


'''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       _snake_case     (  A				,					A				,					A				,					A				,					A=True				,					A="pt" )		->					Tuple:
							lowerCAmelCase__      =			{'''add_prefix_space''': True} if isinstance(A				,					A ) and not line.startswith(''' ''' ) else {}
							lowerCAmelCase__      =			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       _snake_case     (  A				,					A				,					A=None				,					)		->					str:
							lowerCAmelCase__      =			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 	a__       (							a__						):





							'''simple docstring'''






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







							def __len__(	self     ) ->							Tuple:
														return len(self.src_lens     )







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

														lowerCAmelCase__      =			encode_line(lowerCamelCase_      ,					lowerCamelCase_      ,					self.max_source_length      ,					'''right'''     )
														lowerCAmelCase__      =			encode_line(lowerCamelCase_      ,					lowerCamelCase_      ,					self.max_target_length      ,					'''right'''     )

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







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







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

__UpperCAmelCase					       =							getLogger(__name__)


def       _snake_case     (  A )		->					Tuple:
							return list(itertools.chain.from_iterable(A ) )


def       _snake_case     (  A )		->					None:
							lowerCAmelCase__      =			get_git_info()
							save_json(A				,					os.path.join(A				,					'''git_log.json''' ) )


def       _snake_case     (  A				,					A				,					A=4				,					**A )		->					Optional[int]:
							with open(A				,					'''w''' ) as f:
														json.dump(A				,					A				,					indent=A				,					**A )


def       _snake_case     (  A )		->					Optional[int]:
							with open(A ) as f:
														return json.load(A )


def       _snake_case     (  )		->					str:
							lowerCAmelCase__      =			git.Repo(search_parent_directories=A )
							lowerCAmelCase__      =			{
							    '''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       _snake_case     (  A				,					A )		->					List:
							return list(map(A				,					A ) )


def       _snake_case     (  A				,					A )		->					str:
							with open(A				,					'''wb''' ) as f:
														return pickle.dump(A				,					A )


def       _snake_case     (  A )		->					int:

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

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

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

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

							return white_space_fix(remove_articles(remove_punc(lower(A ) ) ) )


def       _snake_case     (  A				,					A )		->					str:
							lowerCAmelCase__      =			normalize_answer(A ).split()
							lowerCAmelCase__      =			normalize_answer(A ).split()
							lowerCAmelCase__      =			Counter(A ) & Counter(A )
							lowerCAmelCase__      =			sum(common.values() )
							if num_same == 0:
														return 0
							lowerCAmelCase__      =			1.0 * num_same / len(A )
							lowerCAmelCase__      =			1.0 * num_same / len(A )
							lowerCAmelCase__      =			(2 * precision * recall) / (precision + recall)
							return fa


def       _snake_case     (  A				,					A )		->					str:
							return normalize_answer(A ) == normalize_answer(A )


def       _snake_case     (  A				,					A )		->					Dict:
							assert len(A ) == len(A )
							lowerCAmelCase__      =			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       _snake_case     (  A )		->					Union[str, Any]:
							return model_prefix.startswith('''rag''' )






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



'''simple docstring'''






def       _snake_case     (  A				,					A )		->					bool:
							lowerCAmelCase__      =			len(A ) + 1
							lowerCAmelCase__      =			len(A ) + 1

							# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
							# length i of input_string matches with prefix string of length j of
							# given pattern.
							# "dp" stands for dynamic programming.
							lowerCAmelCase__      =			[[0 for i in range(A )] for j in range(A )]

							# since string of zero length match pattern of zero length
							lowerCAmelCase__      =			1

							# since pattern of zero length will never match with string of non-zero length
							for i in range(1				,					A ):
														lowerCAmelCase__      =			0

							# since string of zero length will match with pattern where there
							# is at least one * alternatively
							for j in range(1				,					A ):
														lowerCAmelCase__      =			dp[0][j - 2] if pattern[j - 1] == '''*''' else 0

							# now using bottom-up approach to find for all remaining lengths
							for i in range(1				,					A ):
														for j in range(1				,					A ):
																					if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
																												lowerCAmelCase__      =			dp[i - 1][j - 1]

																					elif pattern[j - 1] == "*":
																												if dp[i][j - 2] == 1:
																																			lowerCAmelCase__      =			1
																												elif pattern[j - 2] in (input_string[i - 1], "."):
																																			lowerCAmelCase__      =			dp[i - 1][j]
																												else:
																																			lowerCAmelCase__      =			0
																					else:
																												lowerCAmelCase__      =			0

							return bool(dp[-1][-1] )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		# inputing the strings
		# input_string = input("input a string :")
		# pattern = input("input a pattern :")

		__UpperCAmelCase					       =							'''aab'''
		__UpperCAmelCase					       =							'''c*a*b'''

		# using function to check whether given string matches the given pattern
		if match_pattern(input_string, pattern):
				print(f"""{input_string} matches the given pattern {pattern}""")
		else:
				print(f"""{input_string} does not match with the given pattern {pattern}""")