
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__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_=True       ,							SCREAMING_SNAKE_CASE_="pt"     )				->		Any:
			lowerCAmelCase__      :      List[Any]         =		{'add_prefix_space': True} if isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     ) and not line.startswith(' '     ) else {}
			lowerCAmelCase__      :      List[Any]         =		padding_side
			return tokenizer(
			    [line]       ,							max_length=SCREAMING_SNAKE_CASE_       ,							padding='max_length' if pad_to_max_length else None       ,							truncation=SCREAMING_SNAKE_CASE_       ,							return_tensors=SCREAMING_SNAKE_CASE_       ,							add_special_tokens=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       ,							)
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_=None       ,							)				->		str:
			lowerCAmelCase__      :      Dict         =		input_ids.ne(SCREAMING_SNAKE_CASE_     ).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__							(     __magic_name__	):






				def __init__(						self					:      Any    ,     a					:      Any    ,     a					:      List[str]    ,     a					:      List[Any]    ,     a					:      Optional[Any]    ,     a					:      Optional[Any]="train"    ,     a					:      Optional[int]=None    ,     a					:      int=None    ,     a					:      str=None    ,     a					:      Optional[int]=""    ,     ):






							'''simple docstring'''







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






				def __len__(						self					:      Optional[Any]   ):






							'''simple docstring'''







							return len(self.src_lens   )






				def __getitem__(						self					:      Union[str, Any]    ,     a					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		index + 1  # linecache starts at 1
							lowerCAmelCase__      :      Union[str, Any]         =		self.prefix + linecache.getline(str(self.src_file   )    ,     a   ).rstrip('\n'   )
							lowerCAmelCase__      :      List[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
							lowerCAmelCase__      :      Any         =		(
							    self.tokenizer.question_encoder if isinstance(self.tokenizer    ,     a   ) else self.tokenizer
							)
							lowerCAmelCase__      :      Optional[int]         =		self.tokenizer.generator if isinstance(self.tokenizer    ,     a   ) else self.tokenizer

							lowerCAmelCase__      :      Optional[int]         =		encode_line(a    ,     a    ,     self.max_source_length    ,     'right'   )
							lowerCAmelCase__      :      List[str]         =		encode_line(a    ,     a    ,     self.max_target_length    ,     'right'   )

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






				@staticmethod
				def 							_lowerCamelCase		(						a					:      Tuple   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      Union[str, Any]    ,     a					:      int   ):






							'''simple docstring'''







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






lowerCamelCase__          =		getLogger(__name__)
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		List[Any]:
			return list(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE_     )     )
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		None:
			lowerCAmelCase__      :      int         =		get_git_info()
			save_json(SCREAMING_SNAKE_CASE_       ,							os.path.join(SCREAMING_SNAKE_CASE_       ,							'git_log.json'     )     )
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_=4       ,							**SCREAMING_SNAKE_CASE_     )				->		Any:
			with open(SCREAMING_SNAKE_CASE_       ,							'w'     ) as f:
						json.dump(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							indent=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_     )
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		Tuple:
			with open(SCREAMING_SNAKE_CASE_     ) as f:
						return json.load(SCREAMING_SNAKE_CASE_     )
def  lowerCAmelCase__			(  )				->		Tuple:
			lowerCAmelCase__      :      Any         =		git.Repo(search_parent_directories=SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      Tuple         =		{
			    'repo_id': str(SCREAMING_SNAKE_CASE_     ),
			    'repo_sha': str(repo.head.object.hexsha     ),
			    'repo_branch': str(repo.active_branch     ),
			    'hostname': str(socket.gethostname()     ),
			}
			return repo_infos
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		List:
			return list(map(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )     )
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Dict:
			with open(SCREAMING_SNAKE_CASE_       ,							'wb'     ) as f:
						return pickle.dump(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		Optional[Any]:

			def remove_articles(SCREAMING_SNAKE_CASE_     ):
						return re.sub(r'\b(a|an|the)\b'       ,							' '       ,							SCREAMING_SNAKE_CASE_     )

			def white_space_fix(SCREAMING_SNAKE_CASE_     ):
						return " ".join(text.split()     )

			def remove_punc(SCREAMING_SNAKE_CASE_     ):
						lowerCAmelCase__      :      Tuple         =		set(string.punctuation     )
						return "".join(ch for ch in text if ch not in exclude     )

			def lower(SCREAMING_SNAKE_CASE_     ):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE_     )     )     )     )
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Tuple:
			lowerCAmelCase__      :      Any         =		normalize_answer(SCREAMING_SNAKE_CASE_     ).split()
			lowerCAmelCase__      :      Tuple         =		normalize_answer(SCREAMING_SNAKE_CASE_     ).split()
			lowerCAmelCase__      :      Union[str, Any]         =		Counter(SCREAMING_SNAKE_CASE_     ) & Counter(SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      str         =		sum(common.values()     )
			if num_same == 0:
						return 0
			lowerCAmelCase__      :      str         =		1.0 * num_same / len(SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      Optional[int]         =		1.0 * num_same / len(SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      Dict         =		(2 * precision * recall) / (precision + recall)
			return fa
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Union[str, Any]:
			return normalize_answer(SCREAMING_SNAKE_CASE_     ) == normalize_answer(SCREAMING_SNAKE_CASE_     )
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Dict:
			assert len(SCREAMING_SNAKE_CASE_     ) == len(SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      Union[str, Any]         =		0
			for hypo, pred in zip(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     ):
						em += exact_match_score(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )
			if len(SCREAMING_SNAKE_CASE_     ) > 0:
						em /= len(SCREAMING_SNAKE_CASE_     )
			return {"em": em}
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		Any:
			return model_prefix.startswith('rag'     )







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

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class      A__							(     __magic_name__	):
				lowercase			    =  42
				lowercase			    =  42






				def __init__(						self					:      Any    ,     a					:      UNetaDModel    ,     a					:      ScoreSdeVeScheduler   ):






							'''simple docstring'''







							super().__init__()
							self.register_modules(unet=a    ,     scheduler=a   )






				@torch.no_grad()
				def __call__(						self					:      List[str]    ,     a					:      int = 1    ,     a					:      int = 2_000    ,     a					:      Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,     a					:      Optional[str] = "pil"    ,     a					:      bool = True    ,     **a					:      int    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		self.unet.config.sample_size
							lowerCAmelCase__      :      Union[str, Any]         =		(batch_size, 3, img_size, img_size)

							lowerCAmelCase__      :      Tuple         =		self.unet

							lowerCAmelCase__      :      Optional[Any]         =		randn_tensor(a    ,     generator=a   ) * self.scheduler.init_noise_sigma
							lowerCAmelCase__      :      int         =		sample.to(self.device   )

							self.scheduler.set_timesteps(a   )
							self.scheduler.set_sigmas(a   )

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps   )   ):
										lowerCAmelCase__      :      Dict         =		self.scheduler.sigmas[i] * torch.ones(shape[0]    ,     device=self.device   )

										# correction step
										for _ in range(self.scheduler.config.correct_steps   ):
													lowerCAmelCase__      :      Optional[Any]         =		self.unet(a    ,     a   ).sample
													lowerCAmelCase__      :      Dict         =		self.scheduler.step_correct(a    ,     a    ,     generator=a   ).prev_sample

										# prediction step
										lowerCAmelCase__      :      Optional[int]         =		model(a    ,     a   ).sample
										lowerCAmelCase__      :      Optional[Any]         =		self.scheduler.step_pred(a    ,     a    ,     a    ,     generator=a   )

										lowerCAmelCase__      ,				lowerCAmelCase__      :      str         =		output.prev_sample, output.prev_sample_mean

							lowerCAmelCase__      :      Any         =		sample_mean.clamp(0    ,     1   )
							lowerCAmelCase__      :      List[str]         =		sample.cpu().permute(0    ,     2    ,     3    ,     1   ).numpy()
							if output_type == "pil":
										lowerCAmelCase__      :      int         =		self.numpy_to_pil(a   )

							if not return_dict:
										return (sample,)

							return ImagePipelineOutput(images=a   )