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


SCREAMING_SNAKE_CASE		:     List[Any]         =	logging.get_logger(__name__)  # pylint: disable=invalid-name






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Union[List, PIL.Image.Image, torch.Tensor]			):
						warnings.warn(
						    """The preprocess method is deprecated and will be removed in a future version. Please"""
						    """ use VaeImageProcessor.preprocess instead"""		, _UpperCamelCase		, )
						if isinstance(_UpperCamelCase		, torch.Tensor			):
												return image
						elif isinstance(_UpperCamelCase		, PIL.Image.Image			):
												UpperCamelCase_		:							int  =		[image]

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

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






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Union[List, PIL.Image.Image, torch.Tensor]			):
						if isinstance(_UpperCamelCase		, torch.Tensor			):
												return mask
						elif isinstance(_UpperCamelCase		, PIL.Image.Image			):
												UpperCamelCase_		:							Optional[int]  =		[mask]

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







class        UpperCamelCase						(				lowerCAmelCase__				):
						a__		:str      =			42
						a__		:Dict      =			42





						def __init__(self			,     __UpperCamelCase			,     __UpperCamelCase						)				->				Union[str, Any]:
												super().__init__()
												self.register_modules(unet=__UpperCAmelCase			,     scheduler=__UpperCAmelCase						)





						@torch.no_grad()
						def __call__(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase = 250			,     __UpperCamelCase = 0.0			,     __UpperCamelCase = 10			,     __UpperCamelCase = 10			,     __UpperCamelCase = None			,     __UpperCamelCase = "pil"			,     __UpperCamelCase = True			,     )				->				Dict:
												UpperCamelCase_		:							List[Any]  =		image

												UpperCamelCase_		:							List[Any]  =		_preprocess_image(__UpperCAmelCase						)
												UpperCamelCase_		:							Optional[Any]  =		original_image.to(device=self.device			,     dtype=self.unet.dtype						)
												UpperCamelCase_		:							Dict  =		_preprocess_mask(__UpperCAmelCase						)
												UpperCamelCase_		:							Union[str, Any]  =		mask_image.to(device=self.device			,     dtype=self.unet.dtype						)

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

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

												UpperCamelCase_		:							List[str]  =		original_image.shape
												UpperCamelCase_		:							int  =		randn_tensor(__UpperCAmelCase			,     generator=__UpperCAmelCase			,     device=self.device			,     dtype=self.unet.dtype						)

												# set step values
												self.scheduler.set_timesteps(__UpperCAmelCase			,     __UpperCAmelCase			,     __UpperCAmelCase			,     self.device						)
												UpperCamelCase_		:							Optional[Any]  =		eta

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

																		else:
																								# compute the reverse: x_t-1 -> x_t
																								UpperCamelCase_		:							Optional[Any]  =		self.scheduler.undo_step(__UpperCAmelCase			,     __UpperCAmelCase			,     __UpperCAmelCase						)
																		UpperCamelCase_		:							str  =		t

												UpperCamelCase_		:							Dict  =		(image / 2 + 0.5).clamp(0			,     1						)
												UpperCamelCase_		:							Tuple  =		image.cpu().permute(0			,     2			,     3			,     1						).numpy()
												if output_type == "pil":
																		UpperCamelCase_		:							List[str]  =		self.numpy_to_pil(__UpperCAmelCase						)

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=__UpperCAmelCase						)











import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 			__lowerCAmelCase  (						lowerCAmelCase__	,						unittest.TestCase ):
 lowerCAmelCase__							       =	XLMTokenizer
 lowerCAmelCase__							       =	False




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    __lowerCamelCase    				=     [
        '''l''',
        '''o''',
        '''w''',
        '''e''',
        '''r''',
        '''s''',
        '''t''',
        '''i''',
        '''d''',
        '''n''',
        '''w</w>''',
        '''r</w>''',
        '''t</w>''',
        '''lo''',
        '''low''',
        '''er</w>''',
        '''low</w>''',
        '''lowest</w>''',
        '''newer</w>''',
        '''wider</w>''',
        '''<unk>''',
    ]
    __lowerCamelCase    				=     dict(zip(__UpperCAmelCase						,					range(len(__UpperCAmelCase					)					)					)					)
    __lowerCamelCase    				=     ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

    __lowerCamelCase    				=     os.path.join(self.tmpdirname						,					VOCAB_FILES_NAMES['''vocab_file''']					)
    __lowerCamelCase    				=     os.path.join(self.tmpdirname						,					VOCAB_FILES_NAMES['''merges_file''']					)
    with open(self.vocab_file						,					'''w'''					) as fp:
       fp.write(json.dumps(__UpperCAmelCase					)					)
    with open(self.merges_file						,					'''w'''					) as fp:
       fp.write('''\n'''.join(__UpperCAmelCase					)					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     '''lower newer'''
    __lowerCamelCase    				=     '''lower newer'''
    return input_text, output_text




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     XLMTokenizer(self.vocab_file						,					self.merges_file					)

    __lowerCamelCase    				=     '''lower'''
    __lowerCamelCase    				=     ['''low''', '''er</w>''']
    __lowerCamelCase    				=     tokenizer.tokenize(__UpperCAmelCase					)
    self.assertListEqual(__UpperCAmelCase						,					__UpperCAmelCase					)

    __lowerCamelCase    				=     tokens + ['''<unk>''']
    __lowerCamelCase    				=     [14, 15, 20]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase					)						,					__UpperCAmelCase					)




 @slow
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''					)

    __lowerCamelCase    				=     tokenizer.encode('''sequence builders'''						,					add_special_tokens=__UpperCAmelCase					)
    __lowerCamelCase    				=     tokenizer.encode('''multi-sequence build'''						,					add_special_tokens=__UpperCAmelCase					)

    __lowerCamelCase    				=     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase					)
    __lowerCamelCase    				=     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase						,					__UpperCAmelCase					)

    assert encoded_sentence == [0] + text + [1]
    assert encoded_pair == [0] + text + [1] + text_a + [1]






