


'''simple docstring'''

from __future__ import annotations

lowerCAmelCase				:str        =      tuple[int, int, int]
lowerCAmelCase				:List[Any]        =      tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
lowerCAmelCase				:Optional[Any]        =      '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''

# -------------------------- default selection --------------------------
# rotors --------------------------
lowerCAmelCase				:Union[str, Any]        =      '''EGZWVONAHDCLFQMSIPJBYUKXTR'''
lowerCAmelCase				:Optional[Any]        =      '''FOBHMDKEXQNRAULPGSJVTYICZW'''
lowerCAmelCase				:Union[str, Any]        =      '''ZJXESIUQLHAVRMDOYGTNFWPBKC'''
# reflector --------------------------
lowerCAmelCase				:List[Any]        =      {
    '''A''': '''N''',
    '''N''': '''A''',
    '''B''': '''O''',
    '''O''': '''B''',
    '''C''': '''P''',
    '''P''': '''C''',
    '''D''': '''Q''',
    '''Q''': '''D''',
    '''E''': '''R''',
    '''R''': '''E''',
    '''F''': '''S''',
    '''S''': '''F''',
    '''G''': '''T''',
    '''T''': '''G''',
    '''H''': '''U''',
    '''U''': '''H''',
    '''I''': '''V''',
    '''V''': '''I''',
    '''J''': '''W''',
    '''W''': '''J''',
    '''K''': '''X''',
    '''X''': '''K''',
    '''L''': '''Y''',
    '''Y''': '''L''',
    '''M''': '''Z''',
    '''Z''': '''M''',
}

# -------------------------- extra rotors --------------------------
lowerCAmelCase				:List[str]        =      '''RMDJXFUWGISLHVTCQNKYPBEZOA'''
lowerCAmelCase				:List[Any]        =      '''SGLCPQWZHKXAREONTFBVIYJUDM'''
lowerCAmelCase				:Dict        =      '''HVSICLTYKQUBXDWAJZOMFGPREN'''
lowerCAmelCase				:Optional[int]        =      '''RZWQHFMVDBKICJLNTUXAGYPSOE'''
lowerCAmelCase				:str        =      '''LFKIJODBEGAMQPXVUHYSTCZRWN'''
lowerCAmelCase				:int        =      '''KOAEGVDHXPQZMLFTYWJNBRCIUS'''





def lowerCamelCase      (      lowerCAmelCase    :     RotorPositionT		,		lowerCAmelCase    :     RotorSelectionT		,		lowerCAmelCase    :     str		):





	"""simple docstring"""







	if (unique_rotsel := len(set(lowerCAmelCase		)		)) < 3:
		__magic_name__	:   Dict					     =					f'Please use 3 unique rotors (not {unique_rotsel})'
		raise Exception(lowerCAmelCase		)

	# Checks if rotor positions are valid
	__magic_name__     ,    __magic_name__     ,    __magic_name__	:   Tuple					     =					rotpos
	if not 0 < rotorposa <= len(lowerCAmelCase		):
		__magic_name__	:   Union[str, Any]					     =					f'First rotor position is not within range of 1..26 ({rotorposa}'
		raise ValueError(lowerCAmelCase		)
	if not 0 < rotorposa <= len(lowerCAmelCase		):
		__magic_name__	:   Tuple					     =					f'Second rotor position is not within range of 1..26 ({rotorposa})'
		raise ValueError(lowerCAmelCase		)
	if not 0 < rotorposa <= len(lowerCAmelCase		):
		__magic_name__	:   Any					     =					f'Third rotor position is not within range of 1..26 ({rotorposa})'
		raise ValueError(lowerCAmelCase		)

	# Validates string and returns dict
	__magic_name__	:   Dict					     =					_plugboard(lowerCAmelCase		)

	return rotpos, rotsel, pbdict





def lowerCamelCase      (      lowerCAmelCase    :     str		):





	"""simple docstring"""







	if not isinstance(lowerCAmelCase		,		lowerCAmelCase		):
		__magic_name__	:   Any					     =					f'Plugboard setting isn\'t type string ({type(lowerCAmelCase		)})'
		raise TypeError(lowerCAmelCase		)
	elif len(lowerCAmelCase		) % 2 != 0:
		__magic_name__	:   Dict					     =					f'Odd number of symbols ({len(lowerCAmelCase		)})'
		raise Exception(lowerCAmelCase		)
	elif pbstring == "":
		return {}

	pbstring.replace(' '		,		''		)

	# Checks if all characters are unique
	__magic_name__	:   List[str]					     =					set()
	for i in pbstring:
		if i not in abc:
			__magic_name__	:   List[Any]					     =					f'\'{i}\' not in list of symbols'
			raise Exception(lowerCAmelCase		)
		elif i in tmppbl:
			__magic_name__	:   Dict					     =					f'Duplicate symbol ({i})'
			raise Exception(lowerCAmelCase		)
		else:
			tmppbl.add(lowerCAmelCase		)
	del tmppbl

	# Created the dictionary
	__magic_name__	:   List[Any]					     =					{}
	for j in range(0		,		len(lowerCAmelCase		) - 1		,		2		):
		__magic_name__	:   List[str]					     =					pbstring[j + 1]
		__magic_name__	:   Any					     =					pbstring[j]

	return pb





def lowerCamelCase      (      lowerCAmelCase    :     str		,		lowerCAmelCase    :     RotorPositionT		,		lowerCAmelCase    :     RotorSelectionT = (rotora, rotora, rotora)		,		lowerCAmelCase    :     str = ""		,		):





	"""simple docstring"""







	__magic_name__	:   Union[str, Any]					     =					text.upper()
	__magic_name__     ,    __magic_name__     ,    __magic_name__	:   List[Any]					     =					_validator(
	    lowerCAmelCase		,		lowerCAmelCase		,		plugb.upper()		)

	__magic_name__     ,    __magic_name__     ,    __magic_name__	:   List[str]					     =					rotor_position
	__magic_name__     ,    __magic_name__     ,    __magic_name__	:   Tuple					     =					rotor_selection
	rotorposa -= 1
	rotorposa -= 1
	rotorposa -= 1

	__magic_name__	:   List[str]					     =					[]

	# encryption/decryption process --------------------------
	for symbol in text:
		if symbol in abc:
			# 1st plugboard --------------------------
			if symbol in plugboard:
				__magic_name__	:   List[str]					     =					plugboard[symbol]

			# rotor ra --------------------------
			__magic_name__	:   List[Any]					     =					abc.index(lowerCAmelCase		) + rotorposa
			__magic_name__	:   Any					     =					rotora[index % len(lowerCAmelCase		)]

			# rotor rb --------------------------
			__magic_name__	:   Union[str, Any]					     =					abc.index(lowerCAmelCase		) + rotorposa
			__magic_name__	:   Optional[Any]					     =					rotora[index % len(lowerCAmelCase		)]

			# rotor rc --------------------------
			__magic_name__	:   Optional[Any]					     =					abc.index(lowerCAmelCase		) + rotorposa
			__magic_name__	:   int					     =					rotora[index % len(lowerCAmelCase		)]

			# reflector --------------------------
			# this is the reason you don't need another machine to decipher

			__magic_name__	:   Union[str, Any]					     =					reflector[symbol]

			# 2nd rotors
			__magic_name__	:   Optional[int]					     =					abc[rotora.index(lowerCAmelCase		) - rotorposa]
			__magic_name__	:   Optional[Any]					     =					abc[rotora.index(lowerCAmelCase		) - rotorposa]
			__magic_name__	:   Optional[Any]					     =					abc[rotora.index(lowerCAmelCase		) - rotorposa]

			# 2nd plugboard
			if symbol in plugboard:
				__magic_name__	:   Any					     =					plugboard[symbol]

			# moves/resets rotor positions
			rotorposa += 1
			if rotorposa >= len(lowerCAmelCase		):
				__magic_name__	:   List[Any]					     =					0
				rotorposa += 1
			if rotorposa >= len(lowerCAmelCase		):
				__magic_name__	:   str					     =					0
				rotorposa += 1
			if rotorposa >= len(lowerCAmelCase		):
				__magic_name__	:   int					     =					0

        # else:
        #    pass
        #    Error could be also raised
        #    raise ValueError(
        #       'Invalid symbol('+repr(symbol)+')')
		result.append(lowerCAmelCase		)

	return "".join(lowerCAmelCase		)


if __name__ == "__main__":
							lowerCAmelCase				:Dict        =      '''This is my Python script that emulates the Enigma machine from WWII.'''
							lowerCAmelCase				:List[str]        =      (1, 1, 1)
							lowerCAmelCase				:int        =      '''pictures'''
							lowerCAmelCase				:Optional[int]        =      (rotora, rotora, rotora)
							lowerCAmelCase				:int        =      enigma(message, rotor_pos, rotor_sel, pb)

							print('''Encrypted message:''', en)
							print('''Decrypted message:''', enigma(en, rotor_pos, rotor_sel, pb))



'''simple docstring'''

from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


lowerCAmelCase				:str        =      logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCAmelCase				:int        =      '''
    Examples:
        ```py
        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")
        >>> pipe_prior.to("cuda")

        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> negative_image_emb = out.negative_image_embeds

        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
        >>> pipe.to("cuda")

        >>> image = pipe(
        ...     prompt,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=negative_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ... ).images

        >>> image[0].save("cat.png")
        ```
'''





def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     Union[str, Any]		,		lowerCAmelCase    :     List[Any]=8		):





	"""simple docstring"""







	__magic_name__	:   List[str]					     =					h // scale_factor**2
	if h % scale_factor**2 != 0:
		new_h += 1
	__magic_name__	:   str					     =					w // scale_factor**2
	if w % scale_factor**2 != 0:
		new_w += 1
	return new_h * scale_factor, new_w * scale_factor





class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		def __init__( self		:						Union[str, Any]  ,					_A		:						MultilingualCLIP  ,					_A		:						XLMRobertaTokenizer  ,					_A		:						UNetaDConditionModel  ,					_A		:						Union[DDIMScheduler, DDPMScheduler]  ,					_A		:						VQModel  ,					)  ->			int:
			super().__init__()

			self.register_modules(
			    text_encoder=_A  ,					tokenizer=_A  ,					unet=_A  ,					scheduler=_A  ,					movq=_A  ,					)
			__magic_name__	:   List[Any]					     =					2 ** (len(self.movq.config.block_out_channels      ) - 1)







		def        __lowerCAmelCase		( self		:						List[Any]  ,					_A		:						Tuple  ,					_A		:						Optional[Any]  ,					_A		:						Optional[int]  ,					_A		:						Dict  ,					_A		:						str  ,					_A		:						List[str]      )  ->			str:
			if latents is None:
				__magic_name__	:   Any					     =					randn_tensor(_A  ,					generator=_A  ,					device=_A  ,					dtype=_A      )
			else:
				if latents.shape != shape:
					raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'      )
				__magic_name__	:   int					     =					latents.to(_A      )

			__magic_name__	:   Union[str, Any]					     =					latents * scheduler.init_noise_sigma
			return latents







		def        __lowerCAmelCase		( self		:						List[Any]  ,					_A		:						List[str]  ,					_A		:						List[str]  ,					_A		:						List[str]  ,					_A		:						List[Any]  ,					_A		:						str=None  ,					)  ->			Dict:
			__magic_name__	:   Optional[Any]					     =					len(_A      ) if isinstance(_A  ,					_A      ) else 1
			# get prompt text embeddings
			__magic_name__	:   str					     =					self.tokenizer(
			    _A  ,					padding='max_length'  ,					truncation=_A  ,					max_length=77  ,					return_attention_mask=_A  ,					add_special_tokens=_A  ,					return_tensors='pt'  ,					)

			__magic_name__	:   Optional[Any]					     =					text_inputs.input_ids
			__magic_name__	:   Optional[Any]					     =					self.tokenizer(_A  ,					padding='longest'  ,					return_tensors='pt'      ).input_ids

			if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(_A  ,					_A      ):
				__magic_name__	:   str					     =					self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]      )
				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}'      )

			__magic_name__	:   Union[str, Any]					     =					text_input_ids.to(_A      )
			__magic_name__	:   Dict					     =					text_inputs.attention_mask.to(_A      )

			__magic_name__     ,    __magic_name__	:   str					     =					self.text_encoder(
			    input_ids=_A  ,					attention_mask=_A      )

			__magic_name__	:   Tuple					     =					prompt_embeds.repeat_interleave(_A  ,					dim=0      )
			__magic_name__	:   int					     =					text_encoder_hidden_states.repeat_interleave(_A  ,					dim=0      )
			__magic_name__	:   Union[str, Any]					     =					text_mask.repeat_interleave(_A  ,					dim=0      )

			if do_classifier_free_guidance:
				__magic_name__	:   List[str]
				if negative_prompt is None:
					__magic_name__	:   Optional[Any]					     =					[''] * batch_size
				elif type(_A      ) is not type(_A      ):
					raise TypeError(
					    F'`negative_prompt` should be the same type to `prompt`, but got {type(_A      )} !='
					    F' {type(_A      )}.'      )
				elif isinstance(_A  ,					_A      ):
					__magic_name__	:   int					     =					[negative_prompt]
				elif batch_size != len(_A      ):
					raise ValueError(
					    F'`negative_prompt`: {negative_prompt} has batch size {len(_A      )}, but `prompt`:'
					    F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
					    ' the batch size of `prompt`.'      )
				else:
					__magic_name__	:   Dict					     =					negative_prompt

				__magic_name__	:   List[str]					     =					self.tokenizer(
				    _A  ,					padding='max_length'  ,					max_length=77  ,					truncation=_A  ,					return_attention_mask=_A  ,					add_special_tokens=_A  ,					return_tensors='pt'  ,					)
				__magic_name__	:   Optional[int]					     =					uncond_input.input_ids.to(_A      )
				__magic_name__	:   Optional[Any]					     =					uncond_input.attention_mask.to(_A      )

				__magic_name__     ,    __magic_name__	:   int					     =					self.text_encoder(
				    input_ids=_A  ,					attention_mask=_A      )

				# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

				__magic_name__	:   List[str]					     =					negative_prompt_embeds.shape[1]
				__magic_name__	:   str					     =					negative_prompt_embeds.repeat(1  ,					_A      )
				__magic_name__	:   Dict					     =					negative_prompt_embeds.view(batch_size * num_images_per_prompt  ,					_A      )

				__magic_name__	:   Any					     =					uncond_text_encoder_hidden_states.shape[1]
				__magic_name__	:   Optional[int]					     =					uncond_text_encoder_hidden_states.repeat(1  ,					_A  ,					1      )
				__magic_name__	:   Tuple					     =					uncond_text_encoder_hidden_states.view(
				    batch_size * num_images_per_prompt  ,					_A  ,					-1      )
				__magic_name__	:   List[Any]					     =					uncond_text_mask.repeat_interleave(_A  ,					dim=0      )

				# done duplicates

				# 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
				__magic_name__	:   Tuple					     =					torch.cat([negative_prompt_embeds, prompt_embeds]      )
				__magic_name__	:   str					     =					torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]      )

				__magic_name__	:   str					     =					torch.cat([uncond_text_mask, text_mask]      )

			return prompt_embeds, text_encoder_hidden_states, text_mask







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						List[Any]=0      )  ->			Tuple:
			if is_accelerate_available():
				from accelerate import cpu_offload
			else:
				raise ImportError('Please install accelerate via `pip install accelerate`'      )

			__magic_name__	:   List[Any]					     =					torch.device(F'cuda:{gpu_id}'      )

			__magic_name__	:   Dict					     =					[
			    self.unet,
			    self.text_encoder,
			    self.movq,
			]
			for cpu_offloaded_model in models:
				if cpu_offloaded_model is not None:
					cpu_offload(_A  ,					_A      )







		def        __lowerCAmelCase		( self		:						List[Any]  ,					_A		:						List[str]=0      )  ->			str:
			if is_accelerate_available() and is_accelerate_version('>='  ,					'0.17.0.dev0'      ):
				from accelerate import cpu_offload_with_hook
			else:
				raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'      )

			__magic_name__	:   int					     =					torch.device(F'cuda:{gpu_id}'      )

			if self.device.type != "cpu":
				self.to('cpu'  ,					silence_dtype_warnings=_A      )
				torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

			__magic_name__	:   Optional[int]					     =					None
			for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
				__magic_name__     ,    __magic_name__	:   Union[str, Any]					     =					cpu_offload_with_hook(_A  ,					_A  ,					prev_module_hook=_A      )

			if self.safety_checker is not None:
				__magic_name__     ,    __magic_name__	:   List[str]					     =					cpu_offload_with_hook(self.safety_checker  ,					_A  ,					prev_module_hook=_A      )

			# We'll offload the last model manually.
			__magic_name__	:   Any					     =					hook







		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def        __lowerCAmelCase		( self		:						int      )  ->			List[str]:
			if not hasattr(self.unet  ,					'_hf_hook'      ):
				return self.device
			for module in self.unet.modules():
				if (
				    hasattr(_A  ,					'_hf_hook'      )
				    and hasattr(module._hf_hook  ,					'execution_device'      )
				    and module._hf_hook.execution_device is not None
				):
					return torch.device(module._hf_hook.execution_device      )
			return self.device







		@torch.no_grad()
		@replace_example_docstring(_A      )
		def __call__( self		:						int  ,					_A		:						Union[str, List[str]]  ,					_A		:						Union[torch.FloatTensor, List[torch.FloatTensor]]  ,					_A		:						Union[torch.FloatTensor, List[torch.FloatTensor]]  ,					_A		:						Optional[Union[str, List[str]]] = None  ,					_A		:						int = 512  ,					_A		:						int = 512  ,					_A		:						int = 100  ,					_A		:						float = 4.0  ,					_A		:						int = 1  ,					_A		:						Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,					_A		:						Optional[torch.FloatTensor] = None  ,					_A		:						Optional[str] = "pil"  ,					_A		:						bool = True  ,					)  ->			Optional[int]:

			if isinstance(_A  ,					_A      ):
				__magic_name__	:   Optional[int]					     =					1
			elif isinstance(_A  ,					_A      ):
				__magic_name__	:   Union[str, Any]					     =					len(_A      )
			else:
				raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(_A      )}'      )

			__magic_name__	:   Tuple					     =					self._execution_device

			__magic_name__	:   Any					     =					batch_size * num_images_per_prompt
			__magic_name__	:   int					     =					guidance_scale > 1.0

			__magic_name__     ,    __magic_name__     ,    __magic_name__	:   Optional[int]					     =					self._encode_prompt(
			    _A  ,					_A  ,					_A  ,					_A  ,					_A      )

			if isinstance(_A  ,					_A      ):
				__magic_name__	:   Union[str, Any]					     =					torch.cat(_A  ,					dim=0      )
			if isinstance(_A  ,					_A      ):
				__magic_name__	:   Dict					     =					torch.cat(_A  ,					dim=0      )

			if do_classifier_free_guidance:
				__magic_name__	:   Dict					     =					image_embeds.repeat_interleave(_A  ,					dim=0      )
				__magic_name__	:   Optional[int]					     =					negative_image_embeds.repeat_interleave(_A  ,					dim=0      )

			__magic_name__	:   Optional[int]					     =					torch.cat([negative_image_embeds, image_embeds]  ,					dim=0      ).to(
			    dtype=prompt_embeds.dtype  ,					device=_A      )

			self.scheduler.set_timesteps(_A  ,					device=_A      )
			__magic_name__	:   Tuple					     =					self.scheduler.timesteps

			__magic_name__	:   Optional[int]					     =					self.unet.config.in_channels

			__magic_name__     ,    __magic_name__	:   Dict					     =					get_new_h_w(_A  ,					_A  ,					self.movq_scale_factor      )

			# create initial latent
			__magic_name__	:   Union[str, Any]					     =					self.prepare_latents(
			    (batch_size, num_channels_latents, height, width)  ,					text_encoder_hidden_states.dtype  ,					_A  ,					_A  ,					_A  ,					self.scheduler  ,					)

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

				__magic_name__	:   Tuple					     =					{'text_embeds': prompt_embeds, 'image_embeds': image_embeds}
				__magic_name__	:   Union[str, Any]					     =					self.unet(
				    sample=_A  ,					timestep=_A  ,					encoder_hidden_states=_A  ,					added_cond_kwargs=_A  ,					return_dict=_A  ,					)[0]

				if do_classifier_free_guidance:
					__magic_name__     ,    __magic_name__	:   Optional[Any]					     =					noise_pred.split(latents.shape[1]  ,					dim=1      )
					__magic_name__     ,    __magic_name__	:   Dict					     =					noise_pred.chunk(2      )
					__magic_name__     ,    __magic_name__	:   List[str]					     =					variance_pred.chunk(2      )
					__magic_name__	:   List[Any]					     =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
					__magic_name__	:   Any					     =					torch.cat([noise_pred, variance_pred_text]  ,					dim=1      )

				if not (
				    hasattr(self.scheduler.config  ,					'variance_type'      )
				    and self.scheduler.config.variance_type in ["learned", "learned_range"]
				):
					__magic_name__     ,    __magic_name__	:   Optional[Any]					     =					noise_pred.split(latents.shape[1]  ,					dim=1      )

				# compute the previous noisy sample x_t -> x_t-1
				__magic_name__	:   List[Any]					     =					self.scheduler.step(
				    _A  ,					_A  ,					_A  ,					generator=_A  ,					).prev_sample
			# post-processing
			__magic_name__	:   int					     =					self.movq.decode(_A  ,					force_not_quantize=_A      )['sample']

			if output_type not in ["pt", "np", "pil"]:
				raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'      )

			if output_type in ["np", "pil"]:
				__magic_name__	:   Dict					     =					image * 0.5 + 0.5
				__magic_name__	:   str					     =					image.clamp(0  ,					1      )
				__magic_name__	:   List[Any]					     =					image.cpu().permute(0  ,					2  ,					3  ,					1      ).float().numpy()

			if output_type == "pil":
				__magic_name__	:   str					     =					self.numpy_to_pil(_A      )

			if not return_dict:
				return (image,)

			return ImagePipelineOutput(images=_A      )