


"""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


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

__snake_case   			=    """=======
>>>>>>>
"""

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

__snake_case   			=    [
    # (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						(						lowercase  :  Namespace )	->						Tuple:


    """simple docstring"""





    return ConvertCommand(args.tfds_path						,							args.datasets_directory )







class _lowerCAmelCase				(      snake_case_	):







   @staticmethod
   def  lowerCamelCase		(	UpperCamelCase__						)	->		Any:

       '''simple docstring'''




       snake_case    :      Dict      					=    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						,	UpperCamelCase__						,	UpperCamelCase__						,	*UpperCamelCase__						)	->		Tuple:

       '''simple docstring'''




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

       snake_case    :      int      					=    tfds_path
       snake_case    :      Dict      					=    datasets_directory







   def  lowerCamelCase		(	self						)	->		Optional[Any]:

       '''simple docstring'''




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

       snake_case    :      Tuple      					=    os.path.abspath(self._datasets_directory						)

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

       snake_case    :      List[str]      					=    []
       snake_case    :      List[str]      					=    []
       snake_case    :      int      					=    {}

       if os.path.isdir(self._tfds_path						):
           snake_case    :      Optional[int]      					=    os.listdir(UpperCamelCase__						)
       else:
           snake_case    :      Tuple      					=    [os.path.basename(self._tfds_path						)]

       for f_name in file_names:
           self._logger.info(F'Looking at file {f_name}'						)
           snake_case    :      List[str]      					=    os.path.join(UpperCamelCase__						,	UpperCamelCase__						)
           snake_case    :      Optional[int]      					=    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:
               snake_case    :      str      					=    f.readlines()

           snake_case    :      List[str]      					=    []
           snake_case    :      Any      					=    False
           snake_case    :      int      					=    False
           snake_case    :      Tuple      					=    []
           for line in lines:
               snake_case    :      Optional[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:
                   snake_case    :      Optional[Any]      					=    "import datasets\n"
               elif "import tensorflow" in out_line:
                   # order is important here
                   snake_case    :      int      					=    ""
                   continue
               elif "from absl import logging" in out_line:
                   snake_case    :      Optional[Any]      					=    "from datasets import logging\n"
               elif "getLogger" in out_line:
                   snake_case    :      Dict      					=    out_line.replace("getLogger"						,	"get_logger"						)
               elif any(expression in out_line for expression in TO_HIGHLIGHT						):
                   snake_case    :      List[Any]      					=    True
                   snake_case    :      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:
                       snake_case    :      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:
                   snake_case    :      Dict      					=    re.match(r"from\stensorflow_datasets.*import\s([^\.\r\n]+)"						,	UpperCamelCase__						)
                   tfds_imports.extend(imp.strip() for imp in match.group(1						).split(","						)						)
                   snake_case    :      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:
                   snake_case    :      str      					=    True
               out_lines.append(UpperCamelCase__						)

           if is_builder or "wmt" in f_name:
               # We create a new directory for each dataset
               snake_case    :      Any      					=    f_name.replace(".py"						,	""						)
               snake_case    :      str      					=    os.path.join(UpperCamelCase__						,	UpperCamelCase__						)
               snake_case    :      List[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:
               snake_case    :      Dict      					=    os.path.basename(UpperCamelCase__						)
               snake_case    :      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 argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


__snake_case   			=    logging.getLogger(__name__)







class _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       Optional[int]							  =  '''token-classification'''







   def __init__(	self						,	UpperCamelCase__						)	->		List[Any]:

       '''simple docstring'''




       if type(UpperCamelCase__						) == dict:
           snake_case    :      Optional[int]      					=    Namespace(**UpperCamelCase__						)
       snake_case    :      Optional[int]      					=    import_module("tasks"						)
       try:
           snake_case    :      Optional[int]      					=    getattr(UpperCamelCase__						,	hparams.task_type						)
           snake_case    :      TokenClassificationTask      					=    token_classification_task_clazz()
       except AttributeError:
           raise ValueError(
               F'Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
               F'Available tasks classes are: {TokenClassificationTask.__subclasses__()}'						)
       snake_case    :      str      					=    self.token_classification_task.get_labels(hparams.labels						)
       snake_case    :      Union[str, Any]      					=    CrossEntropyLoss().ignore_index
       super().__init__(UpperCamelCase__						,	len(self.labels						)						,	self.mode						)







   def  lowerCamelCase		(	self						,	**UpperCamelCase__						)	->		Any:

       '''simple docstring'''




       return self.model(**UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						)	->		Tuple:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
       if self.config.model_type != "distilbert":
           snake_case    :      Optional[int]      					=    (
               batch[2] if self.config.model_type in ["bert", "xlnet"] else None
           )  # XLM and RoBERTa don"t use token_type_ids

       snake_case    :      List[Any]      					=    self(**UpperCamelCase__						)
       snake_case    :      Union[str, Any]      					=    outputs[0]
       # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
       return {"loss": loss}







   def  lowerCamelCase		(	self						)	->		Optional[int]:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    self.hparams
       for mode in ["train", "dev", "test"]:
           snake_case    :      Optional[Any]      					=    self._feature_file(UpperCamelCase__						)
           if os.path.exists(UpperCamelCase__						) and not args.overwrite_cache:
               logger.info("Loading features from cached file %s"						,	UpperCamelCase__						)
               snake_case    :      List[str]      					=    torch.load(UpperCamelCase__						)
           else:
               logger.info("Creating features from dataset file at %s"						,	args.data_dir						)
               snake_case    :      Optional[Any]      					=    self.token_classification_task.read_examples_from_file(args.data_dir						,	UpperCamelCase__						)
               snake_case    :      Dict      					=    self.token_classification_task.convert_examples_to_features(
                   UpperCamelCase__						,	self.labels						,	args.max_seq_length						,	self.tokenizer						,	cls_token_at_end=bool(self.config.model_type in ["xlnet"]						)						,	cls_token=self.tokenizer.cls_token						,	cls_token_segment_id=2 if self.config.model_type in ["xlnet"] else 0						,	sep_token=self.tokenizer.sep_token						,	sep_token_extra=UpperCamelCase__						,	pad_on_left=bool(self.config.model_type in ["xlnet"]						)						,	pad_token=self.tokenizer.pad_token_id						,	pad_token_segment_id=self.tokenizer.pad_token_type_id						,	pad_token_label_id=self.pad_token_label_id						,	)
               logger.info("Saving features into cached file %s"						,	UpperCamelCase__						)
               torch.save(UpperCamelCase__						,	UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__ = False						)	->		DataLoader:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    self._feature_file(UpperCamelCase__						)
       logger.info("Loading features from cached file %s"						,	UpperCamelCase__						)
       snake_case    :      Any      					=    torch.load(UpperCamelCase__						)
       snake_case    :      Tuple      					=    torch.tensor([f.input_ids for f in features]						,	dtype=torch.long						)
       snake_case    :      List[str]      					=    torch.tensor([f.attention_mask for f in features]						,	dtype=torch.long						)
       if features[0].token_type_ids is not None:
           snake_case    :      Tuple      					=    torch.tensor([f.token_type_ids for f in features]						,	dtype=torch.long						)
       else:
           snake_case    :      Union[str, Any]      					=    torch.tensor([0 for f in features]						,	dtype=torch.long						)
           # HACK(we will not use this anymore soon)
       snake_case    :      Optional[int]      					=    torch.tensor([f.label_ids for f in features]						,	dtype=torch.long						)
       return DataLoader(
           TensorDataset(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)						,	batch_size=UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						)	->		Optional[Any]:

       '''simple docstring'''




       """Compute validation""" ""
       snake_case    :      Any      					=    {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
       if self.config.model_type != "distilbert":
           snake_case    :      Optional[int]      					=    (
               batch[2] if self.config.model_type in ["bert", "xlnet"] else None
           )  # XLM and RoBERTa don"t use token_type_ids
       snake_case    :      Optional[int]      					=    self(**UpperCamelCase__						)
       snake_case					,snake_case    :      str      					=    outputs[:2]
       snake_case    :      Optional[int]      					=    logits.detach().cpu().numpy()
       snake_case    :      str      					=    inputs["labels"].detach().cpu().numpy()
       return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Any:

       '''simple docstring'''




       snake_case    :      Dict      					=    torch.stack([x["val_loss"] for x in outputs]						).mean()
       snake_case    :      List[str]      					=    np.concatenate([x["pred"] for x in outputs]						,	axis=0						)
       snake_case    :      Any      					=    np.argmax(UpperCamelCase__						,	axis=2						)
       snake_case    :      Dict      					=    np.concatenate([x["target"] for x in outputs]						,	axis=0						)

       snake_case    :      Tuple      					=    dict(enumerate(self.labels						)						)
       snake_case    :      str      					=    [[] for _ in range(out_label_ids.shape[0]						)]
       snake_case    :      List[Any]      					=    [[] for _ in range(out_label_ids.shape[0]						)]

       for i in range(out_label_ids.shape[0]						):
           for j in range(out_label_ids.shape[1]						):
               if out_label_ids[i, j] != self.pad_token_label_id:
                   out_label_list[i].append(label_map[out_label_ids[i][j]]						)
                   preds_list[i].append(label_map[preds[i][j]]						)

       snake_case    :      Union[str, Any]      					=    {
           "val_loss": val_loss_mean,
           "accuracy_score": accuracy_score(UpperCamelCase__						,	UpperCamelCase__						),
           "precision": precision_score(UpperCamelCase__						,	UpperCamelCase__						),
           "recall": recall_score(UpperCamelCase__						,	UpperCamelCase__						),
           "f1": fa_score(UpperCamelCase__						,	UpperCamelCase__						),
       }

       snake_case    :      int      					=    dict(results.items()						)
       snake_case    :      Union[str, Any]      					=    results
       return ret, preds_list, out_label_list







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		List[str]:

       '''simple docstring'''




       snake_case					,snake_case					,snake_case    :      Optional[Any]      					=    self._eval_end(UpperCamelCase__						)
       snake_case    :      Tuple      					=    ret["log"]
       return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Any:

       '''simple docstring'''




       snake_case					,snake_case					,snake_case    :      List[Any]      					=    self._eval_end(UpperCamelCase__						)

       # Converting to the dict required by pl
       # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
       # pytorch_lightning/trainer/logging.py#L139
       snake_case    :      Optional[Any]      					=    ret["log"]
       # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
       return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







   @staticmethod
   def  lowerCamelCase		(	UpperCamelCase__						,	UpperCamelCase__						)	->		str:

       '''simple docstring'''




       BaseTransformer.add_model_specific_args(UpperCamelCase__						,	UpperCamelCase__						)
       parser.add_argument(
           "--task_type"						,	default="NER"						,	type=UpperCamelCase__						,	help="Task type to fine tune in training (e.g. NER, POS, etc)"						)
       parser.add_argument(
           "--max_seq_length"						,	default=128						,	type=UpperCamelCase__						,	help=(
               "The maximum total input sequence length after tokenization. Sequences longer "
               "than this will be truncated, sequences shorter will be padded."
           )						,	)

       parser.add_argument(
           "--labels"						,	default=""						,	type=UpperCamelCase__						,	help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."						,	)
       parser.add_argument(
           "--gpus"						,	default=0						,	type=UpperCamelCase__						,	help="The number of GPUs allocated for this, it is by default 0 meaning none"						,	)

       parser.add_argument(
           "--overwrite_cache"						,	action="store_true"						,	help="Overwrite the cached training and evaluation sets"						)

       return parser


if __name__ == "__main__":
  __snake_case   			=    argparse.ArgumentParser()
  add_generic_args(parser, os.getcwd())
  __snake_case   			=    NERTransformer.add_model_specific_args(parser, os.getcwd())
  __snake_case   			=    parser.parse_args()
  __snake_case   			=    NERTransformer(args)
  __snake_case   			=    generic_train(model, args)

  if args.do_predict:
    # See https://github.com/huggingface/transformers/issues/3159
    # pl use this default format to create a checkpoint:
    # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
    # /pytorch_lightning/callbacks/model_checkpoint.py#L322
    __snake_case   			=    sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
    __snake_case   			=    model.load_from_checkpoint(checkpoints[-1])
    trainer.test(model)





