




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 		UpperCAmelCase   (				UpperCAmelCase__       ,   UpperCAmelCase__       ,   unittest.TestCase			):







						'''simple docstring'''





						lowerCAmelCase_     		=   StableDiffusionXLImgaImgPipeline
						lowerCAmelCase_     		=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
						lowerCAmelCase_     		=   PipelineTesterMixin.required_optional_params - {'''latents'''}
						lowerCAmelCase_     		=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
						lowerCAmelCase_     		=   IMAGE_TO_IMAGE_IMAGE_PARAMS
						lowerCAmelCase_     		=   IMAGE_TO_IMAGE_IMAGE_PARAMS






						def        snake_case__						(						self							:     Any						):






							"""simple docstring"""






							torch.manual_seed(0						)
							snake_case_		    =     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=__lowercase       ,	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       ,	)
							snake_case_		    =     EulerDiscreteScheduler(
							    beta_start=0.0_0085       ,	beta_end=0.012       ,	steps_offset=1       ,	beta_schedule="scaled_linear"       ,	timestep_spacing="leading"       ,	)
							torch.manual_seed(0						)
							snake_case_		    =     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=1_28       ,	)
							torch.manual_seed(0						)
							snake_case_		    =     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       ,	hidden_act="gelu"       ,	projection_dim=32       ,	)
							snake_case_		    =     CLIPTextModel(__lowercase						)
							snake_case_		    =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       ,	local_files_only=__lowercase						)

							snake_case_		    =     CLIPTextModelWithProjection(__lowercase						)
							snake_case_		    =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       ,	local_files_only=__lowercase						)

							snake_case_		    =     {
							    "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        snake_case__						(						self							:     List[Any]       ,	__lowercase							:     Any       ,	__lowercase							:     Union[str, Any]=0						):






							"""simple docstring"""






							snake_case_		    =     floats_tensor((1, 3, 32, 32)       ,	rng=random.Random(__lowercase						)						).to(__lowercase						)
							snake_case_		    =     image / 2 + 0.5
							if str(__lowercase						).startswith("mps"						):
								snake_case_		    =     torch.manual_seed(__lowercase						)
							else:
								snake_case_		    =     torch.Generator(device=__lowercase						).manual_seed(__lowercase						)
							snake_case_		    =     {
							    "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        snake_case__						(						self							:     List[str]						):






							"""simple docstring"""






							snake_case_		    =     "cpu"  # ensure determinism for the device-dependent torch.Generator
							snake_case_		    =     self.get_dummy_components()
							snake_case_		    =     StableDiffusionXLImgaImgPipeline(**__lowercase						)
							snake_case_		    =     sd_pipe.to(__lowercase						)
							sd_pipe.set_progress_bar_config(disable=__lowercase						)

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

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

							snake_case_		    =     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        snake_case__						(						self							:     Any						):






							"""simple docstring"""






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






						def        snake_case__						(						self							:     Optional[int]						):






							"""simple docstring"""






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






						def        snake_case__						(						self							:     Dict						):






							"""simple docstring"""






							pass






						def        snake_case__						(						self							:     Optional[int]						):






							"""simple docstring"""






							snake_case_		    =     self.get_dummy_components()
							snake_case_		    =     StableDiffusionXLImgaImgPipeline(**__lowercase						)
							snake_case_		    =     sd_pipe.to(__lowercase						)
							snake_case_		    =     sd_pipe.to(__lowercase						)
							sd_pipe.set_progress_bar_config(disable=__lowercase						)

							# forward without prompt embeds
							snake_case_		    =     self.get_dummy_inputs(__lowercase						)
							snake_case_		    =     3 * ["this is a negative prompt"]
							snake_case_		    =     negative_prompt
							snake_case_		    =     3 * [inputs["prompt"]]

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

							# forward with prompt embeds
							snake_case_		    =     self.get_dummy_inputs(__lowercase						)
							snake_case_		    =     3 * ["this is a negative prompt"]
							snake_case_		    =     3 * [inputs.pop("prompt"						)]

							(
							    (
							    snake_case_
							)    ,      (
							    snake_case_
							)    ,      (
							    snake_case_
							)    ,      (
							    snake_case_
							)    ,      
							)		    =     sd_pipe.encode_prompt(__lowercase       ,	negative_prompt=__lowercase						)

							snake_case_		    =     sd_pipe(
							    **__lowercase       ,	prompt_embeds=__lowercase       ,	negative_prompt_embeds=__lowercase       ,	pooled_prompt_embeds=__lowercase       ,	negative_pooled_prompt_embeds=__lowercase       ,	)
							snake_case_		    =     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 		UpperCAmelCase   (				unittest.TestCase			):







						'''simple docstring'''





						def        snake_case__						(						self							:     int						):






							"""simple docstring"""






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






						def        snake_case__						(						self							:     Tuple       ,	__lowercase							:     Tuple       ,	__lowercase							:     List[Any]="cpu"       ,	__lowercase							:     Optional[int]=torch.floataa       ,	__lowercase							:     Dict=0						):






							"""simple docstring"""






							snake_case_		    =     torch.Generator(device=__lowercase						).manual_seed(__lowercase						)
							snake_case_		    =     np.random.RandomState(__lowercase						).standard_normal((1, 4, 64, 64)						)
							snake_case_		    =     torch.from_numpy(__lowercase						).to(device=__lowercase       ,	dtype=__lowercase						)
							snake_case_		    =     {
							    "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        snake_case__						(						self							:     Any						):






							"""simple docstring"""






							snake_case_		    =     DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"						)
							pipe.to(__lowercase						)
							pipe.set_progress_bar_config(disable=__lowercase						)

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

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






def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	_enforce_args(_A     ,	_A							)
	if n == 0:
		return 0
	snake_case_		    =     float("-inf"							)
	for i in range(1     ,	n + 1							):
		snake_case_		    =     max(
		    _A     ,	prices[i - 1] + naive_cut_rod_recursive(n - i     ,	_A							)							)

	return max_revue


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	_enforce_args(_A     ,	_A							)
	snake_case_		    =     [float("-inf"							) for _ in range(n + 1							)]
	return _top_down_cut_rod_recursive(_A     ,	_A     ,	_A							)


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A							):







	'''simple docstring'''





	if max_rev[n] >= 0:
		return max_rev[n]
	elif n == 0:
		return 0
	else:
		snake_case_		    =     float("-inf"							)
		for i in range(1     ,	n + 1							):
			snake_case_		    =     max(
			    _A     ,	prices[i - 1] + _top_down_cut_rod_recursive(n - i     ,	_A     ,	_A							)     ,	)

		snake_case_		    =     max_revenue

	return max_rev[n]


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	_enforce_args(_A     ,	_A							)

	# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
	# length 0.
	snake_case_		    =     [float("-inf"							) for _ in range(n + 1							)]
	snake_case_		    =     0

	for i in range(1     ,	n + 1							):
		snake_case_		    =     max_rev[i]
		for j in range(1     ,	i + 1							):
			snake_case_		    =     max(_A     ,	prices[j - 1] + max_rev[i - j]							)

		snake_case_		    =     max_revenue_i

	return max_rev[n]


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	if n < 0:
		snake_case_		    =     f"n must be greater than or equal to 0. Got n = {n}"
		raise ValueError(_A							)

	if n > len(_A							):
		snake_case_		    =     (
		    "Each integral piece of rod must have a corresponding price. "
		    f"Got n = {n} but length of prices = {len(_A							)}"
		)
		raise ValueError(_A							)


def 						lowerCamelCase__    (		):







	'''simple docstring'''





	snake_case_		    =     [6, 10, 12, 15, 20, 23]
	snake_case_		    =     len(_A							)

	# the best revenue comes from cutting the rod into 6 pieces, each
	# of length 1 resulting in a revenue of 6 * 6 = 36.
	snake_case_		    =     36

	snake_case_		    =     top_down_cut_rod(_A     ,	_A							)
	snake_case_		    =     bottom_up_cut_rod(_A     ,	_A							)
	snake_case_		    =     naive_cut_rod_recursive(_A     ,	_A							)

	assert expected_max_revenue == max_rev_top_down
	assert max_rev_top_down == max_rev_bottom_up
	assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
		main()
