




"""simple docstring"""


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				)						->    str:
      lowerCAmelCase__      :			Optional[int]   =	[tensor.shape for tensor in tensor_list]
      return all(shape == shapes[0] for shape in shapes[1:]				)


class _lowerCamelCase    (					a_       ,	a_       ,	a_       ,	unittest.TestCase						):
   _lowerCamelCase    :str     				=     StableDiffusionLatentUpscalePipeline
   _lowerCamelCase    :Any     				=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
       "height",
       "width",
       "cross_attention_kwargs",
       "negative_prompt_embeds",
       "prompt_embeds",
   }
   _lowerCamelCase    :Optional[int]     				=     PipelineTesterMixin.required_optional_params - {"num_images_per_prompt"}
   _lowerCamelCase    :str     				=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   _lowerCamelCase    :Optional[int]     				=     frozenset(
       []						)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   _lowerCamelCase    :Any     				=     frozenset([]						)

   _lowerCamelCase    :Dict     				=     True






   @property
   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						Any:

         """simple docstring"""
         lowerCAmelCase__      :			List[Any]   =	1
         lowerCAmelCase__      :			Any   =	4
         lowerCAmelCase__      :			int   =	(16, 16)

         lowerCAmelCase__      :			Optional[int]   =	floats_tensor((batch_size, num_channels) + sizes   ,     rng=random.Random(0   )   ).to(UpperCamelCase   )
         return image






   def 	_lowerCAmelCase   (		self       :		int   )			->						Any:

         """simple docstring"""
         torch.manual_seed(0   )
         lowerCAmelCase__      :			Optional[Any]   =	UNetaDConditionModel(
             act_fn="""gelu"""   ,     attention_head_dim=8   ,     norm_num_groups=UpperCamelCase   ,     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=UpperCamelCase   ,     only_cross_attention=UpperCamelCase   ,     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__      :			Tuple   =	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__      :			Optional[Any]   =	EulerDiscreteScheduler(prediction_type="""sample"""   )
         lowerCAmelCase__      :			List[Any]   =	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="""quick_gelu"""   ,     projection_dim=5_12   ,     )
         lowerCAmelCase__      :			Union[str, Any]   =	CLIPTextModel(UpperCamelCase   )
         lowerCAmelCase__      :			List[str]   =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

         lowerCAmelCase__      :			Union[str, Any]   =	{
             """unet""": model.eval(),
             """vae""": vae.eval(),
             """scheduler""": scheduler,
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
         }

         return components






   def 	_lowerCAmelCase   (		self       :		List[str]   ,     UpperCamelCase       :		Optional[Any]   ,     UpperCamelCase       :		List[str]=0   )			->						Tuple:

         """simple docstring"""
         if str(UpperCamelCase   ).startswith("""mps"""   ):
               lowerCAmelCase__      :			List[Any]   =	torch.manual_seed(UpperCamelCase   )
         else:
               lowerCAmelCase__      :			List[str]   =	torch.Generator(device=UpperCamelCase   ).manual_seed(UpperCamelCase   )
         lowerCAmelCase__      :			int   =	{
             """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 	_lowerCAmelCase   (		self       :		str   )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			List[Any]   =	"""cpu"""

         lowerCAmelCase__      :			Optional[int]   =	self.get_dummy_components()
         lowerCAmelCase__      :			Dict   =	self.pipeline_class(**UpperCamelCase   )
         pipe.to(UpperCamelCase   )
         pipe.set_progress_bar_config(disable=UpperCamelCase   )

         lowerCAmelCase__      :			Union[str, Any]   =	self.get_dummy_inputs(UpperCamelCase   )
         lowerCAmelCase__      :			Any   =	pipe(**UpperCamelCase   ).images
         lowerCAmelCase__      :			List[str]   =	image[0, -3:, -3:, -1]

         self.assertEqual(image.shape   ,     (1, 2_56, 2_56, 3)   )
         lowerCAmelCase__      :			str   =	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__      :			Dict   =	np.abs(image_slice.flatten() - expected_slice   ).max()
         self.assertLessEqual(UpperCamelCase   ,     1E-3   )






   def 	_lowerCAmelCase   (		self       :		Dict   )			->						List[Any]:

         """simple docstring"""
         super().test_attention_slicing_forward_pass(expected_max_diff=7E-3   )






   def 	_lowerCAmelCase   (		self       :		Dict   )			->						List[Any]:

         """simple docstring"""
         super().test_cpu_offload_forward_pass(expected_max_diff=3E-3   )






   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						Dict:

         """simple docstring"""
         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3   )






   def 	_lowerCAmelCase   (		self       :		str   )			->						str:

         """simple docstring"""
         super().test_inference_batch_single_identical(expected_max_diff=7E-3   )






   def 	_lowerCAmelCase   (		self       :		int   )			->						Any:

         """simple docstring"""
         super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3   )






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

         """simple docstring"""
         super().test_save_load_local(expected_max_difference=3E-3   )






   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						List[Any]:

         """simple docstring"""
         super().test_save_load_optional_components(expected_max_difference=3E-3   )






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	[
             """DDIMScheduler""",
             """DDPMScheduler""",
             """PNDMScheduler""",
             """HeunDiscreteScheduler""",
             """EulerAncestralDiscreteScheduler""",
             """KDPM2DiscreteScheduler""",
             """KDPM2AncestralDiscreteScheduler""",
             """DPMSolverSDEScheduler""",
         ]
         lowerCAmelCase__      :			Union[str, Any]   =	self.get_dummy_components()
         lowerCAmelCase__      :			List[str]   =	self.pipeline_class(**UpperCamelCase   )

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

         pipe.to(UpperCamelCase   )
         pipe.set_progress_bar_config(disable=UpperCamelCase   )
         lowerCAmelCase__      :			Any   =	self.get_dummy_inputs(UpperCamelCase   )
         lowerCAmelCase__      :			Tuple   =	2

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

               lowerCAmelCase__      :			Tuple   =	getattr(UpperCamelCase   ,     scheduler_enum.name   )
               lowerCAmelCase__      :			int   =	scheduler_cls.from_config(pipe.scheduler.config   )
               lowerCAmelCase__      :			int   =	pipe(**UpperCamelCase   )[0]
               outputs.append(UpperCamelCase   )

         assert check_same_shape(UpperCamelCase   )






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






   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						Tuple:

         """simple docstring"""
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Optional[int]:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	torch.manual_seed(33   )

         lowerCAmelCase__      :			List[str]   =	StableDiffusionPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""   ,     torch_dtype=torch.floataa   )
         pipe.to("""cuda"""   )

         lowerCAmelCase__      :			List[str]   =	StableDiffusionLatentUpscalePipeline.from_pretrained(
             """stabilityai/sd-x2-latent-upscaler"""   ,     torch_dtype=torch.floataa   )
         upscaler.to("""cuda"""   )

         lowerCAmelCase__      :			int   =	"""a photo of an astronaut high resolution, unreal engine, ultra realistic"""

         lowerCAmelCase__      :			int   =	pipe(UpperCamelCase   ,     generator=UpperCamelCase   ,     output_type="""latent"""   ).images

         lowerCAmelCase__      :			Optional[Any]   =	upscaler(
             prompt=UpperCamelCase   ,     image=UpperCamelCase   ,     num_inference_steps=20   ,     guidance_scale=0   ,     generator=UpperCamelCase   ,     output_type="""np"""   ,     ).images[0]

         lowerCAmelCase__      :			Any   =	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 	_lowerCAmelCase   (		self       :		Tuple   )			->						List[str]:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	torch.manual_seed(33   )

         lowerCAmelCase__      :			Optional[Any]   =	StableDiffusionLatentUpscalePipeline.from_pretrained(
             """stabilityai/sd-x2-latent-upscaler"""   ,     torch_dtype=torch.floataa   )
         upscaler.to("""cuda"""   )

         lowerCAmelCase__      :			Optional[int]   =	"""the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"""

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

         lowerCAmelCase__      :			str   =	upscaler(
             prompt=UpperCamelCase   ,     image=UpperCamelCase   ,     num_inference_steps=20   ,     guidance_scale=0   ,     generator=UpperCamelCase   ,     output_type="""np"""   ,     ).images[0]

         lowerCAmelCase__      :			List[str]   =	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











"""simple docstring"""


# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


_A													=      open  # noqa: we just need to have a builtin inside this module to test it properly





