





"""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__        =      logging.get_logger(__name__)  # pylint: disable=invalid-name


def    a__       (       self	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						image.to(device=_SCREAMING_SNAKE_CASE     )
		UpperCamelCase =						image  # take image as image_embeddings
		UpperCamelCase =						image_embeddings.unsqueeze(1     )

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

		if do_classifier_free_guidance:
				UpperCamelCase =						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 =						torch.cat([uncond_embeddings, image_embeddings]     )

		return image_embeddings






class 							_lowerCamelCase   (							_lowercase			):


						def __init__(self ,				__a ,				__a ,				__a ,				__a ,				__a = None ,				)			-> Union[str, Any]:
								super().__init__()

								UpperCamelCase =						{"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

								UpperCamelCase =						decoder_pipe_kwargs.get("torch_dtype" ,				__a		) or prior.dtype

								UpperCamelCase =						StableDiffusionImageVariationPipeline.from_pretrained(
								    "lambdalabs/sd-image-variations-diffusers" ,				**__a		)

								# replace `_encode_image` method
								UpperCamelCase =						types.MethodType(_encode_image ,				self.decoder_pipe		)

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


						def      snake_case_ (self ,				__a ,				__a ,				__a ,				__a ,				__a = None ,				__a = None ,				)			-> Any:
								if text_model_output is None:
										UpperCamelCase =						len(__a		) if isinstance(__a ,				__a		) else 1
										# get prompt text embeddings
										UpperCamelCase =						self.tokenizer(
										    __a ,				padding="max_length" ,				max_length=self.tokenizer.model_max_length ,				return_tensors="pt" ,				)
										UpperCamelCase =						text_inputs.input_ids
										UpperCamelCase =						text_inputs.attention_mask.bool().to(__a		)

										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(__a		)		)

										UpperCamelCase =						text_encoder_output.text_embeds
										UpperCamelCase =						text_encoder_output.last_hidden_state

								else:
										UpperCamelCase =						text_model_output[0].shape[0]
										UpperCamelCase ,					UpperCamelCase =						text_model_output[0], text_model_output[1]
										UpperCamelCase =						text_attention_mask

								UpperCamelCase =						text_embeddings.repeat_interleave(__a ,				dim=0		)
								UpperCamelCase =						text_encoder_hidden_states.repeat_interleave(__a ,				dim=0		)
								UpperCamelCase =						text_mask.repeat_interleave(__a ,				dim=0		)

								if do_classifier_free_guidance:
										UpperCamelCase =						[""] * batch_size

										UpperCamelCase =						self.tokenizer(
										    __a ,				padding="max_length" ,				max_length=self.tokenizer.model_max_length ,				truncation=__a ,				return_tensors="pt" ,				)
										UpperCamelCase =						uncond_input.attention_mask.bool().to(__a		)
										UpperCamelCase =						self.text_encoder(uncond_input.input_ids.to(__a		)		)

										UpperCamelCase =						uncond_embeddings_text_encoder_output.text_embeds
										UpperCamelCase =						uncond_embeddings_text_encoder_output.last_hidden_state

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

										UpperCamelCase =						uncond_embeddings.shape[1]
										UpperCamelCase =						uncond_embeddings.repeat(1 ,				__a		)
										UpperCamelCase =						uncond_embeddings.view(batch_size * num_images_per_prompt ,				__a		)

										UpperCamelCase =						uncond_text_encoder_hidden_states.shape[1]
										UpperCamelCase =						uncond_text_encoder_hidden_states.repeat(1 ,				__a ,				1		)
										UpperCamelCase =						uncond_text_encoder_hidden_states.view(
										    batch_size * num_images_per_prompt ,				__a ,				-1		)
										UpperCamelCase =						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
										UpperCamelCase =						torch.cat([uncond_embeddings, text_embeddings]		)
										UpperCamelCase =						torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]		)

										UpperCamelCase =						torch.cat([uncond_text_mask, text_mask]		)

								return text_embeddings, text_encoder_hidden_states, text_mask


						@property
						def      snake_case_ (self		)			-> List[str]:
								if self.device != torch.device("meta"		) or not hasattr(self.prior ,				"_hf_hook"		):
										return self.device
								for module in self.prior.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


						def      snake_case_ (self ,				__a ,				__a ,				__a ,				__a ,				__a ,				__a		)			-> str:
								if latents is None:
										UpperCamelCase =						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}"		)
										UpperCamelCase =						latents.to(__a		)

								UpperCamelCase =						latents * scheduler.init_noise_sigma
								return latents


						def      snake_case_ (self ,				__a = None		)			-> Optional[int]:
								self.decoder_pipe.to(__a		)
								super().to(__a		)




						@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 ,				)			-> str:
								if prompt is not None:
										if isinstance(__a ,				__a		):
												UpperCamelCase =						1
										elif isinstance(__a ,				__a		):
												UpperCamelCase =						len(__a		)
										else:
												raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(__a		)}"		)
								else:
										UpperCamelCase =						text_model_output[0].shape[0]

								UpperCamelCase =						self._execution_device

								UpperCamelCase =						batch_size * num_images_per_prompt

								UpperCamelCase =						prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

								UpperCamelCase ,					UpperCamelCase ,					UpperCamelCase =						self._encode_prompt(
								    __a ,				__a ,				__a ,				__a ,				__a ,				__a		)

								# prior

								self.prior_scheduler.set_timesteps(__a ,				device=__a		)
								UpperCamelCase =						self.prior_scheduler.timesteps

								UpperCamelCase =						self.prior.config.embedding_dim

								UpperCamelCase =						self.prepare_latents(
								    (batch_size, embedding_dim) ,				text_embeddings.dtype ,				__a ,				__a ,				__a ,				self.prior_scheduler ,				)

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

										UpperCamelCase =						self.prior(
										    __a ,				timestep=__a ,				proj_embedding=__a ,				encoder_hidden_states=__a ,				attention_mask=__a ,				).predicted_image_embedding

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

										if i + 1 == prior_timesteps_tensor.shape[0]:
												UpperCamelCase =						None
										else:
												UpperCamelCase =						prior_timesteps_tensor[i + 1]

										UpperCamelCase =						self.prior_scheduler.step(
										    __a ,				timestep=__a ,				sample=__a ,				generator=__a ,				prev_timestep=__a ,				).prev_sample

								UpperCamelCase =						self.prior.post_process_latents(__a		)

								UpperCamelCase =						prior_latents

								UpperCamelCase =						self.decoder_pipe(
								    image=__a ,				height=__a ,				width=__a ,				num_inference_steps=__a ,				guidance_scale=__a ,				generator=__a ,				output_type=__a ,				return_dict=__a ,				num_images_per_prompt=__a ,				eta=__a ,				)
								return output













"""simple docstring"""





def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						sorted(_SCREAMING_SNAKE_CASE	,						key=lambda _SCREAMING_SNAKE_CASE     : value[2]	,						reverse=_SCREAMING_SNAKE_CASE     )

		# Create a list of size equal to the maximum deadline
		# and initialize it with -1
		UpperCamelCase =						max(_SCREAMING_SNAKE_CASE	,						key=lambda _SCREAMING_SNAKE_CASE     : value[1]     )[1]
		UpperCamelCase =						[-1] * max_deadline

		# Finding the maximum profit and the count of jobs
		UpperCamelCase =						0
		UpperCamelCase =						0
		for job in jobs:
				# Find a free time slot for this job
				# (Note that we start from the last possible slot)
				for i in range(job[1] - 1	,						-1	,						-1     ):
						if time_slots[i] == -1:
								UpperCamelCase =						job[0]
								count += 1
								max_profit += job[2]
								break
		return [count, max_profit]


if __name__ == "__main__":
						import doctest

						doctest.testmod()






