



import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		[tensor.shape for tensor in tensor_list]
      return all(shape == shapes[0] for shape in shapes[1:] )





class    lowerCamelCase__  (						UpperCAmelCase_       ,    UpperCAmelCase_       ,    UpperCAmelCase_       ,    unittest.TestCase):
     """simple docstring"""



     _A       =			StableDiffusionLatentUpscalePipeline
     _A       =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         'height',
         'width',
         'cross_attention_kwargs',
         'negative_prompt_embeds',
         'prompt_embeds',
     }
     _A       =			PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
     _A       =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     _A       =			frozenset(
         [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     _A       =			frozenset([])

     _A       =			True







     @property
     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		1
           lowerCamelCase								=		4
           lowerCamelCase								=		(16, 16)

           lowerCamelCase								=		floats_tensor((batch_size, num_channels) + sizes				,			rng=random.Random(0    )    ).to(__a    )
           return image







     def 						_a      (self    ):






           '''simple docstring'''



           torch.manual_seed(0    )
           lowerCamelCase								=		UNetaDConditionModel(
               act_fn="gelu"				,			attention_head_dim=8				,			norm_num_groups=__a				,			block_out_channels=[32, 32, 64, 64]				,			time_cond_proj_dim=1_60				,			conv_in_kernel=1				,			conv_out_kernel=1				,			cross_attention_dim=32				,			down_block_types=(
                   "KDownBlock2D",
                   "KCrossAttnDownBlock2D",
                   "KCrossAttnDownBlock2D",
                   "KCrossAttnDownBlock2D",
               )				,			in_channels=8				,			mid_block_type=__a				,			only_cross_attention=__a				,			out_channels=5				,			resnet_time_scale_shift="scale_shift"				,			time_embedding_type="fourier"				,			timestep_post_act="gelu"				,			up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")				,			)
           lowerCamelCase								=		AutoencoderKL(
               block_out_channels=[32, 32, 64, 64]				,			in_channels=3				,			out_channels=3				,			down_block_types=[
                   "DownEncoderBlock2D",
                   "DownEncoderBlock2D",
                   "DownEncoderBlock2D",
                   "DownEncoderBlock2D",
               ]				,			up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]				,			latent_channels=4				,			)
           lowerCamelCase								=		EulerDiscreteScheduler(prediction_type="sample"    )
           lowerCamelCase								=		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=10_00				,			hidden_act="quick_gelu"				,			projection_dim=5_12				,			)
           lowerCamelCase								=		CLIPTextModel(__a    )
           lowerCamelCase								=		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )

           lowerCamelCase								=		{
               "unet": model.eval(),
               "vae": vae.eval(),
               "scheduler": scheduler,
               "text_encoder": text_encoder,
               "tokenizer": tokenizer,
           }

           return components







     def 						_a      (self				,			__a				,			__a=0    ):






           '''simple docstring'''



           if str(__a    ).startswith("mps"    ):
                 lowerCamelCase								=		torch.manual_seed(__a    )
           else:
                 lowerCamelCase								=		torch.Generator(device=__a    ).manual_seed(__a    )
           lowerCamelCase								=		{
               "prompt": "A painting of a squirrel eating a burger",
               "image": self.dummy_image.cpu(),
               "generator": generator,
               "num_inference_steps": 2,
               "output_type": "numpy",
           }
           return inputs







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		"cpu"

           lowerCamelCase								=		self.get_dummy_components()
           lowerCamelCase								=		self.pipeline_class(**__a    )
           pipe.to(__a    )
           pipe.set_progress_bar_config(disable=__a    )

           lowerCamelCase								=		self.get_dummy_inputs(__a    )
           lowerCamelCase								=		pipe(**__a    ).images
           lowerCamelCase								=		image[0, -3:, -3:, -1]

           self.assertEqual(image.shape				,			(1, 2_56, 2_56, 3)    )
           lowerCamelCase								=		np.array(
               [0.4722_2412, 0.4192_1633, 0.4471_7434, 0.4687_4192, 0.4258_8258, 0.4615_0726, 0.467_7534, 0.4558_3832, 0.4857_9055]    )
           lowerCamelCase								=		np.abs(image_slice.flatten() - expected_slice    ).max()
           self.assertLessEqual(__a				,			1E-3    )







     def 						_a      (self    ):






           '''simple docstring'''



           super().test_attention_slicing_forward_pass(expected_max_diff=7E-3    )







     def 						_a      (self    ):






           '''simple docstring'''



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







     def 						_a      (self    ):






           '''simple docstring'''



           super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3    )







     def 						_a      (self    ):






           '''simple docstring'''



           super().test_inference_batch_single_identical(expected_max_diff=7E-3    )







     def 						_a      (self    ):






           '''simple docstring'''



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







     def 						_a      (self    ):






           '''simple docstring'''



           super().test_save_load_local(expected_max_difference=3E-3    )







     def 						_a      (self    ):






           '''simple docstring'''



           super().test_save_load_optional_components(expected_max_difference=3E-3    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		[
               "DDIMScheduler",
               "DDPMScheduler",
               "PNDMScheduler",
               "HeunDiscreteScheduler",
               "EulerAncestralDiscreteScheduler",
               "KDPM2DiscreteScheduler",
               "KDPM2AncestralDiscreteScheduler",
               "DPMSolverSDEScheduler",
           ]
           lowerCamelCase								=		self.get_dummy_components()
           lowerCamelCase								=		self.pipeline_class(**__a    )

           # make sure that PNDM does not need warm-up
           pipe.scheduler.register_to_config(skip_prk_steps=__a    )

           pipe.to(__a    )
           pipe.set_progress_bar_config(disable=__a    )
           lowerCamelCase								=		self.get_dummy_inputs(__a    )
           lowerCamelCase								=		2

           lowerCamelCase								=		[]
           for scheduler_enum in KarrasDiffusionSchedulers:
                 if scheduler_enum.name in skip_schedulers:
                       # no sigma schedulers are not supported
                       # no schedulers
                       continue

                 lowerCamelCase								=		getattr(__a				,			scheduler_enum.name    )
                 lowerCamelCase								=		scheduler_cls.from_config(pipe.scheduler.config    )
                 lowerCamelCase								=		pipe(**__a    )[0]
                 outputs.append(__a    )

           assert check_same_shape(__a    )





@require_torch_gpu
@slow
class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     def 						_a      (self    ):






           '''simple docstring'''



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







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		torch.manual_seed(33    )

           lowerCamelCase								=		StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"				,			torch_dtype=torch.floataa    )
           pipe.to("cuda"    )

           lowerCamelCase								=		StableDiffusionLatentUpscalePipeline.from_pretrained(
               "stabilityai/sd-x2-latent-upscaler"				,			torch_dtype=torch.floataa    )
           upscaler.to("cuda"    )

           lowerCamelCase								=		"a photo of an astronaut high resolution, unreal engine, ultra realistic"

           lowerCamelCase								=		pipe(__a				,			generator=__a				,			output_type="latent"    ).images

           lowerCamelCase								=		upscaler(
               prompt=__a				,			image=__a				,			num_inference_steps=20				,			guidance_scale=0				,			generator=__a				,			output_type="np"				,			).images[0]

           lowerCamelCase								=		load_numpy(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy"    )
           assert np.abs((expected_image - image).mean()    ) < 5E-2







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		torch.manual_seed(33    )

           lowerCamelCase								=		StableDiffusionLatentUpscalePipeline.from_pretrained(
               "stabilityai/sd-x2-latent-upscaler"				,			torch_dtype=torch.floataa    )
           upscaler.to("cuda"    )

           lowerCamelCase								=		"the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"

           lowerCamelCase								=		load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png"    )

           lowerCamelCase								=		upscaler(
               prompt=__a				,			image=__a				,			num_inference_steps=20				,			guidance_scale=0				,			generator=__a				,			output_type="np"				,			).images[0]

           lowerCamelCase								=		load_numpy(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy"    )
           assert np.abs((expected_image - image).max()    ) < 5E-2




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			['image_processor', 'tokenizer']
     _A       =			'CLIPImageProcessor'
     _A       =			('CLIPTokenizer', 'CLIPTokenizerFast')







     def __init__(self				,			__a=None				,			__a=None				,			**__a    ):






           '''simple docstring'''



           lowerCamelCase								=		None
           if "feature_extractor" in kwargs:
                 warnings.warn(
                     "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                     " instead."				,			__a				,			)
                 lowerCamelCase								=		kwargs.pop("feature_extractor"    )

           lowerCamelCase								=		image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
                 raise ValueError("You need to specify an `image_processor`."    )
           if tokenizer is None:
                 raise ValueError("You need to specify a `tokenizer`."    )

           super().__init__(__a				,			__a    )







     def __call__(self				,			__a=None				,			__a=None				,			__a=None				,			**__a    ):






           '''simple docstring'''



           if text is None and images is None:
                 raise ValueError("You have to specify either text or images. Both cannot be none."    )

           if text is not None:
                 lowerCamelCase								=		self.tokenizer(__a				,			return_tensors=__a				,			**__a    )

           if images is not None:
                 lowerCamelCase								=		self.image_processor(__a				,			return_tensors=__a				,			**__a    )

           if text is not None and images is not None:
                 lowerCamelCase								=		image_features.pixel_values
                 return encoding
           elif text is not None:
                 return encoding
           else:
                 return BatchEncoding(data=dict(**__a    )				,			tensor_type=__a    )







     def 						_a      (self				,			*__a				,			**__a    ):






           '''simple docstring'''



           return self.tokenizer.batch_decode(*__a				,			**__a    )







     def 						_a      (self				,			*__a				,			**__a    ):






           '''simple docstring'''



           return self.tokenizer.decode(*__a				,			**__a    )







     @property
     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.tokenizer.model_input_names
           lowerCamelCase								=		self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )







     @property
     def 						_a      (self    ):






           '''simple docstring'''



           warnings.warn(
               "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,			__a				,			)
           return self.image_processor_class







     @property
     def 						_a      (self    ):






           '''simple docstring'''



           warnings.warn(
               "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,			__a				,			)
           return self.image_processor