


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 				lowerCamelCase__	(      a	:   Dict	,  a	:   Tuple	,  a	:   Tuple	,  a	:   Optional[Any]	,  a	:   int=True	,  a	:   List[str]="pt"				)    ->	Dict:





			"""simple docstring"""
			a__ :List[str]						  =    {"add_prefix_space": True} if isinstance(a	,  a				) and not line.startswith(" "				) else {}
			a__ :Union[str, Any]						  =    padding_side
			return tokenizer(
			    [line]	,  max_length=a	,  padding="max_length" if pad_to_max_length else None	,  truncation=a	,  return_tensors=a	,  add_special_tokens=a	,  **a	,  )


def 				lowerCamelCase__	(      a	:   Any	,  a	:   int	,  a	:   str=None	,  )    ->	Any:





			"""simple docstring"""
			a__ :Dict						  =    input_ids.ne(a				).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 					lowerCAmelCase_ (       _a):




						def __init__(	self						:   List[str]   ,     __A						:   Tuple   ,     __A						:   Optional[Any]   ,     __A						:   Optional[int]   ,     __A						:   Tuple   ,     __A						:   Tuple="train"   ,     __A						:   str=None   ,     __A						:   Optional[int]=None   ,     __A						:   Dict=None   ,     __A						:   Any=""   ,     )      ->Tuple:






									"""simple docstring"""






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




						def __len__(	self						:   Union[str, Any]						)      ->Optional[int]:






									"""simple docstring"""






									return len(self.src_lens						)




						def __getitem__(	self						:   Any   ,     __A						:   int						)      ->Dict[str, torch.Tensor]:






									"""simple docstring"""






									a__ :Optional[int]						  =    index + 1  # linecache starts at 1
									a__ :int						  =    self.prefix + linecache.getline(str(self.src_file						)   ,     __A						).rstrip("\n"						)
									a__ :Optional[Any]						  =    linecache.getline(str(self.tgt_file						)   ,     __A						).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   ,     __A						):
												source_line += self.tokenizer.eos_token
												tgt_line += self.tokenizer.eos_token

									# Pad source and target to the right
									a__ :Optional[int]						  =    (
									    self.tokenizer.question_encoder if isinstance(self.tokenizer   ,     __A						) else self.tokenizer
									)
									a__ :Union[str, Any]						  =    self.tokenizer.generator if isinstance(self.tokenizer   ,     __A						) else self.tokenizer

									a__ :Tuple						  =    encode_line(__A   ,     __A   ,     self.max_source_length   ,     "right"						)
									a__ :int						  =    encode_line(__A   ,     __A   ,     self.max_target_length   ,     "right"						)

									a__ :Optional[int]						  =    source_inputs["input_ids"].squeeze()
									a__ :Union[str, Any]						  =    target_inputs["input_ids"].squeeze()
									a__ :str						  =    source_inputs["attention_mask"].squeeze()
									return {
									    "input_ids": source_ids,
									    "attention_mask": src_mask,
									    "decoder_input_ids": target_ids,
									}




						@staticmethod
						def  _snake_case					(	__A						:   Dict						)      ->int:






									"""simple docstring"""






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






						def  _snake_case					(	self						:   int   ,     __A						:   List[str]						)      ->Dict[str, torch.Tensor]:






									"""simple docstring"""






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






snake_case__			 =						getLogger(__name__)


def 				lowerCamelCase__	(      a	:   List[List]				)    ->	int:





			"""simple docstring"""
			return list(itertools.chain.from_iterable(a				)				)


def 				lowerCamelCase__	(      a	:   str				)    ->	None:





			"""simple docstring"""
			a__ :Dict						  =    get_git_info()
			save_json(a	,  os.path.join(a	,  "git_log.json"				)				)


def 				lowerCamelCase__	(      a	:   Optional[Any]	,  a	:   Union[str, Any]	,  a	:   Optional[int]=4	,  **a	:   Any				)    ->	Dict:





			"""simple docstring"""
			with open(a	,  "w"				) as f:
						json.dump(a	,  a	,  indent=a	,  **a				)


def 				lowerCamelCase__	(      a	:   List[str]				)    ->	Optional[Any]:





			"""simple docstring"""
			with open(a				) as f:
						return json.load(a				)


def 				lowerCamelCase__	(      )    ->	List[Any]:





			"""simple docstring"""
			a__ :Union[str, Any]						  =    git.Repo(search_parent_directories=a				)
			a__ :int						  =    {
			    "repo_id": str(a				),
			    "repo_sha": str(repo.head.object.hexsha				),
			    "repo_branch": str(repo.active_branch				),
			    "hostname": str(socket.gethostname()				),
			}
			return repo_infos


def 				lowerCamelCase__	(      a	:   Callable	,  a	:   Iterable				)    ->	List:





			"""simple docstring"""
			return list(map(a	,  a				)				)


def 				lowerCamelCase__	(      a	:   Optional[int]	,  a	:   Union[str, Any]				)    ->	str:





			"""simple docstring"""
			with open(a	,  "wb"				) as f:
						return pickle.dump(a	,  a				)


def 				lowerCamelCase__	(      a	:   List[Any]				)    ->	Optional[int]:





			"""simple docstring"""

			def remove_articles(a	:   List[Any]				):
						return re.sub(r"\b(a|an|the)\b"	,  " "	,  a				)

			def white_space_fix(a	:   Tuple				):
						return " ".join(text.split()				)

			def remove_punc(a	:   Dict				):
						a__ :int						  =    set(string.punctuation				)
						return "".join(ch for ch in text if ch not in exclude				)

			def lower(a	:   Dict				):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(a				)				)				)				)


def 				lowerCamelCase__	(      a	:   List[Any]	,  a	:   List[Any]				)    ->	List[str]:





			"""simple docstring"""
			a__ :List[str]						  =    normalize_answer(a				).split()
			a__ :Tuple						  =    normalize_answer(a				).split()
			a__ :List[str]						  =    Counter(a				) & Counter(a				)
			a__ :Any						  =    sum(common.values()				)
			if num_same == 0:
						return 0
			a__ :Union[str, Any]						  =    1.0 * num_same / len(a				)
			a__ :int						  =    1.0 * num_same / len(a				)
			a__ :str						  =    (2 * precision * recall) / (precision + recall)
			return fa


def 				lowerCamelCase__	(      a	:   Union[str, Any]	,  a	:   Tuple				)    ->	Optional[Any]:





			"""simple docstring"""
			return normalize_answer(a				) == normalize_answer(a				)


def 				lowerCamelCase__	(      a	:   List[str]	,  a	:   List[str]				)    ->	Dict:





			"""simple docstring"""
			assert len(a				) == len(a				)
			a__ :List[str]						  =    0
			for hypo, pred in zip(a	,  a				):
						em += exact_match_score(a	,  a				)
			if len(a				) > 0:
						em /= len(a				)
			return {"em": em}


def 				lowerCamelCase__	(      a	:   Optional[Any]				)    ->	Optional[int]:





			"""simple docstring"""
			return model_prefix.startswith("rag"				)







def 				lowerCamelCase__	(      a	:   List[Any]	,  a	:   Dict	,  a	:   str				)    ->	Union[str, Any]:





			"""simple docstring"""
			a__ :str						  =    {p: p for p in extra_params}
			# T5 models don't have `dropout` param, they have `dropout_rate` instead
			a__ :Dict						  =    "dropout_rate"
			for p in extra_params:
						if getattr(a	,  a	,  a				):
									if not hasattr(a	,  a				) and not hasattr(a	,  equivalent_param[p]				):
												logger.info("config doesn't have a `{}` attribute".format(a				)				)
												delattr(a	,  a				)
												continue
									a__ :List[str]						  =    p if hasattr(a	,  a				) else equivalent_param[p]
									setattr(a	,  a	,  getattr(a	,  a				)				)
									delattr(a	,  a				)
			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__":
		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.''',
		)
		snake_case__			 =						parser.parse_args()

		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)


