
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							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_=True	,	UpperCamelCase_="pt"		)		->		int:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= {"""add_prefix_space""": True} if isinstance(UpperCamelCase_	,	UpperCamelCase_		) and not line.startswith(' '		) else {}
					SCREAMING_SNAKE_CASE__													= 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 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_=None	,	)		->		Optional[Any]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= 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__  (						_a  ):
	def __init__(			self							:   int       ,   UpperCAmelCase_							:   List[str]       ,   UpperCAmelCase_							:   Any       ,   UpperCAmelCase_							:   int       ,   UpperCAmelCase_							:   Optional[Any]       ,   UpperCAmelCase_							:   List[Any]="train"       ,   UpperCAmelCase_							:   Optional[int]=None       ,   UpperCAmelCase_							:   str=None       ,   UpperCAmelCase_							:   Dict=None       ,   UpperCAmelCase_							:   Optional[Any]=""       ,   ):
						super().__init__()
						SCREAMING_SNAKE_CASE__													= Path(__lowerCamelCase							).joinpath(type_path + '.source'							)
						SCREAMING_SNAKE_CASE__													= Path(__lowerCamelCase							).joinpath(type_path + '.target'							)
						SCREAMING_SNAKE_CASE__													= self.get_char_lens(self.src_file							)
						SCREAMING_SNAKE_CASE__													= max_source_length
						SCREAMING_SNAKE_CASE__													= max_target_length
						assert min(self.src_lens							) > 0, F'found empty line in {self.src_file}'
						SCREAMING_SNAKE_CASE__													= tokenizer
						SCREAMING_SNAKE_CASE__													= prefix
						if n_obs is not None:
											SCREAMING_SNAKE_CASE__													= self.src_lens[:n_obs]
						SCREAMING_SNAKE_CASE__													= src_lang
						SCREAMING_SNAKE_CASE__													= tgt_lang
	def __len__(			self							:   List[str]							):
						return len(self.src_lens							)
	def __getitem__(			self							:   Union[str, Any]       ,   UpperCAmelCase_							:   Dict							):
						SCREAMING_SNAKE_CASE__													= index + 1  # linecache starts at 1
						SCREAMING_SNAKE_CASE__													= self.prefix + linecache.getline(str(self.src_file							)       ,   __lowerCamelCase							).rstrip('\n'							)
						SCREAMING_SNAKE_CASE__													= 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
						SCREAMING_SNAKE_CASE__													= (
						    self.tokenizer.question_encoder if isinstance(self.tokenizer       ,   __lowerCamelCase							) else self.tokenizer
						)
						SCREAMING_SNAKE_CASE__													= self.tokenizer.generator if isinstance(self.tokenizer       ,   __lowerCamelCase							) else self.tokenizer

						SCREAMING_SNAKE_CASE__													= encode_line(__lowerCamelCase       ,   __lowerCamelCase       ,   self.max_source_length       ,   'right'							)
						SCREAMING_SNAKE_CASE__													= encode_line(__lowerCamelCase       ,   __lowerCamelCase       ,   self.max_target_length       ,   'right'							)

						SCREAMING_SNAKE_CASE__													= source_inputs["""input_ids"""].squeeze()
						SCREAMING_SNAKE_CASE__													= target_inputs["""input_ids"""].squeeze()
						SCREAMING_SNAKE_CASE__													= source_inputs["""attention_mask"""].squeeze()
						return {
						    "input_ids": source_ids,
						    "attention_mask": src_mask,
						    "decoder_input_ids": target_ids,
						}
	@staticmethod
	def      A_				(			UpperCAmelCase_							:   Tuple							):
						return [len(__lowerCamelCase							) for x in Path(__lowerCamelCase							).open().readlines()]





	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   int							):
						SCREAMING_SNAKE_CASE__													= torch.stack([x['input_ids'] for x in batch]							)
						SCREAMING_SNAKE_CASE__													= torch.stack([x['attention_mask'] for x in batch]							)
						SCREAMING_SNAKE_CASE__													= torch.stack([x['decoder_input_ids'] for x in batch]							)
						SCREAMING_SNAKE_CASE__													= (
						    self.tokenizer.generator.pad_token_id
						    if isinstance(self.tokenizer       ,   __lowerCamelCase							)
						    else self.tokenizer.pad_token_id
						)
						SCREAMING_SNAKE_CASE__													= (
						    self.tokenizer.question_encoder.pad_token_id
						    if isinstance(self.tokenizer       ,   __lowerCamelCase							)
						    else self.tokenizer.pad_token_id
						)
						SCREAMING_SNAKE_CASE__													= trim_batch(__lowerCamelCase       ,   __lowerCamelCase							)
						SCREAMING_SNAKE_CASE__													= trim_batch(__lowerCamelCase       ,   __lowerCamelCase       ,   attention_mask=__lowerCamelCase							)
						SCREAMING_SNAKE_CASE__													= {
						    """input_ids""": source_ids,
						    """attention_mask""": source_mask,
						    """decoder_input_ids""": y,
						}
						return batch





__snake_case								=  getLogger(__name__)






def 							_lowercase							(     UpperCamelCase_		)		->		Dict:
					'''simple docstring'''


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






def 							_lowercase							(     UpperCamelCase_		)		->		None:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= get_git_info()
					save_json(UpperCamelCase_	,	os.path.join(UpperCamelCase_	,	'git_log.json'		)		)






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_=4	,	**UpperCamelCase_		)		->		str:
					'''simple docstring'''


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






def 							_lowercase							(     UpperCamelCase_		)		->		Tuple:
					'''simple docstring'''


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






def 							_lowercase							(     )		->		Union[str, Any]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= git.Repo(search_parent_directories=UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= {
					    """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 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		List:
					'''simple docstring'''


					return list(map(UpperCamelCase_	,	UpperCamelCase_		)		)






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		Optional[Any]:
					'''simple docstring'''


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






def 							_lowercase							(     UpperCamelCase_		)		->		List[str]:
					'''simple docstring'''


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

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

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

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

					return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase_		)		)		)		)






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		Optional[int]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= normalize_answer(UpperCamelCase_		).split()
					SCREAMING_SNAKE_CASE__													= normalize_answer(UpperCamelCase_		).split()
					SCREAMING_SNAKE_CASE__													= Counter(UpperCamelCase_		) & Counter(UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= sum(common.values()		)
					if num_same == 0:
										return 0
					SCREAMING_SNAKE_CASE__													= 1.0 * num_same / len(UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= 1.0 * num_same / len(UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= (2 * precision * recall) / (precision + recall)
					return fa






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		List[str]:
					'''simple docstring'''


					return normalize_answer(UpperCamelCase_		) == normalize_answer(UpperCamelCase_		)






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		Dict:
					'''simple docstring'''


					assert len(UpperCamelCase_		) == len(UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= 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 							_lowercase							(     UpperCamelCase_		)		->		Union[str, Any]:
					'''simple docstring'''


					return model_prefix.startswith('rag'		)






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		Optional[Any]:
					'''simple docstring'''


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













import re
import string

import numpy as np

import datasets


UpperCAmelCase_ :						Dict      =      '''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''

UpperCAmelCase_ :						Any      =      '''
Args:
    predictions: List of predicted texts.
    references: List of reference texts.
    regexes_to_ignore: List, defaults to None. Regex expressions of characters to
        ignore when calculating the exact matches. Note: these regexes are removed
        from the input data before the changes based on the options below (e.g. ignore_case,
        ignore_punctuation, ignore_numbers) are applied.
    ignore_case: Boolean, defaults to False. If true, turns everything
        to lowercase so that capitalization differences are ignored.
    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
Returns:
    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
Examples:
    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results["exact_match"], 1))
    25.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results["exact_match"], 1))
    50.0


    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results["exact_match"], 1))
    75.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
    >>> print(round(results["exact_match"], 1))
    100.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]
    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results["exact_match"], 1))
    33.3

'''

UpperCAmelCase_ :						Tuple      =      '''
'''

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,   _KWARGS_DESCRIPTION      )
class     _SCREAMING_SNAKE_CASE       (			datasets.Metric      ):





	def 						_A (		self				:					Optional[int]	):
		return datasets.MetricInfo(
		    description=_DESCRIPTION ,  citation=_CITATION ,  inputs_description=_KWARGS_DESCRIPTION ,  features=datasets.Features(
		        {
		            """predictions""": datasets.Value("""string""" ,  id="""sequence"""	),
		            """references""": datasets.Value("""string""" ,  id="""sequence"""	),
		        }	) ,  reference_urls=[] ,  )





	def 						_A (		self				:					Optional[int] ,  __lowerCamelCase				:					Union[str, Any] ,  __lowerCamelCase				:					List[str] ,  __lowerCamelCase				:					List[str]=None ,  __lowerCamelCase				:					Optional[int]=False ,  __lowerCamelCase				:					int=False ,  __lowerCamelCase				:					Optional[int]=False ,  ):
		if regexes_to_ignore is not None:
			for s in regexes_to_ignore:
				UpperCamelCase     :str          =	np.array([re.sub(__lowerCamelCase ,  """""" ,  __lowerCamelCase	) for x in predictions]	)
				UpperCamelCase     :Tuple          =	np.array([re.sub(__lowerCamelCase ,  """""" ,  __lowerCamelCase	) for x in references]	)
		else:
			UpperCamelCase     :Any          =	np.asarray(__lowerCamelCase	)
			UpperCamelCase     :str          =	np.asarray(__lowerCamelCase	)

		if ignore_case:
			UpperCamelCase     :Tuple          =	np.char.lower(__lowerCamelCase	)
			UpperCamelCase     :Any          =	np.char.lower(__lowerCamelCase	)

		if ignore_punctuation:
			UpperCamelCase     :Optional[int]          =	string.punctuation.maketrans("""""" ,  """""" ,  string.punctuation	)
			UpperCamelCase     :Optional[Any]          =	np.char.translate(__lowerCamelCase ,  table=__lowerCamelCase	)
			UpperCamelCase     :List[str]          =	np.char.translate(__lowerCamelCase ,  table=__lowerCamelCase	)

		if ignore_numbers:
			UpperCamelCase     :Tuple          =	string.digits.maketrans("""""" ,  """""" ,  string.digits	)
			UpperCamelCase     :Dict          =	np.char.translate(__lowerCamelCase ,  table=__lowerCamelCase	)
			UpperCamelCase     :Tuple          =	np.char.translate(__lowerCamelCase ,  table=__lowerCamelCase	)

		UpperCamelCase     :int          =	predictions == references

		return {"exact_match": np.mean(__lowerCamelCase	) * 100}
