




'''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 			SCREAMING_SNAKE_CASE_			(							__A    :			str	,					__A    :			Union[str, Any]	,					__A    :			Dict	,					__A    :			List[Any]	,					__A    :			Tuple=True	,					__A    :			Any="pt"  )			->			str:
   _SCREAMING_SNAKE_CASE	=				{"add_prefix_space": True} if isinstance(__A	,					__A  ) and not line.startswith(" "  ) else {}
   _SCREAMING_SNAKE_CASE	=				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 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[int]	,					__A    :			List[Any]	,					__A    :			Union[str, Any]=None	,					)			->			Optional[int]:
   _SCREAMING_SNAKE_CASE	=				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 		lowercase_   (			A       ):


   """simple docstring"""

   def __init__(   self :     List[str]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     Dict="train"  ,				__lowerCamelCase :     List[str]=None  ,				__lowerCamelCase :     str=None  ,				__lowerCamelCase :     Optional[Any]=None  ,				__lowerCamelCase :     List[str]=""  ,				):

      """simple docstring"""
      super().__init__()
      _SCREAMING_SNAKE_CASE	=				Path(__lowerCamelCase   ).joinpath(type_path + ".source"   )
      _SCREAMING_SNAKE_CASE	=				Path(__lowerCamelCase   ).joinpath(type_path + ".target"   )
      _SCREAMING_SNAKE_CASE	=				self.get_char_lens(self.src_file   )
      _SCREAMING_SNAKE_CASE	=				max_source_length
      _SCREAMING_SNAKE_CASE	=				max_target_length
      assert min(self.src_lens   ) > 0, F"""found empty line in {self.src_file}"""
      _SCREAMING_SNAKE_CASE	=				tokenizer
      _SCREAMING_SNAKE_CASE	=				prefix
      if n_obs is not None:
         _SCREAMING_SNAKE_CASE	=				self.src_lens[:n_obs]
      _SCREAMING_SNAKE_CASE	=				src_lang
      _SCREAMING_SNAKE_CASE	=				tgt_lang



   def __len__(   self :     Union[str, Any]   ):

      """simple docstring"""
      return len(self.src_lens   )



   def __getitem__(   self :     Tuple  ,				__lowerCamelCase :     int   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				index + 1  # linecache starts at 1
      _SCREAMING_SNAKE_CASE	=				self.prefix + linecache.getline(str(self.src_file   )  ,				__lowerCamelCase   ).rstrip("\n"   )
      _SCREAMING_SNAKE_CASE	=				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
      _SCREAMING_SNAKE_CASE	=				(
          self.tokenizer.question_encoder if isinstance(self.tokenizer  ,				__lowerCamelCase   ) else self.tokenizer
      )
      _SCREAMING_SNAKE_CASE	=				self.tokenizer.generator if isinstance(self.tokenizer  ,				__lowerCamelCase   ) else self.tokenizer

      _SCREAMING_SNAKE_CASE	=				encode_line(__lowerCamelCase  ,				__lowerCamelCase  ,				self.max_source_length  ,				"right"   )
      _SCREAMING_SNAKE_CASE	=				encode_line(__lowerCamelCase  ,				__lowerCamelCase  ,				self.max_target_length  ,				"right"   )

      _SCREAMING_SNAKE_CASE	=				source_inputs["input_ids"].squeeze()
      _SCREAMING_SNAKE_CASE	=				target_inputs["input_ids"].squeeze()
      _SCREAMING_SNAKE_CASE	=				source_inputs["attention_mask"].squeeze()
      return {
          "input_ids": source_ids,
          "attention_mask": src_mask,
          "decoder_input_ids": target_ids,
      }



   @staticmethod
   def 							lowerCAmelCase_      (   __lowerCamelCase :     str   ):

      """simple docstring"""
      return [len(__lowerCamelCase   ) for x in Path(__lowerCamelCase   ).open().readlines()]



   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				torch.stack([x["input_ids"] for x in batch]   )
      _SCREAMING_SNAKE_CASE	=				torch.stack([x["attention_mask"] for x in batch]   )
      _SCREAMING_SNAKE_CASE	=				torch.stack([x["decoder_input_ids"] for x in batch]   )
      _SCREAMING_SNAKE_CASE	=				(
          self.tokenizer.generator.pad_token_id
          if isinstance(self.tokenizer  ,				__lowerCamelCase   )
          else self.tokenizer.pad_token_id
      )
      _SCREAMING_SNAKE_CASE	=				(
          self.tokenizer.question_encoder.pad_token_id
          if isinstance(self.tokenizer  ,				__lowerCamelCase   )
          else self.tokenizer.pad_token_id
      )
      _SCREAMING_SNAKE_CASE	=				trim_batch(__lowerCamelCase  ,				__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				trim_batch(__lowerCamelCase  ,				__lowerCamelCase  ,				attention_mask=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				{
          "input_ids": source_ids,
          "attention_mask": source_mask,
          "decoder_input_ids": y,
      }
      return batch


lowerCamelCase_			=						getLogger(__name__)
def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[List]  )			->			Any:
   return list(itertools.chain.from_iterable(__A  )  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			str  )			->			None:
   _SCREAMING_SNAKE_CASE	=				get_git_info()
   save_json(__A	,					os.path.join(__A	,					"git_log.json"  )  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[Any]	,					__A    :			Optional[Any]	,					__A    :			Optional[int]=4	,					**__A    :			Optional[int]  )			->			List[Any]:
   with open(__A	,					"w"  ) as f:
      json.dump(__A	,					__A	,					indent=__A	,					**__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Any  )			->			List[Any]:
   with open(__A  ) as f:
      return json.load(__A  )
def 			SCREAMING_SNAKE_CASE_			(							)			->			str:
   _SCREAMING_SNAKE_CASE	=				git.Repo(search_parent_directories=__A  )
   _SCREAMING_SNAKE_CASE	=				{
       "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 			SCREAMING_SNAKE_CASE_			(							__A    :			Callable	,					__A    :			Iterable  )			->			List:
   return list(map(__A	,					__A  )  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[Any]	,					__A    :			str  )			->			Any:
   with open(__A	,					"wb"  ) as f:
      return pickle.dump(__A	,					__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[Any]  )			->			Tuple:

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

   def white_space_fix(__A    :			str  ):
      return " ".join(text.split()  )

   def remove_punc(__A    :			List[str]  ):
      _SCREAMING_SNAKE_CASE	=				set(string.punctuation  )
      return "".join(ch for ch in text if ch not in exclude  )

   def lower(__A    :			Dict  ):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(__A  )  )  )  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Any	,					__A    :			Optional[int]  )			->			Any:
   _SCREAMING_SNAKE_CASE	=				normalize_answer(__A  ).split()
   _SCREAMING_SNAKE_CASE	=				normalize_answer(__A  ).split()
   _SCREAMING_SNAKE_CASE	=				Counter(__A  ) & Counter(__A  )
   _SCREAMING_SNAKE_CASE	=				sum(common.values()  )
   if num_same == 0:
      return 0
   _SCREAMING_SNAKE_CASE	=				1.0 * num_same / len(__A  )
   _SCREAMING_SNAKE_CASE	=				1.0 * num_same / len(__A  )
   _SCREAMING_SNAKE_CASE	=				(2 * precision * recall) / (precision + recall)
   return fa
def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[str]	,					__A    :			List[Any]  )			->			Optional[Any]:
   return normalize_answer(__A  ) == normalize_answer(__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[str]	,					__A    :			List[str]  )			->			Dict:
   assert len(__A  ) == len(__A  )
   _SCREAMING_SNAKE_CASE	=				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 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[int]  )			->			str:
   return model_prefix.startswith("rag"  )






def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[Any]	,					__A    :			Optional[Any]	,					__A    :			List[Any]  )			->			Union[str, Any]:
   _SCREAMING_SNAKE_CASE	=				{p: p for p in extra_params}
   # T5 models don't have `dropout` param, they have `dropout_rate` instead
   _SCREAMING_SNAKE_CASE	=				"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
         _SCREAMING_SNAKE_CASE	=				p if hasattr(__A	,					__A  ) else equivalent_param[p]
         setattr(__A	,					__A	,					getattr(__A	,					__A  )  )
         delattr(__A	,					__A  )
   return hparams, config







'''simple docstring'''



import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


lowerCamelCase_			=						'<<<<<<< This should probably be modified because it mentions: '

lowerCamelCase_			=						'=======\n>>>>>>>\n'

lowerCamelCase_			=						[
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

lowerCamelCase_			=						[
    # (pattern, replacement)
    # Order is important here for some replacements
    (r'tfds\.core', r'datasets'),
    (r'tf\.io\.gfile\.GFile', r'open'),
    (r'tf\.([\w\d]+)', r'datasets.Value(\'\1\')'),
    (r'tfds\.features\.Text\(\)', r'datasets.Value(\'string\')'),
    (r'tfds\.features\.Text\(', r'datasets.Value(\'string\'),'),
    (r'features\s*=\s*tfds.features.FeaturesDict\(', r'features=datasets.Features('),
    (r'tfds\.features\.FeaturesDict\(', r'dict('),
    (r'The TensorFlow Datasets Authors', r'The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'),
    (r'tfds\.', r'datasets.'),
    (r'dl_manager\.manual_dir', r'self.config.data_dir'),
    (r'self\.builder_config', r'self.config'),
]
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Namespace  )			->			List[Any]:
   return ConvertCommand(args.tfds_path	,					args.datasets_directory  )







class 		lowercase_   (			A       ):


   """simple docstring"""

   @staticmethod
   def 							lowerCAmelCase_      (   __lowerCamelCase :     ArgumentParser   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				parser.add_parser(
          "convert"  ,				help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."  ,				)
      train_parser.add_argument(
          "--tfds_path"  ,				type=__lowerCamelCase  ,				required=__lowerCamelCase  ,				help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."  ,				)
      train_parser.add_argument(
          "--datasets_directory"  ,				type=__lowerCamelCase  ,				required=__lowerCamelCase  ,				help="Path to the HuggingFace Datasets folder."   )
      train_parser.set_defaults(func=__lowerCamelCase   )



   def __init__(   self :     Dict  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     str  ,				*__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				get_logger("datasets-cli/converting"   )

      _SCREAMING_SNAKE_CASE	=				tfds_path
      _SCREAMING_SNAKE_CASE	=				datasets_directory



   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      if os.path.isdir(self._tfds_path   ):
         _SCREAMING_SNAKE_CASE	=				os.path.abspath(self._tfds_path   )
      elif os.path.isfile(self._tfds_path   ):
         _SCREAMING_SNAKE_CASE	=				os.path.dirname(self._tfds_path   )
      else:
         raise ValueError("--tfds_path is neither a directory nor a file. Please check path."   )

      _SCREAMING_SNAKE_CASE	=				os.path.abspath(self._datasets_directory   )

      self._logger.info(F"""Converting datasets from {abs_tfds_path} to {abs_datasets_path}"""   )

      _SCREAMING_SNAKE_CASE	=				[]
      _SCREAMING_SNAKE_CASE	=				[]
      _SCREAMING_SNAKE_CASE	=				{}

      if os.path.isdir(self._tfds_path   ):
         _SCREAMING_SNAKE_CASE	=				os.listdir(__lowerCamelCase   )
      else:
         _SCREAMING_SNAKE_CASE	=				[os.path.basename(self._tfds_path   )]

      for f_name in file_names:
         self._logger.info(F"""Looking at file {f_name}"""   )
         _SCREAMING_SNAKE_CASE	=				os.path.join(__lowerCamelCase  ,				__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				os.path.join(__lowerCamelCase  ,				__lowerCamelCase   )

         if not os.path.isfile(__lowerCamelCase   ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
            self._logger.info("Skipping file"   )
            continue

         with open(__lowerCamelCase  ,				encoding="utf-8"   ) as f:
            _SCREAMING_SNAKE_CASE	=				f.readlines()

         _SCREAMING_SNAKE_CASE	=				[]
         _SCREAMING_SNAKE_CASE	=				False
         _SCREAMING_SNAKE_CASE	=				False
         _SCREAMING_SNAKE_CASE	=				[]
         for line in lines:
            _SCREAMING_SNAKE_CASE	=				line

            # Convert imports
            if "import tensorflow.compat.v2 as tf" in out_line:
               continue
            elif "@tfds.core" in out_line:
               continue
            elif "builder=self" in out_line:
               continue
            elif "import tensorflow_datasets.public_api as tfds" in out_line:
               _SCREAMING_SNAKE_CASE	=				"import datasets\n"
            elif "import tensorflow" in out_line:
               # order is important here
               _SCREAMING_SNAKE_CASE	=				""
               continue
            elif "from absl import logging" in out_line:
               _SCREAMING_SNAKE_CASE	=				"from datasets import logging\n"
            elif "getLogger" in out_line:
               _SCREAMING_SNAKE_CASE	=				out_line.replace("getLogger"  ,				"get_logger"   )
            elif any(expression in out_line for expression in TO_HIGHLIGHT   ):
               _SCREAMING_SNAKE_CASE	=				True
               _SCREAMING_SNAKE_CASE	=				list(filter(lambda __lowerCamelCase   : e in out_line  ,				__lowerCamelCase   )   )
               out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(__lowerCamelCase   ) + "\n"   )
               out_lines.append(__lowerCamelCase   )
               out_lines.append(__lowerCamelCase   )
               continue
            else:
               for pattern, replacement in TO_CONVERT:
                  _SCREAMING_SNAKE_CASE	=				re.sub(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase   )

                # Take care of saving utilities (to later move them together with main script)
            if "tensorflow_datasets" in out_line:
               _SCREAMING_SNAKE_CASE	=				re.match(r"from\stensorflow_datasets.*import\s([^\.\r\n]+)"  ,				__lowerCamelCase   )
               tfds_imports.extend(imp.strip() for imp in match.group(1   ).split(","   )   )
               _SCREAMING_SNAKE_CASE	=				"from . import " + match.group(1   )

            # Check we have not forget anything
            if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
               raise ValueError(F"""Error converting {out_line.strip()}"""   )

            if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
               _SCREAMING_SNAKE_CASE	=				True
            out_lines.append(__lowerCamelCase   )

         if is_builder or "wmt" in f_name:
            # We create a new directory for each dataset
            _SCREAMING_SNAKE_CASE	=				f_name.replace(".py"  ,				""   )
            _SCREAMING_SNAKE_CASE	=				os.path.join(__lowerCamelCase  ,				__lowerCamelCase   )
            _SCREAMING_SNAKE_CASE	=				os.path.join(__lowerCamelCase  ,				__lowerCamelCase   )
            os.makedirs(__lowerCamelCase  ,				exist_ok=__lowerCamelCase   )
            self._logger.info(F"""Adding directory {output_dir}"""   )
            imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}   )
         else:
            # Utilities will be moved at the end
            utils_files.append(__lowerCamelCase   )

         if needs_manual_update:
            with_manual_update.append(__lowerCamelCase   )

         with open(__lowerCamelCase  ,				"w"  ,				encoding="utf-8"   ) as f:
            f.writelines(__lowerCamelCase   )
         self._logger.info(F"""Converted in {output_file}"""   )

      for utils_file in utils_files:
         try:
            _SCREAMING_SNAKE_CASE	=				os.path.basename(__lowerCamelCase   )
            _SCREAMING_SNAKE_CASE	=				imports_to_builder_map[f_name.replace(".py"  ,				""   )]
            self._logger.info(F"""Moving {dest_folder} to {utils_file}"""   )
            shutil.copy(__lowerCamelCase  ,				__lowerCamelCase   )
         except KeyError:
            self._logger.error(F"""Cannot find destination folder for {utils_file}. Please copy manually."""   )

      if with_manual_update:
         for file_path in with_manual_update:
            self._logger.warning(
                F"""You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'."""   )

