


'''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


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





def lowerCamelCase      (      lowerCAmelCase    :     Union[List, PIL.Image.Image, torch.Tensor]		):





	"""simple docstring"""







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

	if isinstance(image[0]		,		PIL.Image.Image		):
		__magic_name__     ,    __magic_name__	:   Optional[int]					     =					image[0].size
		__magic_name__     ,    __magic_name__	:   Dict					     =					(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

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





def lowerCamelCase      (      lowerCAmelCase    :     Union[List, PIL.Image.Image, torch.Tensor]		):





	"""simple docstring"""







	if isinstance(lowerCAmelCase		,		torch.Tensor		):
		return mask
	elif isinstance(lowerCAmelCase		,		PIL.Image.Image		):
		__magic_name__	:   Union[str, Any]					     =					[mask]

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





class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      UNetaDModel
		A_     :      RePaintScheduler







		def __init__( self		:						Tuple  ,					_A		:						Any  ,					_A		:						Tuple      )  ->			str:
			super().__init__()
			self.register_modules(unet=_A  ,					scheduler=_A      )







		@torch.no_grad()
		def __call__( self		:						List[Any]  ,					_A		:						Union[torch.Tensor, PIL.Image.Image]  ,					_A		:						Union[torch.Tensor, PIL.Image.Image]  ,					_A		:						int = 250  ,					_A		:						float = 0.0  ,					_A		:						int = 10  ,					_A		:						int = 10  ,					_A		:						Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,					_A		:						Optional[str] = "pil"  ,					_A		:						bool = True  ,					)  ->			Union[ImagePipelineOutput, Tuple]:
			__magic_name__	:   Optional[Any]					     =					image

			__magic_name__	:   List[str]					     =					_preprocess_image(_A      )
			__magic_name__	:   List[Any]					     =					original_image.to(device=self.device  ,					dtype=self.unet.dtype      )
			__magic_name__	:   int					     =					_preprocess_mask(_A      )
			__magic_name__	:   Optional[int]					     =					mask_image.to(device=self.device  ,					dtype=self.unet.dtype      )

			__magic_name__	:   Optional[Any]					     =					original_image.shape[0]

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

			__magic_name__	:   Dict					     =					original_image.shape
			__magic_name__	:   List[str]					     =					randn_tensor(_A  ,					generator=_A  ,					device=self.device  ,					dtype=self.unet.dtype      )

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

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

				else:
					# compute the reverse: x_t-1 -> x_t
					__magic_name__	:   Optional[int]					     =					self.scheduler.undo_step(_A  ,					_A  ,					_A      )
				__magic_name__	:   List[Any]					     =					t

			__magic_name__	:   int					     =					(image / 2 + 0.5).clamp(0  ,					1      )
			__magic_name__	:   Optional[Any]					     =					image.cpu().permute(0  ,					2  ,					3  ,					1      ).numpy()
			if output_type == "pil":
				__magic_name__	:   Any					     =					self.numpy_to_pil(_A      )

			if not return_dict:
				return (image,)

			return ImagePipelineOutput(images=_A      )



'''simple docstring'''

import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _lowerCamelCase	(						lowercase__		,					unittest.TestCase							):



		'''simple docstring'''







		A_     :      Optional[Any]				   =    LxmertTokenizer
		A_     :      List[Any]				   =    LxmertTokenizerFast
		A_     :      int				   =    True
		A_     :      Any				   =    True







		def        __lowerCAmelCase		( self		:						List[str]      )  ->			Tuple:
			super().setUp()

			__magic_name__	:   str					     =					[
			    '[UNK]',
			    '[CLS]',
			    '[SEP]',
			    'want',
			    '##want',
			    '##ed',
			    'wa',
			    'un',
			    'runn',
			    '##ing',
			    ',',
			    'low',
			    'lowest',
			]
			__magic_name__	:   List[str]					     =					os.path.join(self.tmpdirname  ,					VOCAB_FILES_NAMES['vocab_file']      )
			with open(self.vocab_file  ,					'w'  ,					encoding='utf-8'      ) as vocab_writer:
				vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )







		def        __lowerCAmelCase		( self		:						Any  ,					_A		:						str      )  ->			List[Any]:
			__magic_name__	:   Dict					     =					'UNwant\u00E9d,running'
			__magic_name__	:   Dict					     =					'unwanted, running'
			return input_text, output_text







		def        __lowerCAmelCase		( self		:						Tuple      )  ->			Optional[int]:
			__magic_name__	:   Optional[Any]					     =					self.tokenizer_class(self.vocab_file      )

			__magic_name__	:   List[str]					     =					tokenizer.tokenize('UNwant\u00E9d,running'      )
			self.assertListEqual(_A  ,					['un', '##want', '##ed', ',', 'runn', '##ing']      )
			self.assertListEqual(tokenizer.convert_tokens_to_ids(_A      )  ,					[7, 4, 5, 10, 8, 9]      )







		def        __lowerCAmelCase		( self		:						int      )  ->			List[Any]:
			if not self.test_rust_tokenizer:
				return

			__magic_name__	:   Any					     =					self.get_tokenizer()
			__magic_name__	:   Optional[Any]					     =					self.get_rust_tokenizer()

			__magic_name__	:   Union[str, Any]					     =					'I was born in 92000, and this is falsé.'

			__magic_name__	:   List[Any]					     =					tokenizer.tokenize(_A      )
			__magic_name__	:   Dict					     =					rust_tokenizer.tokenize(_A      )
			self.assertListEqual(_A  ,					_A      )

			__magic_name__	:   int					     =					tokenizer.encode(_A  ,					add_special_tokens=_A      )
			__magic_name__	:   Union[str, Any]					     =					rust_tokenizer.encode(_A  ,					add_special_tokens=_A      )
			self.assertListEqual(_A  ,					_A      )

			__magic_name__	:   List[Any]					     =					self.get_rust_tokenizer()
			__magic_name__	:   str					     =					tokenizer.encode(_A      )
			__magic_name__	:   Optional[int]					     =					rust_tokenizer.encode(_A      )
			self.assertListEqual(_A  ,					_A      )