import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 					snake_case__       (				lowerCamelCase_					,						unittest.TestCase							):
			_SCREAMING_SNAKE_CASE	: Optional[Any]						    =      RePaintPipeline
			_SCREAMING_SNAKE_CASE	: List[Any]						    =      IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
			_SCREAMING_SNAKE_CASE	: List[Any]						    =      PipelineTesterMixin.required_optional_params - {
			    """latents""",
			    """num_images_per_prompt""",
			    """callback""",
			    """callback_steps""",
			}
			_SCREAMING_SNAKE_CASE	: List[Any]						    =      IMAGE_INPAINTING_BATCH_PARAMS
			_SCREAMING_SNAKE_CASE	: List[str]						    =      False



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




									'''simple docstring'''




									torch.manual_seed(0   )
									torch.manual_seed(0   )
									snake_case_     :			Union[str, Any]       =       UNetaDModel(
									    block_out_channels=(32, 64)			,	layers_per_block=2			,	sample_size=32			,	in_channels=3			,	out_channels=3			,	down_block_types=("DownBlock2D", "AttnDownBlock2D")			,	up_block_types=("AttnUpBlock2D", "UpBlock2D")			,	)
									snake_case_     :			Union[str, Any]       =       RePaintScheduler()
									snake_case_     :			Optional[Any]       =       {"unet": unet, "scheduler": scheduler}
									return components



			def 					UpperCAmelCase__       (				self   :   int			,	A__   :   Dict			,	A__   :   Dict=0   )							->     Union[str, Any]:




									'''simple docstring'''




									if str(A__   ).startswith("mps"   ):
															snake_case_     :			str       =       torch.manual_seed(A__   )
									else:
															snake_case_     :			List[Any]       =       torch.Generator(device=A__   ).manual_seed(A__   )
									snake_case_     :			Any       =       np.random.RandomState(A__   ).standard_normal((1, 3, 32, 32)   )
									snake_case_     :			Any       =       torch.from_numpy(A__   ).to(device=A__			,	dtype=torch.floataa   )
									snake_case_     :			List[str]       =       (image > 0).to(device=A__			,	dtype=torch.floataa   )
									snake_case_     :			int       =       {
									    "image": image,
									    "mask_image": mask,
									    "generator": generator,
									    "num_inference_steps": 5,
									    "eta": 0.0,
									    "jump_length": 2,
									    "jump_n_sample": 2,
									    "output_type": "numpy",
									}
									return inputs



			def 					UpperCAmelCase__       (				self   :   int   )							->     str:




									'''simple docstring'''




									snake_case_     :			List[Any]       =       "cpu"  # ensure determinism for the device-dependent torch.Generator
									snake_case_     :			Any       =       self.get_dummy_components()
									snake_case_     :			Optional[int]       =       RePaintPipeline(**A__   )
									snake_case_     :			Optional[Any]       =       sd_pipe.to(A__   )
									sd_pipe.set_progress_bar_config(disable=A__   )

									snake_case_     :			Any       =       self.get_dummy_inputs(A__   )
									snake_case_     :			Dict       =       sd_pipe(**A__   ).images
									snake_case_     :			Optional[int]       =       image[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									snake_case_     :			List[str]       =       np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]   )

									assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3



			@skip_mps
			def 					UpperCAmelCase__       (				self   :   List[Any]   )							->     int:




									'''simple docstring'''




									return super().test_save_load_local()



			@unittest.skip("non-deterministic pipeline"   )
			def 					UpperCAmelCase__       (				self   :   Dict   )							->     Tuple:




									'''simple docstring'''




									return super().test_inference_batch_single_identical()



			@skip_mps
			def 					UpperCAmelCase__       (				self   :   Tuple   )							->     int:




									'''simple docstring'''




									return super().test_dict_tuple_outputs_equivalent()



			@skip_mps
			def 					UpperCAmelCase__       (				self   :   Dict   )							->     Tuple:




									'''simple docstring'''




									return super().test_save_load_optional_components()



			@skip_mps
			def 					UpperCAmelCase__       (				self   :   List[Any]   )							->     Optional[int]:




									'''simple docstring'''




									return super().test_attention_slicing_forward_pass()



@nightly
@require_torch_gpu
class 					snake_case__       (				unittest.TestCase							):



			def 					UpperCAmelCase__       (				self   :   List[Any]   )							->     Tuple:




									'''simple docstring'''




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



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




									'''simple docstring'''




									snake_case_     :			Optional[int]       =       load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
									    "repaint/celeba_hq_256.png"   )
									snake_case_     :			Any       =       load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"   )
									snake_case_     :			List[Any]       =       load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
									    "repaint/celeba_hq_256_result.npy"   )

									snake_case_     :			str       =       "google/ddpm-ema-celebahq-256"
									snake_case_     :			Optional[Any]       =       UNetaDModel.from_pretrained(A__   )
									snake_case_     :			Optional[int]       =       RePaintScheduler.from_pretrained(A__   )

									snake_case_     :			str       =       RePaintPipeline(unet=A__			,	scheduler=A__   ).to(A__   )
									repaint.set_progress_bar_config(disable=A__   )
									repaint.enable_attention_slicing()

									snake_case_     :			Union[str, Any]       =       torch.manual_seed(0   )
									snake_case_     :			int       =       repaint(
									    A__			,	A__			,	num_inference_steps=2_50			,	eta=0.0			,	jump_length=10			,	jump_n_sample=10			,	generator=A__			,	output_type="np"			,	)
									snake_case_     :			List[Any]       =       output.images[0]

									assert image.shape == (2_56, 2_56, 3)
									assert np.abs(expected_image - image   ).mean() < 1E-2




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase							=       {
    "configuration_nystromformer": ["NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "NystromformerConfig"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase							=       [
         "NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "NystromformerForMaskedLM",
         "NystromformerForMultipleChoice",
         "NystromformerForQuestionAnswering",
         "NystromformerForSequenceClassification",
         "NystromformerForTokenClassification",
         "NystromformerLayer",
         "NystromformerModel",
         "NystromformerPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_nystromformer import (
              NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              NystromformerForMaskedLM,
              NystromformerForMultipleChoice,
              NystromformerForQuestionAnswering,
              NystromformerForSequenceClassification,
              NystromformerForTokenClassification,
              NystromformerLayer,
              NystromformerModel,
              NystromformerPreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase							=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



