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      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase   ,							unittest.TestCase    ):
       A							:	Dict          =	RePaintPipeline
       A							:	Optional[int]          =	IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
       A							:	Optional[Any]          =	PipelineTesterMixin.required_optional_params - {
           "latents",
           "num_images_per_prompt",
           "callback",
           "callback_steps",
       }
       A							:	Union[str, Any]          =	IMAGE_INPAINTING_BATCH_PARAMS
       A							:	Dict          =	False

       def 			snake_case__    (							self					:     List[str]			):
           torch.manual_seed(0			)
           torch.manual_seed(0			)
           __snake_case       :				List[str] 			=    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       :				Optional[int] 			=    RePaintScheduler()
           __snake_case       :				Optional[Any] 			=    {"""unet""": unet, """scheduler""": scheduler}
           return components

       def 			snake_case__    (							self					:     Optional[int]			,			_lowerCAmelCase					:     int			,			_lowerCAmelCase					:     Any=0			):
           if str(_lowerCAmelCase			).startswith("""mps"""			):
               __snake_case       :				Union[str, Any] 			=    torch.manual_seed(_lowerCAmelCase			)
           else:
               __snake_case       :				Optional[int] 			=    torch.Generator(device=_lowerCAmelCase			).manual_seed(_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    np.random.RandomState(_lowerCAmelCase			).standard_normal((1, 3, 32, 32)			)
           __snake_case       :				List[str] 			=    torch.from_numpy(_lowerCAmelCase			).to(device=_lowerCAmelCase			,			dtype=torch.floataa			)
           __snake_case       :				Tuple 			=    (image > 0).to(device=_lowerCAmelCase			,			dtype=torch.floataa			)
           __snake_case       :				Union[str, Any] 			=    {
               """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 			snake_case__    (							self					:     List[Any]			):
           __snake_case       :				List[str] 			=    """cpu"""  # ensure determinism for the device-dependent torch.Generator
           __snake_case       :				Optional[int] 			=    self.get_dummy_components()
           __snake_case       :				List[Any] 			=    RePaintPipeline(**_lowerCAmelCase			)
           __snake_case       :				Dict 			=    sd_pipe.to(_lowerCAmelCase			)
           sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

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

           assert image.shape == (1, 32, 32, 3)
           __snake_case       :				Optional[int] 			=    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 			snake_case__    (							self					:     List[Any]			):
           return super().test_save_load_local()

       @unittest.skip("""non-deterministic pipeline"""			)
       def 			snake_case__    (							self					:     str			):
           return super().test_inference_batch_single_identical()

       @skip_mps
       def 			snake_case__    (							self					:     List[Any]			):
           return super().test_dict_tuple_outputs_equivalent()

       @skip_mps
       def 			snake_case__    (							self					:     Optional[Any]			):
           return super().test_save_load_optional_components()





       @skip_mps
       def 			snake_case__    (							self					:     Optional[Any]			):
           return super().test_attention_slicing_forward_pass()







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

       def 			snake_case__    (							self					:     str			):
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()





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

           __snake_case       :				Dict 			=    """google/ddpm-ema-celebahq-256"""
           __snake_case       :				int 			=    UNetaDModel.from_pretrained(_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    RePaintScheduler.from_pretrained(_lowerCAmelCase			)

           __snake_case       :				List[Any] 			=    RePaintPipeline(unet=_lowerCAmelCase			,			scheduler=_lowerCAmelCase			).to(_lowerCAmelCase			)
           repaint.set_progress_bar_config(disable=_lowerCAmelCase			)
           repaint.enable_attention_slicing()

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

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




import warnings

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







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	Optional[Any]          =	["image_processor", "tokenizer"]
       A							:	Dict          =	"VideoMAEImageProcessor"
       A							:	Tuple          =	("CLIPTokenizer", "CLIPTokenizerFast")

       def __init__(							self					:     str			,			_lowerCAmelCase					:     Tuple=None			,			_lowerCAmelCase					:     Any=None			,			**_lowerCAmelCase					:     Union[str, Any]			):
           __snake_case       :				int 			=    None
           if "feature_extractor" in kwargs:
               warnings.warn(
                   """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                   """ instead."""			,			_lowerCAmelCase			,			)
               __snake_case       :				Optional[Any] 			=    kwargs.pop("""feature_extractor"""			)

           __snake_case       :				int 			=    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__(_lowerCAmelCase			,			_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    self.image_processor

       def __call__(							self					:     str			,			_lowerCAmelCase					:     int=None			,			_lowerCAmelCase					:     Dict=None			,			_lowerCAmelCase					:     Optional[int]=None			,			**_lowerCAmelCase					:     List[str]			):

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

           if text is not None:
               __snake_case       :				Optional[Any] 			=    self.tokenizer(_lowerCAmelCase			,			return_tensors=_lowerCAmelCase			,			**_lowerCAmelCase			)

           if videos is not None:
               __snake_case       :				Optional[int] 			=    self.image_processor(_lowerCAmelCase			,			return_tensors=_lowerCAmelCase			,			**_lowerCAmelCase			)

           if text is not None and videos is not None:
               __snake_case       :				Union[str, Any] 			=    image_features.pixel_values
               return encoding
           elif text is not None:
               return encoding
           else:
               return BatchEncoding(data=dict(**_lowerCAmelCase			)			,			tensor_type=_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Tuple			,			*_lowerCAmelCase					:     str			,			**_lowerCAmelCase					:     List[Any]			):
           return self.tokenizer.batch_decode(*_lowerCAmelCase			,			**_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Tuple			,			*_lowerCAmelCase					:     List[Any]			,			**_lowerCAmelCase					:     Dict			):
           return self.tokenizer.decode(*_lowerCAmelCase			,			**_lowerCAmelCase			)

       @property
       def 			snake_case__    (							self					:     str			):
           return ["input_ids", "attention_mask", "position_ids", "pixel_values"]

       @property
       def 			snake_case__    (							self					:     str			):
           warnings.warn(
               """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,			_lowerCAmelCase			,			)
           return self.image_processor_class





       @property
       def 			snake_case__    (							self					:     Union[str, Any]			):
           warnings.warn(
               """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,			_lowerCAmelCase			,			)
           return self.image_processor



