

"""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


lowerCamelCase__			:					Optional[Any]							  =					logging.get_logger(__name__)  # pylint: disable=invalid-name
def  UpperCamelCase   (     self       :  Optional[int],					_lowerCAmelCase       :  Dict,					_lowerCAmelCase       :  Tuple,					_lowerCAmelCase       :  Union[str, Any],					_lowerCAmelCase       :  int	)			->     Any:
	_UpperCAmelCase      :							int       =   image.to(device=SCREAMING_SNAKE_CASE__	)
	_UpperCAmelCase      :							str       =   image  # take image as image_embeddings
	_UpperCAmelCase      :							Optional[Any]       =   image_embeddings.unsqueeze(1	)

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

	if do_classifier_free_guidance:
		_UpperCAmelCase      :							Optional[int]       =   torch.zeros_like(SCREAMING_SNAKE_CASE__	)

		# 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      :							Union[str, Any]       =   torch.cat([uncond_embeddings, image_embeddings]	)

	return image_embeddings






class       _UpperCAmelCase     (						UpperCamelCase_):
			def __init__(						self ,						_A ,						_A ,						_A ,						_A ,						_A = None ,						)      ->      Dict:






				'''simple docstring'''







				super().__init__()

				_UpperCAmelCase      :							Optional[Any]       =   {'image_encoder': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

				_UpperCAmelCase      :							int       =   decoder_pipe_kwargs.get("""torch_dtype""" ,						lowercase_       ) or prior.dtype

				_UpperCAmelCase      :							str       =   StableDiffusionImageVariationPipeline.from_pretrained(
				    """lambdalabs/sd-image-variations-diffusers""" ,						**lowercase_       )

				# replace `_encode_image` method
				_UpperCAmelCase      :							Union[str, Any]       =   types.MethodType(_encode_image ,						self.decoder_pipe       )

				self.register_modules(
				    prior=lowercase_ ,						tokenizer=lowercase_ ,						text_encoder=lowercase_ ,						prior_scheduler=lowercase_ ,						)
			def 							__snake_case	(						self ,						_A ,						_A ,						_A ,						_A ,						_A = None ,						_A = None ,						)      ->      Optional[int]:






				'''simple docstring'''







				if text_model_output is None:
					_UpperCAmelCase      :							Dict       =   len(lowercase_       ) if isinstance(lowercase_ ,						lowercase_       ) else 1
					# get prompt text embeddings
					_UpperCAmelCase      :							Tuple       =   self.tokenizer(
					    lowercase_ ,						padding="""max_length""" ,						max_length=self.tokenizer.model_max_length ,						return_tensors="""pt""" ,						)
					_UpperCAmelCase      :							Optional[Any]       =   text_inputs.input_ids
					_UpperCAmelCase      :							Any       =   text_inputs.attention_mask.bool().to(lowercase_       )

					if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
						_UpperCAmelCase      :							List[str]       =   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      :							Any       =   text_input_ids[:, : self.tokenizer.model_max_length]

					_UpperCAmelCase      :							int       =   self.text_encoder(text_input_ids.to(lowercase_       )       )

					_UpperCAmelCase      :							List[Any]       =   text_encoder_output.text_embeds
					_UpperCAmelCase      :							Union[str, Any]       =   text_encoder_output.last_hidden_state

				else:
					_UpperCAmelCase      :							List[str]       =   text_model_output[0].shape[0]
					_UpperCAmelCase      :							Optional[Any]       =   text_model_output[0], text_model_output[1]
					_UpperCAmelCase      :							int       =   text_attention_mask

				_UpperCAmelCase      :							List[Any]       =   text_embeddings.repeat_interleave(lowercase_ ,						dim=0       )
				_UpperCAmelCase      :							Optional[int]       =   text_encoder_hidden_states.repeat_interleave(lowercase_ ,						dim=0       )
				_UpperCAmelCase      :							Union[str, Any]       =   text_mask.repeat_interleave(lowercase_ ,						dim=0       )

				if do_classifier_free_guidance:
					_UpperCAmelCase      :							Any       =   [''] * batch_size

					_UpperCAmelCase      :							Optional[Any]       =   self.tokenizer(
					    lowercase_ ,						padding="""max_length""" ,						max_length=self.tokenizer.model_max_length ,						truncation=lowercase_ ,						return_tensors="""pt""" ,						)
					_UpperCAmelCase      :							Optional[int]       =   uncond_input.attention_mask.bool().to(lowercase_       )
					_UpperCAmelCase      :							Union[str, Any]       =   self.text_encoder(uncond_input.input_ids.to(lowercase_       )       )

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

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

					_UpperCAmelCase      :							int       =   uncond_embeddings.shape[1]
					_UpperCAmelCase      :							str       =   uncond_embeddings.repeat(1 ,						lowercase_       )
					_UpperCAmelCase      :							str       =   uncond_embeddings.view(batch_size * num_images_per_prompt ,						lowercase_       )

					_UpperCAmelCase      :							Optional[int]       =   uncond_text_encoder_hidden_states.shape[1]
					_UpperCAmelCase      :							int       =   uncond_text_encoder_hidden_states.repeat(1 ,						lowercase_ ,						1       )
					_UpperCAmelCase      :							Optional[Any]       =   uncond_text_encoder_hidden_states.view(
					    batch_size * num_images_per_prompt ,						lowercase_ ,						-1       )
					_UpperCAmelCase      :							Optional[Any]       =   uncond_text_mask.repeat_interleave(lowercase_ ,						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
					_UpperCAmelCase      :							List[Any]       =   torch.cat([uncond_embeddings, text_embeddings]       )
					_UpperCAmelCase      :							Dict       =   torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]       )

					_UpperCAmelCase      :							Optional[int]       =   torch.cat([uncond_text_mask, text_mask]       )

				return text_embeddings, text_encoder_hidden_states, text_mask
			@property
			def 							__snake_case	(						self       )      ->      Tuple:






				'''simple docstring'''







				if self.device != torch.device("""meta"""       ) or not hasattr(self.prior ,						"""_hf_hook"""       ):
					return self.device
				for module in self.prior.modules():
					if (
					    hasattr(lowercase_ ,						"""_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 							__snake_case	(						self ,						_A ,						_A ,						_A ,						_A ,						_A ,						_A       )      ->      List[str]:






				'''simple docstring'''







				if latents is None:
					_UpperCAmelCase      :							int       =   randn_tensor(lowercase_ ,						generator=lowercase_ ,						device=lowercase_ ,						dtype=lowercase_       )
				else:
					if latents.shape != shape:
						raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''       )
					_UpperCAmelCase      :							List[Any]       =   latents.to(lowercase_       )

				_UpperCAmelCase      :							Union[str, Any]       =   latents * scheduler.init_noise_sigma
				return latents
			def 							__snake_case	(						self ,						_A = None       )      ->      List[str]:






				'''simple docstring'''







				self.decoder_pipe.to(lowercase_       )
				super().to(lowercase_       )
			@torch.no_grad()
			def __call__(						self ,						_A = None ,						_A = None ,						_A = None ,						_A = 1 ,						_A = 25 ,						_A = None ,						_A = None ,						_A = None ,						_A = None ,						_A = 4.0 ,						_A = 8.0 ,						_A = 50 ,						_A = 1 ,						_A = 0.0 ,						_A = "pil" ,						_A = True ,						)      ->      Dict:






				'''simple docstring'''







				if prompt is not None:
					if isinstance(lowercase_ ,						lowercase_       ):
						_UpperCAmelCase      :							List[Any]       =   1
					elif isinstance(lowercase_ ,						lowercase_       ):
						_UpperCAmelCase      :							Optional[Any]       =   len(lowercase_       )
					else:
						raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowercase_       )}'''       )
				else:
					_UpperCAmelCase      :							Optional[Any]       =   text_model_output[0].shape[0]

				_UpperCAmelCase      :							Dict       =   self._execution_device

				_UpperCAmelCase      :							str       =   batch_size * num_images_per_prompt

				_UpperCAmelCase      :							Union[str, Any]       =   prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

				_UpperCAmelCase      :							Tuple       =   self._encode_prompt(
				    lowercase_ ,						lowercase_ ,						lowercase_ ,						lowercase_ ,						lowercase_ ,						lowercase_       )

				# prior

				self.prior_scheduler.set_timesteps(lowercase_ ,						device=lowercase_       )
				_UpperCAmelCase      :							List[str]       =   self.prior_scheduler.timesteps

				_UpperCAmelCase      :							Any       =   self.prior.config.embedding_dim

				_UpperCAmelCase      :							int       =   self.prepare_latents(
				    (batch_size, embedding_dim) ,						text_embeddings.dtype ,						lowercase_ ,						lowercase_ ,						lowercase_ ,						self.prior_scheduler ,						)

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

					_UpperCAmelCase      :							Dict       =   self.prior(
					    lowercase_ ,						timestep=lowercase_ ,						proj_embedding=lowercase_ ,						encoder_hidden_states=lowercase_ ,						attention_mask=lowercase_ ,						).predicted_image_embedding

					if do_classifier_free_guidance:
						_UpperCAmelCase      :							Tuple       =   predicted_image_embedding.chunk(2       )
						_UpperCAmelCase      :							List[Any]       =   predicted_image_embedding_uncond + prior_guidance_scale * (
						    predicted_image_embedding_text - predicted_image_embedding_uncond
						)

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

					_UpperCAmelCase      :							Optional[int]       =   self.prior_scheduler.step(
					    lowercase_ ,						timestep=lowercase_ ,						sample=lowercase_ ,						generator=lowercase_ ,						prev_timestep=lowercase_ ,						).prev_sample

				_UpperCAmelCase      :							Optional[int]       =   self.prior.post_process_latents(lowercase_       )

				_UpperCAmelCase      :							Union[str, Any]       =   prior_latents

				_UpperCAmelCase      :							Optional[Any]       =   self.decoder_pipe(
				    image=lowercase_ ,						height=lowercase_ ,						width=lowercase_ ,						num_inference_steps=lowercase_ ,						guidance_scale=lowercase_ ,						generator=lowercase_ ,						output_type=lowercase_ ,						return_dict=lowercase_ ,						num_images_per_prompt=lowercase_ ,						eta=lowercase_ ,						)
				return output








"""simple docstring"""






def  UpperCamelCase   (     _lowerCAmelCase       :  float,					_lowerCAmelCase       :  float,					_lowerCAmelCase       :  float	)			->     float:
	if mass < 0:
		raise ValueError("""The mass of the body cannot be negative"""	)
	if radius <= 0:
		raise ValueError("""The radius is always a positive non zero integer"""	)
	return (mass * (velocity) ** 2) / radius


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)





