






import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
       from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
       __magic_name__   :  Any        =							get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
       from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

__magic_name__   :  Optional[Any]        =							12_8022
__magic_name__   :  Dict        =							12_8028





@require_sentencepiece
class 					A__     (     __snake_case   ,    unittest.TestCase ):

     '''simple docstring'''






     snake_case__					=					MaMaaaTokenizer
     snake_case__					=					False
     snake_case__					=					False
     snake_case__					=					True







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str] ):






           """simple docstring"""




           super().setUp()

           UpperCamelCase					       =  ['</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est', '\u0120', '<pad>']
           UpperCamelCase					       =  dict(zip(_SCREAMING_SNAKE_CASE   ,						range(len(_SCREAMING_SNAKE_CASE ) ) ) )
           UpperCamelCase					       =  Path(self.tmpdirname )
           save_json(_SCREAMING_SNAKE_CASE   ,						save_dir / VOCAB_FILES_NAMES['vocab_file'] )
           if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
                 copyfile(_SCREAMING_SNAKE_CASE   ,						save_dir / VOCAB_FILES_NAMES['spm_file'] )

           UpperCamelCase					       =  MaMaaaTokenizer.from_pretrained(self.tmpdirname )
           tokenizer.save_pretrained(self.tmpdirname )







     def   _SCREAMING_SNAKE_CASE     ( self      :    str   ,						**_SCREAMING_SNAKE_CASE      :    List[str] ):






           """simple docstring"""




           return MaMaaaTokenizer.from_pretrained(self.tmpdirname   ,						**_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any] ):






           """simple docstring"""




           return (
               "This is a test",
               "This is a test",
           )







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  '</s>'
           UpperCamelCase					       =  0

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







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  self.get_tokenizer()
           UpperCamelCase					       =  list(tokenizer.get_vocab().keys() )

           self.assertEqual(vocab_keys[0]   ,						'</s>' )
           self.assertEqual(vocab_keys[1]   ,						'<unk>' )
           self.assertEqual(vocab_keys[-1]   ,						'<s>' )
           self.assertEqual(len(_SCREAMING_SNAKE_CASE )   ,						tokenizer.vocab_size + len(tokenizer.get_added_vocab() ) )







     @unittest.skip('Skip this test while all models are still to be uploaded.' )
     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           pass







     def   _SCREAMING_SNAKE_CASE     ( self      :    Any ):






           """simple docstring"""




           UpperCamelCase					       =  self.get_tokenizer()

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

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE )   ,						[2, 3, 4, 5, 6]   ,						)

           UpperCamelCase					       =  tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6] )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						['▁This', '▁is', '▁a', '▁t', 'est'] )

           UpperCamelCase					       =  tokenizer.convert_tokens_to_string(_SCREAMING_SNAKE_CASE )
           self.assertEqual(_SCREAMING_SNAKE_CASE   ,						'This is a test' )







     @slow
     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  {'input_ids': [[12_8022, 11_0108, 397, 11, 3_8272, 2247, 12_4811, 285, 1_8105, 1586, 207, 7, 3_9534, 4428, 397, 1019, 1_8105, 1586, 207, 7, 4_1337, 1_6786, 241, 7, 2_0214, 17, 12_5690, 1_0398, 7, 4_4378, 5_8069, 6_8342, 7798, 7343, 11, 299, 3_3310, 4, 158, 3_7350, 9_4077, 4569, 299, 3_3310, 90, 4, 5_2840, 290, 4, 3_1270, 112, 299, 682, 4, 5_2840, 3_9953, 1_4079, 193, 5_2519, 9_0894, 1_7894, 12_0697, 11, 4_0445, 551, 17, 1019, 5_2519, 9_0894, 1_7756, 963, 11, 4_0445, 480, 17, 9792, 1120, 5173, 1393, 6240, 1_6786, 241, 12_0996, 28, 1245, 1393, 11_8240, 1_1123, 1019, 9_3612, 2691, 1_0618, 9_8058, 12_0409, 1928, 279, 4, 4_0683, 367, 178, 207, 1019, 103, 10_3121, 506, 6_5296, 5, 2], [12_8022, 2_1217, 367, 117, 12_5450, 128, 719, 7, 7308, 40, 9_3612, 1_2669, 1116, 1_6704, 71, 1_7785, 3699, 1_5592, 35, 144, 9584, 241, 1_1943, 713, 950, 799, 2247, 8_8427, 150, 149, 11_8813, 12_0706, 1019, 10_6906, 8_1518, 28, 1224, 2_2799, 397, 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], [12_8022, 1658, 12_3311, 5155, 5578, 4722, 279, 1_4947, 2366, 1120, 1197, 14, 1348, 9232, 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, 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, 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], [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, 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=_SCREAMING_SNAKE_CASE   ,						model_name='facebook/m2m100_418M'   ,						revision='c168bae485c864188cf9aa0e4108b0b6934dc91e'   ,						)





@require_torch
@require_sentencepiece
@require_tokenizers
class 					A__     (     unittest.TestCase ):

     '''simple docstring'''






     snake_case__					=					"""facebook/m2m100_418M"""
     snake_case__					=					[
         """In my opinion, there are two levels of response from the French government.""",
         """NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
     ]
     snake_case__					=					[
         """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
         """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
     ]

     # fmt: off
     snake_case__					=					[EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]







     @classmethod
     def   _SCREAMING_SNAKE_CASE     ( cls      :    Any ):






           """simple docstring"""




           UpperCamelCase					       =  MaMaaaTokenizer.from_pretrained(
               cls.checkpoint_name   ,						src_lang='en'   ,						tgt_lang='fr' )
           UpperCamelCase					       =  1
           return cls







     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           self.assertEqual(self.tokenizer.get_lang_id('ar' )   ,						12_8006 )
           self.assertEqual(self.tokenizer.get_lang_id('en' )   ,						12_8022 )
           self.assertEqual(self.tokenizer.get_lang_id('ro' )   ,						12_8076 )
           self.assertEqual(self.tokenizer.get_lang_id('mr' )   ,						12_8063 )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           UpperCamelCase					       =  self.tokenizer.get_vocab()
           self.assertEqual(len(_SCREAMING_SNAKE_CASE )   ,						self.tokenizer.vocab_size )
           self.assertEqual(vocab['<unk>']   ,						3 )
           self.assertIn(self.tokenizer.get_lang_token('en' )   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  'en'
           UpperCamelCase					       =  self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
           self.assertListEqual(self.expected_src_tokens   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any] ):






           """simple docstring"""




           self.assertIn(_SCREAMING_SNAKE_CASE   ,						self.tokenizer.all_special_ids )
           # fmt: off
           UpperCamelCase					       =  [FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 1_4028, 136, 3286, 9706, 6, 9_0797, 6, 14_4012, 162, 8_8128, 3_0061, 5, 2]
           # fmt: on
           UpperCamelCase					       =  self.tokenizer.decode(_SCREAMING_SNAKE_CASE   ,						skip_special_tokens=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  self.tokenizer.decode(generated_ids[1:]   ,						skip_special_tokens=_SCREAMING_SNAKE_CASE )
           self.assertEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           self.assertNotIn(self.tokenizer.eos_token   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  tempfile.mkdtemp()
           UpperCamelCase					       =  self.tokenizer.lang_token_to_id
           self.tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  MaMaaaTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE )
           self.assertDictEqual(new_tok.lang_token_to_id   ,						_SCREAMING_SNAKE_CASE )







     @require_torch
     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any] ):






           """simple docstring"""




           UpperCamelCase					       =  'en'
           UpperCamelCase					       =  'fr'

           UpperCamelCase					       =  self.tokenizer(self.src_text   ,						text_target=self.tgt_text   ,						padding=_SCREAMING_SNAKE_CASE   ,						return_tensors='pt' )

           UpperCamelCase					       =  shift_tokens_right(
               batch['labels']   ,						self.tokenizer.pad_token_id   ,						self.tokenizer.eos_token_id )

           for k in batch:
                 UpperCamelCase					       =  batch[k].tolist()
           # batch = {k: v.tolist() for k,v in batch.items()}
           # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
           # batch.decoder_inputs_ids[0][0] ==
           assert batch.input_ids[1][0] == EN_CODE
           assert batch.input_ids[1][-1] == 2
           assert batch.labels[1][0] == FR_CODE
           assert batch.labels[1][-1] == 2
           assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]







     @require_torch
     def   _SCREAMING_SNAKE_CASE     ( self      :    Any ):






           """simple docstring"""




           UpperCamelCase					       =  'mr'
           self.assertListEqual(self.tokenizer.prefix_tokens   ,						[self.tokenizer.get_lang_id('mr' )] )
           self.assertListEqual(self.tokenizer.suffix_tokens   ,						[self.tokenizer.eos_token_id] )

           UpperCamelCase					       =  'zh'
           self.assertListEqual(self.tokenizer.prefix_tokens   ,						[self.tokenizer.get_lang_id('zh' )] )
           self.assertListEqual(self.tokenizer.suffix_tokens   ,						[self.tokenizer.eos_token_id] )







     @require_torch
     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           UpperCamelCase					       =  'mr'
           self.tokenizer._switch_to_target_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens   ,						[self.tokenizer.get_lang_id('mr' )] )
           self.assertListEqual(self.tokenizer.suffix_tokens   ,						[self.tokenizer.eos_token_id] )
           self.tokenizer._switch_to_input_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens   ,						[self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )

           UpperCamelCase					       =  'zh'
           self.tokenizer._switch_to_target_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens   ,						[self.tokenizer.get_lang_id('zh' )] )
           self.assertListEqual(self.tokenizer.suffix_tokens   ,						[self.tokenizer.eos_token_id] )
           self.tokenizer._switch_to_input_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens   ,						[self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )







     @require_torch
     def   _SCREAMING_SNAKE_CASE     ( self      :    Any ):






           """simple docstring"""




           UpperCamelCase					       =  self.tokenizer._build_translation_inputs('A test'   ,						return_tensors='pt'   ,						src_lang='en'   ,						tgt_lang='ar' )

           self.assertEqual(
               nested_simplify(_SCREAMING_SNAKE_CASE )   ,						{
                   # en_XX, A, test, EOS
                   'input_ids': [[12_8022, 58, 4183, 2]],
                   'attention_mask': [[1, 1, 1, 1]],
                   # ar_AR
                   'forced_bos_token_id': 12_8006,
               }   ,						)











import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


__magic_name__   :  Any        =							logging.get_logger(__name__)  # pylint: disable=invalid-name





class 					A__     (     __snake_case ):

     '''simple docstring'''






     def __init__( self      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    WhisperForConditionalGeneration   ,						_SCREAMING_SNAKE_CASE      :    WhisperProcessor   ,						_SCREAMING_SNAKE_CASE      :    AutoencoderKL   ,						_SCREAMING_SNAKE_CASE      :    CLIPTextModel   ,						_SCREAMING_SNAKE_CASE      :    CLIPTokenizer   ,						_SCREAMING_SNAKE_CASE      :    UNetaDConditionModel   ,						_SCREAMING_SNAKE_CASE      :    Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]   ,						_SCREAMING_SNAKE_CASE      :    StableDiffusionSafetyChecker   ,						_SCREAMING_SNAKE_CASE      :    CLIPImageProcessor   ,						):






           """simple docstring"""




           super().__init__()

           if safety_checker is None:
                 logger.warning(
                     f'You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'
                     ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
                     ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
                     ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
                     ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
                     ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .' )

           self.register_modules(
               speech_model=_SCREAMING_SNAKE_CASE   ,						speech_processor=_SCREAMING_SNAKE_CASE   ,						vae=_SCREAMING_SNAKE_CASE   ,						text_encoder=_SCREAMING_SNAKE_CASE   ,						tokenizer=_SCREAMING_SNAKE_CASE   ,						unet=_SCREAMING_SNAKE_CASE   ,						scheduler=_SCREAMING_SNAKE_CASE   ,						feature_extractor=_SCREAMING_SNAKE_CASE   ,						)







     def   _SCREAMING_SNAKE_CASE     ( self      :    str   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[str, int]] = "auto" ):






           """simple docstring"""




           if slice_size == "auto":
                 UpperCamelCase					       =  self.unet.config.attention_head_dim // 2
           self.unet.set_attention_slice(_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           self.enable_attention_slicing(_SCREAMING_SNAKE_CASE )







     @torch.no_grad()
     def __call__( self      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]=1_6000   ,						_SCREAMING_SNAKE_CASE      :    int = 512   ,						_SCREAMING_SNAKE_CASE      :    int = 512   ,						_SCREAMING_SNAKE_CASE      :    int = 50   ,						_SCREAMING_SNAKE_CASE      :    float = 7.5   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[str, List[str]]] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[int] = 1   ,						_SCREAMING_SNAKE_CASE      :    float = 0.0   ,						_SCREAMING_SNAKE_CASE      :    Optional[torch.Generator] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[torch.FloatTensor] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[str] = "pil"   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						_SCREAMING_SNAKE_CASE      :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None   ,						_SCREAMING_SNAKE_CASE      :    int = 1   ,						**_SCREAMING_SNAKE_CASE      :    str   ,						):






           """simple docstring"""




           UpperCamelCase					       =  self.speech_processor.feature_extractor(
               _SCREAMING_SNAKE_CASE   ,						return_tensors='pt'   ,						sampling_rate=_SCREAMING_SNAKE_CASE ).input_features.to(self.device )
           UpperCamelCase					       =  self.speech_model.generate(_SCREAMING_SNAKE_CASE   ,						max_length=48_0000 )

           UpperCamelCase					       =  self.speech_processor.tokenizer.batch_decode(_SCREAMING_SNAKE_CASE   ,						skip_special_tokens=_SCREAMING_SNAKE_CASE   ,						normalize=_SCREAMING_SNAKE_CASE )[
               0
           ]

           if isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ):
                 UpperCamelCase					       =  1
           elif isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ):
                 UpperCamelCase					       =  len(_SCREAMING_SNAKE_CASE )
           else:
                 raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(_SCREAMING_SNAKE_CASE )}' )

           if height % 8 != 0 or width % 8 != 0:
                 raise ValueError(f'`height` and `width` have to be divisible by 8 but are {height} and {width}.' )

           if (callback_steps is None) or (
               callback_steps is not None and (not isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ) or callback_steps <= 0)
           ):
                 raise ValueError(
                     f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
                     f' {type(_SCREAMING_SNAKE_CASE )}.' )

           # get prompt text embeddings
           UpperCamelCase					       =  self.tokenizer(
               _SCREAMING_SNAKE_CASE   ,						padding='max_length'   ,						max_length=self.tokenizer.model_max_length   ,						return_tensors='pt'   ,						)
           UpperCamelCase					       =  text_inputs.input_ids

           if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                 UpperCamelCase					       =  self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
                 logger.warning(
                     'The following part of your input was truncated because CLIP can only handle sequences up to'
                     f' {self.tokenizer.model_max_length} tokens: {removed_text}' )
                 UpperCamelCase					       =  text_input_ids[:, : self.tokenizer.model_max_length]
           UpperCamelCase					       =  self.text_encoder(text_input_ids.to(self.device ) )[0]

           # duplicate text embeddings for each generation per prompt, using mps friendly method
           UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  text_embeddings.shape
           UpperCamelCase					       =  text_embeddings.repeat(1   ,						_SCREAMING_SNAKE_CASE   ,						1 )
           UpperCamelCase					       =  text_embeddings.view(bs_embed * num_images_per_prompt   ,						_SCREAMING_SNAKE_CASE   ,						-1 )

           # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
           # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
           # corresponds to doing no classifier free guidance.
           UpperCamelCase					       =  guidance_scale > 1.0
           # get unconditional embeddings for classifier free guidance
           if do_classifier_free_guidance:
                 UpperCamelCase					       =  42
                 if negative_prompt is None:
                       UpperCamelCase					       =  [''] * batch_size
                 elif type(_SCREAMING_SNAKE_CASE ) is not type(_SCREAMING_SNAKE_CASE ):
                       raise TypeError(
                           f'`negative_prompt` should be the same type to `prompt`, but got {type(_SCREAMING_SNAKE_CASE )} !='
                           f' {type(_SCREAMING_SNAKE_CASE )}.' )
                 elif isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ):
                       UpperCamelCase					       =  [negative_prompt]
                 elif batch_size != len(_SCREAMING_SNAKE_CASE ):
                       raise ValueError(
                           f'`negative_prompt`: {negative_prompt} has batch size {len(_SCREAMING_SNAKE_CASE )}, but `prompt`:'
                           f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
                           ' the batch size of `prompt`.' )
                 else:
                       UpperCamelCase					       =  negative_prompt

                 UpperCamelCase					       =  text_input_ids.shape[-1]
                 UpperCamelCase					       =  self.tokenizer(
                     _SCREAMING_SNAKE_CASE   ,						padding='max_length'   ,						max_length=_SCREAMING_SNAKE_CASE   ,						truncation=_SCREAMING_SNAKE_CASE   ,						return_tensors='pt'   ,						)
                 UpperCamelCase					       =  self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]

                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                 UpperCamelCase					       =  uncond_embeddings.shape[1]
                 UpperCamelCase					       =  uncond_embeddings.repeat(1   ,						_SCREAMING_SNAKE_CASE   ,						1 )
                 UpperCamelCase					       =  uncond_embeddings.view(batch_size * num_images_per_prompt   ,						_SCREAMING_SNAKE_CASE   ,						-1 )

                 # For classifier free guidance, we need to do two forward passes.
                 # Here we concatenate the unconditional and text embeddings into a single batch
                 # to avoid doing two forward passes
                 UpperCamelCase					       =  torch.cat([uncond_embeddings, text_embeddings] )

           # get the initial random noise unless the user supplied it

           # Unlike in other pipelines, latents need to be generated in the target device
           # for 1-to-1 results reproducibility with the CompVis implementation.
           # However this currently doesn't work in `mps`.
           UpperCamelCase					       =  (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
           UpperCamelCase					       =  text_embeddings.dtype
           if latents is None:
                 if self.device.type == "mps":
                       # randn does not exist on mps
                       UpperCamelCase					       =  torch.randn(_SCREAMING_SNAKE_CASE   ,						generator=_SCREAMING_SNAKE_CASE   ,						device='cpu'   ,						dtype=_SCREAMING_SNAKE_CASE ).to(
                           self.device )
                 else:
                       UpperCamelCase					       =  torch.randn(_SCREAMING_SNAKE_CASE   ,						generator=_SCREAMING_SNAKE_CASE   ,						device=self.device   ,						dtype=_SCREAMING_SNAKE_CASE )
           else:
                 if latents.shape != latents_shape:
                       raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}' )
                 UpperCamelCase					       =  latents.to(self.device )

           # set timesteps
           self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE )

           # Some schedulers like PNDM have timesteps as arrays
           # It's more optimized to move all timesteps to correct device beforehand
           UpperCamelCase					       =  self.scheduler.timesteps.to(self.device )

           # scale the initial noise by the standard deviation required by the scheduler
           UpperCamelCase					       =  latents * self.scheduler.init_noise_sigma

           # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
           # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
           # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
           # and should be between [0, 1]
           UpperCamelCase					       =  'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
           UpperCamelCase					       =  {}
           if accepts_eta:
                 UpperCamelCase					       =  eta

           for i, t in enumerate(self.progress_bar(_SCREAMING_SNAKE_CASE ) ):
                 # expand the latents if we are doing classifier free guidance
                 UpperCamelCase					       =  torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
                 UpperCamelCase					       =  self.scheduler.scale_model_input(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

                 # predict the noise residual
                 UpperCamelCase					       =  self.unet(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						encoder_hidden_states=_SCREAMING_SNAKE_CASE ).sample

                 # perform guidance
                 if do_classifier_free_guidance:
                       UpperCamelCase       ,  UpperCamelCase					       =  noise_pred.chunk(2 )
                       UpperCamelCase					       =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                 # compute the previous noisy sample x_t -> x_t-1
                 UpperCamelCase					       =  self.scheduler.step(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE ).prev_sample

                 # call the callback, if provided
                 if callback is not None and i % callback_steps == 0:
                       callback(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  1 / 0.1_8_2_1_5 * latents
           UpperCamelCase					       =  self.vae.decode(_SCREAMING_SNAKE_CASE ).sample

           UpperCamelCase					       =  (image / 2 + 0.5).clamp(0   ,						1 )

           # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
           UpperCamelCase					       =  image.cpu().permute(0   ,						2   ,						3   ,						1 ).float().numpy()

           if output_type == "pil":
                 UpperCamelCase					       =  self.numpy_to_pil(_SCREAMING_SNAKE_CASE )

           if not return_dict:
                 return image

           return StableDiffusionPipelineOutput(images=_SCREAMING_SNAKE_CASE   ,						nsfw_content_detected=_SCREAMING_SNAKE_CASE )



