



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__(				lowerCamelCase  ,				lowerCamelCase  ,				unittest.TestCase						):

   '''simple docstring'''




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


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




         """simple docstring"""

         torch.manual_seed(0)
         lowercase__  =	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      ,					)
         lowercase__  =	EulerDiscreteScheduler(
             beta_start=0.0_00_85      ,					beta_end=0.0_12      ,					steps_offset=1      ,					beta_schedule='scaled_linear'      ,					timestep_spacing='leading'      ,					)
         torch.manual_seed(0)
         lowercase__  =	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)
         lowercase__  =	CLIPTextConfig(
             bos_token_id=0      ,					eos_token_id=2      ,					hidden_size=32      ,					intermediate_size=37      ,					layer_norm_eps=1E-0_5      ,					num_attention_heads=4      ,					num_hidden_layers=5      ,					pad_token_id=1      ,					vocab_size=10_00      ,					hidden_act='gelu'      ,					projection_dim=32      ,					)
         lowercase__  =	CLIPTextModel(lowerCAmelCase)
         lowercase__  =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      ,					local_files_only=lowerCAmelCase)

         lowercase__  =	CLIPTextModelWithProjection(lowerCAmelCase)
         lowercase__  =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      ,					local_files_only=lowerCAmelCase)

         lowercase__  =	{
             '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	:      Optional[Any]      ,					lowerCAmelCase	:      int      ,					lowerCAmelCase	:      int=0)						->					Dict:




         """simple docstring"""

         lowercase__  =	floats_tensor((1, 3, 32, 32)      ,					rng=random.Random(lowerCAmelCase)).to(lowerCAmelCase)
         lowercase__  =	image / 2 + 0.5
         if str(lowerCAmelCase).startswith('mps'):
               lowercase__  =	torch.manual_seed(lowerCAmelCase)
         else:
               lowercase__  =	torch.Generator(device=lowerCAmelCase).manual_seed(lowerCAmelCase)
         lowercase__  =	{
             '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	:      Optional[Any])						->					str:




         """simple docstring"""

         lowercase__  =	'cpu'  # ensure determinism for the device-dependent torch.Generator
         lowercase__  =	self.get_dummy_components()
         lowercase__  =	StableDiffusionXLImgaImgPipeline(**lowerCAmelCase)
         lowercase__  =	sd_pipe.to(lowerCAmelCase)
         sd_pipe.set_progress_bar_config(disable=lowerCAmelCase)

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

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

         lowercase__  =	np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65])

         assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2


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




         """simple docstring"""

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


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




         """simple docstring"""

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


   def       UpperCAmelCase		(    self	:      str)						->					Tuple:




         """simple docstring"""

         pass



   def       UpperCAmelCase		(    self	:      int)						->					Dict:




         """simple docstring"""

         lowercase__  =	self.get_dummy_components()
         lowercase__  =	StableDiffusionXLImgaImgPipeline(**lowerCAmelCase)
         lowercase__  =	sd_pipe.to(lowerCAmelCase)
         lowercase__  =	sd_pipe.to(lowerCAmelCase)
         sd_pipe.set_progress_bar_config(disable=lowerCAmelCase)

         # forward without prompt embeds
         lowercase__  =	self.get_dummy_inputs(lowerCAmelCase)
         lowercase__  =	3 * ['this is a negative prompt']
         lowercase__  =	negative_prompt
         lowercase__  =	3 * [inputs['prompt']]

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

         # forward with prompt embeds
         lowercase__  =	self.get_dummy_inputs(lowerCAmelCase)
         lowercase__  =	3 * ['this is a negative prompt']
         lowercase__  =	3 * [inputs.pop('prompt')]

         (
             (
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						
         )  =	sd_pipe.encode_prompt(lowerCAmelCase      ,					negative_prompt=lowerCAmelCase)

         lowercase__  =	sd_pipe(
             **lowerCAmelCase      ,					prompt_embeds=lowerCAmelCase      ,					negative_prompt_embeds=lowerCAmelCase      ,					pooled_prompt_embeds=lowerCAmelCase      ,					negative_pooled_prompt_embeds=lowerCAmelCase      ,					)
         lowercase__  =	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	:      List[str])						->					Tuple:




         """simple docstring"""

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


   def       UpperCAmelCase		(    self	:      List[Any]      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      int="cpu"      ,					lowerCAmelCase	:      List[Any]=torch.floataa      ,					lowerCAmelCase	:      str=0)						->					str:




         """simple docstring"""

         lowercase__  =	torch.Generator(device=lowerCAmelCase).manual_seed(lowerCAmelCase)
         lowercase__  =	np.random.RandomState(lowerCAmelCase).standard_normal((1, 4, 64, 64))
         lowercase__  =	torch.from_numpy(lowerCAmelCase).to(device=lowerCAmelCase      ,					dtype=lowerCAmelCase)
         lowercase__  =	{
             '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	:      Any)						->					Dict:




         """simple docstring"""

         lowercase__  =	DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base')
         pipe.to(lowerCAmelCase)
         pipe.set_progress_bar_config(disable=lowerCAmelCase)

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

         assert image.shape == (1, 5_12, 5_12, 3)
         lowercase__  =	np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06])
         assert np.abs(image_slice - expected_slice).max() < 7E-3





import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


a__   :					Optional[Any] 						=      logging.get_logger(__name__)



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   def __init__(    self	:      Optional[Any]      ,					*lowerCAmelCase	:      Optional[Any]      ,					**lowerCAmelCase	:      List[Any])						->					None:




         """simple docstring"""

         warnings.warn(
             'The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
             ' use GLPNImageProcessor instead.'      ,					lowerCAmelCase      ,					)
         super().__init__(*lowerCAmelCase      ,					**lowerCAmelCase)
