
import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class        lowercase      (	_SCREAMING_SNAKE_CASE						,     _SCREAMING_SNAKE_CASE						,     unittest.TestCase     ):
		__lowercase : str			  =       StableDiffusionXLImgaImgPipeline
		__lowercase : List[str]			  =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
		__lowercase : Any			  =       PipelineTesterMixin.required_optional_params - {"latents"}
		__lowercase : str			  =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		__lowercase : Optional[Any]			  =       IMAGE_TO_IMAGE_IMAGE_PARAMS
		__lowercase : Tuple			  =       IMAGE_TO_IMAGE_IMAGE_PARAMS



		def __UpperCamelCase				(				self )						->      List[str]:
				"""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')       ,   attention_head_dim=(2, 4)       ,   use_linear_projection=A_       ,   addition_embed_type='text_time'       ,   addition_time_embed_dim=8       ,   transformer_layers_per_block=(1, 2)       ,   projection_class_embeddings_input_dim=80       ,   cross_attention_dim=64       ,   )
				UpperCamelCase						=  EulerDiscreteScheduler(
				    beta_start=0.0_0085       ,   beta_end=0.012       ,   steps_offset=1       ,   beta_schedule='scaled_linear'       ,   timestep_spacing='leading'       ,   )
				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       ,   sample_size=128       ,   )
				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=1_000       ,   hidden_act='gelu'       ,   projection_dim=32       ,   )
				UpperCamelCase						=  CLIPTextModel(A_ )
				UpperCamelCase						=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       ,   local_files_only=A_ )

				UpperCamelCase						=  CLIPTextModelWithProjection(A_ )
				UpperCamelCase						=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       ,   local_files_only=A_ )

				UpperCamelCase						=  {
				    'unet': unet,
				    'scheduler': scheduler,
				    'vae': vae,
				    'text_encoder': text_encoder,
				    'tokenizer': tokenizer,
				    'text_encoder_2': text_encoder_a,
				    'tokenizer_2': tokenizer_a,
				    # "safety_checker": None,
				    # "feature_extractor": None,
				}
				return components



		def __UpperCamelCase				(				self       ,   A_       ,   A_=0 )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase						=  floats_tensor((1, 3, 32, 32)       ,   rng=random.Random(A_ ) ).to(A_ )
				UpperCamelCase						=  image / 2 + 0.5
				if str(A_ ).startswith('mps' ):
						UpperCamelCase						=  torch.manual_seed(A_ )
				else:
						UpperCamelCase						=  torch.Generator(device=A_ ).manual_seed(A_ )
				UpperCamelCase						=  {
				    'prompt': 'A painting of a squirrel eating a burger',
				    'image': image,
				    'generator': generator,
				    'num_inference_steps': 2,
				    'guidance_scale': 5.0,
				    'output_type': 'numpy',
				    'strength': 0.75,
				}
				return inputs



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase						=  'cpu'  # ensure determinism for the device-dependent torch.Generator
				UpperCamelCase						=  self.get_dummy_components()
				UpperCamelCase						=  StableDiffusionXLImgaImgPipeline(**A_ )
				UpperCamelCase						=  sd_pipe.to(A_ )
				sd_pipe.set_progress_bar_config(disable=A_ )

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

				assert image.shape == (1, 32, 32, 3)

				UpperCamelCase						=  np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165] )

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



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				super().test_attention_slicing_forward_pass(expected_max_diff=3e-3 )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				super().test_inference_batch_single_identical(expected_max_diff=3e-3 )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				pass



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  self.get_dummy_components()
				UpperCamelCase						=  StableDiffusionXLImgaImgPipeline(**A_ )
				UpperCamelCase						=  sd_pipe.to(A_ )
				UpperCamelCase						=  sd_pipe.to(A_ )
				sd_pipe.set_progress_bar_config(disable=A_ )

				# forward without prompt embeds
				UpperCamelCase						=  self.get_dummy_inputs(A_ )
				UpperCamelCase						=  3 * ['this is a negative prompt']
				UpperCamelCase						=  negative_prompt
				UpperCamelCase						=  3 * [inputs['prompt']]

				UpperCamelCase						=  sd_pipe(**A_ )
				UpperCamelCase						=  output.images[0, -3:, -3:, -1]

				# forward with prompt embeds
				UpperCamelCase						=  self.get_dummy_inputs(A_ )
				UpperCamelCase						=  3 * ['this is a negative prompt']
				UpperCamelCase						=  3 * [inputs.pop('prompt' )]

				(
				    (
				    UpperCamelCase
				)			,						(
				    UpperCamelCase
				)			,						(
				    UpperCamelCase
				)			,						(
				    UpperCamelCase
				)			,						
				)						=  sd_pipe.encode_prompt(A_       ,   negative_prompt=A_ )

				UpperCamelCase						=  sd_pipe(
				    **A_       ,   prompt_embeds=A_       ,   negative_prompt_embeds=A_       ,   pooled_prompt_embeds=A_       ,   negative_pooled_prompt_embeds=A_       ,   )
				UpperCamelCase						=  output.images[0, -3:, -3:, -1]

				# make sure that it's equal
				assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4






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



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase				(				self       ,   A_       ,   A_="cpu"       ,   A_=torch.floataa       ,   A_=0 )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  torch.Generator(device=A_ ).manual_seed(A_ )
				UpperCamelCase						=  np.random.RandomState(A_ ).standard_normal((1, 4, 64, 64) )
				UpperCamelCase						=  torch.from_numpy(A_ ).to(device=A_       ,   dtype=A_ )
				UpperCamelCase						=  {
				    'prompt': 'a photograph of an astronaut riding a horse',
				    'latents': latents,
				    'generator': generator,
				    'num_inference_steps': 3,
				    'guidance_scale': 7.5,
				    'output_type': 'numpy',
				}
				return inputs



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base' )
				pipe.to(A_ )
				pipe.set_progress_bar_config(disable=A_ )

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

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase						=  np.array([0.4_9493, 0.4_7896, 0.4_0798, 0.5_4214, 0.5_3212, 0.4_8202, 0.4_7656, 0.4_6329, 0.4_8506] )
				assert np.abs(image_slice - expected_slice ).max() < 7e-3


from collections.abc import Callable


def    A (      lowercase					, lowercase					, lowercase				)     ->						float:
		'''simple docstring'''

		UpperCamelCase						=  a
		UpperCamelCase						=  b
		if function(lowercase				) == 0:  # one of the a or b is a root for the function
				return a
		elif function(lowercase				) == 0:
				return b
		elif (
		    function(lowercase				) * function(lowercase				) > 0
		):  # if none of these are root and they are both positive or negative,
				# then this algorithm can't find the root
				raise ValueError('could not find root in given interval.'				)
		else:
				UpperCamelCase						=  start + (end - start) / 2.0
				while abs(start - mid				) > 10**-7:  # until precisely equals to 10^-7
						if function(lowercase				) == 0:
								return mid
						elif function(lowercase				) * function(lowercase				) < 0:
								UpperCamelCase						=  mid
						else:
								UpperCamelCase						=  mid
						UpperCamelCase						=  start + (end - start) / 2.0
				return mid


def    A (      lowercase				)     ->						float:
		'''simple docstring'''

		return x**3 - 2 * x - 5


if __name__ == "__main__":
					print(bisection(f, 1, 1_000))

					import doctest

					doctest.testmod()
