





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


snake_case__					:							int													=     get_tests_dir('fixtures/test_sentencepiece.model')

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

snake_case__					:							Tuple													=     250004
snake_case__					:							int													=     250020





@require_sentencepiece
@require_tokenizers
class A_					(			_lowerCamelCase ,      unittest.TestCase   ):
   lowerCAmelCase__        = MBartaaTokenizer
   lowerCAmelCase__        = MBartaaTokenizerFast
   lowerCAmelCase__        = True
   lowerCAmelCase__        = True




   def _lowerCAmelCase      (self	:List[Any] )->					List[Any]:
         super().setUp()

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




   def _lowerCAmelCase      (self	:Any )->					Tuple:
         __A          =				'''<s>'''
         __A          =				0

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




   def _lowerCAmelCase      (self	:Optional[int] )->					List[Any]:
         __A          =				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(_UpperCamelCase )    ,   1054 )




   def _lowerCAmelCase      (self	:Tuple )->					Union[str, Any]:
         self.assertEqual(self.get_tokenizer().vocab_size    ,   1054 )




   def _lowerCAmelCase      (self	:Union[str, Any] )->					Union[str, Any]:
         __A          =				MBartaaTokenizer(_UpperCamelCase    ,   src_lang='''en_XX'''    ,   tgt_lang='''ro_RO'''    ,   keep_accents=_UpperCamelCase )

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

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(_UpperCamelCase )    ,   [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]    ,   )

         __A          =				tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
         self.assertListEqual(
             _UpperCamelCase    ,   [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''', '''.''']    ,   )
         __A          =				tokenizer.convert_tokens_to_ids(_UpperCamelCase )
         self.assertListEqual(
             _UpperCamelCase    ,   [
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
             ]    ,   )

         __A          =				tokenizer.convert_ids_to_tokens(_UpperCamelCase )
         self.assertListEqual(
             _UpperCamelCase    ,   [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 _lowerCAmelCase      (self	:str )->					Optional[Any]:
         # fmt: off
         __A          =				{'''input_ids''': [[25_0004, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [25_0004, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 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_0004, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 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=_UpperCamelCase    ,   model_name='''facebook/mbart-large-50'''    ,   revision='''d3913889c59cd5c9e456b269c376325eabad57e2'''    ,   )






   def _lowerCAmelCase      (self	:Dict )->					int:
         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

         __A          =				(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})""" ):
                     __A          =				self.rust_tokenizer_class.from_pretrained(_UpperCamelCase    ,   **_UpperCamelCase )
                     __A          =				self.tokenizer_class.from_pretrained(_UpperCamelCase    ,   **_UpperCamelCase )

                     __A          =				tempfile.mkdtemp()

                     __A          =				tokenizer_r.save_pretrained(_UpperCamelCase )
                     __A          =				tokenizer_p.save_pretrained(_UpperCamelCase )

                     # 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 ) )
                     __A          =				tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
                     self.assertSequenceEqual(_UpperCamelCase    ,   _UpperCamelCase )

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

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

                     shutil.rmtree(_UpperCamelCase )

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

                     __A          =				tokenizer_r.save_pretrained(_UpperCamelCase    ,   legacy_format=_UpperCamelCase )
                     __A          =				tokenizer_p.save_pretrained(_UpperCamelCase )

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

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

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

                     shutil.rmtree(_UpperCamelCase )

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

                     __A          =				tokenizer_r.save_pretrained(_UpperCamelCase    ,   legacy_format=_UpperCamelCase )
                     __A          =				tokenizer_p.save_pretrained(_UpperCamelCase )

                     # 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
                     __A          =				tokenizer_r.from_pretrained(_UpperCamelCase )
                     __A          =				tokenizer_p.from_pretrained(_UpperCamelCase )

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

                     shutil.rmtree(_UpperCamelCase )





@require_torch
@require_sentencepiece
@require_tokenizers
class A_					(			unittest.TestCase   ):
   lowerCAmelCase__        = """facebook/mbart-large-50-one-to-many-mmt"""
   lowerCAmelCase__        = [
       """ 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.""",
   ]
   lowerCAmelCase__        = [
       """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.""",
   ]
   lowerCAmelCase__        = [EN_CODE, 8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2]




   @classmethod
   def _lowerCAmelCase      (cls	:List[Any] )->					str:
         __A          =				MBartaaTokenizer.from_pretrained(
             cls.checkpoint_name    ,   src_lang='''en_XX'''    ,   tgt_lang='''ro_RO''' )
         __A          =				1
         return cls




   def _lowerCAmelCase      (self	:Union[str, Any] )->					Union[str, Any]:
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR''']    ,   25_0001 )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN''']    ,   25_0004 )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO''']    ,   25_0020 )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''mr_IN''']    ,   25_0038 )




   def _lowerCAmelCase      (self	:Union[str, Any] )->					Optional[Any]:
         __A          =				self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
         self.assertListEqual(self.expected_src_tokens    ,   _UpperCamelCase )




   def _lowerCAmelCase      (self	:Tuple )->					List[Any]:
         self.assertIn(_UpperCamelCase    ,   self.tokenizer.all_special_ids )
         __A          =				[RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
         __A          =				self.tokenizer.decode(_UpperCamelCase    ,   skip_special_tokens=_UpperCamelCase )
         __A          =				self.tokenizer.decode(generated_ids[1:]    ,   skip_special_tokens=_UpperCamelCase )
         self.assertEqual(_UpperCamelCase    ,   _UpperCamelCase )
         self.assertNotIn(self.tokenizer.eos_token    ,   _UpperCamelCase )




   def _lowerCAmelCase      (self	:List[str] )->					Any:
         __A          =				['''this is gunna be a long sentence ''' * 20]
         assert isinstance(src_text[0]    ,   _UpperCamelCase )
         __A          =				10
         __A          =				self.tokenizer(_UpperCamelCase    ,   max_length=_UpperCamelCase    ,   truncation=_UpperCamelCase ).input_ids[0]
         self.assertEqual(ids[0]    ,   _UpperCamelCase )
         self.assertEqual(ids[-1]    ,   2 )
         self.assertEqual(len(_UpperCamelCase )    ,   _UpperCamelCase )




   def _lowerCAmelCase      (self	:Any )->					str:
         self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR'''] )    ,   [25_0053, 25_0001] )




   def _lowerCAmelCase      (self	:Tuple )->					Union[str, Any]:
         __A          =				tempfile.mkdtemp()
         __A          =				self.tokenizer.fairseq_tokens_to_ids
         self.tokenizer.save_pretrained(_UpperCamelCase )
         __A          =				MBartaaTokenizer.from_pretrained(_UpperCamelCase )
         self.assertDictEqual(new_tok.fairseq_tokens_to_ids    ,   _UpperCamelCase )




   @require_torch
   def _lowerCAmelCase      (self	:int )->					List[str]:
         __A          =				self.tokenizer(self.src_text    ,   text_target=self.tgt_text    ,   padding=_UpperCamelCase    ,   return_tensors='''pt''' )
         __A          =				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 _lowerCAmelCase      (self	:List[Any] )->					Tuple:
         __A          =				self.tokenizer(
             self.src_text    ,   text_target=self.tgt_text    ,   padding=_UpperCamelCase    ,   truncation=_UpperCamelCase    ,   max_length=len(self.expected_src_tokens )    ,   return_tensors='''pt'''    ,   )
         __A          =				shift_tokens_right(batch['''labels''']    ,   self.tokenizer.pad_token_id )

         self.assertIsInstance(_UpperCamelCase    ,   _UpperCamelCase )

         self.assertEqual((2, 14)    ,   batch.input_ids.shape )
         self.assertEqual((2, 14)    ,   batch.attention_mask.shape )
         __A          =				batch.input_ids.tolist()[0]
         self.assertListEqual(self.expected_src_tokens    ,   _UpperCamelCase )
         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 _lowerCAmelCase      (self	:Tuple )->					Optional[Any]:
         __A          =				self.tokenizer(self.src_text    ,   padding=_UpperCamelCase    ,   truncation=_UpperCamelCase    ,   max_length=3    ,   return_tensors='''pt''' )
         __A          =				self.tokenizer(
             text_target=self.tgt_text    ,   padding=_UpperCamelCase    ,   truncation=_UpperCamelCase    ,   max_length=10    ,   return_tensors='''pt''' )
         __A          =				targets['''input_ids''']
         __A          =				shift_tokens_right(_UpperCamelCase    ,   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 _lowerCAmelCase      (self	:Dict )->					Optional[Any]:
         __A          =				self.tokenizer._build_translation_inputs(
             '''A test'''    ,   return_tensors='''pt'''    ,   src_lang='''en_XX'''    ,   tgt_lang='''ar_AR''' )

         self.assertEqual(
             nested_simplify(_UpperCamelCase )    ,   {
                 # en_XX, A, test, EOS
                 '''input_ids''': [[25_0004, 62, 3034, 2]],
                 '''attention_mask''': [[1, 1, 1, 1]],
                 # ar_AR
                 '''forced_bos_token_id''': 25_0001,
             }    ,   )












from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


snake_case__					:							int													=     transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)



def _a				(	lowerCamelCase:   List[Any] )	->						List[Any]:







      '''simple docstring'''






      if isinstance(lowerCamelCase					,				torch.Tensor ):
            return image
      elif isinstance(lowerCamelCase					,				PIL.Image.Image ):
            __A          =				[image]

      __A          =				[trans(img.convert('''RGB''' ) ) for img in image]
      __A          =				torch.stack(lowerCamelCase )
      return image







class A_					(			_lowerCamelCase   ):




   def __init__(self	:List[str]    ,   _UpperCamelCase	:List[Any]    ,   _UpperCamelCase	:List[str] )->					List[Any]:
         super().__init__()

         # make sure scheduler can always be converted to DDIM
         __A          =				DDIMScheduler.from_config(scheduler.config )

         self.register_modules(unet=_UpperCamelCase    ,   scheduler=_UpperCamelCase )




   def _lowerCAmelCase      (self	:int    ,   _UpperCamelCase	:Optional[Any] )->					Union[str, Any]:
         if strength < 0 or strength > 1:
               raise ValueError(f"""The value of strength should in [0.0, 1.0] but is {strength}""" )




   def _lowerCAmelCase      (self	:Optional[Any]    ,   _UpperCamelCase	:Dict    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:List[str] )->					Union[str, Any]:
         # get the original timestep using init_timestep
         __A          =				min(int(num_inference_steps * strength )    ,   _UpperCamelCase )

         __A          =				max(num_inference_steps - init_timestep    ,   0 )
         __A          =				self.scheduler.timesteps[t_start:]

         return timesteps, num_inference_steps - t_start




   def _lowerCAmelCase      (self	:str    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:Dict=None )->					List[str]:
         if not isinstance(_UpperCamelCase    ,   (torch.Tensor, PIL.Image.Image, list) ):
               raise ValueError(
                   f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_UpperCamelCase )}""" )

         __A          =				image.to(device=_UpperCamelCase    ,   dtype=_UpperCamelCase )

         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.""" )

         __A          =				init_latents.shape
         __A          =				randn_tensor(_UpperCamelCase    ,   generator=_UpperCamelCase    ,   device=_UpperCamelCase    ,   dtype=_UpperCamelCase )

         # get latents
         print('''add noise to latents at timestep'''    ,   _UpperCamelCase )
         __A          =				self.scheduler.add_noise(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )
         __A          =				init_latents

         return latents






   @torch.no_grad()
   def __call__(self	:List[str]    ,   _UpperCamelCase	:Union[torch.FloatTensor, PIL.Image.Image] = None    ,   _UpperCamelCase	:float = 0.8    ,   _UpperCamelCase	:int = 1    ,   _UpperCamelCase	:Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,   _UpperCamelCase	:float = 0.0    ,   _UpperCamelCase	:int = 50    ,   _UpperCamelCase	:Optional[bool] = None    ,   _UpperCamelCase	:Optional[str] = "pil"    ,   _UpperCamelCase	:bool = True    ,   )->					Union[ImagePipelineOutput, Tuple]:
         self.check_inputs(_UpperCamelCase )

         # 2. Preprocess image
         __A          =				preprocess(_UpperCamelCase )

         # 3. set timesteps
         self.scheduler.set_timesteps(_UpperCamelCase    ,   device=self.device )
         __A   ,						__A          =				self.get_timesteps(_UpperCamelCase    ,   _UpperCamelCase    ,   self.device )
         __A          =				timesteps[:1].repeat(_UpperCamelCase )

         # 4. Prepare latent variables
         __A          =				self.prepare_latents(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   self.unet.dtype    ,   self.device    ,   _UpperCamelCase )
         __A          =				latents

         # 5. Denoising loop
         for t in self.progress_bar(_UpperCamelCase ):
               # 1. predict noise model_output
               __A          =				self.unet(_UpperCamelCase    ,   _UpperCamelCase ).sample

               # 2. predict previous mean of image x_t-1 and add variance depending on eta
               # eta corresponds to η in paper and should be between [0, 1]
               # do x_t -> x_t-1
               __A          =				self.scheduler.step(
                   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   eta=_UpperCamelCase    ,   use_clipped_model_output=_UpperCamelCase    ,   generator=_UpperCamelCase    ,   ).prev_sample

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

         if not return_dict:
               return (image, latent_timestep.item())

         return ImagePipelineOutput(images=_UpperCamelCase )





