




__lowerCamelCase   :						Dict				=			'''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

__lowerCamelCase   :						Optional[int]				=			[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
__lowerCamelCase   :						List[Any]				=			{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}









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 		lowercase__   (		__A:	str							,__A:	Optional[Any]							,__A:	List[Any]							,__A:	str							,__A:	List[str]=True							,__A:	List[Any]="pt"				):





					'''simple docstring'''






					__magic_name__							:	Optional[Any]    							=							{'''add_prefix_space''': True} if isinstance(__A							,__A				) and not line.startswith(''' '''				) else {}
					__magic_name__							:	List[str]    							=							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 		lowercase__   (		__A:	int							,__A:	int							,__A:	Optional[Any]=None							,):





					'''simple docstring'''






					__magic_name__							:	List[Any]    							=							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    (		_lowerCamelCase						):






			'''simple docstring'''





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

			def __len__( self			:   Optional[int]  )						->   List[Any]:
								return len(self.src_lens  )

			def __getitem__( self			:   Optional[int]       ,						lowerCamelCase_			:   Optional[int]  )						->   Dict[str, torch.Tensor]:
								__magic_name__							:	Optional[int]    							=							index + 1  # linecache starts at 1
								__magic_name__							:	Optional[int]    							=							self.prefix + linecache.getline(str(self.src_file  )       ,						lowerCamelCase_  ).rstrip('''\n'''  )
								__magic_name__							:	Dict    							=							linecache.getline(str(self.tgt_file  )       ,						lowerCamelCase_  ).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       ,						lowerCamelCase_  ):
													source_line += self.tokenizer.eos_token
													tgt_line += self.tokenizer.eos_token

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

								__magic_name__							:	Tuple    							=							encode_line(lowerCamelCase_       ,						lowerCamelCase_       ,						self.max_source_length       ,						'''right'''  )
								__magic_name__							:	int    							=							encode_line(lowerCamelCase_       ,						lowerCamelCase_       ,						self.max_target_length       ,						'''right'''  )

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

			@staticmethod
			def 							UpperCAmelCase__       ( lowerCamelCase_			:   List[str]  )						->   Dict:
								return [len(lowerCamelCase_  ) for x in Path(lowerCamelCase_  ).open().readlines()]


			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Optional[Any]  )						->   Dict[str, torch.Tensor]:
								__magic_name__							:	str    							=							torch.stack([x['''input_ids'''] for x in batch]  )
								__magic_name__							:	Any    							=							torch.stack([x['''attention_mask'''] for x in batch]  )
								__magic_name__							:	Tuple    							=							torch.stack([x['''decoder_input_ids'''] for x in batch]  )
								__magic_name__							:	Tuple    							=							(
								    self.tokenizer.generator.pad_token_id
								    if isinstance(self.tokenizer       ,						lowerCamelCase_  )
								    else self.tokenizer.pad_token_id
								)
								__magic_name__							:	Optional[Any]    							=							(
								    self.tokenizer.question_encoder.pad_token_id
								    if isinstance(self.tokenizer       ,						lowerCamelCase_  )
								    else self.tokenizer.pad_token_id
								)
								__magic_name__							:	Dict    							=							trim_batch(lowerCamelCase_       ,						lowerCamelCase_  )
								__magic_name__							,    __magic_name__							:	Optional[int]    							=							trim_batch(lowerCamelCase_       ,						lowerCamelCase_       ,						attention_mask=lowerCamelCase_  )
								__magic_name__							:	Optional[Any]    							=							{
								    '''input_ids''': source_ids,
								    '''attention_mask''': source_mask,
								    '''decoder_input_ids''': y,
								}
								return batch






__lowerCamelCase   :						Dict				=			getLogger(__name__)







def 		lowercase__   (		__A:	List[List]				):





					'''simple docstring'''






					return list(itertools.chain.from_iterable(__A				)				)







def 		lowercase__   (		__A:	str				):





					'''simple docstring'''






					__magic_name__							:	Any    							=							get_git_info()
					save_json(__A							,os.path.join(__A							,'''git_log.json'''				)				)







def 		lowercase__   (		__A:	Optional[int]							,__A:	Union[str, Any]							,__A:	Union[str, Any]=4							,**__A:	Optional[Any]				):





					'''simple docstring'''






					with open(__A							,'''w'''				) as f:
										json.dump(__A							,__A							,indent=__A							,**__A				)







def 		lowercase__   (		__A:	List[Any]				):





					'''simple docstring'''






					with open(__A				) as f:
										return json.load(__A				)







def 		lowercase__   (		):





					'''simple docstring'''






					__magic_name__							:	int    							=							git.Repo(search_parent_directories=__A				)
					__magic_name__							:	str    							=							{
					    '''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 		lowercase__   (		__A:	Callable							,__A:	Iterable				):





					'''simple docstring'''






					return list(map(__A							,__A				)				)







def 		lowercase__   (		__A:	int							,__A:	Dict				):





					'''simple docstring'''






					with open(__A							,'''wb'''				) as f:
										return pickle.dump(__A							,__A				)







def 		lowercase__   (		__A:	Any				):





					'''simple docstring'''






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

					def white_space_fix(__A:	Optional[int]				):
										return " ".join(text.split()				)

					def remove_punc(__A:	Any				):
										__magic_name__							:	Union[str, Any]    							=							set(string.punctuation				)
										return "".join(ch for ch in text if ch not in exclude				)

					def lower(__A:	Optional[int]				):
										return text.lower()

					return white_space_fix(remove_articles(remove_punc(lower(__A				)				)				)				)







def 		lowercase__   (		__A:	int							,__A:	Optional[Any]				):





					'''simple docstring'''






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







def 		lowercase__   (		__A:	List[Any]							,__A:	Dict				):





					'''simple docstring'''






					return normalize_answer(__A				) == normalize_answer(__A				)







def 		lowercase__   (		__A:	List[str]							,__A:	List[str]				):





					'''simple docstring'''






					assert len(__A				) == len(__A				)
					__magic_name__							:	Optional[int]    							=							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 		lowercase__   (		__A:	int				):





					'''simple docstring'''






					return model_prefix.startswith('''rag'''				)







def 		lowercase__   (		__A:	Optional[Any]							,__A:	List[str]							,__A:	Dict				):





					'''simple docstring'''






					__magic_name__							:	Tuple    							=							{p: p for p in extra_params}
					# T5 models don't have `dropout` param, they have `dropout_rate` instead
					__magic_name__							:	Any    							=							'''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
															__magic_name__							:	str    							=							p if hasattr(__A							,__A				) else equivalent_param[p]
															setattr(__A							,__A							,getattr(__A							,__A				)				)
															delattr(__A							,__A				)
					return hparams, config



