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    (     _lowercase       ,_lowercase       ,_lowercase       ,unittest.TestCase      ):
					__lowerCamelCase			   =  StableDiffusionModelEditingPipeline
					__lowerCamelCase			   =  TEXT_TO_IMAGE_PARAMS
					__lowerCamelCase			   =  TEXT_TO_IMAGE_BATCH_PARAMS
					__lowerCamelCase			   =  TEXT_TO_IMAGE_IMAGE_PARAMS
					__lowerCamelCase			   =  TEXT_TO_IMAGE_IMAGE_PARAMS




					def      __a     (  self						)	->    int:



									'''simple docstring'''







									torch.manual_seed(0						)
									snake_case__   :      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       ,			)
									snake_case__   :      Optional[Any]   				= DDIMScheduler()
									torch.manual_seed(0						)
									snake_case__   :      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						)
									snake_case__   :      Optional[Any]   				= 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       ,			)
									snake_case__   :      str   				= CLIPTextModel(__lowerCAmelCase						)
									snake_case__   :      int   				= CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

									snake_case__   :      str   				= {
									    'unet': unet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}
									return components




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase=0						)	->    Tuple:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= torch.manual_seed(__lowerCAmelCase						)
									snake_case__   :      List[str]   				= {
									    '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      __a     (  self						)	->    Any:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= 'cpu'  # ensure determinism for the device-dependent torch.Generator
									snake_case__   :      Dict   				= self.get_dummy_components()
									snake_case__   :      Dict   				= StableDiffusionModelEditingPipeline(**__lowerCAmelCase						)
									snake_case__   :      Union[str, Any]   				= sd_pipe.to(__lowerCAmelCase						)
									sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

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

									snake_case__   :      Optional[int]   				= 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      __a     (  self						)	->    Any:



									'''simple docstring'''







									snake_case__   :      Any   				= 'cpu'  # ensure determinism for the device-dependent torch.Generator
									snake_case__   :      Optional[int]   				= self.get_dummy_components()
									snake_case__   :      Optional[Any]   				= StableDiffusionModelEditingPipeline(**__lowerCAmelCase						)
									snake_case__   :      Optional[Any]   				= sd_pipe.to(__lowerCAmelCase						)
									sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

									snake_case__   :      Dict   				= self.get_dummy_inputs(__lowerCAmelCase						)
									snake_case__   :      Optional[Any]   				= 'french fries'
									snake_case__   :      Tuple   				= sd_pipe(**__lowerCAmelCase       ,			negative_prompt=__lowerCAmelCase						)
									snake_case__   :      Any   				= output.images
									snake_case__   :      Any   				= image[0, -3:, -3:, -1]

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

									snake_case__   :      Optional[int]   				= 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      __a     (  self						)	->    Tuple:



									'''simple docstring'''







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

									snake_case__   :      Any   				= self.get_dummy_inputs(__lowerCAmelCase						)
									snake_case__   :      Union[str, Any]   				= sd_pipe(**__lowerCAmelCase						).images
									snake_case__   :      List[Any]   				= image[0, -3:, -3:, -1]

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

									snake_case__   :      Union[str, Any]   				= 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      __a     (  self						)	->    Tuple:



									'''simple docstring'''







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

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




					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







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




					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







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





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




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



									'''simple docstring'''







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




					def      __a     (  self       ,			__UpperCamelCase=0						)	->    str:



									'''simple docstring'''







									snake_case__   :      Tuple   				= torch.manual_seed(__lowerCAmelCase						)
									snake_case__   :      Tuple   				= {
									    'prompt': 'A field of roses',
									    'generator': generator,
									    'num_inference_steps': 3,
									    'guidance_scale': 7.5,
									    'output_type': 'numpy',
									}
									return inputs




					def      __a     (  self						)	->    Tuple:



									'''simple docstring'''







									snake_case__   :      List[str]   				= 'CompVis/stable-diffusion-v1-4'
									snake_case__   :      List[str]   				= StableDiffusionModelEditingPipeline.from_pretrained(__lowerCAmelCase       ,			safety_checker=__lowerCAmelCase						)
									pipe.to(__lowerCAmelCase						)
									pipe.set_progress_bar_config(disable=__lowerCAmelCase						)
									pipe.enable_attention_slicing()

									snake_case__   :      Union[str, Any]   				= self.get_inputs()
									snake_case__   :      Union[str, Any]   				= pipe(**__lowerCAmelCase						).images
									snake_case__   :      Dict   				= image[0, -3:, -3:, -1].flatten()

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

									snake_case__   :      str   				= 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'						)

									snake_case__   :      List[Any]   				= pipe(**__lowerCAmelCase						).images
									snake_case__   :      List[str]   				= image[0, -3:, -3:, -1].flatten()

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

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




					def      __a     (  self						)	->    str:



									'''simple docstring'''







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

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

									snake_case__   :      List[str]   				= self.get_inputs()
									snake_case__   :      Tuple   				= pipe(**__lowerCAmelCase						)

									snake_case__   :      List[str]   				= torch.cuda.max_memory_allocated()
									# make sure that less than 4.4 GB is allocated
									assert mem_bytes < 4.4 * 10**9









import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class 	_UpperCAmelCase						( _lowercase			,							unittest.TestCase       ):
					__A        = KandinskyPipeline
					__A        = [
					    "prompt",
					    "image_embeds",
					    "negative_image_embeds",
					]
					__A        = ["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
					__A        = [
					    "generator",
					    "height",
					    "width",
					    "latents",
					    "guidance_scale",
					    "negative_prompt",
					    "num_inference_steps",
					    "return_dict",
					    "guidance_scale",
					    "num_images_per_prompt",
					    "output_type",
					    "return_dict",
					]
					__A        = False




					@property
					def 						snake_case_		(    self							)    ->	Any:
							return 32




					@property
					def 						snake_case_		(    self							)    ->	Optional[Any]:
							return 32




					@property
					def 						snake_case_		(    self							)    ->	List[Any]:
							return self.time_input_dim




					@property
					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							return self.time_input_dim * 4




					@property
					def 						snake_case_		(    self							)    ->	Dict:
							return 100




					@property
					def 						snake_case_		(    self							)    ->	List[str]:
							UpperCamelCase__								=						XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""							)
							return tokenizer




					@property
					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							torch.manual_seed(0							)
							UpperCamelCase__								=						MCLIPConfig(
							    numDims=self.cross_attention_dim     ,   transformerDimensions=self.text_embedder_hidden_size     ,   hidden_size=self.text_embedder_hidden_size     ,   intermediate_size=37     ,   num_attention_heads=4     ,   num_hidden_layers=5     ,   vocab_size=1_005     ,   )

							UpperCamelCase__								=						MultilingualCLIP(__lowerCAmelCase							)
							UpperCamelCase__								=						text_encoder.eval()

							return text_encoder




					@property
					def 						snake_case_		(    self							)    ->	Optional[Any]:
							torch.manual_seed(0							)

							UpperCamelCase__								=						{
							    """in_channels""": 4,
							    # Out channels is double in channels because predicts mean and variance
							    """out_channels""": 8,
							    """addition_embed_type""": """text_image""",
							    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
							    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
							    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
							    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
							    """layers_per_block""": 1,
							    """encoder_hid_dim""": self.text_embedder_hidden_size,
							    """encoder_hid_dim_type""": """text_image_proj""",
							    """cross_attention_dim""": self.cross_attention_dim,
							    """attention_head_dim""": 4,
							    """resnet_time_scale_shift""": """scale_shift""",
							    """class_embed_type""": None,
							}

							UpperCamelCase__								=						UNetaDConditionModel(**__lowerCAmelCase							)
							return model




					@property
					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							return {
							    "block_out_channels": [32, 64],
							    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
							    "in_channels": 3,
							    "latent_channels": 4,
							    "layers_per_block": 1,
							    "norm_num_groups": 8,
							    "norm_type": "spatial",
							    "num_vq_embeddings": 12,
							    "out_channels": 3,
							    "up_block_types": [
							        "AttnUpDecoderBlock2D",
							        "UpDecoderBlock2D",
							    ],
							    "vq_embed_dim": 4,
							}




					@property
					def 						snake_case_		(    self							)    ->	Optional[int]:
							torch.manual_seed(0							)
							UpperCamelCase__								=						VQModel(**self.dummy_movq_kwargs							)
							return model




					def 						snake_case_		(    self							)    ->	Dict:
							UpperCamelCase__								=						self.dummy_text_encoder
							UpperCamelCase__								=						self.dummy_tokenizer
							UpperCamelCase__								=						self.dummy_unet
							UpperCamelCase__								=						self.dummy_movq

							UpperCamelCase__								=						DDIMScheduler(
							    num_train_timesteps=1_000     ,   beta_schedule="""linear"""     ,   beta_start=0.0_0_0_8_5     ,   beta_end=0.0_1_2     ,   clip_sample=__lowerCAmelCase     ,   set_alpha_to_one=__lowerCAmelCase     ,   steps_offset=1     ,   prediction_type="""epsilon"""     ,   thresholding=__lowerCAmelCase     ,   )

							UpperCamelCase__								=						{
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """movq""": movq,
							}
							return components




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase=0							)    ->	str:
							UpperCamelCase__								=						floats_tensor((1, self.cross_attention_dim)     ,   rng=random.Random(__lowerCAmelCase							)							).to(__lowerCAmelCase							)
							UpperCamelCase__								=						floats_tensor((1, self.cross_attention_dim)     ,   rng=random.Random(seed + 1							)							).to(__lowerCAmelCase							)
							if str(__lowerCAmelCase							).startswith("""mps"""							):
									UpperCamelCase__								=						torch.manual_seed(__lowerCAmelCase							)
							else:
									UpperCamelCase__								=						torch.Generator(device=__lowerCAmelCase							).manual_seed(__lowerCAmelCase							)
							UpperCamelCase__								=						{
							    """prompt""": """horse""",
							    """image_embeds""": image_embeds,
							    """negative_image_embeds""": negative_image_embeds,
							    """generator""": generator,
							    """height""": 64,
							    """width""": 64,
							    """guidance_scale""": 4.0,
							    """num_inference_steps""": 2,
							    """output_type""": """np""",
							}
							return inputs




					def 						snake_case_		(    self							)    ->	Tuple:
							UpperCamelCase__								=						"""cpu"""

							UpperCamelCase__								=						self.get_dummy_components()

							UpperCamelCase__								=						self.pipeline_class(**__lowerCAmelCase							)
							UpperCamelCase__								=						pipe.to(__lowerCAmelCase							)

							pipe.set_progress_bar_config(disable=__lowerCAmelCase							)

							UpperCamelCase__								=						pipe(**self.get_dummy_inputs(__lowerCAmelCase							)							)
							UpperCamelCase__								=						output.images

							UpperCamelCase__								=						pipe(
							    **self.get_dummy_inputs(__lowerCAmelCase							)     ,   return_dict=__lowerCAmelCase     ,   )[0]

							UpperCamelCase__								=						image[0, -3:, -3:, -1]
							UpperCamelCase__								=						image_from_tuple[0, -3:, -3:, -1]

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

							UpperCamelCase__								=						np.array(
							    [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_3]							)

							assert (
							    np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
							), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
							assert (
							    np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2
							), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""




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




					def 						snake_case_		(    self							)    ->	str:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




					def 						snake_case_		(    self							)    ->	Any:
							UpperCamelCase__								=						load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinsky/kandinsky_text2img_cat_fp16.npy"""							)

							UpperCamelCase__								=						KandinskyPriorPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1-prior"""     ,   torch_dtype=torch.floataa							)
							pipe_prior.to(__lowerCAmelCase							)

							UpperCamelCase__								=						KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1"""     ,   torch_dtype=torch.floataa							)
							UpperCamelCase__								=						pipeline.to(__lowerCAmelCase							)
							pipeline.set_progress_bar_config(disable=__lowerCAmelCase							)

							UpperCamelCase__								=						"""red cat, 4k photo"""

							UpperCamelCase__								=						torch.Generator(device="""cuda"""							).manual_seed(0							)
							UpperCamelCase__	,      UpperCamelCase__								=						pipe_prior(
							    __lowerCAmelCase     ,   generator=__lowerCAmelCase     ,   num_inference_steps=5     ,   negative_prompt=""""""     ,   ).to_tuple()

							UpperCamelCase__								=						torch.Generator(device="""cuda"""							).manual_seed(0							)
							UpperCamelCase__								=						pipeline(
							    __lowerCAmelCase     ,   image_embeds=__lowerCAmelCase     ,   negative_image_embeds=__lowerCAmelCase     ,   generator=__lowerCAmelCase     ,   num_inference_steps=100     ,   output_type="""np"""     ,   )

							UpperCamelCase__								=						output.images[0]

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

							assert_mean_pixel_difference(__lowerCAmelCase     ,   __lowerCAmelCase							)





