
"""simple docstring"""




import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors





@require_tokenizers
class  snake_case     (							__lowercase       ,			unittest.TestCase   ):
							UpperCAmelCase__						     =      MvpTokenizer
							UpperCAmelCase__						     =      MvpTokenizerFast
							UpperCAmelCase__						     =      True
							UpperCAmelCase__						     =      filter_roberta_detectors

							def _lowercase					(self    ):

									"""simple docstring"""







									super().setUp()
									SCREAMING_SNAKE_CASE_						 = [
									    '''l''',
									    '''o''',
									    '''w''',
									    '''e''',
									    '''r''',
									    '''s''',
									    '''t''',
									    '''i''',
									    '''d''',
									    '''n''',
									    '''\u0120''',
									    '''\u0120l''',
									    '''\u0120n''',
									    '''\u0120lo''',
									    '''\u0120low''',
									    '''er''',
									    '''\u0120lowest''',
									    '''\u0120newer''',
									    '''\u0120wider''',
									    '''<unk>''',
									]
									SCREAMING_SNAKE_CASE_						 = dict(zip(SCREAMING_SNAKE_CASE_       ,       range(len(SCREAMING_SNAKE_CASE_    )    )    )    )
									SCREAMING_SNAKE_CASE_						 = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
									SCREAMING_SNAKE_CASE_						 = {'''unk_token''': '''<unk>'''}

									SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES['''vocab_file''']    )
									SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES['''merges_file''']    )
									with open(self.vocab_file       ,       '''w'''       ,       encoding='''utf-8'''    ) as fp:
											fp.write(json.dumps(SCREAMING_SNAKE_CASE_    ) + '''\n'''    )
									with open(self.merges_file       ,       '''w'''       ,       encoding='''utf-8'''    ) as fp:
											fp.write('''\n'''.join(SCREAMING_SNAKE_CASE_    )    )

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									kwargs.update(self.special_tokens_map    )
									return self.tokenizer_class.from_pretrained(self.tmpdirname       ,       **SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									kwargs.update(self.special_tokens_map    )
									return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,       **SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									return "lower newer", "lower newer"

							@cached_property
							def _lowercase					(self    ):

									"""simple docstring"""







									return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''    )

							@cached_property
							def _lowercase					(self    ):

									"""simple docstring"""







									return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''    )

							@require_torch
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
									SCREAMING_SNAKE_CASE_						 = [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											SCREAMING_SNAKE_CASE_						 = tokenizer(SCREAMING_SNAKE_CASE_       ,       max_length=len(SCREAMING_SNAKE_CASE_    )       ,       padding=SCREAMING_SNAKE_CASE_       ,       return_tensors='''pt'''    )
											self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

											self.assertEqual((2, 9)       ,       batch.input_ids.shape    )
											self.assertEqual((2, 9)       ,       batch.attention_mask.shape    )
											SCREAMING_SNAKE_CASE_						 = batch.input_ids.tolist()[0]
											self.assertListEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											# Test that special tokens are reset

							@require_torch
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											SCREAMING_SNAKE_CASE_						 = tokenizer(SCREAMING_SNAKE_CASE_       ,       padding=SCREAMING_SNAKE_CASE_       ,       return_tensors='''pt'''    )
											# check if input_ids are returned and no labels
											self.assertIn('''input_ids'''       ,       SCREAMING_SNAKE_CASE_    )
											self.assertIn('''attention_mask'''       ,       SCREAMING_SNAKE_CASE_    )
											self.assertNotIn('''labels'''       ,       SCREAMING_SNAKE_CASE_    )
											self.assertNotIn('''decoder_attention_mask'''       ,       SCREAMING_SNAKE_CASE_    )

							@require_torch
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = [
									    '''Summary of the text.''',
									    '''Another summary.''',
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											SCREAMING_SNAKE_CASE_						 = tokenizer(text_target=SCREAMING_SNAKE_CASE_       ,       max_length=32       ,       padding='''max_length'''       ,       return_tensors='''pt'''    )
											self.assertEqual(32       ,       targets['''input_ids'''].shape[1]    )

							@require_torch
							def _lowercase					(self    ):

									"""simple docstring"""







									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											SCREAMING_SNAKE_CASE_						 = tokenizer(
											    ['''I am a small frog''' * 10_24, '''I am a small frog''']       ,       padding=SCREAMING_SNAKE_CASE_       ,       truncation=SCREAMING_SNAKE_CASE_       ,       return_tensors='''pt'''    )
											self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertEqual(batch.input_ids.shape       ,       (2, 10_24)    )

							@require_torch
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = ['''A long paragraph for summarization.''']
									SCREAMING_SNAKE_CASE_						 = [
									    '''Summary of the text.''',
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											SCREAMING_SNAKE_CASE_						 = tokenizer(SCREAMING_SNAKE_CASE_       ,       text_target=SCREAMING_SNAKE_CASE_       ,       return_tensors='''pt'''    )
											SCREAMING_SNAKE_CASE_						 = inputs['''input_ids''']
											SCREAMING_SNAKE_CASE_						 = inputs['''labels''']
											self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()    )
											self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()    )
											self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()    )
											self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()    )

							def _lowercase					(self    ):

									"""simple docstring"""







									pass







							def _lowercase					(self    ):

									"""simple docstring"""







									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'    ):
													SCREAMING_SNAKE_CASE_						 = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )
													SCREAMING_SNAKE_CASE_						 = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )
													SCREAMING_SNAKE_CASE_						 = '''A, <mask> AllenNLP sentence.'''
													SCREAMING_SNAKE_CASE_						 = tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE_       ,       add_special_tokens=SCREAMING_SNAKE_CASE_       ,       return_token_type_ids=SCREAMING_SNAKE_CASE_    )
													SCREAMING_SNAKE_CASE_						 = tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE_       ,       add_special_tokens=SCREAMING_SNAKE_CASE_       ,       return_token_type_ids=SCREAMING_SNAKE_CASE_    )

													# token_type_ids should put 0 everywhere
													self.assertEqual(sum(tokens_r['''token_type_ids''']    )       ,       sum(tokens_p['''token_type_ids''']    )    )

													# attention_mask should put 1 everywhere, so sum over length should be 1
													self.assertEqual(
													    sum(tokens_r['''attention_mask''']    ) / len(tokens_r['''attention_mask''']    )       ,       sum(tokens_p['''attention_mask''']    ) / len(tokens_p['''attention_mask''']    )       ,       )

													SCREAMING_SNAKE_CASE_						 = tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']    )
													SCREAMING_SNAKE_CASE_						 = tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']    )

													# Rust correctly handles the space before the mask while python doesnt
													self.assertSequenceEqual(tokens_p['''input_ids''']       ,       [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]    )
													self.assertSequenceEqual(tokens_r['''input_ids''']       ,       [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]    )

													self.assertSequenceEqual(
													    SCREAMING_SNAKE_CASE_       ,       ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']    )
													self.assertSequenceEqual(
													    SCREAMING_SNAKE_CASE_       ,       ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']    )

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



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

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

				SCREAMING_SNAKE_CASE_						 = [np.array(i.resize((w, h),							resample=PIL_INTERPOLATION['''lanczos''']    )    )[None, :] for i in image]
				SCREAMING_SNAKE_CASE_						 = np.concatenate(__a,							axis=0    )
				SCREAMING_SNAKE_CASE_						 = np.array(__a    ).astype(np.floataa    ) / 2_5_5.0
				SCREAMING_SNAKE_CASE_						 = image.transpose(0,							3,							1,							2    )
				SCREAMING_SNAKE_CASE_						 = 2.0 * image - 1.0
				SCREAMING_SNAKE_CASE_						 = torch.from_numpy(__a    )
		elif isinstance(image[0],							torch.Tensor    ):
				SCREAMING_SNAKE_CASE_						 = torch.cat(__a,							dim=0    )
		return image



def        _lowerCamelCase ( __a    ):
		if isinstance(__a,							torch.Tensor    ):
				return mask
		elif isinstance(__a,							PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE_						 = [mask]

		if isinstance(mask[0],							PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = mask[0].size
				SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
				SCREAMING_SNAKE_CASE_						 = [np.array(m.convert('''L'''    ).resize((w, h),							resample=PIL_INTERPOLATION['''nearest''']    )    )[None, :] for m in mask]
				SCREAMING_SNAKE_CASE_						 = np.concatenate(__a,							axis=0    )
				SCREAMING_SNAKE_CASE_						 = mask.astype(np.floataa    ) / 2_5_5.0
				SCREAMING_SNAKE_CASE_						 = 0
				SCREAMING_SNAKE_CASE_						 = 1
				SCREAMING_SNAKE_CASE_						 = torch.from_numpy(__a    )
		elif isinstance(mask[0],							torch.Tensor    ):
				SCREAMING_SNAKE_CASE_						 = torch.cat(__a,							dim=0    )
		return mask





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      42
							UpperCAmelCase__						     =      42

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







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







							@torch.no_grad()
							def __call__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = 2_50       ,       SCREAMING_SNAKE_CASE_ = 0.0       ,       SCREAMING_SNAKE_CASE_ = 10       ,       SCREAMING_SNAKE_CASE_ = 10       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = "pil"       ,       SCREAMING_SNAKE_CASE_ = True       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = image

									SCREAMING_SNAKE_CASE_						 = _preprocess_image(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = original_image.to(device=self.device       ,       dtype=self.unet.dtype    )
									SCREAMING_SNAKE_CASE_						 = _preprocess_mask(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = mask_image.to(device=self.device       ,       dtype=self.unet.dtype    )

									SCREAMING_SNAKE_CASE_						 = original_image.shape[0]

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

									SCREAMING_SNAKE_CASE_						 = original_image.shape
									SCREAMING_SNAKE_CASE_						 = randn_tensor(SCREAMING_SNAKE_CASE_       ,       generator=SCREAMING_SNAKE_CASE_       ,       device=self.device       ,       dtype=self.unet.dtype    )

									# set step values
									self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       self.device    )
									SCREAMING_SNAKE_CASE_						 = eta

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

											else:
													# compute the reverse: x_t-1 -> x_t
													SCREAMING_SNAKE_CASE_						 = self.scheduler.undo_step(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = t

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

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_    )