


'''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 							__magic_name__(  lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase=True, lowerCamelCase="pt"):
						__lowerCAmelCase											= {'''add_prefix_space''': True} if isinstance(_snake_case, _snake_case) and not line.startswith(''' ''') else {}
						__lowerCAmelCase											= padding_side
						return tokenizer(
						    [line], max_length=_snake_case, padding='''max_length''' if pad_to_max_length else None, truncation=_snake_case, return_tensors=_snake_case, add_special_tokens=_snake_case, **_snake_case, )

def 							__magic_name__(  lowerCamelCase, lowerCamelCase, lowerCamelCase=None, ):
						__lowerCAmelCase											= input_ids.ne(_snake_case).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				,	__lowercase				,	__lowercase				,	__lowercase				,	__lowercase				,	__lowercase="train"				,	__lowercase=None				,	__lowercase=None				,	__lowercase=None				,	__lowercase=""				,	):
										super().__init__()
										__lowerCAmelCase											= Path(UpperCamelCase_						).joinpath(type_path + '''.source'''						)
										__lowerCAmelCase											= Path(UpperCamelCase_						).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						):
										return len(self.src_lens						)




				def __getitem__(self				,	__lowercase						):
										__lowerCAmelCase											= index + 1  # linecache starts at 1
										__lowerCAmelCase											= self.prefix + linecache.getline(str(self.src_file						)				,	UpperCamelCase_						).rstrip('''\n'''						)
										__lowerCAmelCase											= linecache.getline(str(self.tgt_file						)				,	UpperCamelCase_						).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				,	UpperCamelCase_						):
																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				,	UpperCamelCase_						) else self.tokenizer
										)
										__lowerCAmelCase											= self.tokenizer.generator if isinstance(self.tokenizer				,	UpperCamelCase_						) else self.tokenizer

										__lowerCAmelCase											= encode_line(UpperCamelCase_				,	UpperCamelCase_				,	self.max_source_length				,	'''right'''						)
										__lowerCAmelCase											= encode_line(UpperCamelCase_				,	UpperCamelCase_				,	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     _snake_case     (__lowercase						):
										return [len(UpperCamelCase_						) for x in Path(UpperCamelCase_						).open().readlines()]




				def     _snake_case     (self				,	__lowercase						):
										__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				,	UpperCamelCase_						)
										    else self.tokenizer.pad_token_id
										)
										__lowerCAmelCase											= (
										    self.tokenizer.question_encoder.pad_token_id
										    if isinstance(self.tokenizer				,	UpperCamelCase_						)
										    else self.tokenizer.pad_token_id
										)
										__lowerCAmelCase											= trim_batch(UpperCamelCase_				,	UpperCamelCase_						)
										__lowerCAmelCase											= trim_batch(UpperCamelCase_				,	UpperCamelCase_				,	attention_mask=UpperCamelCase_						)
										__lowerCAmelCase											= {
										    '''input_ids''': source_ids,
										    '''attention_mask''': source_mask,
										    '''decoder_input_ids''': y,
										}
										return batch

_UpperCAmelCase :       Dict										= getLogger(__name__)

def 							__magic_name__(  lowerCamelCase):
						return list(itertools.chain.from_iterable(_snake_case))

def 							__magic_name__(  lowerCamelCase):
						__lowerCAmelCase											= get_git_info()
						save_json(_snake_case, os.path.join(_snake_case, '''git_log.json'''))

def 							__magic_name__(  lowerCamelCase, lowerCamelCase, lowerCamelCase=4, **lowerCamelCase):
						with open(_snake_case, '''w''') as f:
												json.dump(_snake_case, _snake_case, indent=_snake_case, **_snake_case)

def 							__magic_name__(  lowerCamelCase):
						with open(_snake_case) as f:
												return json.load(_snake_case)

def 							__magic_name__(  ):
						__lowerCAmelCase											= git.Repo(search_parent_directories=_snake_case)
						__lowerCAmelCase											= {
						    '''repo_id''': str(_snake_case),
						    '''repo_sha''': str(repo.head.object.hexsha),
						    '''repo_branch''': str(repo.active_branch),
						    '''hostname''': str(socket.gethostname()),
						}
						return repo_infos

def 							__magic_name__(  lowerCamelCase, lowerCamelCase):
						return list(map(_snake_case, _snake_case))

def 							__magic_name__(  lowerCamelCase, lowerCamelCase):
						with open(_snake_case, '''wb''') as f:
												return pickle.dump(_snake_case, _snake_case)

def 							__magic_name__(  lowerCamelCase):

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

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

						def remove_punc(lowerCamelCase):
												__lowerCAmelCase											= 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(_snake_case))))

def 							__magic_name__(  lowerCamelCase, lowerCamelCase):
						__lowerCAmelCase											= normalize_answer(_snake_case).split()
						__lowerCAmelCase											= normalize_answer(_snake_case).split()
						__lowerCAmelCase											= Counter(_snake_case) & Counter(_snake_case)
						__lowerCAmelCase											= sum(common.values())
						if num_same == 0:
												return 0
						__lowerCAmelCase											= 1.0 * num_same / len(_snake_case)
						__lowerCAmelCase											= 1.0 * num_same / len(_snake_case)
						__lowerCAmelCase											= (2 * precision * recall) / (precision + recall)
						return fa

def 							__magic_name__(  lowerCamelCase, lowerCamelCase):
						return normalize_answer(_snake_case) == normalize_answer(_snake_case)

def 							__magic_name__(  lowerCamelCase, lowerCamelCase):
						assert len(_snake_case) == len(_snake_case)
						__lowerCAmelCase											= 0
						for hypo, pred in zip(_snake_case, _snake_case):
												em += exact_match_score(_snake_case, _snake_case)
						if len(_snake_case) > 0:
												em /= len(_snake_case)
						return {"em": em}

def 							__magic_name__(  lowerCamelCase):
						return model_prefix.startswith('''rag''')




def 							__magic_name__(  lowerCamelCase, lowerCamelCase, lowerCamelCase):
						__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(_snake_case, _snake_case, _snake_case):
																		if not hasattr(_snake_case, _snake_case) and not hasattr(_snake_case, equivalent_param[p]):
																								logger.info('''config doesn\'t have a `{}` attribute'''.format(_snake_case))
																								delattr(_snake_case, _snake_case)
																								continue
																		__lowerCAmelCase											= p if hasattr(_snake_case, _snake_case) else equivalent_param[p]
																		setattr(_snake_case, _snake_case, getattr(_snake_case, _snake_case))
																		delattr(_snake_case, _snake_case)
						return hparams, config





"""simple docstring"""





from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def    _snake_case  (  _snake_case :	np.ndarray       ,      _snake_case :	np.ndarray	):
      return math.sqrt(sum(pow(a - b       ,      2	) for a, b in zip(_snake_case       ,      _snake_case	)	)	)


def    _snake_case  (  _snake_case :	np.ndarray       ,      _snake_case :	np.ndarray	):

      if dataset.ndim != value_array.ndim:
            lowerCAmelCase    :		List[Any]				=      (
                '''Wrong input data\'s dimensions... '''
                f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
            )
            raise ValueError(_snake_case	)

      try:
            if dataset.shape[1] != value_array.shape[1]:
                  lowerCAmelCase    :		Dict				=      (
                      '''Wrong input data\'s shape... '''
                      f'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
                  )
                  raise ValueError(_snake_case	)
      except IndexError:
            if dataset.ndim != value_array.ndim:
                  raise TypeError('''Wrong shape'''	)

      if dataset.dtype != value_array.dtype:
            lowerCAmelCase    :		Optional[Any]				=      (
                '''Input data have different datatype... '''
                f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
            )
            raise TypeError(_snake_case	)

      lowerCAmelCase    :		str				=      []

      for value in value_array:
            lowerCAmelCase    :		int				=      euclidean(_snake_case       ,      dataset[0]	)
            lowerCAmelCase    :		Union[str, Any]				=      dataset[0].tolist()

            for dataset_value in dataset[1:]:
                  lowerCAmelCase    :		Any				=      euclidean(_snake_case       ,      _snake_case	)

                  if dist > temp_dist:
                        lowerCAmelCase    :		List[Any]				=      temp_dist
                        lowerCAmelCase    :		Tuple				=      dataset_value.tolist()

            answer.append([vector, dist]	)

      return answer


def    _snake_case  (  _snake_case :	np.ndarray       ,      _snake_case :	np.ndarray	):
      return np.dot(_snake_case       ,      _snake_case	) / (norm(_snake_case	) * norm(_snake_case	))


if __name__ == "__main__":
 import doctest

 doctest.testmod()


