





"""simple docstring"""





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 							snake_case							(			lowerCamelCase__      ,  lowerCamelCase__      ,  lowerCamelCase__      ,  unittest.TestCase ):

      '''simple docstring'''



      _A     :       Optional[int]         =       AltDiffusionPipeline
      _A     :       Optional[int]         =       TEXT_TO_IMAGE_PARAMS
      _A     :       Dict         =       TEXT_TO_IMAGE_BATCH_PARAMS
      _A     :       Dict         =       TEXT_TO_IMAGE_IMAGE_PARAMS
      _A     :       Optional[Any]         =       TEXT_TO_IMAGE_IMAGE_PARAMS

      def       A_					(							self       :      Union[str, Any] ):


             '''simple docstring'''




             torch.manual_seed(0 )
             __UpperCAmelCase    :						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						,				)
             __UpperCAmelCase    :						List[str]       =						DDIMScheduler(
                 beta_start=0.0_0_0_8_5						,				beta_end=0.0_1_2						,				beta_schedule='''scaled_linear'''						,				clip_sample=__lowerCamelCase						,				set_alpha_to_one=__lowerCamelCase						,				)
             torch.manual_seed(0 )
             __UpperCAmelCase    :						Optional[Any]       =						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 )
             __UpperCAmelCase    :						Dict       =						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						,				)
             __UpperCAmelCase    :						int       =						CLIPTextModel(__lowerCamelCase )

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

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

      def       A_					(							self       :      int						,				__lowercase       :      List[Any]						,				__lowercase       :      Optional[int]=0 ):


             '''simple docstring'''




             if str(__lowerCamelCase ).startswith('''mps''' ):
                    __UpperCAmelCase    :						Tuple       =						torch.manual_seed(__lowerCamelCase )
             else:
                    __UpperCAmelCase    :						Any       =						torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
             __UpperCAmelCase    :						Optional[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       A_					(							self       :      Tuple ):


             '''simple docstring'''




             super().test_attention_slicing_forward_pass(expected_max_diff=3e-3 )

      def       A_					(							self       :      str ):


             '''simple docstring'''




             super().test_inference_batch_single_identical(expected_max_diff=3e-3 )

      def       A_					(							self       :      str ):


             '''simple docstring'''




             __UpperCAmelCase    :						List[str]       =						'''cpu'''  # ensure determinism for the device-dependent torch.Generator

             __UpperCAmelCase    :						int       =						self.get_dummy_components()
             torch.manual_seed(0 )
             __UpperCAmelCase    :						str       =						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
             __UpperCAmelCase    :						Any       =						RobertaSeriesModelWithTransformation(__lowerCamelCase )
             __UpperCAmelCase    :						List[str]       =						text_encoder

             __UpperCAmelCase    :						List[Any]       =						AltDiffusionPipeline(**__lowerCamelCase )
             __UpperCAmelCase    :						Optional[Any]       =						alt_pipe.to(__lowerCamelCase )
             alt_pipe.set_progress_bar_config(disable=__lowerCamelCase )

             __UpperCAmelCase    :						List[str]       =						self.get_dummy_inputs(__lowerCamelCase )
             __UpperCAmelCase    :						str       =						'''A photo of an astronaut'''
             __UpperCAmelCase    :						Any       =						alt_pipe(**__lowerCamelCase )
             __UpperCAmelCase    :						int       =						output.images
             __UpperCAmelCase    :						List[Any]       =						image[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)
             __UpperCAmelCase    :						Optional[Any]       =						np.array(
                 [0.5_7_4_8_1_6_2, 0.6_0_4_4_7_1_4_5, 0.4_8_8_2_1_2_1_7, 0.5_0_1_0_0_6_3_6, 0.5_4_3_1_1_8_5, 0.4_5_7_6_3_6_8_3, 0.4_9_6_5_7_6_9_6, 0.4_8_1_3_2_7_3_3, 0.4_7_5_7_3_0_9_3] )

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







      def       A_					(							self       :      List[Any] ):


             '''simple docstring'''




             __UpperCAmelCase    :						List[Any]       =						'''cpu'''  # ensure determinism for the device-dependent torch.Generator

             __UpperCAmelCase    :						str       =						self.get_dummy_components()
             __UpperCAmelCase    :						Optional[Any]       =						PNDMScheduler(skip_prk_steps=__lowerCamelCase )
             torch.manual_seed(0 )
             __UpperCAmelCase    :						int       =						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
             __UpperCAmelCase    :						Tuple       =						RobertaSeriesModelWithTransformation(__lowerCamelCase )
             __UpperCAmelCase    :						List[str]       =						text_encoder
             __UpperCAmelCase    :						List[Any]       =						AltDiffusionPipeline(**__lowerCamelCase )
             __UpperCAmelCase    :						str       =						alt_pipe.to(__lowerCamelCase )
             alt_pipe.set_progress_bar_config(disable=__lowerCamelCase )

             __UpperCAmelCase    :						Union[str, Any]       =						self.get_dummy_inputs(__lowerCamelCase )
             __UpperCAmelCase    :						Optional[int]       =						alt_pipe(**__lowerCamelCase )
             __UpperCAmelCase    :						Optional[int]       =						output.images
             __UpperCAmelCase    :						str       =						image[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)
             __UpperCAmelCase    :						Union[str, Any]       =						np.array(
                 [0.5_1_6_0_5_0_9_3, 0.5_7_0_7_2_4_1, 0.4_7_3_6_5_5_0_7, 0.5_0_5_7_8_8_8_6, 0.5_6_3_3_8_7_7, 0.4_6_4_2_5_0_3, 0.5_1_8_2_0_8_1, 0.4_8_7_6_3_4_8_4, 0.4_9_0_8_4_2_3_7] )

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




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

      '''simple docstring'''



      def       A_					(							self       :      List[Any] ):


             '''simple docstring'''




             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()

      def       A_					(							self       :      List[str] ):


             '''simple docstring'''




             __UpperCAmelCase    :						Union[str, Any]       =						AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''						,				safety_checker=__lowerCamelCase )
             __UpperCAmelCase    :						List[Any]       =						alt_pipe.to(__lowerCamelCase )
             alt_pipe.set_progress_bar_config(disable=__lowerCamelCase )

             __UpperCAmelCase    :						Any       =						'''A painting of a squirrel eating a burger'''
             __UpperCAmelCase    :						List[Any]       =						torch.manual_seed(0 )
             __UpperCAmelCase    :						str       =						alt_pipe([prompt]						,				generator=__lowerCamelCase						,				guidance_scale=6.0						,				num_inference_steps=20						,				output_type='''np''' )

             __UpperCAmelCase    :						str       =						output.images

             __UpperCAmelCase    :						int       =						image[0, -3:, -3:, -1]

             assert image.shape == (1, 512, 512, 3)
             __UpperCAmelCase    :						Dict       =						np.array([0.1_0_1_0, 0.0_8_0_0, 0.0_7_9_4, 0.0_8_8_5, 0.0_8_4_3, 0.0_7_6_2, 0.0_7_6_9, 0.0_7_2_9, 0.0_5_8_6] )

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







      def       A_					(							self       :      List[str] ):


             '''simple docstring'''




             __UpperCAmelCase    :						List[Any]       =						DDIMScheduler.from_pretrained('''BAAI/AltDiffusion'''						,				subfolder='''scheduler''' )

             __UpperCAmelCase    :						List[str]       =						AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''						,				scheduler=__lowerCamelCase						,				safety_checker=__lowerCamelCase )
             __UpperCAmelCase    :						Union[str, Any]       =						alt_pipe.to(__lowerCamelCase )
             alt_pipe.set_progress_bar_config(disable=__lowerCamelCase )

             __UpperCAmelCase    :						List[Any]       =						'''A painting of a squirrel eating a burger'''
             __UpperCAmelCase    :						Optional[Any]       =						torch.manual_seed(0 )

             __UpperCAmelCase    :						List[str]       =						alt_pipe([prompt]						,				generator=__lowerCamelCase						,				num_inference_steps=2						,				output_type='''numpy''' )
             __UpperCAmelCase    :						List[str]       =						output.images

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

             assert image.shape == (1, 512, 512, 3)
             __UpperCAmelCase    :						List[Any]       =						np.array([0.4_0_1_9, 0.4_0_5_2, 0.3_8_1_0, 0.4_1_1_9, 0.3_9_1_6, 0.3_9_8_2, 0.4_6_5_1, 0.4_1_9_5, 0.5_3_2_3] )

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






"""simple docstring"""





def       lowerCamelCase_       (   UpperCAmelCase_  )						->float:





  """simple docstring"""


  return 10 - x * x


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->float:





  """simple docstring"""


  if equation(UpperCAmelCase_  ) * equation(UpperCAmelCase_  ) >= 0:
    raise ValueError('''Wrong space!'''  )

  __UpperCAmelCase    :						Tuple       =						a
  while (b - a) >= 0.01:
    # Find middle point
    __UpperCAmelCase    :						List[str]       =						(a + b) / 2
    # Check if middle point is root
    if equation(UpperCAmelCase_  ) == 0.0:
      break
    # Decide the side to repeat the steps
    if equation(UpperCAmelCase_  ) * equation(UpperCAmelCase_  ) < 0:
      __UpperCAmelCase    :						Union[str, Any]       =						c
    else:
      __UpperCAmelCase    :						str       =						c
  return c


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(bisection(-2, 5))
  print(bisection(0, 6))