
"""simple docstring"""






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 __A						(   a_ :							str		,		a_ :							List[str]		,		a_ :							Dict		,		a_ :							Union[str, Any]		,		a_ :							int=True		,		a_ :							Optional[Any]="pt"     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[Any]				= {"""add_prefix_space""": True} if isinstance(UpperCAmelCase__		,		UpperCAmelCase__     ) and not line.startswith(''' '''     ) else {}
		SCREAMING_SNAKE_CASE       :     List[str]				= padding_side
		return tokenizer(
		    [line]		,		max_length=UpperCAmelCase__		,		padding='''max_length''' if pad_to_max_length else None		,		truncation=UpperCAmelCase__		,		return_tensors=UpperCAmelCase__		,		add_special_tokens=UpperCAmelCase__		,		**UpperCAmelCase__		,		)





def __A						(   a_ :							Tuple		,		a_ :							List[Any]		,		a_ :							Optional[int]=None		,		)->							List[Any]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= input_ids.ne(UpperCAmelCase__     ).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 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

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







			'''simple docstring'''




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





	def __len__(						self    :Optional[Any]					)							->			Any:







			'''simple docstring'''




			return len(self.src_lens					)





	def __getitem__(						self    :Optional[int]		, lowerCamelCase_    :List[str]					)							->			Optional[int]:







			'''simple docstring'''




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

			SCREAMING_SNAKE_CASE       :     str				= encode_line(lowercase_		, lowercase_		, self.max_source_length		, '''right'''					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= encode_line(lowercase_		, lowercase_		, self.max_target_length		, '''right'''					)

			SCREAMING_SNAKE_CASE       :     Any				= source_inputs["""input_ids"""].squeeze()
			SCREAMING_SNAKE_CASE       :     Dict				= target_inputs["""input_ids"""].squeeze()
			SCREAMING_SNAKE_CASE       :     int				= source_inputs["""attention_mask"""].squeeze()
			return {
			    "input_ids": source_ids,
			    "attention_mask": src_mask,
			    "decoder_input_ids": target_ids,
			}





	@staticmethod
	def   __lowerCAmelCase (						lowerCamelCase_    :int					)							->			Any:







			'''simple docstring'''




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







	def   __lowerCAmelCase (						self    :List[str]		, lowerCamelCase_    :List[str]					)							->			List[str]:







			'''simple docstring'''




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





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





def __A						(   a_ :							List[List]     )->							Optional[Any]:




		'''simple docstring'''






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





def __A						(   a_ :							str     )->							None:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Dict				= get_git_info()
		save_json(UpperCAmelCase__		,		os.path.join(UpperCAmelCase__		,		'''git_log.json'''     )     )





def __A						(   a_ :							Optional[Any]		,		a_ :							List[Any]		,		a_ :							int=4		,		**a_ :							List[Any]     )->							int:




		'''simple docstring'''






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





def __A						(   a_ :							str     )->							Union[str, Any]:




		'''simple docstring'''






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





def __A						(   )->							Optional[int]:




		'''simple docstring'''






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





def __A						(   a_ :							Callable		,		a_ :							Iterable     )->							List:




		'''simple docstring'''






		return list(map(UpperCAmelCase__		,		UpperCAmelCase__     )     )





def __A						(   a_ :							List[str]		,		a_ :							Optional[Any]     )->							Any:




		'''simple docstring'''






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





def __A						(   a_ :							List[str]     )->							Dict:




		'''simple docstring'''






		def remove_articles(a_ :							List[str]     ):
				return re.sub(r'''\b(a|an|the)\b'''		,		''' '''		,		UpperCAmelCase__     )

		def white_space_fix(a_ :							str     ):
				return " ".join(text.split()     )

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

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

		return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase__     )     )     )     )





def __A						(   a_ :							List[str]		,		a_ :							Optional[Any]     )->							Union[str, Any]:




		'''simple docstring'''






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





def __A						(   a_ :							str		,		a_ :							Any     )->							Tuple:




		'''simple docstring'''






		return normalize_answer(UpperCAmelCase__     ) == normalize_answer(UpperCAmelCase__     )





def __A						(   a_ :							List[str]		,		a_ :							List[str]     )->							Dict:




		'''simple docstring'''






		assert len(UpperCAmelCase__     ) == len(UpperCAmelCase__     )
		SCREAMING_SNAKE_CASE       :     str				= 0
		for hypo, pred in zip(UpperCAmelCase__		,		UpperCAmelCase__     ):
				em += exact_match_score(UpperCAmelCase__		,		UpperCAmelCase__     )
		if len(UpperCAmelCase__     ) > 0:
				em /= len(UpperCAmelCase__     )
		return {"em": em}





def __A						(   a_ :							Any     )->							Any:




		'''simple docstring'''






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





def __A						(   a_ :							Union[str, Any]		,		a_ :							str		,		a_ :							List[str]     )->							Optional[Any]:




		'''simple docstring'''






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







"""simple docstring"""






import os
import sys

lowerCamelCase__ :       List[Any]						     =			os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowerCamelCase__ :       str						     =			[
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]





@add_start_docstrings(AutoConfig.__doc__     )
def __A						(   *a_ :							Any		,		**a_ :							Union[str, Any]     )->							Dict:




		'''simple docstring'''






		return AutoConfig.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoTokenizer.__doc__     )
def __A						(   *a_ :							str		,		**a_ :							Union[str, Any]     )->							Union[str, Any]:




		'''simple docstring'''






		return AutoTokenizer.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModel.__doc__     )
def __A						(   *a_ :							List[str]		,		**a_ :							int     )->							Dict:




		'''simple docstring'''






		return AutoModel.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForCausalLM.__doc__     )
def __A						(   *a_ :							Any		,		**a_ :							Tuple     )->							Dict:




		'''simple docstring'''






		return AutoModelForCausalLM.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForMaskedLM.__doc__     )
def __A						(   *a_ :							Dict		,		**a_ :							Optional[Any]     )->							Optional[int]:




		'''simple docstring'''






		return AutoModelForMaskedLM.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForSequenceClassification.__doc__     )
def __A						(   *a_ :							Optional[int]		,		**a_ :							str     )->							Optional[int]:




		'''simple docstring'''






		return AutoModelForSequenceClassification.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__     )
def __A						(   *a_ :							List[str]		,		**a_ :							int     )->							List[Any]:




		'''simple docstring'''






		return AutoModelForQuestionAnswering.from_pretrained(*a_		,		**a_     )





