





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

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


__A									=				"<<<<<<< This should probably be modified because it mentions: "

__A									=				"=======\n>>>>>>>\n"

__A									=				[
    "TextEncoderConfig",
    "ByteTextEncoder",
    "SubwordTextEncoder",
    "encoder_config",
    "maybe_build_from_corpus",
    "manual_dir",
]

__A									=				[
    # (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      lowerCAmelCase_ (       __a		)     ->		Optional[Any]:







  """simple docstring"""





  return ConvertCommand(args.tfds_path	,    args.datasets_directory		)






class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

 @staticmethod
 def 							SCREAMING_SNAKE_CASE_					(UpperCAmelCase_				:   ArgumentParser)       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      List[str]			  =parser.add_parser(
       "convert"       , help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."       , )
   train_parser.add_argument(
       "--tfds_path"       , type=UpperCAmelCase_       , required=UpperCAmelCase_       , help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."       , )
   train_parser.add_argument(
       "--datasets_directory"       , type=UpperCAmelCase_       , required=UpperCAmelCase_       , help="Path to the HuggingFace Datasets folder.")
   train_parser.set_defaults(func=UpperCAmelCase_)

 def __init__(self				:   Any       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   str       , *UpperCAmelCase_				:   List[str])       ->int:



   '''simple docstring'''



   lowerCamelCase__:      str			  =get_logger("datasets-cli/converting")

   lowerCamelCase__:      Tuple			  =tfds_path
   lowerCamelCase__:      Union[str, Any]			  =datasets_directory







 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->Union[str, Any]:



   '''simple docstring'''



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

   lowerCamelCase__:      Any			  =os.path.abspath(self._datasets_directory)

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

   lowerCamelCase__:      List[str]			  =[]
   lowerCamelCase__:      Optional[int]			  =[]
   lowerCamelCase__:      int			  ={}

   if os.path.isdir(self._tfds_path):
     lowerCamelCase__:      Tuple			  =os.listdir(UpperCAmelCase_)
   else:
     lowerCamelCase__:      int			  =[os.path.basename(self._tfds_path)]

   for f_name in file_names:
     self._logger.info(F"""Looking at file {f_name}""")
     lowerCamelCase__:      Tuple			  =os.path.join(UpperCAmelCase_       , UpperCAmelCase_)
     lowerCamelCase__:      List[str]			  =os.path.join(UpperCAmelCase_       , UpperCAmelCase_)

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

     with open(UpperCAmelCase_       , encoding="utf-8") as f:
       lowerCamelCase__:      Union[str, Any]			  =f.readlines()

     lowerCamelCase__:      int			  =[]
     lowerCamelCase__:      Any			  =False
     lowerCamelCase__:      int			  =False
     lowerCamelCase__:      int			  =[]
     for line in lines:
       lowerCamelCase__:      List[Any]			  =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:
         lowerCamelCase__:      List[Any]			  ="import datasets\n"
       elif "import tensorflow" in out_line:
         # order is important here
         lowerCamelCase__:      Union[str, Any]			  =""
         continue
       elif "from absl import logging" in out_line:
         lowerCamelCase__:      Tuple			  ="from datasets import logging\n"
       elif "getLogger" in out_line:
         lowerCamelCase__:      List[str]			  =out_line.replace("getLogger"       , "get_logger")
       elif any(expression in out_line for expression in TO_HIGHLIGHT):
         lowerCamelCase__:      str			  =True
         lowerCamelCase__:      List[Any]			  =list(filter(lambda UpperCAmelCase_: e in out_line       , UpperCAmelCase_))
         out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(UpperCAmelCase_) + "\n")
         out_lines.append(UpperCAmelCase_)
         out_lines.append(UpperCAmelCase_)
         continue
       else:
         for pattern, replacement in TO_CONVERT:
           lowerCamelCase__:      Dict			  =re.sub(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_)

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

     if is_builder or "wmt" in f_name:
       # We create a new directory for each dataset
       lowerCamelCase__:      Tuple			  =f_name.replace(".py"       , "")
       lowerCamelCase__:      Optional[int]			  =os.path.join(UpperCAmelCase_       , UpperCAmelCase_)
       lowerCamelCase__:      Optional[Any]			  =os.path.join(UpperCAmelCase_       , UpperCAmelCase_)
       os.makedirs(UpperCAmelCase_       , exist_ok=UpperCAmelCase_)
       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(UpperCAmelCase_)

     if needs_manual_update:
       with_manual_update.append(UpperCAmelCase_)

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

   for utils_file in utils_files:
     try:
       lowerCamelCase__:      Union[str, Any]			  =os.path.basename(UpperCAmelCase_)
       lowerCamelCase__:      Optional[Any]			  =imports_to_builder_map[f_name.replace(".py"       , "")]
       self._logger.info(F"""Moving {dest_folder} to {utils_file}""")
       shutil.copy(UpperCAmelCase_       , UpperCAmelCase_)
     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'.""")








'''simple docstring'''

import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
  import torch






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""char"""
 __lowercase   		=				"""bpe"""
 __lowercase   		=				"""wp"""

__lowerCAmelCase							:	Union[str, Any]			=(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				["""image_processor""", """char_tokenizer"""]
 __lowercase   		=				"""ViTImageProcessor"""
 __lowercase   		=				"""MgpstrTokenizer"""
 def __init__(				self      :int						,       lowercase_      :int=None						,       lowercase_      :List[str]=None						,       **lowercase_      :List[Any]							)->							Optional[Any]:
     A__				=			None
     if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."						,       lowercase_						,       )
         A__				=			kwargs.pop("feature_extractor"							)

     A__				=			image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."							)
     if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."							)

     A__				=			tokenizer
     A__				=			AutoTokenizer.from_pretrained("gpt2"							)
     A__				=			AutoTokenizer.from_pretrained("bert-base-uncased"							)

     super().__init__(lowercase_						,       lowercase_							)
 def __call__(				self      :Optional[Any]						,       lowercase_      :Any=None						,       lowercase_      :Tuple=None						,       lowercase_      :List[str]=None						,       **lowercase_      :Union[str, Any]							)->							Optional[Any]:
     if images is None and text is None:
         raise ValueError("You need to specify either an `images` or `text` input to process."							)

     if images is not None:
         A__				=			self.image_processor(lowercase_						,       return_tensors=lowercase_						,       **lowercase_							)
     if text is not None:
         A__				=			self.char_tokenizer(lowercase_						,       return_tensors=lowercase_						,       **lowercase_							)

     if text is None:
         return inputs
     elif images is None:
         return encodings
     else:
         A__				=			encodings["input_ids"]
         return inputs
 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :int							)->							int:
     A__,	A__,	A__				=			sequences
     A__				=			char_preds.size(0							)

     A__,	A__				=			self._decode_helper(lowercase_						,       "char"							)
     A__,	A__				=			self._decode_helper(lowercase_						,       "bpe"							)
     A__,	A__				=			self._decode_helper(lowercase_						,       "wp"							)

     A__				=			[]
     A__				=			[]
     for i in range(lowercase_							):
         A__				=			[char_scores[i], bpe_scores[i], wp_scores[i]]
         A__				=			[char_strs[i], bpe_strs[i], wp_strs[i]]
         A__				=			scores.index(max(lowercase_							)							)
         final_strs.append(strs[max_score_index]							)
         final_scores.append(scores[max_score_index]							)

     A__				=			{}
     A__				=			final_strs
     A__				=			final_scores
     A__				=			char_strs
     A__				=			bpe_strs
     A__				=			wp_strs
     return out
 def      UpperCAmelCase_							(				self      :Union[str, Any]						,       lowercase_      :List[str]						,       lowercase_      :str							)->							Optional[Any]:
     if format == DecodeType.CHARACTER:
         A__				=			self.char_decode
         A__				=			1
         A__				=			"[s]"
     elif format == DecodeType.BPE:
         A__				=			self.bpe_decode
         A__				=			2
         A__				=			"#"
     elif format == DecodeType.WORDPIECE:
         A__				=			self.wp_decode
         A__				=			1_02
         A__				=			"[SEP]"
     else:
         raise ValueError(F"Format {format} is not supported."							)

     A__,	A__				=			[], []
     A__				=			pred_logits.size(0							)
     A__				=			pred_logits.size(1							)
     A__,	A__				=			pred_logits.topk(1						,       dim=-1						,       largest=lowercase_						,       sorted=lowercase_							)
     A__				=			preds_index.view(-1						,       lowercase_							)[:, 1:]
     A__				=			decoder(lowercase_							)
     A__,	A__				=			torch.nn.functional.softmax(lowercase_						,       dim=2							).max(dim=2							)
     A__				=			preds_max_prob[:, 1:]

     for index in range(lowercase_							):
         A__				=			preds_str[index].find(lowercase_							)
         A__				=			preds_str[index][:pred_eos]
         A__				=			preds_index[index].cpu().tolist()
         A__				=			pred_index.index(lowercase_							) if eos_token in pred_index else -1
         A__				=			preds_max_prob[index][: pred_eos_index + 1]
         A__				=			pred_max_prob.cumprod(dim=0							)[-1] if pred_max_prob.nelement() != 0 else 0.0
         dec_strs.append(lowercase_							)
         conf_scores.append(lowercase_							)

     return dec_strs, conf_scores
 def      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :Optional[Any]							)->							int:
     A__				=			[seq.replace(" "						,       ""							) for seq in self.char_tokenizer.batch_decode(lowercase_							)]
     return decode_strs
 def      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :Optional[Any]							)->							List[str]:
     return self.bpe_tokenizer.batch_decode(lowercase_							)



 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :List[str]							)->							Union[str, Any]:
     A__				=			[seq.replace(" "						,       ""							) for seq in self.wp_tokenizer.batch_decode(lowercase_							)]
     return decode_strs



