






"""simple docstring"""


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


   '''simple docstring'''

   A_						:     List[str]									=			StableDiffusionModelEditingPipeline
   A_						:     Any									=			TEXT_TO_IMAGE_PARAMS
   A_						:     Any									=			TEXT_TO_IMAGE_BATCH_PARAMS
   A_						:     Dict									=			TEXT_TO_IMAGE_IMAGE_PARAMS
   A_						:     Optional[Any]									=			TEXT_TO_IMAGE_IMAGE_PARAMS


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

     '''simple docstring'''
     torch.manual_seed(0  )
     __A						      =			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,	)
     __A						      =			DDIMScheduler()
     torch.manual_seed(0  )
     __A						      =			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  )
     __A						      =			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,	)
     __A						      =			CLIPTextModel(_A  )
     __A						      =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

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


   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[Any],	_lowerCamelCase			:					Optional[Any],	_lowerCamelCase			:					Union[str, Any]=0  ):

     '''simple docstring'''
     __A						      =			torch.manual_seed(_A  )
     __A						      =			{
         '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     _SCREAMING_SNAKE_CASE    (     self			:					Any  ):

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

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

     __A						      =			np.array([0.47_55, 0.51_32, 0.49_76, 0.39_04, 0.35_54, 0.47_65, 0.51_39, 0.51_58, 0.48_89]  )

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


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[Any]  ):

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

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

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

     __A						      =			np.array([0.49_92, 0.51_01, 0.50_04, 0.39_49, 0.36_04, 0.47_35, 0.52_16, 0.52_04, 0.49_13]  )

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


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

     '''simple docstring'''
     __A						      =			'cpu'  # ensure determinism for the device-dependent torch.Generator
     __A						      =			self.get_dummy_components()
     __A						      =			EulerAncestralDiscreteScheduler(
         beta_start=0.0_00_85,	beta_end=0.0_12,	beta_schedule='''scaled_linear'''  )
     __A						      =			StableDiffusionModelEditingPipeline(**_A  )
     __A						      =			sd_pipe.to(_A  )
     sd_pipe.set_progress_bar_config(disable=_A  )

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

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

     __A						      =			np.array([0.47_47, 0.53_72, 0.47_79, 0.49_82, 0.55_43, 0.48_16, 0.52_38, 0.49_04, 0.50_27]  )

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


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str]  ):

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

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


   def     _SCREAMING_SNAKE_CASE    (     self			:					int  ):

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






   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str]  ):

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





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


   '''simple docstring'''

   def     _SCREAMING_SNAKE_CASE    (     self			:					str  ):

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


   def     _SCREAMING_SNAKE_CASE    (     self			:					Dict,	_lowerCamelCase			:					List[str]=0  ):

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


   def     _SCREAMING_SNAKE_CASE    (     self			:					int  ):

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

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

     assert image.shape == (1, 5_12, 5_12, 3)

     __A						      =			np.array(
         [0.6_74_94_96, 0.6_38_64_53, 0.51_44_32_67, 0.66_09_49_05, 0.61_92_12_15, 0.5_49_13_32, 0.5_74_44_17, 0.58_07_51_06, 0.5_17_46_58]  )

     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'''  )

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

     assert image.shape == (1, 5_12, 5_12, 3)

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






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

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

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

     __A						      =			self.get_inputs()
     __A						      =			pipe(**_A  )

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












"""simple docstring"""


def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  if not isinstance(__UpperCamelCase		,	__UpperCamelCase			):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 8]'''			)
  if len(__UpperCamelCase			) == 0:
    raise ValueError('''Input list must be a non empty list'''			)
  if len(__UpperCamelCase			) == 1:
    return True
  try:
    __A						      =			series[1] / series[0]
    for index in range(len(__UpperCamelCase			) - 1			):
      if series[index + 1] / series[index] != common_ratio:
        return False
  except ZeroDivisionError:
    return False
  return True



def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  if not isinstance(__UpperCamelCase		,	__UpperCamelCase			):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 8]'''			)
  if len(__UpperCamelCase			) == 0:
    raise ValueError('''Input list must be a non empty list'''			)
  __A						      =			1
  for value in series:
    answer *= value
  return pow(__UpperCamelCase		,	1 / len(__UpperCamelCase			)			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()




