




'''simple docstring'''

import os
from collections import deque

import torch
from torch.utils.data import Dataset






class 		a							(  _lowerCamelCase	):


				def __init__(						self       :   Any			,							lowercase_       :   Optional[Any]=""			,							lowercase_       :   Any="train"   ):
									assert os.path.isdir(lowercase_   )

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


				def __len__(						self       :   List[str]   ):
									return len(self.documents   )


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







def 						__magic_name__       (      __UpperCAmelCase      )						->   Union[str, Any]:

					'''simple docstring'''

					snake_case_		     =							list(filter(lambda __UpperCAmelCase      : len(__UpperCAmelCase      ) != 0, [line.strip() for line in raw_story.split('''\n'''      )]      )      )

					# for some unknown reason some lines miss a period, add it
					snake_case_		     =							[_add_missing_period(__UpperCAmelCase      ) for line in nonempty_lines]

					# gather article lines
					snake_case_		     =							[]
					snake_case_		     =							deque(__UpperCAmelCase      )
					while True:
										try:
															snake_case_		     =							lines.popleft()
															if element.startswith('''@highlight'''      ):
																				break
															story_lines.append(__UpperCAmelCase      )
										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
					snake_case_		     =							list(filter(lambda __UpperCAmelCase      : not t.startswith('''@highlight'''      ), __UpperCAmelCase      )      )

					return story_lines, summary_lines


def 						__magic_name__       (      __UpperCAmelCase      )						->   Optional[Any]:

					'''simple docstring'''

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


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   int:

					'''simple docstring'''

					if len(__UpperCAmelCase      ) > block_size:
										return sequence[:block_size]
					else:
										sequence.extend([pad_token_id] * (block_size - len(__UpperCAmelCase      ))      )
										return sequence


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[Any]:

					'''simple docstring'''

					snake_case_		     =							torch.ones_like(__UpperCAmelCase      )
					snake_case_		     =							sequence == pad_token_id
					snake_case_		     =							0
					return mask


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   List[Any]:

					'''simple docstring'''

					snake_case_		     =							[tokenizer.encode(__UpperCAmelCase      ) for line in story_lines]
					snake_case_		     =							[token for sentence in story_lines_token_ids for token in sentence]
					snake_case_		     =							[tokenizer.encode(__UpperCAmelCase      ) for line in summary_lines]
					snake_case_		     =							[token for sentence in summary_lines_token_ids for token in sentence]

					return story_token_ids, summary_token_ids


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   List[Any]:

					'''simple docstring'''

					snake_case_		     =							[]
					for sequence in batch:
										snake_case_		     =							-1
										snake_case_		     =							[]
										for s in sequence:
															if s == separator_token_id:
																				sentence_num += 1
															embeddings.append(sentence_num % 2      )
										batch_embeddings.append(__UpperCAmelCase      )
					return torch.tensor(__UpperCAmelCase      )







'''simple docstring'''

import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
							a    :							List[str]										=    argparse.ArgumentParser()

							parser.add_argument(
							    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
							)
							# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
							parser.add_argument(
							    '--original_config_file',
							    default=None,
							    type=str,
							    help='The YAML config file corresponding to the original architecture.',
							)
							parser.add_argument(
							    '--num_in_channels',
							    default=None,
							    type=int,
							    help='The number of input channels. If `None` number of input channels will be automatically inferred.',
							)
							parser.add_argument(
							    '--scheduler_type',
							    default='pndm',
							    type=str,
							    help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
							)
							parser.add_argument(
							    '--pipeline_type',
							    default=None,
							    type=str,
							    help=(
							        'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
							        '. If `None` pipeline will be automatically inferred.'
							    ),
							)
							parser.add_argument(
							    '--image_size',
							    default=None,
							    type=int,
							    help=(
							        'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
							        ' Base. Use 768 for Stable Diffusion v2.'
							    ),
							)
							parser.add_argument(
							    '--prediction_type',
							    default=None,
							    type=str,
							    help=(
							        'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
							        ' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
							    ),
							)
							parser.add_argument(
							    '--extract_ema',
							    action='store_true',
							    help=(
							        'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
							        ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
							        ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
							    ),
							)
							parser.add_argument(
							    '--upcast_attention',
							    action='store_true',
							    help=(
							        'Whether the attention computation should always be upcasted. This is necessary when running stable'
							        ' diffusion 2.1.'
							    ),
							)
							parser.add_argument(
							    '--from_safetensors',
							    action='store_true',
							    help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
							)
							parser.add_argument(
							    '--to_safetensors',
							    action='store_true',
							    help='Whether to store pipeline in safetensors format or not.',
							)
							parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
							parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
							parser.add_argument(
							    '--stable_unclip',
							    type=str,
							    default=None,
							    required=False,
							    help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
							)
							parser.add_argument(
							    '--stable_unclip_prior',
							    type=str,
							    default=None,
							    required=False,
							    help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
							)
							parser.add_argument(
							    '--clip_stats_path',
							    type=str,
							    help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
							    required=False,
							)
							parser.add_argument(
							    '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
							)
							parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
							parser.add_argument(
							    '--vae_path',
							    type=str,
							    default=None,
							    required=False,
							    help='Set to a path, hub id to an already converted vae to not convert it again.',
							)
							a    :							str										=    parser.parse_args()

							a    :							List[Any]										=    download_from_original_stable_diffusion_ckpt(
							    checkpoint_path=args.checkpoint_path,
							    original_config_file=args.original_config_file,
							    image_size=args.image_size,
							    prediction_type=args.prediction_type,
							    model_type=args.pipeline_type,
							    extract_ema=args.extract_ema,
							    scheduler_type=args.scheduler_type,
							    num_in_channels=args.num_in_channels,
							    upcast_attention=args.upcast_attention,
							    from_safetensors=args.from_safetensors,
							    device=args.device,
							    stable_unclip=args.stable_unclip,
							    stable_unclip_prior=args.stable_unclip_prior,
							    clip_stats_path=args.clip_stats_path,
							    controlnet=args.controlnet,
							    vae_path=args.vae_path,
							)

							if args.half:
														pipe.to(torch_dtype=torch.floataa)

							if args.controlnet:
														# only save the controlnet model
														pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
							else:
														pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

