


import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class   SCREAMING_SNAKE_CASE_			(a__						,       a__						,       a__						,       unittest.TestCase						):




       '''simple docstring'''



       _a   							=							AltDiffusionPipeline
       _a   							=							TEXT_TO_IMAGE_PARAMS
       _a   							=							TEXT_TO_IMAGE_BATCH_PARAMS
       _a   							=							TEXT_TO_IMAGE_IMAGE_PARAMS
       _a   							=							TEXT_TO_IMAGE_IMAGE_PARAMS



       def 				_lowerCAmelCase							(						self				:				Any							)					->Tuple:
          torch.manual_seed(0							)
          lowerCamelCase_						:   int		= UNetaDConditionModel(
              block_out_channels=(32, 64)		,				layers_per_block=2		,				sample_size=32		,				in_channels=4		,				out_channels=4		,				down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")		,				up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")		,				cross_attention_dim=32		,				)
          lowerCamelCase_						:   Tuple		= DDIMScheduler(
              beta_start=0.00_085		,				beta_end=0.012		,				beta_schedule="""scaled_linear"""		,				clip_sample=__a		,				set_alpha_to_one=__a		,				)
          torch.manual_seed(0							)
          lowerCamelCase_						:   int		= AutoencoderKL(
              block_out_channels=[32, 64]		,				in_channels=3		,				out_channels=3		,				down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]		,				up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]		,				latent_channels=4		,				)

          # TODO: address the non-deterministic text encoder (fails for save-load tests)
          # torch.manual_seed(0)
          # text_encoder_config = RobertaSeriesConfig(
          #     hidden_size=32,
          #     project_dim=32,
          #     intermediate_size=37,
          #     layer_norm_eps=1e-05,
          #     num_attention_heads=4,
          #     num_hidden_layers=5,
          #     vocab_size=5002,
          # )
          # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

          torch.manual_seed(0							)
          lowerCamelCase_						:   str		= CLIPTextConfig(
              bos_token_id=0		,				eos_token_id=2		,				hidden_size=32		,				projection_dim=32		,				intermediate_size=37		,				layer_norm_eps=1e-05		,				num_attention_heads=4		,				num_hidden_layers=5		,				pad_token_id=1		,				vocab_size=5_002		,				)
          lowerCamelCase_						:   Dict		= CLIPTextModel(__a							)

          lowerCamelCase_						:   Union[str, Any]		= XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""							)
          lowerCamelCase_						:   str		= 77

          lowerCamelCase_						:   str		= {
              """unet""": unet,
              """scheduler""": scheduler,
              """vae""": vae,
              """text_encoder""": text_encoder,
              """tokenizer""": tokenizer,
              """safety_checker""": None,
              """feature_extractor""": None,
          }
          return components



       def 				_lowerCAmelCase							(						self				:				List[str]		,				__a				:				str		,				__a				:				Dict=0							)					->str:
          if str(__a							).startswith("""mps"""							):
             lowerCamelCase_						:   List[str]		= torch.manual_seed(__a							)
          else:
             lowerCamelCase_						:   Union[str, Any]		= torch.Generator(device=__a							).manual_seed(__a							)
          lowerCamelCase_						:   List[Any]		= {
              """prompt""": """A painting of a squirrel eating a burger""",
              """generator""": generator,
              """num_inference_steps""": 2,
              """guidance_scale""": 6.0,
              """output_type""": """numpy""",
          }
          return inputs



       def 				_lowerCAmelCase							(						self				:				List[str]							)					->int:
          super().test_attention_slicing_forward_pass(expected_max_diff=3e-3							)



       def 				_lowerCAmelCase							(						self				:				Any							)					->Tuple:
          super().test_inference_batch_single_identical(expected_max_diff=3e-3							)



       def 				_lowerCAmelCase							(						self				:				Optional[Any]							)					->Tuple:
          lowerCamelCase_						:   Any		= """cpu"""  # ensure determinism for the device-dependent torch.Generator

          lowerCamelCase_						:   List[str]		= self.get_dummy_components()
          torch.manual_seed(0							)
          lowerCamelCase_						:   Any		= RobertaSeriesConfig(
              hidden_size=32		,				project_dim=32		,				intermediate_size=37		,				layer_norm_eps=1e-05		,				num_attention_heads=4		,				num_hidden_layers=5		,				vocab_size=5_002		,				)
          # TODO: remove after fixing the non-deterministic text encoder
          lowerCamelCase_						:   List[Any]		= RobertaSeriesModelWithTransformation(__a							)
          lowerCamelCase_						:   Union[str, Any]		= text_encoder

          lowerCamelCase_						:   Dict		= AltDiffusionPipeline(**__a							)
          lowerCamelCase_						:   List[Any]		= alt_pipe.to(__a							)
          alt_pipe.set_progress_bar_config(disable=__a							)

          lowerCamelCase_						:   str		= self.get_dummy_inputs(__a							)
          lowerCamelCase_						:   Any		= """A photo of an astronaut"""
          lowerCamelCase_						:   List[Any]		= alt_pipe(**__a							)
          lowerCamelCase_						:   Tuple		= output.images
          lowerCamelCase_						:   str		= image[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)
          lowerCamelCase_						:   str		= np.array(
              [0.5_748_162, 0.60_447_145, 0.48_821_217, 0.50_100_636, 0.5_431_185, 0.45_763_683, 0.49_657_696, 0.48_132_733, 0.47_573_093]							)

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




       def 				_lowerCAmelCase							(						self				:				List[Any]							)					->List[Any]:
          lowerCamelCase_						:   Optional[Any]		= """cpu"""  # ensure determinism for the device-dependent torch.Generator

          lowerCamelCase_						:   List[Any]		= self.get_dummy_components()
          lowerCamelCase_						:   Optional[Any]		= PNDMScheduler(skip_prk_steps=__a							)
          torch.manual_seed(0							)
          lowerCamelCase_						:   Optional[Any]		= RobertaSeriesConfig(
              hidden_size=32		,				project_dim=32		,				intermediate_size=37		,				layer_norm_eps=1e-05		,				num_attention_heads=4		,				num_hidden_layers=5		,				vocab_size=5_002		,				)
          # TODO: remove after fixing the non-deterministic text encoder
          lowerCamelCase_						:   List[str]		= RobertaSeriesModelWithTransformation(__a							)
          lowerCamelCase_						:   str		= text_encoder
          lowerCamelCase_						:   Dict		= AltDiffusionPipeline(**__a							)
          lowerCamelCase_						:   int		= alt_pipe.to(__a							)
          alt_pipe.set_progress_bar_config(disable=__a							)

          lowerCamelCase_						:   Dict		= self.get_dummy_inputs(__a							)
          lowerCamelCase_						:   Any		= alt_pipe(**__a							)
          lowerCamelCase_						:   List[str]		= output.images
          lowerCamelCase_						:   Dict		= image[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)
          lowerCamelCase_						:   Any		= np.array(
              [0.51_605_093, 0.5_707_241, 0.47_365_507, 0.50_578_886, 0.5_633_877, 0.4_642_503, 0.5_182_081, 0.48_763_484, 0.49_084_237]							)

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







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




       '''simple docstring'''



       def 				_lowerCAmelCase							(						self				:				List[Any]							)					->str:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



       def 				_lowerCAmelCase							(						self				:				Optional[Any]							)					->List[Any]:
          # make sure here that pndm scheduler skips prk
          lowerCamelCase_						:   int		= AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""		,				safety_checker=__a							)
          lowerCamelCase_						:   List[str]		= alt_pipe.to(__a							)
          alt_pipe.set_progress_bar_config(disable=__a							)

          lowerCamelCase_						:   Dict		= """A painting of a squirrel eating a burger"""
          lowerCamelCase_						:   Dict		= torch.manual_seed(0							)
          lowerCamelCase_						:   List[Any]		= alt_pipe([prompt]		,				generator=__a		,				guidance_scale=6.0		,				num_inference_steps=20		,				output_type="""np"""							)

          lowerCamelCase_						:   int		= output.images

          lowerCamelCase_						:   List[str]		= image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          lowerCamelCase_						:   List[str]		= np.array([0.1_010, 0.0_800, 0.0_794, 0.0_885, 0.0_843, 0.0_762, 0.0_769, 0.0_729, 0.0_586]							)

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




       def 				_lowerCAmelCase							(						self				:				List[str]							)					->Union[str, Any]:
          lowerCamelCase_						:   List[str]		= DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""		,				subfolder="""scheduler"""							)

          lowerCamelCase_						:   List[Any]		= AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""		,				scheduler=__a		,				safety_checker=__a							)
          lowerCamelCase_						:   List[Any]		= alt_pipe.to(__a							)
          alt_pipe.set_progress_bar_config(disable=__a							)

          lowerCamelCase_						:   Dict		= """A painting of a squirrel eating a burger"""
          lowerCamelCase_						:   List[str]		= torch.manual_seed(0							)

          lowerCamelCase_						:   Optional[int]		= alt_pipe([prompt]		,				generator=__a		,				num_inference_steps=2		,				output_type="""numpy"""							)
          lowerCamelCase_						:   int		= output.images

          lowerCamelCase_						:   Optional[int]		= image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          lowerCamelCase_						:   Tuple		= np.array([0.4_019, 0.4_052, 0.3_810, 0.4_119, 0.3_916, 0.3_982, 0.4_651, 0.4_195, 0.5_323]							)

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









import requests
from bsa import BeautifulSoup


def    __lowerCamelCase   (      A__						: str = "https://www.worldometers.info/coronavirus"					)     ->    dict:
   lowerCamelCase_						:   List[str]		= BeautifulSoup(requests.get(A__					).text						,						"""html.parser"""					)
   lowerCamelCase_						:   Tuple		= soup.findAll("""h1"""					)
   lowerCamelCase_						:   Tuple		= soup.findAll("""div"""						,						{"""class""": """maincounter-number"""}					)
   keys += soup.findAll("""span"""						,						{"""class""": """panel-title"""}					)
   values += soup.findAll("""div"""						,						{"""class""": """number-table-main"""}					)
   return {key.text.strip(): value.text.strip() for key, value in zip(A__						,						A__					)}


if __name__ == "__main__":
      print('\033[1m' + 'COVID-19 Status of the World' + '\033[0m\n')
      for key, value in world_covidaa_stats().items():
            print(F'{key}\n{value}\n')





