

import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[Any]					, lowerCAmelCase:					List[Any]					, lowerCAmelCase:					Dict					, lowerCAmelCase:					Union[str, Any]					, lowerCAmelCase:					Union[str, Any]=True					, lowerCAmelCase:					Tuple="pt"	):
				_UpperCAmelCase    :						Any							=      {"add_prefix_space": True} if isinstance(lowerCAmelCase					, lowerCAmelCase	) and not line.startswith(" "	) else {}
				_UpperCAmelCase    :						str							=      padding_side
				return tokenizer(
				    [line]					, max_length=lowerCAmelCase					, padding="max_length" if pad_to_max_length else None					, truncation=lowerCAmelCase					, return_tensors=lowerCAmelCase					, add_special_tokens=lowerCAmelCase					, **lowerCAmelCase					, )

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Union[str, Any]					, lowerCAmelCase:					List[Any]					, lowerCAmelCase:					str=None					, ):
				_UpperCAmelCase    :						str							=      input_ids.ne(lowerCAmelCase	).any(dim=0	)
				if attention_mask is None:
								return input_ids[:, keep_column_mask]
				else:
								return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])

class 			a					(		__A			):


						def __init__(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_="train"  ,   A_=None  ,   A_=None  ,   A_=None  ,   A_=""  ,   ):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase    :						Optional[Any]							=      Path(__lowercase		).joinpath(type_path + ".source"		)
										_UpperCAmelCase    :						Any							=      Path(__lowercase		).joinpath(type_path + ".target"		)
										_UpperCAmelCase    :						Optional[int]							=      self.get_char_lens(self.src_file		)
										_UpperCAmelCase    :						List[str]							=      max_source_length
										_UpperCAmelCase    :						Optional[Any]							=      max_target_length
										assert min(self.src_lens		) > 0, f'found empty line in {self.src_file}'
										_UpperCAmelCase    :						Optional[Any]							=      tokenizer
										_UpperCAmelCase    :						Any							=      prefix
										if n_obs is not None:
														_UpperCAmelCase    :						Tuple							=      self.src_lens[:n_obs]
										_UpperCAmelCase    :						Any							=      src_lang
										_UpperCAmelCase    :						str							=      tgt_lang


						def __len__(    self		):







										'''simple docstring'''


										return len(self.src_lens		)


						def __getitem__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      index + 1  # linecache starts at 1
										_UpperCAmelCase    :						Any							=      self.prefix + linecache.getline(str(self.src_file		)  ,   __lowercase		).rstrip("\n"		)
										_UpperCAmelCase    :						Any							=      linecache.getline(str(self.tgt_file		)  ,   __lowercase		).rstrip("\n"		)
										assert source_line, f'empty source line for index {index}'
										assert tgt_line, f'empty tgt line for index {index}'

										# Need to add eos token manually for T5
										if isinstance(self.tokenizer  ,   __lowercase		):
														source_line += self.tokenizer.eos_token
														tgt_line += self.tokenizer.eos_token

										# Pad source and target to the right
										_UpperCAmelCase    :						Dict							=      (
										    self.tokenizer.question_encoder if isinstance(self.tokenizer  ,   __lowercase		) else self.tokenizer
										)
										_UpperCAmelCase    :						Dict							=      self.tokenizer.generator if isinstance(self.tokenizer  ,   __lowercase		) else self.tokenizer

										_UpperCAmelCase    :						int							=      encode_line(__lowercase  ,   __lowercase  ,   self.max_source_length  ,   "right"		)
										_UpperCAmelCase    :						Any							=      encode_line(__lowercase  ,   __lowercase  ,   self.max_target_length  ,   "right"		)

										_UpperCAmelCase    :						List[Any]							=      source_inputs["input_ids"].squeeze()
										_UpperCAmelCase    :						List[Any]							=      target_inputs["input_ids"].squeeze()
										_UpperCAmelCase    :						str							=      source_inputs["attention_mask"].squeeze()
										return {
										    "input_ids": source_ids,
										    "attention_mask": src_mask,
										    "decoder_input_ids": target_ids,
										}


						@staticmethod
						def     _UpperCAmelCase			(    A_		):







										'''simple docstring'''


										return [len(__lowercase		) for x in Path(__lowercase		).open().readlines()]





						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      torch.stack([x["input_ids"] for x in batch]		)
										_UpperCAmelCase    :						List[str]							=      torch.stack([x["attention_mask"] for x in batch]		)
										_UpperCAmelCase    :						Union[str, Any]							=      torch.stack([x["decoder_input_ids"] for x in batch]		)
										_UpperCAmelCase    :						List[Any]							=      (
										    self.tokenizer.generator.pad_token_id
										    if isinstance(self.tokenizer  ,   __lowercase		)
										    else self.tokenizer.pad_token_id
										)
										_UpperCAmelCase    :						Tuple							=      (
										    self.tokenizer.question_encoder.pad_token_id
										    if isinstance(self.tokenizer  ,   __lowercase		)
										    else self.tokenizer.pad_token_id
										)
										_UpperCAmelCase    :						List[Any]							=      trim_batch(__lowercase  ,   __lowercase		)
										_UpperCAmelCase						,							_UpperCAmelCase    :						List[Any]							=      trim_batch(__lowercase  ,   __lowercase  ,   attention_mask=__lowercase		)
										_UpperCAmelCase    :						int							=      {
										    "input_ids": source_ids,
										    "attention_mask": source_mask,
										    "decoder_input_ids": y,
										}
										return batch







SCREAMING_SNAKE_CASE_            = getLogger(__name__)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Any	):
				return list(itertools.chain.from_iterable(lowerCAmelCase	)	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Dict	):
				_UpperCAmelCase    :						Optional[int]							=      get_git_info()
				save_json(lowerCAmelCase					, os.path.join(lowerCAmelCase					, "git_log.json"	)	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int					, lowerCAmelCase:					Optional[Any]					, lowerCAmelCase:					List[str]=4					, **lowerCAmelCase:					List[str]	):
				with open(lowerCAmelCase					, "w"	) as f:
								json.dump(lowerCAmelCase					, lowerCAmelCase					, indent=lowerCAmelCase					, **lowerCAmelCase	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Dict	):
				with open(lowerCAmelCase	) as f:
								return json.load(lowerCAmelCase	)

def 							__SCREAMING_SNAKE_CASE							(			):
				_UpperCAmelCase    :						int							=      git.Repo(search_parent_directories=lowerCAmelCase	)
				_UpperCAmelCase    :						int							=      {
				    "repo_id": str(lowerCAmelCase	),
				    "repo_sha": str(repo.head.object.hexsha	),
				    "repo_branch": str(repo.active_branch	),
				    "hostname": str(socket.gethostname()	),
				}
				return repo_infos

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[int]					, lowerCAmelCase:					List[str]	):
				return list(map(lowerCAmelCase					, lowerCAmelCase	)	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int					, lowerCAmelCase:					List[Any]	):
				with open(lowerCAmelCase					, "wb"	) as f:
								return pickle.dump(lowerCAmelCase					, lowerCAmelCase	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[Any]	):

				def remove_articles(lowerCAmelCase:					int	):
								return re.sub(R"\b(a|an|the)\b"					, " "					, lowerCAmelCase	)

				def white_space_fix(lowerCAmelCase:					Dict	):
								return " ".join(text.split()	)

				def remove_punc(lowerCAmelCase:					Tuple	):
								_UpperCAmelCase    :						Optional[int]							=      set(string.punctuation	)
								return "".join(ch for ch in text if ch not in exclude	)

				def lower(lowerCAmelCase:					Optional[Any]	):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase	)	)	)	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[str]					, lowerCAmelCase:					Any	):
				_UpperCAmelCase    :						Union[str, Any]							=      normalize_answer(lowerCAmelCase	).split()
				_UpperCAmelCase    :						Tuple							=      normalize_answer(lowerCAmelCase	).split()
				_UpperCAmelCase    :						Dict							=      Counter(lowerCAmelCase	) & Counter(lowerCAmelCase	)
				_UpperCAmelCase    :						str							=      sum(common.values()	)
				if num_same == 0:
								return 0
				_UpperCAmelCase    :						Union[str, Any]							=      1.0 * num_same / len(lowerCAmelCase	)
				_UpperCAmelCase    :						int							=      1.0 * num_same / len(lowerCAmelCase	)
				_UpperCAmelCase    :						int							=      (2 * precision * recall) / (precision + recall)
				return fa

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[Any]					, lowerCAmelCase:					str	):
				return normalize_answer(lowerCAmelCase	) == normalize_answer(lowerCAmelCase	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Tuple					, lowerCAmelCase:					str	):
				assert len(lowerCAmelCase	) == len(lowerCAmelCase	)
				_UpperCAmelCase    :						Optional[int]							=      0
				for hypo, pred in zip(lowerCAmelCase					, lowerCAmelCase	):
								em += exact_match_score(lowerCAmelCase					, lowerCAmelCase	)
				if len(lowerCAmelCase	) > 0:
								em /= len(lowerCAmelCase	)
				return {"em": em}

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int	):
				return model_prefix.startswith("rag"	)





def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[str]					, lowerCAmelCase:					Dict					, lowerCAmelCase:					Optional[int]	):
				_UpperCAmelCase    :						List[str]							=      {p: p for p in extra_params}
				# T5 models don't have `dropout` param, they have `dropout_rate` instead
				_UpperCAmelCase    :						List[str]							=      "dropout_rate"
				for p in extra_params:
								if getattr(lowerCAmelCase					, lowerCAmelCase					, lowerCAmelCase	):
												if not hasattr(lowerCAmelCase					, lowerCAmelCase	) and not hasattr(lowerCAmelCase					, equivalent_param[p]	):
																logger.info("config doesn\'t have a `{}` attribute".format(lowerCAmelCase	)	)
																delattr(lowerCAmelCase					, lowerCAmelCase	)
																continue
												_UpperCAmelCase    :						str							=      p if hasattr(lowerCAmelCase					, lowerCAmelCase	) else equivalent_param[p]
												setattr(lowerCAmelCase					, lowerCAmelCase					, getattr(lowerCAmelCase					, lowerCAmelCase	)	)
												delattr(lowerCAmelCase					, lowerCAmelCase	)
				return hparams, config





import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = 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.',
					)
					SCREAMING_SNAKE_CASE_            = parser.parse_args()

					SCREAMING_SNAKE_CASE_            = 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)


