



"""simple docstring"""







import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


UpperCAmelCase:  int			     =				logging.get_logger(__name__)  # pylint: disable=invalid-name


def  __SCREAMING_SNAKE_CASE (  self   ,				__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      ):
		_lowercase				: Optional[Any]     =   image.to(device=__UpperCAmelCase      )
		_lowercase				: List[str]     =   image  # take image as image_embeddings
		_lowercase				: str     =   image_embeddings.unsqueeze(1      )

		# duplicate image embeddings for each generation per prompt, using mps friendly method
		_lowercase     ,  _lowercase     ,  _lowercase				: Any     =   image_embeddings.shape
		_lowercase				: Optional[int]     =   image_embeddings.repeat(1   ,				__UpperCAmelCase   ,				1      )
		_lowercase				: Any     =   image_embeddings.view(bs_embed * num_images_per_prompt   ,				__UpperCAmelCase   ,				-1      )

		if do_classifier_free_guidance:
				_lowercase				: List[Any]     =   torch.zeros_like(__UpperCAmelCase      )

				# 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
				_lowercase				: Tuple     =   torch.cat([uncond_embeddings, image_embeddings]      )

		return image_embeddings





class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""






	def __init__(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_ = None				,):
			super().__init__()

			_lowercase				: str     =   {"""image_encoder""": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

			_lowercase				: List[Any]     =   decoder_pipe_kwargs.get("""torch_dtype"""				,UpperCAmelCase_   ) or prior.dtype

			_lowercase				: List[Any]     =   StableDiffusionImageVariationPipeline.from_pretrained(
			    """lambdalabs/sd-image-variations-diffusers"""				,**UpperCAmelCase_   )

			# replace `_encode_image` method
			_lowercase				: Optional[Any]     =   types.MethodType(_encode_image				,self.decoder_pipe   )

			self.register_modules(
			    prior=UpperCAmelCase_				,tokenizer=UpperCAmelCase_				,text_encoder=UpperCAmelCase_				,prior_scheduler=UpperCAmelCase_				,)






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_ = None				,UpperCAmelCase_ = None				,):
			if text_model_output is None:
					_lowercase				: Tuple     =   len(UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ) else 1
					# get prompt text embeddings
					_lowercase				: List[str]     =   self.tokenizer(
					    UpperCAmelCase_				,padding="""max_length"""				,max_length=self.tokenizer.model_max_length				,return_tensors="""pt"""				,)
					_lowercase				: str     =   text_inputs.input_ids
					_lowercase				: int     =   text_inputs.attention_mask.bool().to(UpperCAmelCase_   )

					if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
							_lowercase				: Optional[Any]     =   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}"""   )
							_lowercase				: str     =   text_input_ids[:, : self.tokenizer.model_max_length]

					_lowercase				: int     =   self.text_encoder(text_input_ids.to(UpperCAmelCase_   )   )

					_lowercase				: Optional[Any]     =   text_encoder_output.text_embeds
					_lowercase				: Any     =   text_encoder_output.last_hidden_state

			else:
					_lowercase				: Tuple     =   text_model_output[0].shape[0]
					_lowercase     ,  _lowercase				: Dict     =   text_model_output[0], text_model_output[1]
					_lowercase				: str     =   text_attention_mask

			_lowercase				: Tuple     =   text_embeddings.repeat_interleave(UpperCAmelCase_				,dim=0   )
			_lowercase				: str     =   text_encoder_hidden_states.repeat_interleave(UpperCAmelCase_				,dim=0   )
			_lowercase				: Tuple     =   text_mask.repeat_interleave(UpperCAmelCase_				,dim=0   )

			if do_classifier_free_guidance:
					_lowercase				: Optional[Any]     =   [""""""] * batch_size

					_lowercase				: Dict     =   self.tokenizer(
					    UpperCAmelCase_				,padding="""max_length"""				,max_length=self.tokenizer.model_max_length				,truncation=UpperCAmelCase_				,return_tensors="""pt"""				,)
					_lowercase				: Optional[Any]     =   uncond_input.attention_mask.bool().to(UpperCAmelCase_   )
					_lowercase				: List[str]     =   self.text_encoder(uncond_input.input_ids.to(UpperCAmelCase_   )   )

					_lowercase				: str     =   uncond_embeddings_text_encoder_output.text_embeds
					_lowercase				: Any     =   uncond_embeddings_text_encoder_output.last_hidden_state

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

					_lowercase				: Union[str, Any]     =   uncond_embeddings.shape[1]
					_lowercase				: Dict     =   uncond_embeddings.repeat(1				,UpperCAmelCase_   )
					_lowercase				: int     =   uncond_embeddings.view(batch_size * num_images_per_prompt				,UpperCAmelCase_   )

					_lowercase				: Dict     =   uncond_text_encoder_hidden_states.shape[1]
					_lowercase				: Tuple     =   uncond_text_encoder_hidden_states.repeat(1				,UpperCAmelCase_				,1   )
					_lowercase				: Tuple     =   uncond_text_encoder_hidden_states.view(
					    batch_size * num_images_per_prompt				,UpperCAmelCase_				,-1   )
					_lowercase				: List[Any]     =   uncond_text_mask.repeat_interleave(UpperCAmelCase_				,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
					_lowercase				: Any     =   torch.cat([uncond_embeddings, text_embeddings]   )
					_lowercase				: Tuple     =   torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]   )

					_lowercase				: Union[str, Any]     =   torch.cat([uncond_text_mask, text_mask]   )

			return text_embeddings, text_encoder_hidden_states, text_mask






	@property
	def    lowerCamelCase__					(     self   ):
			if self.device != torch.device("""meta"""   ) or not hasattr(self.prior				,"""_hf_hook"""   ):
					return self.device
			for module in self.prior.modules():
					if (
					    hasattr(UpperCAmelCase_				,"""_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






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   ):
			if latents is None:
					_lowercase				: Optional[Any]     =   randn_tensor(UpperCAmelCase_				,generator=UpperCAmelCase_				,device=UpperCAmelCase_				,dtype=UpperCAmelCase_   )
			else:
					if latents.shape != shape:
							raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""   )
					_lowercase				: Optional[Any]     =   latents.to(UpperCAmelCase_   )

			_lowercase				: Optional[Any]     =   latents * scheduler.init_noise_sigma
			return latents






	def    lowerCamelCase__					(     self				,UpperCAmelCase_ = None   ):
			self.decoder_pipe.to(UpperCAmelCase_   )
			super().to(UpperCAmelCase_   )






	@torch.no_grad()
	def __call__(     self				,UpperCAmelCase_ = None				,UpperCAmelCase_ = None				,UpperCAmelCase_ = None				,UpperCAmelCase_ = 1				,UpperCAmelCase_ = 25				,UpperCAmelCase_ = None				,UpperCAmelCase_ = None				,UpperCAmelCase_ = None				,UpperCAmelCase_ = None				,UpperCAmelCase_ = 4.0				,UpperCAmelCase_ = 8.0				,UpperCAmelCase_ = 50				,UpperCAmelCase_ = 1				,UpperCAmelCase_ = 0.0				,UpperCAmelCase_ = "pil"				,UpperCAmelCase_ = True				,):
			if prompt is not None:
					if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ):
							_lowercase				: Optional[Any]     =   1
					elif isinstance(UpperCAmelCase_				,UpperCAmelCase_   ):
							_lowercase				: Union[str, Any]     =   len(UpperCAmelCase_   )
					else:
							raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(UpperCAmelCase_   )}"""   )
			else:
					_lowercase				: List[str]     =   text_model_output[0].shape[0]

			_lowercase				: List[str]     =   self._execution_device

			_lowercase				: str     =   batch_size * num_images_per_prompt

			_lowercase				: List[Any]     =   prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

			_lowercase     ,  _lowercase     ,  _lowercase				: List[Any]     =   self._encode_prompt(
			    UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   )

			# prior

			self.prior_scheduler.set_timesteps(UpperCAmelCase_				,device=UpperCAmelCase_   )
			_lowercase				: List[Any]     =   self.prior_scheduler.timesteps

			_lowercase				: Union[str, Any]     =   self.prior.config.embedding_dim

			_lowercase				: Tuple     =   self.prepare_latents(
			    (batch_size, embedding_dim)				,text_embeddings.dtype				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,self.prior_scheduler				,)

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

					_lowercase				: Union[str, Any]     =   self.prior(
					    UpperCAmelCase_				,timestep=UpperCAmelCase_				,proj_embedding=UpperCAmelCase_				,encoder_hidden_states=UpperCAmelCase_				,attention_mask=UpperCAmelCase_				,).predicted_image_embedding

					if do_classifier_free_guidance:
							_lowercase     ,  _lowercase				: Tuple     =   predicted_image_embedding.chunk(2   )
							_lowercase				: str     =   predicted_image_embedding_uncond + prior_guidance_scale * (
							    predicted_image_embedding_text - predicted_image_embedding_uncond
							)

					if i + 1 == prior_timesteps_tensor.shape[0]:
							_lowercase				: List[Any]     =   None
					else:
							_lowercase				: str     =   prior_timesteps_tensor[i + 1]

					_lowercase				: Optional[Any]     =   self.prior_scheduler.step(
					    UpperCAmelCase_				,timestep=UpperCAmelCase_				,sample=UpperCAmelCase_				,generator=UpperCAmelCase_				,prev_timestep=UpperCAmelCase_				,).prev_sample

			_lowercase				: int     =   self.prior.post_process_latents(UpperCAmelCase_   )

			_lowercase				: Union[str, Any]     =   prior_latents

			_lowercase				: Union[str, Any]     =   self.decoder_pipe(
			    image=UpperCAmelCase_				,height=UpperCAmelCase_				,width=UpperCAmelCase_				,num_inference_steps=UpperCAmelCase_				,guidance_scale=UpperCAmelCase_				,generator=UpperCAmelCase_				,output_type=UpperCAmelCase_				,return_dict=UpperCAmelCase_				,num_images_per_prompt=UpperCAmelCase_				,eta=UpperCAmelCase_				,)
			return output






from math import factorial




def        __UpperCamelCase     (      _A					:							Union[str, Any]      ,  _A					:							Tuple		)				->Any:



							"""simple docstring"""
							return factorial(_A		) / (factorial(_A		) * factorial(n - r		))




def        __UpperCamelCase     (      )				->Any:



							"""simple docstring"""
							lowerCamelCase_								=0

							for i in range(1      ,  101		):
														for j in range(1      ,  i + 1		):
																					if combinations(_A      ,  _A		) > 1E6:
																												total += 1
							return total


if __name__ == "__main__":
	print(solution())
