import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


_lowercase:	Tuple       			=		get_tests_dir('''fixtures/test_sentencepiece.model''')

if is_torch_available():
				from transformers.models.mbart.modeling_mbart import shift_tokens_right

_lowercase:	Union[str, Any]       			=		2_5_0_0_0_4
_lowercase:	Dict       			=		2_5_0_0_2_0



@require_sentencepiece
@require_tokenizers
class        lowerCamelCase__				(   UpperCAmelCase       ,unittest.TestCase				):
			UpperCamelCase__    							=MBartaaTokenizer
			UpperCamelCase__    							=MBartaaTokenizerFast
			UpperCamelCase__    							=True
			UpperCamelCase__    							=True





			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										super().setUp()

										# We have a SentencePiece fixture for testing
										_lowerCAmelCase							=							MBartaaTokenizer(lowercase__  ,  src_lang='en_XX'  ,  tgt_lang='ro_RO'  ,  keep_accents=lowercase__						)
										tokenizer.save_pretrained(self.tmpdirname						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase							=							'<s>'
										_lowerCAmelCase							=							0

										self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase__						)  ,  lowercase__						)
										self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase__						)  ,  lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										_lowerCAmelCase							=							list(self.get_tokenizer().get_vocab().keys()						)

										self.assertEqual(vocab_keys[0]  ,  '<s>'						)
										self.assertEqual(vocab_keys[1]  ,  '<pad>'						)
										self.assertEqual(vocab_keys[-1]  ,  '<mask>'						)
										self.assertEqual(len(lowercase__						)  ,  10_54						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										self.assertEqual(self.get_tokenizer().vocab_size  ,  10_54						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase							=							MBartaaTokenizer(lowercase__  ,  src_lang='en_XX'  ,  tgt_lang='ro_RO'  ,  keep_accents=lowercase__						)

										_lowerCAmelCase							=							tokenizer.tokenize('This is a test'						)
										self.assertListEqual(lowercase__  ,  ['▁This', '▁is', '▁a', '▁t', 'est']						)

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(lowercase__						)  ,  [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]  ,  )

										_lowerCAmelCase							=							tokenizer.tokenize('I was born in 92000, and this is falsé.'						)
										self.assertListEqual(
										    lowercase__  ,  [SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '9', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', 'é', '.']  ,  )
										_lowerCAmelCase							=							tokenizer.convert_tokens_to_ids(lowercase__						)
										self.assertListEqual(
										    lowercase__  ,  [
										        value + tokenizer.fairseq_offset
										        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
										    ]  ,  )

										_lowerCAmelCase							=							tokenizer.convert_ids_to_tokens(lowercase__						)
										self.assertListEqual(
										    lowercase__  ,  [SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '<unk>', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', '<unk>', '.']  ,  )





			@slow
			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										# fmt: off
										_lowerCAmelCase							=							{'input_ids': [[25_00_04, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [25_00_04, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_00_04, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
										# fmt: on

										self.tokenizer_integration_test_util(
										    expected_encoding=lowercase__  ,  model_name='facebook/mbart-large-50'  ,  revision='d3913889c59cd5c9e456b269c376325eabad57e2'  ,  )





			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]						):
										if not self.test_slow_tokenizer:
																	# as we don't have a slow version, we can't compare the outputs between slow and fast versions
																	return

										_lowerCAmelCase							=							(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart50', {})
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'						):
																								_lowerCAmelCase							=							self.rust_tokenizer_class.from_pretrained(lowercase__  ,  **lowercase__						)
																								_lowerCAmelCase							=							self.tokenizer_class.from_pretrained(lowercase__  ,  **lowercase__						)

																								_lowerCAmelCase							=							tempfile.mkdtemp()

																								_lowerCAmelCase							=							tokenizer_r.save_pretrained(lowercase__						)
																								_lowerCAmelCase							=							tokenizer_p.save_pretrained(lowercase__						)

																								# Checks it save with the same files + the tokenizer.json file for the fast one
																								self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files						)						)
																								_lowerCAmelCase							=							tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f						)
																								self.assertSequenceEqual(lowercase__  ,  lowercase__						)

																								# Checks everything loads correctly in the same way
																								_lowerCAmelCase							=							tokenizer_r.from_pretrained(lowercase__						)
																								_lowerCAmelCase							=							tokenizer_p.from_pretrained(lowercase__						)

																								# Check special tokens are set accordingly on Rust and Python
																								for key in tokenizer_pp.special_tokens_map:
																															self.assertTrue(hasattr(lowercase__  ,  lowercase__						)						)
																															# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																															# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																								shutil.rmtree(lowercase__						)

																								# Save tokenizer rust, legacy_format=True
																								_lowerCAmelCase							=							tempfile.mkdtemp()

																								_lowerCAmelCase							=							tokenizer_r.save_pretrained(lowercase__  ,  legacy_format=lowercase__						)
																								_lowerCAmelCase							=							tokenizer_p.save_pretrained(lowercase__						)

																								# Checks it save with the same files
																								self.assertSequenceEqual(lowercase__  ,  lowercase__						)

																								# Checks everything loads correctly in the same way
																								_lowerCAmelCase							=							tokenizer_r.from_pretrained(lowercase__						)
																								_lowerCAmelCase							=							tokenizer_p.from_pretrained(lowercase__						)

																								# Check special tokens are set accordingly on Rust and Python
																								for key in tokenizer_pp.special_tokens_map:
																															self.assertTrue(hasattr(lowercase__  ,  lowercase__						)						)

																								shutil.rmtree(lowercase__						)

																								# Save tokenizer rust, legacy_format=False
																								_lowerCAmelCase							=							tempfile.mkdtemp()

																								_lowerCAmelCase							=							tokenizer_r.save_pretrained(lowercase__  ,  legacy_format=lowercase__						)
																								_lowerCAmelCase							=							tokenizer_p.save_pretrained(lowercase__						)

																								# Checks it saved the tokenizer.json file
																								self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files						)						)

																								# Checks everything loads correctly in the same way
																								_lowerCAmelCase							=							tokenizer_r.from_pretrained(lowercase__						)
																								_lowerCAmelCase							=							tokenizer_p.from_pretrained(lowercase__						)

																								# Check special tokens are set accordingly on Rust and Python
																								for key in tokenizer_pp.special_tokens_map:
																															self.assertTrue(hasattr(lowercase__  ,  lowercase__						)						)

																								shutil.rmtree(lowercase__						)







@require_torch
@require_sentencepiece
@require_tokenizers
class        lowerCamelCase__				(   unittest.TestCase				):
			UpperCamelCase__    							="facebook/mbart-large-50-one-to-many-mmt"
			UpperCamelCase__    							=[
			    " UN Chief Says There Is No Military Solution in Syria",
			    " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
			]
			UpperCamelCase__    							=[
			    "Şeful ONU declară că nu există o soluţie militară în Siria",
			    "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
			    " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
			    " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
			]
			UpperCamelCase__    							=[EN_CODE, 8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2]





			@classmethod
			def   SCREAMING_SNAKE_CASE__     (       cls	:	Tuple						):
										_lowerCAmelCase							=							MBartaaTokenizer.from_pretrained(
										    cls.checkpoint_name  ,  src_lang='en_XX'  ,  tgt_lang='ro_RO'						)
										_lowerCAmelCase							=							1
										return cls





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']  ,  25_00_01						)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']  ,  25_00_04						)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']  ,  25_00_20						)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN']  ,  25_00_38						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]						):
										_lowerCAmelCase							=							self.tokenizer.batch_encode_plus(self.src_text						).input_ids[0]
										self.assertListEqual(self.expected_src_tokens  ,  lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										self.assertIn(lowercase__  ,  self.tokenizer.all_special_ids						)
										_lowerCAmelCase							=							[RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
										_lowerCAmelCase							=							self.tokenizer.decode(lowercase__  ,  skip_special_tokens=lowercase__						)
										_lowerCAmelCase							=							self.tokenizer.decode(generated_ids[1:]  ,  skip_special_tokens=lowercase__						)
										self.assertEqual(lowercase__  ,  lowercase__						)
										self.assertNotIn(self.tokenizer.eos_token  ,  lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]						):
										_lowerCAmelCase							=							['this is gunna be a long sentence ' * 20]
										assert isinstance(src_text[0]  ,  lowercase__						)
										_lowerCAmelCase							=							10
										_lowerCAmelCase							=							self.tokenizer(lowercase__  ,  max_length=lowercase__  ,  truncation=lowercase__						).input_ids[0]
										self.assertEqual(ids[0]  ,  lowercase__						)
										self.assertEqual(ids[-1]  ,  2						)
										self.assertEqual(len(lowercase__						)  ,  lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']						)  ,  [25_00_53, 25_00_01]						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										_lowerCAmelCase							=							tempfile.mkdtemp()
										_lowerCAmelCase							=							self.tokenizer.fairseq_tokens_to_ids
										self.tokenizer.save_pretrained(lowercase__						)
										_lowerCAmelCase							=							MBartaaTokenizer.from_pretrained(lowercase__						)
										self.assertDictEqual(new_tok.fairseq_tokens_to_ids  ,  lowercase__						)





			@require_torch
			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										_lowerCAmelCase							=							self.tokenizer(self.src_text  ,  text_target=self.tgt_text  ,  padding=lowercase__  ,  return_tensors='pt'						)
										_lowerCAmelCase							=							shift_tokens_right(batch['labels']  ,  self.tokenizer.pad_token_id						)

										# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
										assert batch.input_ids[1][0] == EN_CODE
										assert batch.input_ids[1][-1] == 2
										assert batch.labels[1][0] == RO_CODE
										assert batch.labels[1][-1] == 2
										assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]





			@require_torch
			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										_lowerCAmelCase							=							self.tokenizer(
										    self.src_text  ,  text_target=self.tgt_text  ,  padding=lowercase__  ,  truncation=lowercase__  ,  max_length=len(self.expected_src_tokens						)  ,  return_tensors='pt'  ,  )
										_lowerCAmelCase							=							shift_tokens_right(batch['labels']  ,  self.tokenizer.pad_token_id						)

										self.assertIsInstance(lowercase__  ,  lowercase__						)

										self.assertEqual((2, 14)  ,  batch.input_ids.shape						)
										self.assertEqual((2, 14)  ,  batch.attention_mask.shape						)
										_lowerCAmelCase							=							batch.input_ids.tolist()[0]
										self.assertListEqual(self.expected_src_tokens  ,  lowercase__						)
										self.assertEqual(2  ,  batch.decoder_input_ids[0, 0]						)  # decoder_start_token_id
										# Test that special tokens are reset
										self.assertEqual(self.tokenizer.prefix_tokens  ,  [EN_CODE]						)
										self.assertEqual(self.tokenizer.suffix_tokens  ,  [self.tokenizer.eos_token_id]						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										_lowerCAmelCase							=							self.tokenizer(self.src_text  ,  padding=lowercase__  ,  truncation=lowercase__  ,  max_length=3  ,  return_tensors='pt'						)
										_lowerCAmelCase							=							self.tokenizer(
										    text_target=self.tgt_text  ,  padding=lowercase__  ,  truncation=lowercase__  ,  max_length=10  ,  return_tensors='pt'						)
										_lowerCAmelCase							=							targets['input_ids']
										_lowerCAmelCase							=							shift_tokens_right(lowercase__  ,  self.tokenizer.pad_token_id						)

										self.assertEqual(batch.input_ids.shape[1]  ,  3						)
										self.assertEqual(batch.decoder_input_ids.shape[1]  ,  10						)





			@require_torch
			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]						):
										_lowerCAmelCase							=							self.tokenizer._build_translation_inputs(
										    'A test'  ,  return_tensors='pt'  ,  src_lang='en_XX'  ,  tgt_lang='ar_AR'						)

										self.assertEqual(
										    nested_simplify(lowercase__						)  ,  {
										        # en_XX, A, test, EOS
										        'input_ids': [[25_00_04, 62, 30_34, 2]],
										        'attention_mask': [[1, 1, 1, 1]],
										        # ar_AR
										        'forced_bos_token_id': 25_00_01,
										    }  ,  )







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


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






def _lowerCamelCase  (		snake_case  ):
							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  ):
														_lowerCAmelCase							=							[image]

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

														_lowerCAmelCase							=							[np.array(i.resize((w, h)     , resample=PIL_INTERPOLATION['lanczos']  )  )[None, :] for i in image]
														_lowerCAmelCase							=							np.concatenate(snake_case     , axis=0  )
														_lowerCAmelCase							=							np.array(snake_case  ).astype(np.floataa  ) / 2_55.0
														_lowerCAmelCase							=							image.transpose(0     , 3     , 1     , 2  )
														_lowerCAmelCase							=							2.0 * image - 1.0
														_lowerCAmelCase							=							torch.from_numpy(snake_case  )
							elif isinstance(image[0]     , torch.Tensor  ):
														_lowerCAmelCase							=							torch.cat(snake_case     , dim=0  )
							return image






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

							if isinstance(mask[0]     , PIL.Image.Image  ):
														_lowerCAmelCase			,		_lowerCAmelCase							=							mask[0].size
														_lowerCAmelCase			,		_lowerCAmelCase							=							(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
														_lowerCAmelCase							=							[np.array(m.convert('L'  ).resize((w, h)     , resample=PIL_INTERPOLATION['nearest']  )  )[None, :] for m in mask]
														_lowerCAmelCase							=							np.concatenate(snake_case     , axis=0  )
														_lowerCAmelCase							=							mask.astype(np.floataa  ) / 2_55.0
														_lowerCAmelCase							=							0
														_lowerCAmelCase							=							1
														_lowerCAmelCase							=							torch.from_numpy(snake_case  )
							elif isinstance(mask[0]     , torch.Tensor  ):
														_lowerCAmelCase							=							torch.cat(snake_case     , dim=0  )
							return mask



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							=42
			UpperCamelCase__    							=42





			def __init__(       self	:	int  ,  lowercase__	:	List[Any]  ,  lowercase__	:	Optional[Any]						):
										super().__init__()
										self.register_modules(unet=lowercase__  ,  scheduler=lowercase__						)





			@torch.no_grad()
			def __call__(       self	:	Any  ,  lowercase__	:	Union[torch.Tensor, PIL.Image.Image]  ,  lowercase__	:	Union[torch.Tensor, PIL.Image.Image]  ,  lowercase__	:	int = 2_50  ,  lowercase__	:	float = 0.0  ,  lowercase__	:	int = 10  ,  lowercase__	:	int = 10  ,  lowercase__	:	Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,  lowercase__	:	Optional[str] = "pil"  ,  lowercase__	:	bool = True  ,  ):
										_lowerCAmelCase							=							image

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

										_lowerCAmelCase							=							original_image.shape[0]

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

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

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

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

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

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

										if not return_dict:
																	return (image,)

										return ImagePipelineOutput(images=lowercase__						)






