





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    __snake_case	(							__lowerCamelCase       ,      __lowerCamelCase       ,      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			:   Union[str, Any]		):
         torch.manual_seed(0		)
         __snake_case:						List[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=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							,    )
         __snake_case:						List[Any]			  =						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:						Dict			  =						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		)
         __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=1_000							,    hidden_act="""gelu"""							,    projection_dim=32							,    )
         __snake_case:						Optional[int]			  =						CLIPTextModel(A		)
         __snake_case:						Optional[Any]			  =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							,    local_files_only=A		)

         __snake_case:						List[Any]			  =						CLIPTextModelWithProjection(A		)
         __snake_case:						List[Any]			  =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							,    local_files_only=A		)

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

         __snake_case:						List[Any]			  =						self.get_dummy_inputs(A		)
         __snake_case:						Tuple			  =						sd_pipe(**A		).images
         __snake_case:						int			  =						image[0, -3:, -3:, -1]

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

         __snake_case:						List[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			:   int		):
         super().test_attention_slicing_forward_pass(expected_max_diff=3E-3		)




       def 							UpperCAmelCase__			(  self			:   Any		):
         super().test_inference_batch_single_identical(expected_max_diff=3E-3		)




       def 							UpperCAmelCase__			(  self			:   List[Any]		):
         pass






       def 							UpperCAmelCase__			(  self			:   Any		):
         __snake_case:						Any			  =						self.get_dummy_components()
         __snake_case:						Tuple			  =						StableDiffusionXLImgaImgPipeline(**A		)
         __snake_case:						int			  =						sd_pipe.to(A		)
         __snake_case:						List[str]			  =						sd_pipe.to(A		)
         sd_pipe.set_progress_bar_config(disable=A		)

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

         __snake_case:						Optional[Any]			  =						sd_pipe(**A		)
         __snake_case:						List[Any]			  =						output.images[0, -3:, -3:, -1]

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

         (
             (
             __snake_case
         )  ,       (
             __snake_case
         )  ,       (
             __snake_case
         )  ,       (
             __snake_case
         )  ,       
         ):						int			  =						sd_pipe.encode_prompt(A							,    negative_prompt=A		)

         __snake_case:						Optional[Any]			  =						sd_pipe(
             **A							,    prompt_embeds=A							,    negative_prompt_embeds=A							,    pooled_prompt_embeds=A							,    negative_pooled_prompt_embeds=A							,    )
         __snake_case:						str			  =						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    __snake_case	(							unittest.TestCase      ):





       '''simple docstring'''







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




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

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

         assert image.shape == (1, 512, 512, 3)
         __snake_case:						Dict			  =						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













import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    "The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
    " StableDiffusionImg2ImgPipeline` instead."
)






