





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 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   , __a=True   , __a="pt"   )     ->   Union[str, Any]:
	snake_case_		:			Optional[int]											=	{"""add_prefix_space""": True} if isinstance(__a   , __a   ) and not line.startswith(' '   ) else {}
	snake_case_		:			Optional[int]											=	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 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a=None   , )     ->   Tuple:
	snake_case_		:			Dict											=	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 SCREAMING_SNAKE_CASE_						(					__SCREAMING_SNAKE_CASE					):






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







							"""simple docstring"""


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






						def __len__(      self      :      Tuple	)							->    Union[str, Any]:







							"""simple docstring"""


							return len(self.src_lens	)






						def __getitem__(      self      :      List[str]		,   _A      :      Tuple	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			int											=	index + 1  # linecache starts at 1
							snake_case_		:			Union[str, Any]											=	self.prefix + linecache.getline(str(self.src_file	)		,   _a	).rstrip('\n'	)
							snake_case_		:			Dict											=	linecache.getline(str(self.tgt_file	)		,   _a	).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		,   _a	):
								source_line += self.tokenizer.eos_token
								tgt_line += self.tokenizer.eos_token

							# Pad source and target to the right
							snake_case_		:			List[str]											=	(
							    self.tokenizer.question_encoder if isinstance(self.tokenizer		,   _a	) else self.tokenizer
							)
							snake_case_		:			Any											=	self.tokenizer.generator if isinstance(self.tokenizer		,   _a	) else self.tokenizer

							snake_case_		:			Optional[int]											=	encode_line(_a		,   _a		,   self.max_source_length		,   'right'	)
							snake_case_		:			Optional[int]											=	encode_line(_a		,   _a		,   self.max_target_length		,   'right'	)

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






						@staticmethod
						def UpperCAmelCase_				(      _A      :      int	)							->    List[str]:







							"""simple docstring"""


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






						def UpperCAmelCase_				(      self      :      Optional[int]		,   _A      :      Dict	)							->    Optional[Any]:







							"""simple docstring"""


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


def 							SCREAMING_SNAKE_CASE__						(    __a   )     ->   List[Any]:
	return list(itertools.chain.from_iterable(__a   )   )


def 							SCREAMING_SNAKE_CASE__						(    __a   )     ->   int:
	snake_case_		:			Union[str, Any]											=	get_git_info()
	save_json(__a   , os.path.join(__a   , 'git_log.json'   )   )


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


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


def 							SCREAMING_SNAKE_CASE__						(    )     ->   List[Any]:
	snake_case_		:			List[str]											=	git.Repo(search_parent_directories=__a   )
	snake_case_		:			Union[str, Any]											=	{
	    """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 							SCREAMING_SNAKE_CASE__						(    __a   , __a   )     ->   str:
	return list(map(__a   , __a   )   )


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   )     ->   Union[str, Any]:
	with open(__a   , 'wb'   ) as f:
		return pickle.dump(__a   , __a   )


def 							SCREAMING_SNAKE_CASE__						(    __a   )     ->   Tuple:

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


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   )     ->   Tuple:
	return normalize_answer(__a   ) == normalize_answer(__a   )


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   )     ->   int:
	assert len(__a   ) == len(__a   )
	snake_case_		:			Any											=	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 							SCREAMING_SNAKE_CASE__						(    __a   )     ->   Union[str, Any]:
	return model_prefix.startswith('rag'   )


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








from __future__ import annotations


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a = None   , __a = None   , __a = False   , ):
	snake_case_		:			int											=	cipher_alphabet or [chr(__a   ) for i in range(97   , 1_23   )]

	# If the argument is None or the user provided an empty dictionary
	if not frequencies_dict:
		# Frequencies of letters in the english language (how much they show up)
		snake_case_		:			Dict											=	{
		    'a': 0.08497,
		    'b': 0.01492,
		    'c': 0.02202,
		    'd': 0.04253,
		    'e': 0.11162,
		    'f': 0.02228,
		    'g': 0.02015,
		    'h': 0.06094,
		    'i': 0.07546,
		    'j': 0.00153,
		    'k': 0.01292,
		    'l': 0.04025,
		    'm': 0.02406,
		    'n': 0.06749,
		    'o': 0.07507,
		    'p': 0.01929,
		    'q': 0.00095,
		    'r': 0.07587,
		    's': 0.06327,
		    't': 0.09356,
		    'u': 0.02758,
		    'v': 0.00978,
		    'w': 0.02560,
		    'x': 0.00150,
		    'y': 0.01994,
		    'z': 0.00077,
		}
	else:
		# Custom frequencies dictionary
		snake_case_		:			int											=	frequencies_dict

	if not case_sensitive:
		snake_case_		:			Optional[Any]											=	ciphertext.lower()

	# Chi squared statistic values
	snake_case_		:			dict[int, tuple[float, str]]											=	{}

	# cycle through all of the shifts
	for shift in range(len(__a   )   ):
		snake_case_		:			int											=	''

		# decrypt the message with the shift
		for letter in ciphertext:
			try:
				# Try to index the letter in the alphabet
				snake_case_		:			List[Any]											=	(alphabet_letters.index(letter.lower()   ) - shift) % len(
				    __a   )
				decrypted_with_shift += (
				    alphabet_letters[new_key].upper()
				    if case_sensitive and letter.isupper()
				    else alphabet_letters[new_key]
				)
			except ValueError:
				# Append the character if it isn't in the alphabet
				decrypted_with_shift += letter

		snake_case_		:			Union[str, Any]											=	0.0

		# Loop through each letter in the decoded message with the shift
		for letter in decrypted_with_shift:
			if case_sensitive:
				snake_case_		:			List[Any]											=	letter.lower()
				if letter in frequencies:
					# Get the amount of times the letter occurs in the message
					snake_case_		:			Optional[Any]											=	decrypted_with_shift.lower().count(__a   )

					# Get the excepcted amount of times the letter should appear based
					# on letter frequencies
					snake_case_		:			Tuple											=	frequencies[letter] * occurrences

					# Complete the chi squared statistic formula
					snake_case_		:			Optional[int]											=	((occurrences - expected) ** 2) / expected

					# Add the margin of error to the total chi squared statistic
					chi_squared_statistic += chi_letter_value
			else:
				if letter.lower() in frequencies:
					# Get the amount of times the letter occurs in the message
					snake_case_		:			Any											=	decrypted_with_shift.count(__a   )

					# Get the excepcted amount of times the letter should appear based
					# on letter frequencies
					snake_case_		:			List[str]											=	frequencies[letter] * occurrences

					# Complete the chi squared statistic formula
					snake_case_		:			Optional[Any]											=	((occurrences - expected) ** 2) / expected

					# Add the margin of error to the total chi squared statistic
					chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
		snake_case_		:			Any											=	(
		    chi_squared_statistic,
		    decrypted_with_shift,
		)

	# Get the most likely cipher by finding the cipher with the smallest chi squared
	# statistic
	def chi_squared_statistic_values_sorting_key(__a   ) -> tuple[float, str]:
		return chi_squared_statistic_values[key]

	snake_case_		:			int											=	min(
	    __a   , key=__a   , )

	# Get all the data from the most likely cipher (key, decoded message)
	(
	    (
	    snake_case_
	)						,(
	    snake_case_
	)						,
	)		:			str											=	chi_squared_statistic_values[most_likely_cipher]

	# Return the data on the most likely shift
	return (
	    most_likely_cipher,
	    most_likely_cipher_chi_squared_value,
	    decoded_most_likely_cipher,
	)

