


'''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(lowerCamelCase_,				lowerCamelCase_) and not line.startswith(''' ''') else {}
      __lowerCAmelCase							       =				padding_side
      return tokenizer(
          [line],				max_length=lowerCamelCase_,				padding='''max_length''' if pad_to_max_length else None,				truncation=lowerCamelCase_,				return_tensors=lowerCamelCase_,				add_special_tokens=lowerCamelCase_,				**lowerCamelCase_,				)

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase=None,				):
      __lowerCAmelCase							       =				input_ids.ne(lowerCamelCase_).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__				( lowercase_      ):







    """simple docstring"""



    def __init__(self       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase="train"       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=""       ,			):
          super().__init__()
          __lowerCAmelCase							       =				Path(lowerCamelCase_		).joinpath(type_path + '''.source'''		)
          __lowerCAmelCase							       =				Path(lowerCamelCase_		).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		)       ,			lowerCamelCase_		).rstrip('''\n'''		)
          __lowerCAmelCase							       =				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
          __lowerCAmelCase							       =				(
              self.tokenizer.question_encoder if isinstance(self.tokenizer       ,			lowerCamelCase_		) else self.tokenizer
          )
          __lowerCAmelCase							       =				self.tokenizer.generator if isinstance(self.tokenizer       ,			lowerCamelCase_		) else self.tokenizer

          __lowerCAmelCase							       =				encode_line(lowerCamelCase_       ,			lowerCamelCase_       ,			self.max_source_length       ,			'''right'''		)
          __lowerCAmelCase							       =				encode_line(lowerCamelCase_       ,			lowerCamelCase_       ,			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(lowerCamelCase_		) for x in Path(lowerCamelCase_		).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       ,			lowerCamelCase_		)
              else self.tokenizer.pad_token_id
          )
          __lowerCAmelCase							       =				(
              self.tokenizer.question_encoder.pad_token_id
              if isinstance(self.tokenizer       ,			lowerCamelCase_		)
              else self.tokenizer.pad_token_id
          )
          __lowerCAmelCase							       =				trim_batch(lowerCamelCase_       ,			lowerCamelCase_		)
          __lowerCAmelCase							       =				trim_batch(lowerCamelCase_       ,			lowerCamelCase_       ,			attention_mask=lowerCamelCase_		)
          __lowerCAmelCase							       =				{
              """input_ids""": source_ids,
              """attention_mask""": source_mask,
              """decoder_input_ids""": y,
          }
          return batch

_UpperCAmelCase				: Any							    =				getLogger(__name__)

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

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

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase=4,				**lowerCamelCase):



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

def 							__magic_name__(						lowerCamelCase):



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

def 							__magic_name__(						):
      __lowerCAmelCase							       =				git.Repo(search_parent_directories=lowerCamelCase_)
      __lowerCAmelCase							       =				{
          """repo_id""": str(lowerCamelCase_),
          """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(lowerCamelCase_,				lowerCamelCase_))

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):



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

def 							__magic_name__(						lowerCamelCase):



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

      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(lowerCamelCase_))))

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

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

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
      assert len(lowerCamelCase_) == len(lowerCamelCase_)
      __lowerCAmelCase							       =				0
      for hypo, pred in zip(lowerCamelCase_,				lowerCamelCase_):
            em += exact_match_score(lowerCamelCase_,				lowerCamelCase_)
      if len(lowerCamelCase_) > 0:
            em /= len(lowerCamelCase_)
      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(lowerCamelCase_,				lowerCamelCase_,				lowerCamelCase_):
                  if not hasattr(lowerCamelCase_,				lowerCamelCase_) and not hasattr(lowerCamelCase_,				equivalent_param[p]):
                        logger.info('''config doesn\'t have a `{}` attribute'''.format(lowerCamelCase_))
                        delattr(lowerCamelCase_,				lowerCamelCase_)
                        continue
                  __lowerCAmelCase							       =				p if hasattr(lowerCamelCase_,				lowerCamelCase_) else equivalent_param[p]
                  setattr(lowerCamelCase_,				lowerCamelCase_,				getattr(lowerCamelCase_,				lowerCamelCase_))
                  delattr(lowerCamelCase_,				lowerCamelCase_)
      return hparams, config





'''simple docstring'''

from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


_UpperCAmelCase				: str							    =				{
    """configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
    """tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase				: Union[str, Any]							    =				[
	    """GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTNeoXJapaneseForCausalLM""",
	    """GPTNeoXJapaneseLayer""",
	    """GPTNeoXJapaneseModel""",
	    """GPTNeoXJapanesePreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
	from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox_japanese import (
		    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXJapaneseForCausalLM,
		    GPTNeoXJapaneseLayer,
		    GPTNeoXJapaneseModel,
		    GPTNeoXJapanesePreTrainedModel,
		)


else:
	import sys

	_UpperCAmelCase				: int							    =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

