


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 (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_=True ,   lowerCAmelCase_="pt" )-> str:
				lowerCAmelCase_ :      int    				=				{'''add_prefix_space''': True} if isinstance(lowerCAmelCase_ ,   lowerCAmelCase_ ) and not line.startswith(''' ''' ) else {}
				lowerCAmelCase_ :      Optional[int]    				=				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 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_=None ,   )-> Any:
				lowerCAmelCase_ :      Dict    				=				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 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




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




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




						def __getitem__(				self   ,						__lowercase							)					->    Dict[str, torch.Tensor]:
										lowerCAmelCase_ :      List[Any]    				=				index + 1  # linecache starts at 1
										lowerCAmelCase_ :      Any    				=				self.prefix + linecache.getline(str(self.src_file							)   ,						__lowercase							).rstrip('''\n'''							)
										lowerCAmelCase_ :      Dict    				=				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
										lowerCAmelCase_ :      Optional[Any]    				=				(
										    self.tokenizer.question_encoder if isinstance(self.tokenizer   ,						__lowercase							) else self.tokenizer
										)
										lowerCAmelCase_ :      Dict    				=				self.tokenizer.generator if isinstance(self.tokenizer   ,						__lowercase							) else self.tokenizer

										lowerCAmelCase_ :      List[Any]    				=				encode_line(__lowercase   ,						__lowercase   ,						self.max_source_length   ,						'''right'''							)
										lowerCAmelCase_ :      Optional[Any]    				=				encode_line(__lowercase   ,						__lowercase   ,						self.max_target_length   ,						'''right'''							)

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




						@staticmethod
						def 						lowercase_ (				__lowercase							)					->    Dict:
										return [len(__lowercase							) for x in Path(__lowercase							).open().readlines()]




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






_UpperCAmelCase			:  Union[str, Any]     =getLogger(__name__)



def 			lowerCAmelCase (		lowerCAmelCase_ )-> Dict:
				return list(itertools.chain.from_iterable(lowerCAmelCase_ ) )



def 			lowerCAmelCase (		lowerCAmelCase_ )-> None:
				lowerCAmelCase_ :      Tuple    				=				get_git_info()
				save_json(lowerCAmelCase_ ,   os.path.join(lowerCAmelCase_ ,   '''git_log.json''' ) )



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_=4 ,   **lowerCAmelCase_ )-> List[Any]:
				with open(lowerCAmelCase_ ,   '''w''' ) as f:
								json.dump(lowerCAmelCase_ ,   lowerCAmelCase_ ,   indent=lowerCAmelCase_ ,   **lowerCAmelCase_ )



def 			lowerCAmelCase (		lowerCAmelCase_ )-> Tuple:
				with open(lowerCAmelCase_ ) as f:
								return json.load(lowerCAmelCase_ )



def 			lowerCAmelCase (		)-> List[Any]:
				lowerCAmelCase_ :      List[str]    				=				git.Repo(search_parent_directories=lowerCAmelCase_ )
				lowerCAmelCase_ :      Optional[Any]    				=				{
				    '''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 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ )-> List:
				return list(map(lowerCAmelCase_ ,   lowerCAmelCase_ ) )



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ )-> List[Any]:
				with open(lowerCAmelCase_ ,   '''wb''' ) as f:
								return pickle.dump(lowerCAmelCase_ ,   lowerCAmelCase_ )



def 			lowerCAmelCase (		lowerCAmelCase_ )-> int:

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

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

				def remove_punc(lowerCAmelCase_ ):
								lowerCAmelCase_ :      List[Any]    				=				set(string.punctuation )
								return "".join(ch for ch in text if ch not in exclude )

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

				return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase_ ) ) ) )



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



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ )-> List[str]:
				return normalize_answer(lowerCAmelCase_ ) == normalize_answer(lowerCAmelCase_ )



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ )-> Dict:
				assert len(lowerCAmelCase_ ) == len(lowerCAmelCase_ )
				lowerCAmelCase_ :      List[Any]    				=				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 			lowerCAmelCase (		lowerCAmelCase_ )-> List[Any]:
				return model_prefix.startswith('''rag''' )




def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> Dict:
				lowerCAmelCase_ :      Optional[int]    				=				{p: p for p in extra_params}
				# T5 models don't have `dropout` param, they have `dropout_rate` instead
				lowerCAmelCase_ :      Optional[int]    				=				'''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
												lowerCAmelCase_ :      List[Any]    				=				p if hasattr(lowerCAmelCase_ ,   lowerCAmelCase_ ) else equivalent_param[p]
												setattr(lowerCAmelCase_ ,   lowerCAmelCase_ ,   getattr(lowerCAmelCase_ ,   lowerCAmelCase_ ) )
												delattr(lowerCAmelCase_ ,   lowerCAmelCase_ )
				return hparams, config



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase			:  str     =logging.get_logger(__name__)







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  Optional[int]					=							"""encoder-decoder"""
						SCREAMING_SNAKE_CASE__      :  str					=							True




						def __init__(				self   ,						**__lowercase							)					->    Union[str, Any]:
										super().__init__(**__lowercase							)
										assert (
										    "encoder" in kwargs and "decoder" in kwargs
										), "Config has to be initialized with encoder and decoder config"
										lowerCAmelCase_ :      str    				=				kwargs.pop('''encoder'''							)
										lowerCAmelCase_ :      int    				=				encoder_config.pop('''model_type'''							)
										lowerCAmelCase_ :      Optional[Any]    				=				kwargs.pop('''decoder'''							)
										lowerCAmelCase_ :      Optional[Any]    				=				decoder_config.pop('''model_type'''							)

										from ..auto.configuration_auto import AutoConfig

										lowerCAmelCase_ :      Union[str, Any]    				=				AutoConfig.for_model(__lowercase   ,						**__lowercase							)
										lowerCAmelCase_ :      List[str]    				=				AutoConfig.for_model(__lowercase   ,						**__lowercase							)
										lowerCAmelCase_ :      Any    				=				True




						@classmethod
						def 						lowercase_ (				cls   ,						__lowercase   ,						__lowercase   ,						**__lowercase							)					->    PretrainedConfig:
										logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''							)
										lowerCAmelCase_ :      int    				=				True
										lowerCAmelCase_ :      List[Any]    				=				True

										return cls(encoder=encoder_config.to_dict()   ,						decoder=decoder_config.to_dict()   ,						**__lowercase							)




						def 						lowercase_ (				self							)					->    Any:
										lowerCAmelCase_ :      Optional[Any]    				=				copy.deepcopy(self.__dict__							)
										lowerCAmelCase_ :      List[str]    				=				self.encoder.to_dict()
										lowerCAmelCase_ :      Dict    				=				self.decoder.to_dict()
										lowerCAmelCase_ :      Optional[Any]    				=				self.__class__.model_type
										return output