

import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


lowercase			:				List[str]   =     logging.get_logger(__name__)  # pylint: disable=invalid-name







class 					__lowercase  (						__A				):





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						)		->       Tuple:
														super().__init__()
														self.register_modules(
														    vae=snake_case_    ,						text_encoder=snake_case_    ,						tokenizer=snake_case_    ,						unet=snake_case_    ,						scheduler=snake_case_    ,						safety_checker=snake_case_    ,						feature_extractor=snake_case_    ,						)




							def       snake_case			(					self    ,						__UpperCAmelCase = "auto"						)		->       List[Any]:


														if slice_size == "auto":
																					# half the attention head size is usually a good trade-off between
																					# speed and memory
																					A      :					List[str]      		=					self.unet.config.attention_head_dim // 2
														self.unet.set_attention_slice(snake_case_						)




							def       snake_case			(					self						)		->       Dict:
														self.enable_attention_slicing(snake_case_						)






							@torch.no_grad()
							def __call__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = 5_12    ,						__UpperCAmelCase = 5_12    ,						__UpperCAmelCase = 50    ,						__UpperCAmelCase = 7.5    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = 1    ,						__UpperCAmelCase = 0.0    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = "pil"    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = 1    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       Optional[int]:


														if isinstance(snake_case_    ,						snake_case_						):
																					A      :					Optional[int]      		=					1
														elif isinstance(snake_case_    ,						snake_case_						):
																					A      :					List[Any]      		=					len(snake_case_						)
														else:
																					raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(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(snake_case_    ,						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(snake_case_						)}.'						)

														# get prompt text embeddings
														A      :					List[str]      		=					self.tokenizer(
														    snake_case_    ,						padding='''max_length'''    ,						max_length=self.tokenizer.model_max_length    ,						return_tensors='''pt'''    ,						)
														A      :					List[Any]      		=					text_inputs.input_ids

														if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																					A      :					int      		=					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}'						)
																					A      :					Tuple      		=					text_input_ids[:, : self.tokenizer.model_max_length]

														if text_embeddings is None:
																					A      :					Any      		=					self.text_encoder(text_input_ids.to(self.device						)						)[0]

														# duplicate text embeddings for each generation per prompt, using mps friendly method
														A      :					str      		=					text_embeddings.shape
														A      :					List[str]      		=					text_embeddings.repeat(1    ,						snake_case_    ,						1						)
														A      :					int      		=					text_embeddings.view(bs_embed * num_images_per_prompt    ,						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.
														A      :					str      		=					guidance_scale > 1.0
														# get unconditional embeddings for classifier free guidance
														if do_classifier_free_guidance:
																					A      :					List[str]
																					if negative_prompt is None:
																												A      :					List[str]      		=					[""""""]
																					elif type(snake_case_						) is not type(snake_case_						):
																												raise TypeError(
																												    f'`negative_prompt` should be the same type to `prompt`, but got {type(snake_case_						)} !='
																												    f' {type(snake_case_						)}.'						)
																					elif isinstance(snake_case_    ,						snake_case_						):
																												A      :					int      		=					[negative_prompt]
																					elif batch_size != len(snake_case_						):
																												raise ValueError(
																												    f'`negative_prompt`: {negative_prompt} has batch size {len(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:
																												A      :					Tuple      		=					negative_prompt

																					A      :					Any      		=					text_input_ids.shape[-1]
																					A      :					Any      		=					self.tokenizer(
																					    snake_case_    ,						padding='''max_length'''    ,						max_length=snake_case_    ,						truncation=snake_case_    ,						return_tensors='''pt'''    ,						)
																					A      :					Optional[Any]      		=					self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]

																					# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																					A      :					Union[str, Any]      		=					uncond_embeddings.shape[1]
																					A      :					List[str]      		=					uncond_embeddings.repeat(snake_case_    ,						snake_case_    ,						1						)
																					A      :					Optional[Any]      		=					uncond_embeddings.view(batch_size * num_images_per_prompt    ,						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
																					A      :					Tuple      		=					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`.
														A      :					Dict      		=					(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
														A      :					Union[str, Any]      		=					(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
														A      :					Union[str, Any]      		=					text_embeddings.dtype
														if latents is None:
																					if self.device.type == "mps":
																												# randn does not exist on mps
																												A      :					str      		=					torch.randn(
																												    snake_case_    ,						generator=snake_case_    ,						device='''cpu'''    ,						dtype=snake_case_						).to(self.device						)
																												A      :					Optional[int]      		=					torch.randn(snake_case_    ,						generator=snake_case_    ,						device='''cpu'''    ,						dtype=snake_case_						).to(
																												    self.device						)
																					else:
																												A      :					Dict      		=					torch.randn(
																												    snake_case_    ,						generator=snake_case_    ,						device=self.device    ,						dtype=snake_case_						)
																												A      :					Optional[int]      		=					torch.randn(snake_case_    ,						generator=snake_case_    ,						device=self.device    ,						dtype=snake_case_						)
														else:
																					if latents_reference.shape != latents_shape:
																												raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'						)
																					A      :					List[str]      		=					latents_reference.to(self.device						)
																					A      :					Union[str, Any]      		=					latents.to(self.device						)

														# This is the key part of the pipeline where we
														# try to ensure that the generated images w/ the same seed
														# but different sizes actually result in similar images
														A      :					int      		=					(latents_shape[3] - latents_shape_reference[3]) // 2
														A      :					List[Any]      		=					(latents_shape[2] - latents_shape_reference[2]) // 2
														A      :					Dict      		=					latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
														A      :					int      		=					latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
														A      :					Optional[int]      		=					0 if dx < 0 else dx
														A      :					List[str]      		=					0 if dy < 0 else dy
														A      :					List[Any]      		=					max(-dx    ,						0						)
														A      :					Any      		=					max(-dy    ,						0						)
														# import pdb
														# pdb.set_trace()
														A      :					int      		=					latents_reference[:, :, dy : dy + h, dx : dx + w]

														# set timesteps
														self.scheduler.set_timesteps(snake_case_						)

														# Some schedulers like PNDM have timesteps as arrays
														# It's more optimized to move all timesteps to correct device beforehand
														A      :					Optional[Any]      		=					self.scheduler.timesteps.to(self.device						)

														# scale the initial noise by the standard deviation required by the scheduler
														A      :					int      		=					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]
														A      :					int      		=					"""eta""" in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
														A      :					Optional[int]      		=					{}
														if accepts_eta:
																					A      :					Union[str, Any]      		=					eta

														for i, t in enumerate(self.progress_bar(snake_case_						)						):
																					# expand the latents if we are doing classifier free guidance
																					A      :					List[Any]      		=					torch.cat([latents] * 2						) if do_classifier_free_guidance else latents
																					A      :					Optional[int]      		=					self.scheduler.scale_model_input(snake_case_    ,						snake_case_						)

																					# predict the noise residual
																					A      :					Any      		=					self.unet(snake_case_    ,						snake_case_    ,						encoder_hidden_states=snake_case_						).sample

																					# perform guidance
																					if do_classifier_free_guidance:
																												A      :					int      		=					noise_pred.chunk(2						)
																												A      :					int      		=					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																					# compute the previous noisy sample x_t -> x_t-1
																					A      :					Optional[int]      		=					self.scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_    ,						**snake_case_						).prev_sample

																					# call the callback, if provided
																					if callback is not None and i % callback_steps == 0:
																												callback(snake_case_    ,						snake_case_    ,						snake_case_						)

														A      :					Optional[Any]      		=					1 / 0.1_8_2_1_5 * latents
														A      :					Union[str, Any]      		=					self.vae.decode(snake_case_						).sample

														A      :					Tuple      		=					(image / 2 + 0.5).clamp(0    ,						1						)

														# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
														A      :					Dict      		=					image.cpu().permute(0    ,						2    ,						3    ,						1						).float().numpy()

														if self.safety_checker is not None:
																					A      :					Dict      		=					self.feature_extractor(self.numpy_to_pil(snake_case_						)    ,						return_tensors='''pt'''						).to(
																					    self.device						)
																					A      :					Union[str, Any]      		=					self.safety_checker(
																					    images=snake_case_    ,						clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype						)						)
														else:
																					A      :					Dict      		=					None

														if output_type == "pil":
																					A      :					Any      		=					self.numpy_to_pil(snake_case_						)

														if not return_dict:
																					return (image, has_nsfw_concept)

														return StableDiffusionPipelineOutput(images=snake_case_    ,						nsfw_content_detected=snake_case_						)







"""simple docstring"""



import os
from collections import deque

import torch
from torch.utils.data import Dataset
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_=""   ,      snake_case_="train"							):







										'''simple docstring'''


										assert os.path.isdir(snake_case_							)

										A__       :     Dict								=			[]
										A__       :     Optional[Any]								=			os.listdir(snake_case_							)
										for story_filename in story_filenames_list:
																	if "summary" in story_filename:
																								continue
																	A__       :     Any								=			os.path.join(snake_case_   ,      snake_case_							)
																	if not os.path.isfile(snake_case_							):
																								continue
																	self.documents.append(snake_case_							)





			def __len__(			self							):







										'''simple docstring'''


										return len(self.documents							)





			def __getitem__(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     List[str]								=			self.documents[idx]
										A__       :     List[Any]								=			document_path.split("""/"""							)[-1]
										with open(snake_case_   ,      encoding="""utf-8"""							) as source:
																	A__       :     Dict								=			source.read()
																	A__				,							A__       :     Dict								=			process_story(snake_case_							)
										return document_name, story_lines, summary_lines





def  _A(		lowerCAmelCase							):
							A__       :     Optional[int]								=			list(filter(lambda lowerCAmelCase							: len(lowerCAmelCase							) != 0			,  [line.strip() for line in raw_story.split("""\n"""							)]							)							)

							# for some unknown reason some lines miss a period, add it
							A__       :     List[str]								=			[_add_missing_period(lowerCAmelCase							) for line in nonempty_lines]

							# gather article lines
							A__       :     Union[str, Any]								=			[]
							A__       :     Optional[Any]								=			deque(lowerCAmelCase							)
							while True:
														try:
																					A__       :     List[str]								=			lines.popleft()
																					if element.startswith("""@highlight"""							):
																												break
																					story_lines.append(lowerCAmelCase							)
														except IndexError:
																					# if "@highlight" is absent from the file we pop
																					# all elements until there is None, raising an exception.
																					return story_lines, []

    # gather summary lines
							A__       :     List[str]								=			list(filter(lambda lowerCAmelCase							: not t.startswith("""@highlight"""							)			,  lowerCAmelCase							)							)

							return story_lines, summary_lines

def  _A(		lowerCAmelCase							):
							A__       :     Tuple								=			[""".""", """!""", """?""", """...""", """'""", """`""", """\"""", """\u2019""", """\u2019""", """)"""]
							if line.startswith("""@highlight"""							):
														return line
							if line[-1] in END_TOKENS:
														return line
							return line + "."

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							if len(lowerCAmelCase							) > block_size:
														return sequence[:block_size]
							else:
														sequence.extend([pad_token_id] * (block_size - len(lowerCAmelCase							))							)
														return sequence

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Any								=			torch.ones_like(lowerCAmelCase							)
							A__       :     Any								=			sequence == pad_token_id
							A__       :     Any								=			0
							return mask

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Dict								=			[tokenizer.encode(lowerCAmelCase							) for line in story_lines]
							A__       :     Any								=			[token for sentence in story_lines_token_ids for token in sentence]
							A__       :     List[str]								=			[tokenizer.encode(lowerCAmelCase							) for line in summary_lines]
							A__       :     int								=			[token for sentence in summary_lines_token_ids for token in sentence]

							return story_token_ids, summary_token_ids



def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     str								=			[]
							for sequence in batch:
														A__       :     List[Any]								=			-1
														A__       :     Dict								=			[]
														for s in sequence:
																					if s == separator_token_id:
																												sentence_num += 1
																					embeddings.append(sentence_num % 2							)
														batch_embeddings.append(lowerCAmelCase							)
							return torch.tensor(lowerCAmelCase							)





