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						(			__lowerCAmelCase			,	__lowerCAmelCase			,	__lowerCAmelCase			,	unittest.TestCase		):





      '''simple docstring'''






      _snake_case     		=							StableDiffusionModelEditingPipeline
      _snake_case     		=							TEXT_TO_IMAGE_PARAMS
      _snake_case     		=							TEXT_TO_IMAGE_BATCH_PARAMS
      _snake_case     		=							TEXT_TO_IMAGE_IMAGE_PARAMS
      _snake_case     		=							TEXT_TO_IMAGE_IMAGE_PARAMS



      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[Any])					->Optional[Any]:


          """simple docstring"""


          torch.manual_seed(0)
          _lowerCamelCase  :		List[str]			    =    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  :		List[Any]			    =    DDIMScheduler()
          torch.manual_seed(0)
          _lowerCamelCase  :		List[str]			    =    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)
          _lowerCamelCase  :		List[Any]			    =    CLIPTextConfig(
              bos_token_id=0     ,	eos_token_id=2     ,	hidden_size=32     ,	intermediate_size=37     ,	layer_norm_eps=1E-0_5     ,	num_attention_heads=4     ,	num_hidden_layers=5     ,	pad_token_id=1     ,	vocab_size=1000     ,	)
          _lowerCamelCase  :		Dict			    =    CLIPTextModel(_UpperCamelCase)
          _lowerCamelCase  :		Optional[Any]			    =    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")

          _lowerCamelCase  :		List[Any]			    =    {
              """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  :					Any     ,	_UpperCamelCase  :					Optional[int]     ,	_UpperCamelCase  :					str=0)					->Any:


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    torch.manual_seed(_UpperCamelCase)
          _lowerCamelCase  :		List[Any]			    =    {
              """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  :					Tuple)					->List[str]:


          """simple docstring"""


          _lowerCamelCase  :		Optional[int]			    =    """cpu"""  # ensure determinism for the device-dependent torch.Generator
          _lowerCamelCase  :		Any			    =    self.get_dummy_components()
          _lowerCamelCase  :		List[str]			    =    StableDiffusionModelEditingPipeline(**_UpperCamelCase)
          _lowerCamelCase  :		Dict			    =    sd_pipe.to(_UpperCamelCase)
          sd_pipe.set_progress_bar_config(disable=_UpperCamelCase)

          _lowerCamelCase  :		List[str]			    =    self.get_dummy_inputs(_UpperCamelCase)
          _lowerCamelCase  :		List[Any]			    =    sd_pipe(**_UpperCamelCase).images
          _lowerCamelCase  :		Optional[int]			    =    image[0, -3:, -3:, -1]
          assert image.shape == (1, 64, 64, 3)

          _lowerCamelCase  :		List[Any]			    =    np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9])

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



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


          """simple docstring"""


          _lowerCamelCase  :		Optional[int]			    =    """cpu"""  # ensure determinism for the device-dependent torch.Generator
          _lowerCamelCase  :		List[str]			    =    self.get_dummy_components()
          _lowerCamelCase  :		int			    =    StableDiffusionModelEditingPipeline(**_UpperCamelCase)
          _lowerCamelCase  :		List[str]			    =    sd_pipe.to(_UpperCamelCase)
          sd_pipe.set_progress_bar_config(disable=_UpperCamelCase)

          _lowerCamelCase  :		int			    =    self.get_dummy_inputs(_UpperCamelCase)
          _lowerCamelCase  :		Union[str, Any]			    =    """french fries"""
          _lowerCamelCase  :		Dict			    =    sd_pipe(**_UpperCamelCase     ,	negative_prompt=_UpperCamelCase)
          _lowerCamelCase  :		List[Any]			    =    output.images
          _lowerCamelCase  :		List[Any]			    =    image[0, -3:, -3:, -1]

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

          _lowerCamelCase  :		Optional[Any]			    =    np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3])

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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Dict)					->int:


          """simple docstring"""


          _lowerCamelCase  :		List[str]			    =    """cpu"""  # ensure determinism for the device-dependent torch.Generator
          _lowerCamelCase  :		Tuple			    =    self.get_dummy_components()
          _lowerCamelCase  :		Dict			    =    EulerAncestralDiscreteScheduler(
              beta_start=0.0_0_0_8_5     ,	beta_end=0.0_1_2     ,	beta_schedule="""scaled_linear""")
          _lowerCamelCase  :		int			    =    StableDiffusionModelEditingPipeline(**_UpperCamelCase)
          _lowerCamelCase  :		str			    =    sd_pipe.to(_UpperCamelCase)
          sd_pipe.set_progress_bar_config(disable=_UpperCamelCase)

          _lowerCamelCase  :		Optional[int]			    =    self.get_dummy_inputs(_UpperCamelCase)
          _lowerCamelCase  :		Dict			    =    sd_pipe(**_UpperCamelCase).images
          _lowerCamelCase  :		Optional[Any]			    =    image[0, -3:, -3:, -1]

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

          _lowerCamelCase  :		List[str]			    =    np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7])

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



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


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    """cpu"""  # ensure determinism for the device-dependent torch.Generator
          _lowerCamelCase  :		Union[str, Any]			    =    self.get_dummy_components()
          _lowerCamelCase  :		Tuple			    =    PNDMScheduler()
          _lowerCamelCase  :		Union[str, Any]			    =    StableDiffusionModelEditingPipeline(**_UpperCamelCase)
          _lowerCamelCase  :		Tuple			    =    sd_pipe.to(_UpperCamelCase)
          sd_pipe.set_progress_bar_config(disable=_UpperCamelCase)

          _lowerCamelCase  :		List[str]			    =    self.get_dummy_inputs(_UpperCamelCase)
          # the pipeline does not expect pndm so test if it raises error.
          with self.assertRaises(_UpperCamelCase):
              _lowerCamelCase  :		List[str]			    =    sd_pipe(**_UpperCamelCase).images



      def 						_SCREAMING_SNAKE_CASE     (       self  :					str)					->Optional[Any]:


          """simple docstring"""


          super().test_inference_batch_single_identical(expected_max_diff=5E-3)




      def 						_SCREAMING_SNAKE_CASE     (       self  :					int)					->Optional[int]:


          """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  :					Optional[Any])					->List[Any]:


          """simple docstring"""


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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple     ,	_UpperCamelCase  :					Any=0)					->List[Any]:


          """simple docstring"""


          _lowerCamelCase  :		Any			    =    torch.manual_seed(_UpperCamelCase)
          _lowerCamelCase  :		Dict			    =    {
              """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  :					List[Any])					->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase  :		Dict			    =    """CompVis/stable-diffusion-v1-4"""
          _lowerCamelCase  :		List[str]			    =    StableDiffusionModelEditingPipeline.from_pretrained(_UpperCamelCase     ,	safety_checker=_UpperCamelCase)
          pipe.to(_UpperCamelCase)
          pipe.set_progress_bar_config(disable=_UpperCamelCase)
          pipe.enable_attention_slicing()

          _lowerCamelCase  :		List[str]			    =    self.get_inputs()
          _lowerCamelCase  :		str			    =    pipe(**_UpperCamelCase).images
          _lowerCamelCase  :		List[str]			    =    image[0, -3:, -3:, -1].flatten()

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

          _lowerCamelCase  :		Tuple			    =    np.array(
              [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8])

          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""")

          _lowerCamelCase  :		Optional[Any]			    =    pipe(**_UpperCamelCase).images
          _lowerCamelCase  :		Tuple			    =    image[0, -3:, -3:, -1].flatten()

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

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




      def 						_SCREAMING_SNAKE_CASE     (       self  :					int)					->Tuple:


          """simple docstring"""


          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()

          _lowerCamelCase  :		Optional[int]			    =    """CompVis/stable-diffusion-v1-4"""
          _lowerCamelCase  :		Dict			    =    DDIMScheduler.from_pretrained(_UpperCamelCase     ,	subfolder="""scheduler""")
          _lowerCamelCase  :		str			    =    StableDiffusionModelEditingPipeline.from_pretrained(
              _UpperCamelCase     ,	scheduler=_UpperCamelCase     ,	safety_checker=_UpperCamelCase)
          _lowerCamelCase  :		int			    =    pipe.to(_UpperCamelCase)
          pipe.set_progress_bar_config(disable=_UpperCamelCase)
          pipe.enable_attention_slicing(1)
          pipe.enable_sequential_cpu_offload()

          _lowerCamelCase  :		List[Any]			    =    self.get_inputs()
          _lowerCamelCase  :		int			    =    pipe(**_UpperCamelCase)

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





lowerCAmelCase      :				Union[str, Any]     			=1000







def     A__	(       __A						):







    '''simple docstring'''
    return x * x







def     A__	(       __A ,			__A ,			__A ,			__A = 4						):







    '''simple docstring'''
    assert callable(
        __A						), F"""the function(object) passed should be callable your input : {function}"""
    assert isinstance(__A ,			(float, int)						), F"""a should be float or integer your input : {a}"""
    assert isinstance(function(__A						) ,			(float, int)						), (
        "the function should return integer or float return type of your function, "
        F"""{type(__A						)}"""
    )
    assert isinstance(__A ,			(float, int)						), F"""b should be float or integer your input : {b}"""
    assert (
        isinstance(__A ,			__A						) and precision > 0
    ), F"""precision should be positive integer your input : {precision}"""

    # just applying the formula of simpson for approximate integration written in
    # mentioned article in first comment of this file and above this function

    _lowerCamelCase  :		Optional[Any]			    =    (b - a) / N_STEPS
    _lowerCamelCase  :		str			    =    function(__A						) + function(__A						)

    for i in range(1 ,			__A						):
        _lowerCamelCase  :		str			    =    a + h * i
        result += function(__A						) * (4 if i % 2 else 2)

    result *= h / 3
    return round(__A ,			__A						)


if __name__ == "__main__":
    import doctest

    doctest.testmod()




