

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				(				__A				,						__A				,						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 				__UpperCamelCase			(      self				:			Any						)				->							Optional[Any]:

				"""simple docstring"""

				torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   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")   ,   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   ,   )
				SCREAMING_SNAKE_CASE :   str					  =							EulerDiscreteScheduler(
				    beta_start=0.0_0085   ,   beta_end=0.012   ,   steps_offset=1   ,   beta_schedule="scaled_linear"   ,   timestep_spacing="leading"   ,   )
				torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Any					  =							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						)
				SCREAMING_SNAKE_CASE :   str					  =							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   ,   hidden_act="gelu"   ,   projection_dim=32   ,   )
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							CLIPTextModel(a						)
				SCREAMING_SNAKE_CASE :   Dict					  =							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   ,   local_files_only=a						)

				SCREAMING_SNAKE_CASE :   str					  =							CLIPTextModelWithProjection(a						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   ,   local_files_only=a						)

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							{
				    "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				:			Tuple   ,   a				:			Any   ,   a				:			Optional[int]=0						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							floats_tensor((1, 3, 32, 32)   ,   rng=random.Random(a						)						).to(a						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							image / 2 + 0.5
				if str(a						).startswith("mps"						):
					SCREAMING_SNAKE_CASE :   int					  =							torch.manual_seed(a						)
				else:
					SCREAMING_SNAKE_CASE :   int					  =							torch.Generator(device=a						).manual_seed(a						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							{
				    "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				:			Tuple						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							"cpu"  # ensure determinism for the device-dependent torch.Generator
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.get_dummy_components()
				SCREAMING_SNAKE_CASE :   Tuple					  =							StableDiffusionXLImgaImgPipeline(**a						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

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

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

				SCREAMING_SNAKE_CASE :   str					  =							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				:			Union[str, Any]						)				->							List[Any]:

				"""simple docstring"""

				super().test_attention_slicing_forward_pass(expected_max_diff=3e-3						)
			def 				__UpperCamelCase			(      self				:			int						)				->							str:

				"""simple docstring"""

				super().test_inference_batch_single_identical(expected_max_diff=3e-3						)
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							Optional[int]:

				"""simple docstring"""

				pass






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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							self.get_dummy_components()
				SCREAMING_SNAKE_CASE :   Tuple					  =							StableDiffusionXLImgaImgPipeline(**a						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							sd_pipe.to(a						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				# forward without prompt embeds
				SCREAMING_SNAKE_CASE :   Any					  =							self.get_dummy_inputs(a						)
				SCREAMING_SNAKE_CASE :   Any					  =							3 * ["this is a negative prompt"]
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							negative_prompt
				SCREAMING_SNAKE_CASE :   int					  =							3 * [inputs["prompt"]]

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							sd_pipe(**a						)
				SCREAMING_SNAKE_CASE :   int					  =							output.images[0, -3:, -3:, -1]

				# forward with prompt embeds
				SCREAMING_SNAKE_CASE :   Tuple					  =							self.get_dummy_inputs(a						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							3 * ["this is a negative prompt"]
				SCREAMING_SNAKE_CASE :   List[Any]					  =							3 * [inputs.pop("prompt"						)]

				(
				    (
				    SCREAMING_SNAKE_CASE
				)  ,(
				    SCREAMING_SNAKE_CASE
				)  ,(
				    SCREAMING_SNAKE_CASE
				)  ,(
				    SCREAMING_SNAKE_CASE
				)  ,
				) :   Tuple					  =							sd_pipe.encode_prompt(a   ,   negative_prompt=a						)

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							sd_pipe(
				    **a   ,   prompt_embeds=a   ,   negative_prompt_embeds=a   ,   pooled_prompt_embeds=a   ,   negative_pooled_prompt_embeds=a   ,   )
				SCREAMING_SNAKE_CASE :   int					  =							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 				__UpperCamelCase			(      self				:			str						)				->							Tuple:

				"""simple docstring"""

				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
			def 				__UpperCamelCase			(      self				:			Any   ,   a				:			int   ,   a				:			str="cpu"   ,   a				:			Dict=torch.floataa   ,   a				:			Any=0						)				->							Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							torch.Generator(device=a						).manual_seed(a						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							np.random.RandomState(a						).standard_normal((1, 4, 64, 64)						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							torch.from_numpy(a						).to(device=a   ,   dtype=a						)
				SCREAMING_SNAKE_CASE :   Dict					  =							{
				    "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				:			Dict						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"						)
				pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.get_inputs(a						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							pipe(**a						).images
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							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 typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a_      =     {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'VanForImageClassification',
			    'VanModel',
			    'VanPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_van import (
						    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
						    VanForImageClassification,
						    VanModel,
						    VanPreTrainedModel,
						)

else:
			import sys

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure)