





"""simple docstring"""





import unittest

from diffusers.models.unet_ad_blocks import *  # noqa F403
from diffusers.utils import torch_device

from .test_unet_blocks_common import UNetBlockTesterMixin






class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:Union[str, Any]						    =   DownBlockaD  # noqa F405
       __magic_name__	:str						    =   """down"""






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				[-0.02_32, -0.98_69, 0.80_54, -0.06_37, -0.16_88, -1.42_64, 0.44_70, -1.33_94, 0.09_04]
         super().test_output(__UpperCAmelCase )






class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:List[Any]						    =   ResnetDownsampleBlockaD  # noqa F405
       __magic_name__	:List[str]						    =   """down"""






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				[0.07_10, 0.24_10, -0.73_20, -1.07_57, -1.13_43, 0.35_40, -0.01_33, -0.25_76, 0.09_48]
         super().test_output(__UpperCAmelCase )






class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:List[Any]						    =   AttnDownBlockaD  # noqa F405
       __magic_name__	:Dict						    =   """down"""






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				[0.06_36, 0.89_64, -0.62_34, -1.01_31, 0.08_44, 0.49_35, 0.34_37, 0.09_11, -0.29_57]
         super().test_output(__UpperCAmelCase )






class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:int						    =   CrossAttnDownBlockaD  # noqa F405
       __magic_name__	:Dict						    =   """down"""






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :Optional[int]											=				super().prepare_init_args_and_inputs_for_common()
         lowerCAmelCase__     :Optional[Any]											=				3_2
         return init_dict, inputs_dict






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				[0.22_38, -0.73_96, -0.22_55, -0.38_29, 0.19_25, 1.16_65, 0.06_03, -0.72_95, 0.19_83]
         super().test_output(__UpperCAmelCase )






class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:str						    =   SimpleCrossAttnDownBlockaD  # noqa F405
       __magic_name__	:Dict						    =   """down"""






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return super().get_dummy_input(include_encoder_hidden_states=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :List[str]											=				super().prepare_init_args_and_inputs_for_common()
         lowerCAmelCase__     :Dict											=				3_2
         return init_dict, inputs_dict






       @unittest.skipIf(torch_device == 'mps'       ,					'MPS result is not consistent' )
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				[0.79_21, -0.09_92, -0.19_62, -0.76_95, -0.42_42, 0.78_04, 0.47_37, 0.27_65, 0.33_38]
         super().test_output(__UpperCAmelCase )






class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:List[str]						    =   SkipDownBlockaD  # noqa F405
       __magic_name__	:str						    =   """down"""






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return super().get_dummy_input(include_skip_sample=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				[-0.08_45, -0.20_87, -0.24_65, 0.09_71, 0.19_00, -0.04_84, 0.26_64, 0.41_79, 0.50_69]
         super().test_output(__UpperCAmelCase )






class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:int						    =   AttnSkipDownBlockaD  # noqa F405
       __magic_name__	:Union[str, Any]						    =   """down"""






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return super().get_dummy_input(include_skip_sample=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				[0.55_39, 0.16_09, 0.49_24, 0.05_37, -0.19_95, 0.40_50, 0.09_79, -0.27_21, -0.06_42]
         super().test_output(__UpperCAmelCase )






class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:Any						    =   DownEncoderBlockaD  # noqa F405
       __magic_name__	:Optional[int]						    =   """down"""






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return super().get_dummy_input(include_temb=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				{
             'in_channels': 3_2,
             'out_channels': 3_2,
         }
         lowerCAmelCase__     :Any											=				self.dummy_input
         return init_dict, inputs_dict






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				[1.11_02, 0.53_02, 0.48_72, -0.00_23, -0.80_42, 0.04_83, -0.34_89, -0.56_32, 0.76_26]
         super().test_output(__UpperCAmelCase )






class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:Any						    =   AttnDownEncoderBlockaD  # noqa F405
       __magic_name__	:Optional[int]						    =   """down"""






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return super().get_dummy_input(include_temb=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				{
             'in_channels': 3_2,
             'out_channels': 3_2,
         }
         lowerCAmelCase__     :Optional[int]											=				self.dummy_input
         return init_dict, inputs_dict






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				[0.89_66, -0.14_86, 0.85_68, 0.81_41, -0.90_46, -0.13_42, -0.09_72, -0.74_17, 0.15_38]
         super().test_output(__UpperCAmelCase )






class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:List[str]						    =   UNetMidBlockaD  # noqa F405
       __magic_name__	:Union[str, Any]						    =   """mid"""






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				{
             'in_channels': 3_2,
             'temb_channels': 1_2_8,
         }
         lowerCAmelCase__     :Union[str, Any]											=				self.dummy_input
         return init_dict, inputs_dict






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				[-0.10_62, 1.72_48, 0.34_94, 1.45_69, -0.09_10, -1.24_21, -0.99_84, 0.67_36, 1.00_28]
         super().test_output(__UpperCAmelCase )






class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:Dict						    =   UNetMidBlockaDCrossAttn  # noqa F405
       __magic_name__	:int						    =   """mid"""






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :str											=				super().prepare_init_args_and_inputs_for_common()
         lowerCAmelCase__     :Union[str, Any]											=				3_2
         return init_dict, inputs_dict






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				[0.01_87, 2.42_20, 0.44_84, 1.12_03, -0.61_21, -1.51_22, -0.82_70, 0.78_51, 1.83_35]
         super().test_output(__UpperCAmelCase )






class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:List[str]						    =   UNetMidBlockaDSimpleCrossAttn  # noqa F405
       __magic_name__	:Any						    =   """mid"""






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return super().get_dummy_input(include_encoder_hidden_states=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :List[str]											=				super().prepare_init_args_and_inputs_for_common()
         lowerCAmelCase__     :Union[str, Any]											=				3_2
         return init_dict, inputs_dict






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				[0.71_43, 1.99_74, 0.54_48, 1.39_77, 0.12_82, -1.12_37, -1.42_38, 0.55_30, 0.88_80]
         super().test_output(__UpperCAmelCase )






class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:List[Any]						    =   UpBlockaD  # noqa F405
       __magic_name__	:str						    =   """up"""






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return super().get_dummy_input(include_res_hidden_states_tuple=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				[-0.20_41, -0.41_65, -0.30_22, 0.00_41, -0.66_28, -0.70_53, 0.19_28, -0.03_25, 0.05_23]
         super().test_output(__UpperCAmelCase )






class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:List[Any]						    =   ResnetUpsampleBlockaD  # noqa F405
       __magic_name__	:str						    =   """up"""






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return super().get_dummy_input(include_res_hidden_states_tuple=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				[0.22_87, 0.35_49, -0.13_46, 0.47_97, -0.17_15, -0.96_49, 0.73_05, -0.58_64, -0.62_44]
         super().test_output(__UpperCAmelCase )






class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:List[Any]						    =   CrossAttnUpBlockaD  # noqa F405
       __magic_name__	:Any						    =   """up"""






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return super().get_dummy_input(include_res_hidden_states_tuple=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :List[str]											=				super().prepare_init_args_and_inputs_for_common()
         lowerCAmelCase__     :Union[str, Any]											=				3_2
         return init_dict, inputs_dict






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				[-0.14_03, -0.35_15, -0.04_20, -0.14_25, 0.31_67, 0.50_94, -0.21_81, 0.59_31, 0.55_82]
         super().test_output(__UpperCAmelCase )






class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:Dict						    =   SimpleCrossAttnUpBlockaD  # noqa F405
       __magic_name__	:Dict						    =   """up"""






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return super().get_dummy_input(include_res_hidden_states_tuple=__UpperCAmelCase       ,					include_encoder_hidden_states=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :Union[str, Any]											=				super().prepare_init_args_and_inputs_for_common()
         lowerCAmelCase__     :List[str]											=				3_2
         return init_dict, inputs_dict






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				[0.26_45, 0.14_80, 0.09_09, 0.80_44, -0.97_58, -0.90_83, 0.09_94, -1.14_53, -0.74_02]
         super().test_output(__UpperCAmelCase )






class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:Dict						    =   AttnUpBlockaD  # noqa F405
       __magic_name__	:Optional[int]						    =   """up"""






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return super().get_dummy_input(include_res_hidden_states_tuple=__UpperCAmelCase )






       @unittest.skipIf(torch_device == 'mps'       ,					'MPS result is not consistent' )
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				[0.09_79, 0.13_26, 0.00_21, 0.06_59, 0.22_49, 0.00_59, 0.11_32, 0.59_52, 0.10_33]
         super().test_output(__UpperCAmelCase )






class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:Union[str, Any]						    =   SkipUpBlockaD  # noqa F405
       __magic_name__	:str						    =   """up"""






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return super().get_dummy_input(include_res_hidden_states_tuple=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				[-0.08_93, -0.12_34, -0.15_06, -0.03_32, 0.01_23, -0.02_11, 0.05_66, 0.01_43, 0.03_62]
         super().test_output(__UpperCAmelCase )






class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:Any						    =   AttnSkipUpBlockaD  # noqa F405
       __magic_name__	:int						    =   """up"""






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return super().get_dummy_input(include_res_hidden_states_tuple=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				[0.03_61, 0.06_17, 0.27_87, -0.03_50, 0.03_42, 0.34_21, -0.08_43, 0.09_13, 0.30_15]
         super().test_output(__UpperCAmelCase )






class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:Optional[int]						    =   UpDecoderBlockaD  # noqa F405
       __magic_name__	:Union[str, Any]						    =   """up"""






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return super().get_dummy_input(include_temb=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				{'in_channels': 3_2, 'out_channels': 3_2}

         lowerCAmelCase__     :Optional[Any]											=				self.dummy_input
         return init_dict, inputs_dict






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				[0.44_04, 0.19_98, -0.98_86, -0.33_20, -0.31_28, -0.70_34, -0.69_55, -0.23_38, -0.31_37]
         super().test_output(__UpperCAmelCase )






class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:List[str]						    =   AttnUpDecoderBlockaD  # noqa F405
       __magic_name__	:Any						    =   """up"""






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return super().get_dummy_input(include_temb=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				{'in_channels': 3_2, 'out_channels': 3_2}

         lowerCAmelCase__     :Any											=				self.dummy_input
         return init_dict, inputs_dict






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				[0.67_38, 0.44_91, 0.10_55, 1.07_10, 0.73_16, 0.33_39, 0.33_52, 0.10_23, 0.35_68]
         super().test_output(__UpperCAmelCase )








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 )



