



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      lowerCAmelCase_					(  _lowercase    :       List[str]						,       _lowercase    :       str						,       _lowercase    :       Optional[Any]						,       _lowercase    :       Tuple						,       _lowercase    :       str=True						,       _lowercase    :       Optional[int]="pt")							->	Any:







			"""simple docstring"""
			a__       :						List[Any]							=  {"""add_prefix_space""": True} if isinstance(_lowercase						,       _lowercase) and not line.startswith(""" """) else {}
			a__       :						Any							=  padding_side
			return tokenizer(
			    [line]						,       max_length=_lowercase						,       padding="""max_length""" if pad_to_max_length else None						,       truncation=_lowercase						,       return_tensors=_lowercase						,       add_special_tokens=_lowercase						,       **_lowercase						,       )

def      lowerCAmelCase_					(  _lowercase    :       Any						,       _lowercase    :       Optional[Any]						,       _lowercase    :       Union[str, Any]=None						,       )							->	Union[str, Any]:







			"""simple docstring"""
			a__       :						Dict							=  input_ids.ne(_lowercase).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        snake_case__    (A__		):







						"""simple docstring"""




						def __init__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase="train" ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       __lowercase="" ,       ) ->				List[str]:
									"""simple docstring"""




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

						def __len__(       self  ) ->				str:
									"""simple docstring"""




									return len(self.src_lens  )

						def __getitem__(       self ,       __lowercase  ) ->				Dict[str, torch.Tensor]:
									"""simple docstring"""




									a__       :						List[Any]							=  index + 1  # linecache starts at 1
									a__       :						Optional[int]							=  self.prefix + linecache.getline(str(self.src_file  ) ,       __lowercase  ).rstrip("""\n"""  )
									a__       :						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
									a__       :						Optional[int]							=  (
									    self.tokenizer.question_encoder if isinstance(self.tokenizer ,       __lowercase  ) else self.tokenizer
									)
									a__       :						Optional[Any]							=  self.tokenizer.generator if isinstance(self.tokenizer ,       __lowercase  ) else self.tokenizer

									a__       :						Any							=  encode_line(__lowercase ,       __lowercase ,       self.max_source_length ,       """right"""  )
									a__       :						Optional[int]							=  encode_line(__lowercase ,       __lowercase ,       self.max_target_length ,       """right"""  )

									a__       :						List[str]							=  source_inputs["""input_ids"""].squeeze()
									a__       :						Optional[int]							=  target_inputs["""input_ids"""].squeeze()
									a__       :						List[str]							=  source_inputs["""attention_mask"""].squeeze()
									return {
									    "input_ids": source_ids,
									    "attention_mask": src_mask,
									    "decoder_input_ids": target_ids,
									}

						@staticmethod
						def 							SCREAMING_SNAKE_CASE__(       __lowercase  ) ->				Tuple:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Dict[str, torch.Tensor]:
									"""simple docstring"""




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




_lowercase		:				Tuple      		=getLogger(__name__)

def      lowerCAmelCase_					(  _lowercase    :       List[List])							->	Optional[int]:







			"""simple docstring"""
			return list(itertools.chain.from_iterable(_lowercase))

def      lowerCAmelCase_					(  _lowercase    :       str)							->	None:







			"""simple docstring"""
			a__       :						List[str]							=  get_git_info()
			save_json(_lowercase						,       os.path.join(_lowercase						,       """git_log.json"""))

def      lowerCAmelCase_					(  _lowercase    :       Optional[int]						,       _lowercase    :       Optional[int]						,       _lowercase    :       int=4						,       **_lowercase    :       List[str])							->	Tuple:







			"""simple docstring"""
			with open(_lowercase						,       """w""") as f:
						json.dump(_lowercase						,       _lowercase						,       indent=_lowercase						,       **_lowercase)

def      lowerCAmelCase_					(  _lowercase    :       List[Any])							->	List[str]:







			"""simple docstring"""
			with open(_lowercase) as f:
						return json.load(_lowercase)

def      lowerCAmelCase_					(  )							->	Optional[int]:







			"""simple docstring"""
			a__       :						Optional[int]							=  git.Repo(search_parent_directories=_lowercase)
			a__       :						Union[str, Any]							=  {
			    """repo_id""": str(_lowercase),
			    """repo_sha""": str(repo.head.object.hexsha),
			    """repo_branch""": str(repo.active_branch),
			    """hostname""": str(socket.gethostname()),
			}
			return repo_infos

def      lowerCAmelCase_					(  _lowercase    :       Callable						,       _lowercase    :       Iterable)							->	List:







			"""simple docstring"""
			return list(map(_lowercase						,       _lowercase))

def      lowerCAmelCase_					(  _lowercase    :       Optional[Any]						,       _lowercase    :       int)							->	List[Any]:







			"""simple docstring"""
			with open(_lowercase						,       """wb""") as f:
						return pickle.dump(_lowercase						,       _lowercase)

def      lowerCAmelCase_					(  _lowercase    :       List[Any])							->	Union[str, Any]:







			"""simple docstring"""

			def remove_articles(_lowercase    :       Optional[Any]):
						return re.sub(R"""\b(a|an|the)\b"""						,       """ """						,       _lowercase)

			def white_space_fix(_lowercase    :       Union[str, Any]):
						return " ".join(text.split())

			def remove_punc(_lowercase    :       Union[str, Any]):
						a__       :						int							=  set(string.punctuation)
						return "".join(ch for ch in text if ch not in exclude)

			def lower(_lowercase    :       Optional[int]):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(_lowercase))))

def      lowerCAmelCase_					(  _lowercase    :       Optional[Any]						,       _lowercase    :       List[str])							->	Union[str, Any]:







			"""simple docstring"""
			a__       :						List[Any]							=  normalize_answer(_lowercase).split()
			a__       :						List[Any]							=  normalize_answer(_lowercase).split()
			a__       :						List[Any]							=  Counter(_lowercase) & Counter(_lowercase)
			a__       :						Union[str, Any]							=  sum(common.values())
			if num_same == 0:
						return 0
			a__       :						List[Any]							=  1.0 * num_same / len(_lowercase)
			a__       :						Any							=  1.0 * num_same / len(_lowercase)
			a__       :						List[Any]							=  (2 * precision * recall) / (precision + recall)
			return fa

def      lowerCAmelCase_					(  _lowercase    :       int						,       _lowercase    :       Optional[Any])							->	Optional[int]:







			"""simple docstring"""
			return normalize_answer(_lowercase) == normalize_answer(_lowercase)

def      lowerCAmelCase_					(  _lowercase    :       List[str]						,       _lowercase    :       List[str])							->	Dict:







			"""simple docstring"""
			assert len(_lowercase) == len(_lowercase)
			a__       :						Dict							=  0
			for hypo, pred in zip(_lowercase						,       _lowercase):
						em += exact_match_score(_lowercase						,       _lowercase)
			if len(_lowercase) > 0:
						em /= len(_lowercase)
			return {"em": em}

def      lowerCAmelCase_					(  _lowercase    :       Any)							->	Dict:







			"""simple docstring"""
			return model_prefix.startswith("""rag""")





def      lowerCAmelCase_					(  _lowercase    :       Tuple						,       _lowercase    :       Tuple						,       _lowercase    :       int)							->	str:







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









def      lowerCAmelCase_					(  _lowercase    :       int)							->	int:







			"""simple docstring"""

			if not isinstance(_lowercase						,       _lowercase):
						raise TypeError("""only integers accepted as input""")
			else:
						a__       :						Any							=  str(abs(_lowercase))
						a__       :						str							=  [list(_lowercase) for char in range(len(_lowercase))]
						for index in range(len(_lowercase)):
									num_transpositions[index].pop(_lowercase)
						return max(
						    int("""""".join(list(_lowercase))) for transposition in num_transpositions)


if __name__ == "__main__":
		__import__("doctest").testmod()




