

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__      (   SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   List[Any]     ,					SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   Tuple=True     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]="pt"      ):



     '''simple docstring'''





     lowercase__   :       Union[str, Any]				=	{'add_prefix_space': True} if isinstance(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      ) and not line.startswith(' '      ) else {}
     lowercase__   :       str				=	padding_side
     return tokenizer(
         [line]     ,					max_length=SCREAMING_SNAKE_CASE_     ,					padding='max_length' if pad_to_max_length else None     ,					truncation=SCREAMING_SNAKE_CASE_     ,					return_tensors=SCREAMING_SNAKE_CASE_     ,					add_special_tokens=SCREAMING_SNAKE_CASE_     ,					**SCREAMING_SNAKE_CASE_     ,					)

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Union[str, Any]     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]     ,					SCREAMING_SNAKE_CASE_		:   Any=None     ,					):



     '''simple docstring'''





     lowercase__   :       Tuple				=	input_ids.ne(SCREAMING_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 				SCREAMING_SNAKE_CASE__ (__snake_case					):





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





    def __len__(				self):
         return len(self.src_lens)





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

         lowercase__   :       Any				=	encode_line(a						,       a						,       self.max_source_length						,       'right')
         lowercase__   :       List[str]				=	encode_line(a						,       a						,       self.max_target_length						,       'right')

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





    @staticmethod
    def 				snake_case_    (				a):
         return [len(a) for x in Path(a).open().readlines()]





    def 				snake_case_    (				self						,       a):
         lowercase__   :       Dict				=	torch.stack([x['input_ids'] for x in batch])
         lowercase__   :       Tuple				=	torch.stack([x['attention_mask'] for x in batch])
         lowercase__   :       Tuple				=	torch.stack([x['decoder_input_ids'] for x in batch])
         lowercase__   :       List[str]				=	(
             self.tokenizer.generator.pad_token_id
             if isinstance(self.tokenizer						,       a)
             else self.tokenizer.pad_token_id
         )
         lowercase__   :       Union[str, Any]				=	(
             self.tokenizer.question_encoder.pad_token_id
             if isinstance(self.tokenizer						,       a)
             else self.tokenizer.pad_token_id
         )
         lowercase__   :       Dict				=	trim_batch(a						,       a)
         lowercase__				,			lowercase__   :       Any				=	trim_batch(a						,       a						,       attention_mask=a)
         lowercase__   :       Union[str, Any]				=	{
             'input_ids': source_ids,
             'attention_mask': source_mask,
             'decoder_input_ids': y,
         }
         return batch



snake_case_        =			getLogger(__name__)

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[List]      ):



     '''simple docstring'''





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

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   str      ):



     '''simple docstring'''





     lowercase__   :       Dict				=	get_git_info()
     save_json(SCREAMING_SNAKE_CASE_     ,					os.path.join(SCREAMING_SNAKE_CASE_     ,					'git_log.json'      )      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   List[str]     ,					SCREAMING_SNAKE_CASE_		:   Dict=4     ,					**SCREAMING_SNAKE_CASE_		:   Any      ):



     '''simple docstring'''





     with open(SCREAMING_SNAKE_CASE_     ,					'w'      ) as f:
          json.dump(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					indent=SCREAMING_SNAKE_CASE_     ,					**SCREAMING_SNAKE_CASE_      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[str]      ):



     '''simple docstring'''





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

def      snake_case__      (   ):



     '''simple docstring'''





     lowercase__   :       Any				=	git.Repo(search_parent_directories=SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Union[str, Any]				=	{
         'repo_id': str(SCREAMING_SNAKE_CASE_      ),
         'repo_sha': str(repo.head.object.hexsha      ),
         'repo_branch': str(repo.active_branch      ),
         'hostname': str(socket.gethostname()      ),
     }
     return repo_infos

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Callable     ,					SCREAMING_SNAKE_CASE_		:   Iterable      ):



     '''simple docstring'''





     return list(map(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   str      ):



     '''simple docstring'''





     with open(SCREAMING_SNAKE_CASE_     ,					'wb'      ) as f:
          return pickle.dump(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[int]      ):



     '''simple docstring'''





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

     def white_space_fix(SCREAMING_SNAKE_CASE_		:   Tuple      ):
          return " ".join(text.split()      )

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

     def lower(SCREAMING_SNAKE_CASE_		:   Union[str, Any]      ):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE_      )      )      )      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]      ):



     '''simple docstring'''





     lowercase__   :       str				=	normalize_answer(SCREAMING_SNAKE_CASE_      ).split()
     lowercase__   :       List[str]				=	normalize_answer(SCREAMING_SNAKE_CASE_      ).split()
     lowercase__   :       str				=	Counter(SCREAMING_SNAKE_CASE_      ) & Counter(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       str				=	sum(common.values()      )
     if num_same == 0:
          return 0
     lowercase__   :       str				=	1.0 * num_same / len(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       List[str]				=	1.0 * num_same / len(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Any				=	(2 * precision * recall) / (precision + recall)
     return fa

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     return normalize_answer(SCREAMING_SNAKE_CASE_      ) == normalize_answer(SCREAMING_SNAKE_CASE_      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[str]     ,					SCREAMING_SNAKE_CASE_		:   List[str]      ):



     '''simple docstring'''





     assert len(SCREAMING_SNAKE_CASE_      ) == len(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       int				=	0
     for hypo, pred in zip(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      ):
          em += exact_match_score(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
     if len(SCREAMING_SNAKE_CASE_      ) > 0:
          em /= len(SCREAMING_SNAKE_CASE_      )
     return {"em": em}

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     return model_prefix.startswith('rag'      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]      ):



     '''simple docstring'''





     lowercase__   :       List[Any]				=	{p: p for p in extra_params}
     # T5 models don't have `dropout` param, they have `dropout_rate` instead
     lowercase__   :       Any				=	'dropout_rate'
     for p in extra_params:
          if getattr(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      ):
               if not hasattr(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      ) and not hasattr(SCREAMING_SNAKE_CASE_     ,					equivalent_param[p]      ):
                    logger.info('config doesn\'t have a `{}` attribute'.format(SCREAMING_SNAKE_CASE_      )      )
                    delattr(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
                    continue
               lowercase__   :       Optional[int]				=	p if hasattr(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      ) else equivalent_param[p]
               setattr(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					getattr(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )      )
               delattr(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
     return hparams, config




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


snake_case_        =			logging.get_logger(__name__)

snake_case_        =			{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_        =			{
    '''vocab_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/tokenizer.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/tokenizer.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/tokenizer.json''',
    },
}

snake_case_        =			{
    '''gpt2''': 1_024,
    '''gpt2-medium''': 1_024,
    '''gpt2-large''': 1_024,
    '''gpt2-xl''': 1_024,
    '''distilgpt2''': 1_024,
}


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	List[str]														=					VOCAB_FILES_NAMES
    __lowerCamelCase						:	Any														=					PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase						:	str														=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase						:	int														=					["""input_ids""", """attention_mask"""]
    __lowerCamelCase						:	str														=					GPTaTokenizer





    def __init__(				self						,       a=None						,       a=None						,       a=None						,       a="<|endoftext|>"						,       a="<|endoftext|>"						,       a="<|endoftext|>"						,       a=False						,       **a						,       ):
         super().__init__(
             a						,       a						,       tokenizer_file=a						,       unk_token=a						,       bos_token=a						,       eos_token=a						,       add_prefix_space=a						,       **a						,       )

         lowercase__   :       int				=	kwargs.pop('add_bos_token'						,       a)

         lowercase__   :       List[str]				=	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
         if pre_tok_state.get('add_prefix_space'						,       a) != add_prefix_space:
              lowercase__   :       Optional[Any]				=	getattr(a						,       pre_tok_state.pop('type'))
              lowercase__   :       List[Any]				=	add_prefix_space
              lowercase__   :       str				=	pre_tok_class(**a)

         lowercase__   :       Tuple				=	add_prefix_space





    def 				snake_case_    (				self						,       *a						,       **a):
         lowercase__   :       Tuple				=	kwargs.get('is_split_into_words'						,       a)
         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*a						,       **a)





    def 				snake_case_    (				self						,       *a						,       **a):
         lowercase__   :       Optional[Any]				=	kwargs.get('is_split_into_words'						,       a)

         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*a						,       **a)





    def 				snake_case_    (				self						,       a						,       a = None):
         lowercase__   :       Any				=	self._tokenizer.model.save(a						,       name=a)
         return tuple(a)





    def 				snake_case_    (				self						,       a):
         lowercase__   :       Union[str, Any]				=	[]
         for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(a						,       add_special_tokens=a) + [self.eos_token_id])

         if len(a) > self.model_max_length:
              lowercase__   :       Union[str, Any]				=	input_ids[-self.model_max_length :]
         return input_ids

