



import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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()

@skip_mps
class 					lowercase						(	__snake_case				, __snake_case				, __snake_case				, unittest.TestCase ):




       '''simple docstring'''







       __SCREAMING_SNAKE_CASE										=					StableDiffusionModelEditingPipeline
       __SCREAMING_SNAKE_CASE										=					TEXT_TO_IMAGE_PARAMS
       __SCREAMING_SNAKE_CASE										=					TEXT_TO_IMAGE_BATCH_PARAMS
       __SCREAMING_SNAKE_CASE										=					TEXT_TO_IMAGE_IMAGE_PARAMS
       __SCREAMING_SNAKE_CASE										=					TEXT_TO_IMAGE_IMAGE_PARAMS






       def 				snake_case_ (						self   )					->		Dict:

           """simple docstring"""
           torch.manual_seed(0   )
           UpperCAmelCase     =    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     =    DDIMScheduler()
           torch.manual_seed(0   )
           UpperCAmelCase     =    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		,       )
           torch.manual_seed(0   )
           UpperCAmelCase     =    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=1000		,       )
           UpperCAmelCase     =    CLIPTextModel(_snake_case   )
           UpperCAmelCase     =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

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






       def 				snake_case_ (						self		,       _snake_case		,       _snake_case=0   )					->		Dict:

           """simple docstring"""
           UpperCAmelCase     =    torch.manual_seed(_snake_case   )
           UpperCAmelCase     =    {
               '''prompt''': '''A field of roses''',
               '''generator''': generator,
               # Setting height and width to None to prevent OOMs on CPU.
               '''height''': None,
               '''width''': None,
               '''num_inference_steps''': 2,
               '''guidance_scale''': 6.0,
               '''output_type''': '''numpy''',
           }
           return inputs






       def 				snake_case_ (						self   )					->		Tuple:

           """simple docstring"""
           UpperCAmelCase     =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           UpperCAmelCase     =    self.get_dummy_components()
           UpperCAmelCase     =    StableDiffusionModelEditingPipeline(**_snake_case   )
           UpperCAmelCase     =    sd_pipe.to(_snake_case   )
           sd_pipe.set_progress_bar_config(disable=_snake_case   )

           UpperCAmelCase     =    self.get_dummy_inputs(_snake_case   )
           UpperCAmelCase     =    sd_pipe(**_snake_case   ).images
           UpperCAmelCase     =    image[0, -3:, -3:, -1]
           assert image.shape == (1, 64, 64, 3)

           UpperCAmelCase     =    np.array([0.4755, 0.5132, 0.4976, 0.3904, 0.3554, 0.4765, 0.5139, 0.5158, 0.4889]   )

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






       def 				snake_case_ (						self   )					->		int:

           """simple docstring"""
           UpperCAmelCase     =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           UpperCAmelCase     =    self.get_dummy_components()
           UpperCAmelCase     =    StableDiffusionModelEditingPipeline(**_snake_case   )
           UpperCAmelCase     =    sd_pipe.to(_snake_case   )
           sd_pipe.set_progress_bar_config(disable=_snake_case   )

           UpperCAmelCase     =    self.get_dummy_inputs(_snake_case   )
           UpperCAmelCase     =    '''french fries'''
           UpperCAmelCase     =    sd_pipe(**_snake_case		,       negative_prompt=_snake_case   )
           UpperCAmelCase     =    output.images
           UpperCAmelCase     =    image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           UpperCAmelCase     =    np.array([0.4992, 0.5101, 0.5004, 0.3949, 0.3604, 0.4735, 0.5216, 0.5204, 0.4913]   )

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






       def 				snake_case_ (						self   )					->		List[str]:

           """simple docstring"""
           UpperCAmelCase     =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           UpperCAmelCase     =    self.get_dummy_components()
           UpperCAmelCase     =    EulerAncestralDiscreteScheduler(
               beta_start=0.0_0085		,       beta_end=0.012		,       beta_schedule='''scaled_linear'''   )
           UpperCAmelCase     =    StableDiffusionModelEditingPipeline(**_snake_case   )
           UpperCAmelCase     =    sd_pipe.to(_snake_case   )
           sd_pipe.set_progress_bar_config(disable=_snake_case   )

           UpperCAmelCase     =    self.get_dummy_inputs(_snake_case   )
           UpperCAmelCase     =    sd_pipe(**_snake_case   ).images
           UpperCAmelCase     =    image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           UpperCAmelCase     =    np.array([0.4747, 0.5372, 0.4779, 0.4982, 0.5543, 0.4816, 0.5238, 0.4904, 0.5027]   )

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






       def 				snake_case_ (						self   )					->		Tuple:

           """simple docstring"""
           UpperCAmelCase     =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           UpperCAmelCase     =    self.get_dummy_components()
           UpperCAmelCase     =    PNDMScheduler()
           UpperCAmelCase     =    StableDiffusionModelEditingPipeline(**_snake_case   )
           UpperCAmelCase     =    sd_pipe.to(_snake_case   )
           sd_pipe.set_progress_bar_config(disable=_snake_case   )

           UpperCAmelCase     =    self.get_dummy_inputs(_snake_case   )
           # the pipeline does not expect pndm so test if it raises error.
           with self.assertRaises(_snake_case   ):
               UpperCAmelCase     =    sd_pipe(**_snake_case   ).images






       def 				snake_case_ (						self   )					->		Tuple:

           """simple docstring"""
           super().test_inference_batch_single_identical(expected_max_diff=5e-3   )






       def 				snake_case_ (						self   )					->		Union[str, Any]:

           """simple docstring"""
           super().test_attention_slicing_forward_pass(expected_max_diff=5e-3   )

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




       '''simple docstring'''







       def 				snake_case_ (						self   )					->		Union[str, Any]:

           """simple docstring"""
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()






       def 				snake_case_ (						self		,       _snake_case=0   )					->		Dict:

           """simple docstring"""
           UpperCAmelCase     =    torch.manual_seed(_snake_case   )
           UpperCAmelCase     =    {
               '''prompt''': '''A field of roses''',
               '''generator''': generator,
               '''num_inference_steps''': 3,
               '''guidance_scale''': 7.5,
               '''output_type''': '''numpy''',
           }
           return inputs






       def 				snake_case_ (						self   )					->		Union[str, Any]:

           """simple docstring"""
           UpperCAmelCase     =    '''CompVis/stable-diffusion-v1-4'''
           UpperCAmelCase     =    StableDiffusionModelEditingPipeline.from_pretrained(_snake_case		,       safety_checker=_snake_case   )
           pipe.to(_snake_case   )
           pipe.set_progress_bar_config(disable=_snake_case   )
           pipe.enable_attention_slicing()

           UpperCAmelCase     =    self.get_inputs()
           UpperCAmelCase     =    pipe(**_snake_case   ).images
           UpperCAmelCase     =    image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 512, 512, 3)

           UpperCAmelCase     =    np.array(
               [0.674_9496, 0.638_6453, 0.5144_3267, 0.6609_4905, 0.6192_1215, 0.549_1332, 0.574_4417, 0.5807_5106, 0.517_4658]   )

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

           # make sure image changes after editing
           pipe.edit_model('''A pack of roses'''		,       '''A pack of blue roses'''   )

           UpperCAmelCase     =    pipe(**_snake_case   ).images
           UpperCAmelCase     =    image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 512, 512, 3)

           assert np.abs(expected_slice - image_slice   ).max() > 1e-1






       def 				snake_case_ (						self   )					->		Optional[Any]:

           """simple docstring"""
           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()

           UpperCAmelCase     =    '''CompVis/stable-diffusion-v1-4'''
           UpperCAmelCase     =    DDIMScheduler.from_pretrained(_snake_case		,       subfolder='''scheduler'''   )
           UpperCAmelCase     =    StableDiffusionModelEditingPipeline.from_pretrained(
               _snake_case		,       scheduler=_snake_case		,       safety_checker=_snake_case   )
           UpperCAmelCase     =    pipe.to(_snake_case   )
           pipe.set_progress_bar_config(disable=_snake_case   )
           pipe.enable_attention_slicing(1   )
           pipe.enable_sequential_cpu_offload()

           UpperCAmelCase     =    self.get_inputs()
           UpperCAmelCase     =    pipe(**_snake_case   )

           UpperCAmelCase     =    torch.cuda.max_memory_allocated()
           # make sure that less than 4.4 GB is allocated
           assert mem_bytes < 4.4 * 10**9







def    UpperCamelCase(					lowercase_							)	->				str:





  '''simple docstring'''





  if len(lowercase_							) == 0 or not all(len(lowercase_							) == 2 for pair in coordinates							):
    raise ValueError("""The program cannot work out a fitting polynomial."""							)

  if len({tuple(lowercase_							) for pair in coordinates}							) != len(lowercase_							):
    raise ValueError("""The program cannot work out a fitting polynomial."""							)

  snake_case_       						=      {x for x, _ in coordinates}
  if len(lowercase_							) == 1:
    return f'''x={coordinates[0][0]}'''

  if len(lowercase_							) != len(lowercase_							):
    raise ValueError("""The program cannot work out a fitting polynomial."""							)

  snake_case_       						=      len(lowercase_							)

  snake_case_       						=      0
  snake_case_       						=      []
  # put the x and x to the power values in a matrix
  while count_of_line < x:
    snake_case_       						=      0
    snake_case_       						=      coordinates[count_of_line][0]
    snake_case_       						=      []
    while count_in_line < x:
      count_line.append(a ** (x - (count_in_line + 1))							)
      count_in_line += 1
    matrix.append(lowercase_							)
    count_of_line += 1

  snake_case_       						=      0
  # put the y values into a vector
  snake_case_       						=      []
  while count_of_line < x:
    vector.append(coordinates[count_of_line][1]							)
    count_of_line += 1

  snake_case_       						=      0

  while count < x:
    snake_case_       						=      0
    while zahlen < x:
      if count == zahlen:
        zahlen += 1
      if zahlen == x:
        break
      snake_case_       						=      matrix[zahlen][count] / matrix[count][count]
      for counting_columns, item in enumerate(matrix[count]							):
        # manipulating all the values in the matrix
        matrix[zahlen][counting_columns] -= item * bruch
      # manipulating the values in the vector
      vector[zahlen] -= vector[count] * bruch
      zahlen += 1
    count += 1

  snake_case_       						=      0
  # make solutions
  snake_case_       						=      []
  while count < x:
    solution.append(str(vector[count] / matrix[count][count]							)							)
    count += 1

  snake_case_       						=      0
  snake_case_       						=      """f(x)="""

  while count < x:
    snake_case_       						=      solution[count].split("""E"""							)
    if len(lowercase_							) > 1:
      snake_case_       						=      f'''{remove_e[0]}*10^{remove_e[1]}'''
    solved += f'''x^{x - (count + 1)}*{solution[count]}'''
    if count + 1 != x:
      solved += "+"
    count += 1

  return solved


if __name__ == "__main__":
    print(points_to_polynomial([]))
    print(points_to_polynomial([[]]))
    print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
    print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
    print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
    print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
    print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
    print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
    print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
    print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))