





"""simple docstring"""



import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


a_					=     logging.get_logger(__name__)  # pylint: disable=invalid-name







def 						__lowercase	(  snake_case_    :    Union[List, PIL.Image.Image, torch.Tensor]		)  ->Optional[int]:



    '''simple docstring'''







    warnings.warn(
        '''The preprocess method is deprecated and will be removed in a future version. Please'''
        ''' use VaeImageProcessor.preprocess instead'''							,snake_case_							,)
    if isinstance(snake_case_							,torch.Tensor		):
        return image
    elif isinstance(snake_case_							,PIL.Image.Image		):
        __A       :		Any							=	[image]

    if isinstance(image[0]							,PIL.Image.Image		):
        __A					,			__A       :		str							=	image[0].size
        __A					,			__A       :		Union[str, Any]							=	(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

        __A       :		Tuple							=	[np.array(i.resize((w, h)							,resample=PIL_INTERPOLATION['''lanczos''']		)		)[None, :] for i in image]
        __A       :		Union[str, Any]							=	np.concatenate(snake_case_							,axis=0		)
        __A       :		Optional[Any]							=	np.array(snake_case_		).astype(np.floataa		) / 255.0
        __A       :		Optional[int]							=	image.transpose(0							,3							,1							,2		)
        __A       :		int							=	2.0 * image - 1.0
        __A       :		Dict							=	torch.from_numpy(snake_case_		)
    elif isinstance(image[0]							,torch.Tensor		):
        __A       :		int							=	torch.cat(snake_case_							,dim=0		)
    return image







def 						__lowercase	(  snake_case_    :    Union[List, PIL.Image.Image, torch.Tensor]		)  ->Dict:



    '''simple docstring'''







    if isinstance(snake_case_							,torch.Tensor		):
        return mask
    elif isinstance(snake_case_							,PIL.Image.Image		):
        __A       :		Any							=	[mask]

    if isinstance(mask[0]							,PIL.Image.Image		):
        __A					,			__A       :		int							=	mask[0].size
        __A					,			__A       :		List[str]							=	(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
        __A       :		Union[str, Any]							=	[np.array(m.convert('''L'''		).resize((w, h)							,resample=PIL_INTERPOLATION['''nearest''']		)		)[None, :] for m in mask]
        __A       :		Tuple							=	np.concatenate(snake_case_							,axis=0		)
        __A       :		int							=	mask.astype(np.floataa		) / 255.0
        __A       :		Optional[Any]							=	0
        __A       :		Union[str, Any]							=	1
        __A       :		Dict							=	torch.from_numpy(snake_case_		)
    elif isinstance(mask[0]							,torch.Tensor		):
        __A       :		Union[str, Any]							=	torch.cat(snake_case_							,dim=0		)
    return mask






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	42
 _lowerCamelCase								=	42

 def __init__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase    ):



     '''simple docstring'''




     super().__init__()
     self.register_modules(unet=__lowerCamelCase   ,		scheduler=__lowerCamelCase    )







 @torch.no_grad()
 def __call__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = 250   ,		__lowerCamelCase = 0.0   ,		__lowerCamelCase = 10   ,		__lowerCamelCase = 10   ,		__lowerCamelCase = None   ,		__lowerCamelCase = "pil"   ,		__lowerCamelCase = True   ,		):



     '''simple docstring'''




     __A       :		Union[str, Any]							=	image

     __A       :		int							=	_preprocess_image(__lowerCamelCase    )
     __A       :		Tuple							=	original_image.to(device=self.device   ,		dtype=self.unet.dtype    )
     __A       :		Union[str, Any]							=	_preprocess_mask(__lowerCamelCase    )
     __A       :		Any							=	mask_image.to(device=self.device   ,		dtype=self.unet.dtype    )

     __A       :		List[str]							=	original_image.shape[0]

     # sample gaussian noise to begin the loop
     if isinstance(__lowerCamelCase   ,		__lowerCamelCase    ) and len(__lowerCamelCase    ) != batch_size:
         raise ValueError(
             F"""You have passed a list of generators of length {len(__lowerCamelCase    )}, but requested an effective batch"""
             F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""    )

     __A       :		Tuple							=	original_image.shape
     __A       :		Dict							=	randn_tensor(__lowerCamelCase   ,		generator=__lowerCamelCase   ,		device=self.device   ,		dtype=self.unet.dtype    )

     # set step values
     self.scheduler.set_timesteps(__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		self.device    )
     __A       :		List[Any]							=	eta

     __A       :		str							=	self.scheduler.timesteps[0] + 1
     __A       :		Union[str, Any]							=	generator[0] if isinstance(__lowerCamelCase   ,		__lowerCamelCase    ) else generator
     for i, t in enumerate(self.progress_bar(self.scheduler.timesteps    )    ):
         if t < t_last:
             # predict the noise residual
             __A       :		Tuple							=	self.unet(__lowerCamelCase   ,		__lowerCamelCase    ).sample
             # compute previous image: x_t -> x_t-1
             __A       :		Optional[int]							=	self.scheduler.step(__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    ).prev_sample

         else:
             # compute the reverse: x_t-1 -> x_t
             __A       :		Dict							=	self.scheduler.undo_step(__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    )
         __A       :		List[str]							=	t

     __A       :		Any							=	(image / 2 + 0.5).clamp(0   ,		1    )
     __A       :		str							=	image.cpu().permute(0   ,		2   ,		3   ,		1    ).numpy()
     if output_type == "pil":
         __A       :		int							=	self.numpy_to_pil(__lowerCamelCase    )

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=__lowerCamelCase    )











"""simple docstring"""



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
       from transformers import AutoModelForSeqaSeqLM, AutoTokenizer






@require_torch
@require_sentencepiece
@require_tokenizers
class 						__snake_case   (						unittest.TestCase	):





 """simple docstring"""


 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Any							=	AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''   ,		return_dict=__lowerCamelCase    ).to(__lowerCamelCase    )
     __A       :		Optional[Any]							=	AutoTokenizer.from_pretrained('''google/mt5-small'''    )

     __A       :		Optional[int]							=	tokenizer('''Hello there'''   ,		return_tensors='''pt'''    ).input_ids
     __A       :		str							=	tokenizer('''Hi I am'''   ,		return_tensors='''pt'''    ).input_ids

     __A       :		Any							=	model(input_ids.to(__lowerCamelCase    )   ,		labels=labels.to(__lowerCamelCase    )    ).loss
     __A       :		Tuple							=	-(labels.shape[-1] * loss.item())

     __A       :		int							=	-8_4.9_1_2_7
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE    ) < 1e-4    )




