import torch

from transformers import AutoTokenizer







class 			SCREAMING_SNAKE_CASE__ (   __SCREAMING_SNAKE_CASE  ):

       def __init__(				self :			Union[str, Any]       ,       _lowerCAmelCase :			List[str]       ):
           __snake_case    :							Optional[Any]             =  AutoTokenizer.from_pretrained(_snake_case       )

       def        snake_case__  (				self :			str       ,       _lowerCAmelCase :			Tuple       ):

           if isinstance(_snake_case       ,       _snake_case       ) and all(isinstance(_x       ,       _snake_case       ) for _x in x       ):
               __snake_case    :							List[str]             =  None
               for l in x:
                   __snake_case    :							int             =  self.tokenizer(
                       _snake_case       ,       padding="""max_length"""       ,       max_length=3_84       ,       truncation=_snake_case       ,       return_tensors="""pt"""       ,       )
                   __snake_case    :							List[str]             =  torch.tensor([len(_snake_case       )]       )
                   if d is not None:
                       for k in d.keys():
                           __snake_case    :							List[str]             =  torch.cat((d[k], t[k])       ,       0       )
                   else:
                       __snake_case    :							Union[str, Any]             =  t

               __snake_case    :							Tuple             =  torch.tensor(self.tokenizer.convert_tokens_to_ids("""[E]"""       )       )
               __snake_case    :							Any             =  torch.tensor(self.tokenizer.convert_tokens_to_ids("""[/E]"""       )       )

           elif isinstance(_snake_case       ,       _snake_case       ) and all(isinstance(_x       ,       _snake_case       ) for _x in x       ):
               __snake_case    :							Union[str, Any]             =  self.tokenizer(
                   _snake_case       ,       padding="""max_length"""       ,       max_length=3_84       ,       truncation=_snake_case       ,       return_tensors="""pt"""       ,       )

           else:
               raise Exception(
                   """Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"""
                   """ strings` for supports are supported."""       )

           return d





       def        snake_case__  (				self :			Optional[int]       ,       _lowerCAmelCase :			Optional[Any]       ,       _lowerCAmelCase :			Optional[Any]       ,       _lowerCAmelCase :			Any       ,       _lowerCAmelCase :			Tuple       ,       _lowerCAmelCase :			int=0.70       ):
           __snake_case    :							Dict             =  []
           for idx in range(len(W_query["""input_ids"""]       )       ):
               __snake_case    :							Tuple             =  range(p_start.shape[1]       )

               __snake_case    :							Tuple             =  []
               for start_id in start_indexes:
                   for end_id in end_indexes:
                       if start_id < end_id:
                           output.append(
                               (
                                   start_id,
                                   end_id,
                                   p_start[idx][start_id].item(),
                                   p_end[idx][end_id].item(),
                               )       )

               output.sort(key=lambda _lowerCAmelCase       : (tup[2] * tup[3])       ,       reverse=_snake_case       )
               __snake_case    :							Union[str, Any]             =  []
               for k in range(len(_snake_case       )       ):
                   if output[k][2] * output[k][3] >= thresh:
                       __snake_case    :							Optional[int]             =  output[k][0], output[k][1]
                       __snake_case    :							Optional[Any]             =  self.tokenizer.decode(W_query["""input_ids"""][idx][c_start_pos:c_end_pos]       )
                       temp.append((decoded, output[k][2] * output[k][3])       )

               final_outputs.append(_snake_case       )

           return final_outputs






'''simple docstring'''
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						(__SCREAMING_SNAKE_CASE       ,       unittest.TestCase):
			__A   :     List[str]          =RePaintPipeline
			__A   :     List[str]          =IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
			__A   :     List[Any]          =PipelineTesterMixin.required_optional_params - {
			    "latents",
			    "num_images_per_prompt",
			    "callback",
			    "callback_steps",
			}
			__A   :     str          =IMAGE_INPAINTING_BATCH_PARAMS
			__A   :     Union[str, Any]          =False






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






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

				UpperCAmelCase_			:	Union[str, Any]      =   self.get_dummy_inputs(_snake_case			)
				UpperCAmelCase_			:	Union[str, Any]      =   sd_pipe(**_snake_case			).images
				UpperCAmelCase_			:	Dict      =   image[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				UpperCAmelCase_			:	List[Any]      =   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			):
				return super().test_save_load_local()






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






			@skip_mps
			def 						UpperCamelCase__				(   self			):
				return super().test_dict_tuple_outputs_equivalent()






			@skip_mps
			def 						UpperCamelCase__				(   self			):
				return super().test_save_load_optional_components()






			@skip_mps
			def 						UpperCamelCase__				(   self			):
				return super().test_attention_slicing_forward_pass()




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






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






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

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

				UpperCAmelCase_			:	Union[str, Any]      =   RePaintPipeline(unet=_snake_case       ,scheduler=_snake_case			).to(_snake_case			)
				repaint.set_progress_bar_config(disable=_snake_case			)
				repaint.enable_attention_slicing()

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

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




