''scheduler''': scheduler}
         return components




   def _lowerCAmelCase      (self	:Optional[int]    ,   _UpperCamelCase	:Dict    ,   _UpperCamelCase	:Optional[Any]=0 )->					Any:
         if str(_UpperCamelCase ).startswith('''mps''' ):
               __A          =				torch.manual_seed(_UpperCamelCase )
         else:
               __A          =				torch.Generator(device=_UpperCamelCase ).manual_seed(_UpperCamelCase )
         __A          =				{
             '''batch_size''': 1,
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }
         return inputs




   def _lowerCAmelCase      (self	:Dict )->					Dict:
         __A          =				'''cpu'''

         __A          =				self.get_dummy_components()
         __A          =				self.pipeline_class(**_UpperCamelCase )
         pipe.to(_UpperCamelCase )
         pipe.set_progress_bar_config(disable=_UpperCamelCase )

         __A          =				self.get_dummy_inputs(_UpperCamelCase )
         __A          =				pipe(**_UpperCamelCase ).images
         __A          =				image[0, -3:, -3:, -1]

         self.assertEqual(image.shape    ,   (1, 32, 32, 3) )
         __A          =				np.array(
             [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04] )
         __A          =				np.abs(image_slice.flatten() - expected_slice ).max()
         self.assertLessEqual(_UpperCamelCase    ,   1e-3 )




   def _lowerCAmelCase      (self	:List[Any] )->					List[str]:
         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )




   def _lowerCAmelCase      (self	:Dict )->					List[Any]:
         super().test_save_load_local(expected_max_difference=3e-3 )




   def _lowerCAmelCase      (self	:Optional[int] )->					Union[str, Any]:
         super().test_save_load_optional_components(expected_max_difference=3e-3 )






   def _lowerCAmelCase      (self	:List[str] )->					Union[str, Any]:
         super().test_inference_batch_single_identical(expected_max_diff=3e-3 )





@slow
@require_torch_gpu
class A_					(			unittest.TestCase   ):




   def _lowerCAmelCase      (self	:Optional[int] )->					Tuple:
         __A          =				'''google/ddpm-cifar10-32'''

         __A          =				UNetaDModel.from_pretrained(_UpperCamelCase )
         __A          =				DDIMScheduler()

         __A          =				DDIMPipeline(unet=_UpperCamelCase    ,   scheduler=_UpperCamelCase )
         ddim.to(_UpperCamelCase )
         ddim.set_progress_bar_config(disable=_UpperCamelCase )

         __A          =				torch.manual_seed(0 )
         __A          =				ddim(generator=_UpperCamelCase    ,   eta=0.0    ,   output_type='''numpy''' ).images

         __A          =				image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         __A          =				np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2






   def _lowerCAmelCase      (self	:Tuple )->					Tuple:
         __A          =				'''google/ddpm-ema-bedroom-256'''

         __A          =				UNetaDModel.from_pretrained(_UpperCamelCase )
         __A          =				DDIMScheduler.from_pretrained(_UpperCamelCase )

         __A          =				DDIMPipeline(unet=_UpperCamelCase    ,   scheduler=_UpperCamelCase )
         ddpm.to(_UpperCamelCase )
         ddpm.set_progress_bar_config(disable=_UpperCamelCase )

         __A          =				torch.manual_seed(0 )
         __A          =				ddpm(generator=_UpperCamelCase    ,   output_type='''numpy''' ).images

         __A          =				image[0, -3:, -3:, -1]

         assert image.shape == (1, 256, 256, 3)
         __A          =				np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2












import requests

snake_case__					:							int													=     'https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='



def _a				(	lowerCamelCase:   str )	->						None:







      '''simple docstring'''






      __A          =				requests.get(_NEWS_API + bbc_news_api_key ).json()
      # each article in the list is a dict
      for i, article in enumerate(bbc_news_page['''articles''']					,				1 ):
            print(F"""{i}.) {article['title']}""" )


if __name__ == "__main__":
       fetch_bbc_news(bbc_news_api_key='<Your BBC News API key goes here>')





