






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    UpperCamelCase						(	_A,   _A,   _A,   _A,   _A=True,   _A="pt"			):






						"""simple docstring"""







						__magic_name__      :       Optional[int]       						= {"""add_prefix_space""": True} if isinstance(_A,   _A			) and not line.startswith(""" """			) else {}
						__magic_name__      :       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    UpperCamelCase						(	_A,   _A,   _A=None,   ):






						"""simple docstring"""







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

							__magic_name__      :       int       						= encode_line(lowerCAmelCase__      ,       lowerCAmelCase__      ,       self.max_source_length      ,       """right"""			)
							__magic_name__      :       List[Any]       						= encode_line(lowerCAmelCase__      ,       lowerCAmelCase__      ,       self.max_target_length      ,       """right"""			)

							__magic_name__      :       Optional[Any]       						= source_inputs["""input_ids"""].squeeze()
							__magic_name__      :       str       						= target_inputs["""input_ids"""].squeeze()
							__magic_name__      :       Any       						= source_inputs["""attention_mask"""].squeeze()
							return {
							    "input_ids": source_ids,
							    "attention_mask": src_mask,
							    "decoder_input_ids": target_ids,
							}
	@staticmethod
	def       __magic_name__    (    lowerCAmelCase__			)							->				int:
							return [len(lowerCAmelCase__			) for x in Path(lowerCAmelCase__			).open().readlines()]







	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				Dict[str, torch.Tensor]:
							__magic_name__      :       Tuple       						= torch.stack([x["""input_ids"""] for x in batch]			)
							__magic_name__      :       Optional[Any]       						= torch.stack([x["""attention_mask"""] for x in batch]			)
							__magic_name__      :       Optional[Any]       						= torch.stack([x["""decoder_input_ids"""] for x in batch]			)
							__magic_name__      :       Tuple       						= (
							    self.tokenizer.generator.pad_token_id
							    if isinstance(self.tokenizer      ,       lowerCAmelCase__			)
							    else self.tokenizer.pad_token_id
							)
							__magic_name__      :       Optional[Any]       						= (
							    self.tokenizer.question_encoder.pad_token_id
							    if isinstance(self.tokenizer      ,       lowerCAmelCase__			)
							    else self.tokenizer.pad_token_id
							)
							__magic_name__      :       int       						= trim_batch(lowerCAmelCase__      ,       lowerCAmelCase__			)
							__magic_name__						,__magic_name__      :       int       						= trim_batch(lowerCAmelCase__      ,       lowerCAmelCase__      ,       attention_mask=lowerCAmelCase__			)
							__magic_name__      :       List[Any]       						= {
							    """input_ids""": source_ids,
							    """attention_mask""": source_mask,
							    """decoder_input_ids""": y,
							}
							return batch



__magic_name__:					int							=     getLogger(__name__)

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







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

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						__magic_name__      :       Union[str, Any]       						= get_git_info()
						save_json(_A,   os.path.join(_A,   """git_log.json"""			)			)

def    UpperCamelCase						(	_A,   _A,   _A=4,   **_A			):






						"""simple docstring"""







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

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







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

def    UpperCamelCase						(	):






						"""simple docstring"""







						__magic_name__      :       str       						= git.Repo(search_parent_directories=_A			)
						__magic_name__      :       Tuple       						= {
						    """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    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						return list(map(_A,   _A			)			)

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						with open(_A,   """wb"""			) as f:
												return pickle.dump(_A,   _A			)

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						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			):
												__magic_name__      :       List[Any]       						= 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    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						__magic_name__      :       List[Any]       						= normalize_answer(_A			).split()
						__magic_name__      :       int       						= normalize_answer(_A			).split()
						__magic_name__      :       Union[str, Any]       						= Counter(_A			) & Counter(_A			)
						__magic_name__      :       Tuple       						= sum(common.values()			)
						if num_same == 0:
												return 0
						__magic_name__      :       Dict       						= 1.0 * num_same / len(_A			)
						__magic_name__      :       Optional[int]       						= 1.0 * num_same / len(_A			)
						__magic_name__      :       Any       						= (2 * precision * recall) / (precision + recall)
						return fa

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						return normalize_answer(_A			) == normalize_answer(_A			)

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						assert len(_A			) == len(_A			)
						__magic_name__      :       Optional[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    UpperCamelCase						(	_A			):






						"""simple docstring"""







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

def    UpperCamelCase						(	_A,   _A,   _A			):






						"""simple docstring"""







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








import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer





class 	snake_case__ (							tf.keras.layers.Layer				):
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = None			)							->				int:
							super().__init__()
							__magic_name__      :       Any       						= pad_token_id
							__magic_name__      :       Any       						= max_length
							__magic_name__      :       List[str]       						= vocab
							__magic_name__      :       List[Any]       						= merges
							__magic_name__      :       int       						= BytePairTokenizer(lowerCAmelCase__      ,       lowerCAmelCase__      ,       sequence_length=lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				Any:
							__magic_name__      :       Union[str, Any]       						= [""" """.join(lowerCAmelCase__			) for m in tokenizer.bpe_ranks.keys()]
							__magic_name__      :       Union[str, Any]       						= tokenizer.get_vocab()
							return cls(lowerCAmelCase__      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				List[str]:
							__magic_name__      :       Optional[Any]       						= GPTaTokenizer.from_pretrained(lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
							return cls.from_tokenizer(lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__			)							->				List[Any]:
							return cls(**lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				int:
							return {
							    "vocab": self.vocab,
							    "merges": self.merges,
							    "max_length": self.max_length,
							    "pad_token_id": self.pad_token_id,
							}







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				int:
							__magic_name__      :       Dict       						= self.tf_tokenizer(lowerCAmelCase__			)
							__magic_name__      :       Dict       						= tf.ones_like(lowerCAmelCase__			)

							if self.pad_token_id is not None:
													# pad the tokens up to max length
													__magic_name__      :       List[Any]       						= max_length if max_length is not None else self.max_length

													if max_length is not None:
																			__magic_name__						,__magic_name__      :       List[Any]       						= pad_model_inputs(
																			    lowerCAmelCase__      ,       max_seq_length=lowerCAmelCase__      ,       pad_value=self.pad_token_id			)

							return {"attention_mask": attention_mask, "input_ids": input_ids}
