

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    (			UpperCAmelCase_		,	UpperCAmelCase_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Optional[int]      							=					StableDiffusionXLImgaImgPipeline
			_UpperCAmelCase       :							Optional[int]      							=					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
			_UpperCAmelCase       :							Dict      							=					PipelineTesterMixin.required_optional_params - {'''latents'''}
			_UpperCAmelCase       :							str      							=					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			_UpperCAmelCase       :							Optional[Any]      							=					IMAGE_TO_IMAGE_IMAGE_PARAMS
			_UpperCAmelCase       :							List[Any]      							=					IMAGE_TO_IMAGE_IMAGE_PARAMS



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	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=lowerCAmelCase__       ,						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_:			Tuple  		=	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_:			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       ,						sample_size=128       ,						)
									torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			List[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[Any]  		=	CLIPTextModel(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       ,						local_files_only=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	CLIPTextModelWithProjection(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       ,						local_files_only=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			str  		=	{
									    "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 	_SCREAMING_SNAKE_CASE      (			self  :					int       ,						lowerCAmelCase__  :					Union[str, Any]       ,						lowerCAmelCase__  :					List[str]=0):
									SCREAMING_SNAKE_CASE_:			Tuple  		=	floats_tensor((1, 3, 32, 32)       ,						rng=random.Random(lowerCAmelCase__)).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	image / 2 + 0.5
									if str(lowerCAmelCase__).startswith("mps"):
															SCREAMING_SNAKE_CASE_:			str  		=	torch.manual_seed(lowerCAmelCase__)
									else:
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	{
									    "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 	_SCREAMING_SNAKE_CASE      (			self  :					Any):
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	"cpu"  # ensure determinism for the device-dependent torch.Generator
									SCREAMING_SNAKE_CASE_:			int  		=	self.get_dummy_components()
									SCREAMING_SNAKE_CASE_:			Any  		=	StableDiffusionXLImgaImgPipeline(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			int  		=	sd_pipe.to(lowerCAmelCase__)
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.get_dummy_inputs(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Dict  		=	sd_pipe(**lowerCAmelCase__).images
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	image[0, -3:, -3:, -1]

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

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	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 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									super().test_attention_slicing_forward_pass(expected_max_diff=3E-3)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									super().test_inference_batch_single_identical(expected_max_diff=3E-3)



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




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.get_dummy_components()
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	StableDiffusionXLImgaImgPipeline(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	sd_pipe.to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	sd_pipe.to(lowerCAmelCase__)
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)

									# forward without prompt embeds
									SCREAMING_SNAKE_CASE_:			List[str]  		=	self.get_dummy_inputs(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			str  		=	3 * ["this is a negative prompt"]
									SCREAMING_SNAKE_CASE_:			List[str]  		=	negative_prompt
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	3 * [inputs["prompt"]]

									SCREAMING_SNAKE_CASE_:			int  		=	sd_pipe(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			str  		=	output.images[0, -3:, -3:, -1]

									# forward with prompt embeds
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.get_dummy_inputs(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	3 * ["this is a negative prompt"]
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	3 * [inputs.pop("prompt")]

									(
									    (
									    SCREAMING_SNAKE_CASE_
									)		,   (
									    SCREAMING_SNAKE_CASE_
									)		,   (
									    SCREAMING_SNAKE_CASE_
									)		,   (
									    SCREAMING_SNAKE_CASE_
									)		,   
									):			Optional[int]  		=	sd_pipe.encode_prompt(lowerCAmelCase__       ,						negative_prompt=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	sd_pipe(
									    **lowerCAmelCase__       ,						prompt_embeds=lowerCAmelCase__       ,						negative_prompt_embeds=lowerCAmelCase__       ,						pooled_prompt_embeds=lowerCAmelCase__       ,						negative_pooled_prompt_embeds=lowerCAmelCase__       ,						)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	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     ):




			"""simple docstring"""







			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					str="cpu"       ,						lowerCAmelCase__  :					Tuple=torch.floataa       ,						lowerCAmelCase__  :					int=0):
									SCREAMING_SNAKE_CASE_:			Any  		=	torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	np.random.RandomState(lowerCAmelCase__).standard_normal((1, 4, 64, 64))
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	torch.from_numpy(lowerCAmelCase__).to(device=lowerCAmelCase__       ,						dtype=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	{
									    "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 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			Any  		=	DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base")
									pipe.to(lowerCAmelCase__)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.get_inputs(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	pipe(**lowerCAmelCase__).images
									SCREAMING_SNAKE_CASE_:			Any  		=	image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 512, 3)
									SCREAMING_SNAKE_CASE_:			str  		=	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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase							:  Optional[int]            =						logging.get_logger(__name__)

lowerCAmelCase							:  int            =						{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							int      							=					'''fnet'''



			def __init__(			self  :					Optional[Any]       ,						lowerCAmelCase__  :					List[str]=3_2000       ,						lowerCAmelCase__  :					Union[str, Any]=768       ,						lowerCAmelCase__  :					Optional[Any]=12       ,						lowerCAmelCase__  :					Tuple=3072       ,						lowerCAmelCase__  :					Any="gelu_new"       ,						lowerCAmelCase__  :					Union[str, Any]=0.1       ,						lowerCAmelCase__  :					Optional[Any]=512       ,						lowerCAmelCase__  :					int=4       ,						lowerCAmelCase__  :					List[Any]=0.02       ,						lowerCAmelCase__  :					Union[str, Any]=1E-12       ,						lowerCAmelCase__  :					Optional[int]=False       ,						lowerCAmelCase__  :					Any=512       ,						lowerCAmelCase__  :					Optional[int]=3       ,						lowerCAmelCase__  :					int=1       ,						lowerCAmelCase__  :					List[Any]=2       ,						**lowerCAmelCase__  :					Optional[int]       ,						):
									super().__init__(pad_token_id=lowerCAmelCase__       ,						bos_token_id=lowerCAmelCase__       ,						eos_token_id=lowerCAmelCase__       ,						**lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			str  		=	vocab_size
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	max_position_embeddings
									SCREAMING_SNAKE_CASE_:			Tuple  		=	hidden_size
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	num_hidden_layers
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	intermediate_size
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	hidden_act
									SCREAMING_SNAKE_CASE_:			Any  		=	hidden_dropout_prob
									SCREAMING_SNAKE_CASE_:			int  		=	initializer_range
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	type_vocab_size
									SCREAMING_SNAKE_CASE_:			int  		=	layer_norm_eps
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	use_tpu_fourier_optimizations
									SCREAMING_SNAKE_CASE_:			int  		=	tpu_short_seq_length






