
import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


lowercase_											=	logging.getLogger(__name__)





@dataclass
class   __UpperCamelCase				:






     """simple docstring"""







     lowerCAmelCase_     =	field(
         metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}   )
     lowerCAmelCase_     =	field(
         default=lowerCAmelCase__				,		metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}   )
     lowerCAmelCase_     =	field(
         default='''NER'''				,		metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}   )
     lowerCAmelCase_     =	field(
         default=lowerCAmelCase__				,		metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}   )
     lowerCAmelCase_     =	field(default=lowerCAmelCase__				,		metadata={'''help''': '''Set this flag to use fast tokenization.'''}   )
     # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
     # or just modify its tokenizer_config.json.
     lowerCAmelCase_     =	field(
         default=lowerCAmelCase__				,		metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}				,		)






@dataclass
class   __UpperCamelCase				:






     """simple docstring"""







     lowerCAmelCase_     =	field(
         metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}   )
     lowerCAmelCase_     =	field(
         metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}   )
     lowerCAmelCase_     =	field(
         default=1_28				,		metadata={
             '''help''': (
                 '''The maximum total input sequence length after tokenization. Sequences longer '''
                 '''than this will be truncated, sequences shorter will be padded.'''
             )
         }				,		)
     lowerCAmelCase_     =	field(
         default=lowerCAmelCase__				,		metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}   )






def     a__			(    ):





  """simple docstring"""
  # See all possible arguments in src/transformers/training_args.py
  # or by passing the --help flag to this script.
  # We now keep distinct sets of args, for a cleaner separation of concerns.
  __SCREAMING_SNAKE_CASE     :					str			=						HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)    )
  __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						parser.parse_args_into_dataclasses()

  if (
      os.path.exists(training_args.output_dir    )
      and os.listdir(training_args.output_dir    )
      and training_args.do_train
      and not training_args.overwrite_output_dir
  ):
    raise ValueError(
        F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
        ''' --overwrite_output_dir to overcome.'''    )

  __SCREAMING_SNAKE_CASE     :					Any			=						import_module('''tasks'''    )

  try:
    __SCREAMING_SNAKE_CASE     :					str			=						getattr(snake_case   ,	model_args.task_type    )
    __SCREAMING_SNAKE_CASE     :					TokenClassificationTask			=						token_classification_task_clazz()
  except AttributeError:
    raise ValueError(
        F'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
        F'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''    )

  # Setup logging
  logging.basicConfig(
      format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''   ,	datefmt='''%m/%d/%Y %H:%M:%S'''   ,	level=logging.INFO   ,	)
  logger.info(
      '''n_replicas: %s, distributed training: %s, 16-bits training: %s'''   ,	training_args.n_replicas   ,	bool(training_args.n_replicas > 1    )   ,	training_args.fpaa   ,	)
  logger.info('''Training/evaluation parameters %s'''   ,	snake_case    )

  # Prepare Token Classification task
  __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						token_classification_task.get_labels(data_args.labels    )
  __SCREAMING_SNAKE_CASE     :					Dict[int, str]			=						dict(enumerate(snake_case    )    )
  __SCREAMING_SNAKE_CASE     :					Tuple			=						len(snake_case    )

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  __SCREAMING_SNAKE_CASE     :					Tuple			=						AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,	num_labels=snake_case   ,	idalabel=snake_case   ,	labelaid={label: i for i, label in enumerate(snake_case    )}   ,	cache_dir=model_args.cache_dir   ,	)
  __SCREAMING_SNAKE_CASE     :					List[Any]			=						AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path   ,	cache_dir=model_args.cache_dir   ,	use_fast=model_args.use_fast   ,	)

  with training_args.strategy.scope():
    __SCREAMING_SNAKE_CASE     :					Dict			=						TFAutoModelForTokenClassification.from_pretrained(
        model_args.model_name_or_path   ,	from_pt=bool('''.bin''' in model_args.model_name_or_path    )   ,	config=snake_case   ,	cache_dir=model_args.cache_dir   ,	)

  # Get datasets
  __SCREAMING_SNAKE_CASE     :					List[Any]			=						(
      TFTokenClassificationDataset(
          token_classification_task=snake_case   ,	data_dir=data_args.data_dir   ,	tokenizer=snake_case   ,	labels=snake_case   ,	model_type=config.model_type   ,	max_seq_length=data_args.max_seq_length   ,	overwrite_cache=data_args.overwrite_cache   ,	mode=Split.train   ,	)
      if training_args.do_train
      else None
  )
  __SCREAMING_SNAKE_CASE     :					int			=						(
      TFTokenClassificationDataset(
          token_classification_task=snake_case   ,	data_dir=data_args.data_dir   ,	tokenizer=snake_case   ,	labels=snake_case   ,	model_type=config.model_type   ,	max_seq_length=data_args.max_seq_length   ,	overwrite_cache=data_args.overwrite_cache   ,	mode=Split.dev   ,	)
      if training_args.do_eval
      else None
  )

  def align_predictions(snake_case   ,	snake_case    ) -> Tuple[List[int], List[int]]:
    __SCREAMING_SNAKE_CASE     :					Optional[int]			=						np.argmax(snake_case   ,	axis=2    )
    __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					List[str]			=						preds.shape
    __SCREAMING_SNAKE_CASE     :					Optional[int]			=						[[] for _ in range(snake_case    )]
    __SCREAMING_SNAKE_CASE     :					Tuple			=						[[] for _ in range(snake_case    )]

    for i in range(snake_case    ):
      for j in range(snake_case    ):
        if label_ids[i, j] != -100:
          out_label_list[i].append(label_map[label_ids[i][j]]    )
          preds_list[i].append(label_map[preds[i][j]]    )

    return preds_list, out_label_list

  def compute_metrics(snake_case    ) -> Dict:
    __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					List[Any]			=						align_predictions(p.predictions   ,	p.label_ids    )

    return {
        "precision": precision_score(snake_case   ,	snake_case    ),
        "recall": recall_score(snake_case   ,	snake_case    ),
        "f1": fa_score(snake_case   ,	snake_case    ),
    }

  # Initialize our Trainer
  __SCREAMING_SNAKE_CASE     :					Dict			=						TFTrainer(
      model=snake_case   ,	args=snake_case   ,	train_dataset=train_dataset.get_dataset() if train_dataset else None   ,	eval_dataset=eval_dataset.get_dataset() if eval_dataset else None   ,	compute_metrics=snake_case   ,	)

  # Training
  if training_args.do_train:
    trainer.train()
    trainer.save_model()
    tokenizer.save_pretrained(training_args.output_dir    )

  # Evaluation
  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						{}
  if training_args.do_eval:
    logger.info('''*** Evaluate ***'''    )

    __SCREAMING_SNAKE_CASE     :					str			=						trainer.evaluate()
    __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						os.path.join(training_args.output_dir   ,	'''eval_results.txt'''    )

    with open(snake_case   ,	'''w'''    ) as writer:
      logger.info('''***** Eval results *****'''    )

      for key, value in result.items():
        logger.info('''  %s = %s'''   ,	snake_case   ,	snake_case    )
        writer.write('''%s = %s\n''' % (key, value)    )

      results.update(snake_case    )

    # Predict
  if training_args.do_predict:
    __SCREAMING_SNAKE_CASE     :					Tuple			=						TFTokenClassificationDataset(
        token_classification_task=snake_case   ,	data_dir=data_args.data_dir   ,	tokenizer=snake_case   ,	labels=snake_case   ,	model_type=config.model_type   ,	max_seq_length=data_args.max_seq_length   ,	overwrite_cache=data_args.overwrite_cache   ,	mode=Split.test   ,	)

    __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					List[str]			=						trainer.predict(test_dataset.get_dataset()    )
    __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Optional[Any]			=						align_predictions(snake_case   ,	snake_case    )
    __SCREAMING_SNAKE_CASE     :					str			=						classification_report(snake_case   ,	snake_case    )

    logger.info('''\n%s'''   ,	snake_case    )

    __SCREAMING_SNAKE_CASE     :					List[Any]			=						os.path.join(training_args.output_dir   ,	'''test_results.txt'''    )

    with open(snake_case   ,	'''w'''    ) as writer:
      writer.write('''%s\n''' % report    )

    # Save predictions
    __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						os.path.join(training_args.output_dir   ,	'''test_predictions.txt'''    )

    with open(snake_case   ,	'''w'''    ) as writer:
      with open(os.path.join(data_args.data_dir   ,	'''test.txt'''    )   ,	'''r'''    ) as f:
        __SCREAMING_SNAKE_CASE     :					Optional[int]			=						0

        for line in f:
          if line.startswith('''-DOCSTART-'''    ) or line == "" or line == "\n":
            writer.write(snake_case    )

            if not preds_list[example_id]:
              example_id += 1
          elif preds_list[example_id]:
            __SCREAMING_SNAKE_CASE     :					List[str]			=						line.split()[0] + ''' ''' + preds_list[example_id].pop(0    ) + '''\n'''

            writer.write(snake_case    )
          else:
            logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.'''   ,	line.split()[0]    )

  return results


if __name__ == "__main__":
   main()


import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowercase_											=	"""src/transformers"""

# Matches is_xxx_available()
lowercase_											=	re.compile(R"""is\_([a-z_]*)_available()""")
# Matches from xxx import bla
lowercase_											=	re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
lowercase_											=	re.compile(R"""^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)""")


lowercase_											=	"""
{0} = None
"""

lowercase_											=	"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


lowercase_											=	"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""


def     a__			(    snake_case    ):





  """simple docstring"""
  if _re_test_backend.search(snake_case    ) is None:
    return None
  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						[b[0] for b in _re_backend.findall(snake_case    )]
  backends.sort()
  return "_and_".join(snake_case    )


def     a__			(    ):





  """simple docstring"""
  with open(os.path.join(snake_case   ,	'''__init__.py'''    )   ,	'''r'''   ,	encoding='''utf-8'''   ,	newline='''\n'''    ) as f:
    __SCREAMING_SNAKE_CASE     :					Tuple			=						f.readlines()

  # Get to the point we do the actual imports for type checking
  __SCREAMING_SNAKE_CASE     :					Any			=						0
  while not lines[line_index].startswith('''if TYPE_CHECKING'''    ):
    line_index += 1

  __SCREAMING_SNAKE_CASE     :					Tuple			=						{}
  # Go through the end of the file
  while line_index < len(snake_case    ):
    # If the line is an if is_backend_available, we grab all objects associated.
    __SCREAMING_SNAKE_CASE     :					int			=						find_backend(lines[line_index]    )
    if backend is not None:
      while not lines[line_index].startswith('''    else:'''    ):
        line_index += 1
      line_index += 1

      __SCREAMING_SNAKE_CASE     :					Any			=						[]
      # Until we unindent, add backend objects to the list
      while len(lines[line_index]    ) <= 1 or lines[line_index].startswith(''' ''' * 8    ):
        __SCREAMING_SNAKE_CASE     :					Dict			=						lines[line_index]
        __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						_re_single_line_import.search(snake_case    )
        if single_line_import_search is not None:
          objects.extend(single_line_import_search.groups()[0].split(''', '''    )    )
        elif line.startswith(''' ''' * 12    ):
          objects.append(line[12:-2]    )
        line_index += 1

      __SCREAMING_SNAKE_CASE     :					List[str]			=						objects
    else:
      line_index += 1

  return backend_specific_objects


def     a__			(    snake_case   ,	snake_case    ):





  """simple docstring"""
  if name.isupper():
    return DUMMY_CONSTANT.format(snake_case    )
  elif name.islower():
    return DUMMY_FUNCTION.format(snake_case   ,	snake_case    )
  else:
    return DUMMY_CLASS.format(snake_case   ,	snake_case    )


def     a__			(    snake_case=None    ):





  """simple docstring"""
  if backend_specific_objects is None:
    __SCREAMING_SNAKE_CASE     :					List[Any]			=						read_init()
  # For special correspondence backend to module name as used in the function requires_modulename
  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						{}

  for backend, objects in backend_specific_objects.items():
    __SCREAMING_SNAKE_CASE     :					Tuple			=						'''[''' + ''', '''.join(F'''"{b}"''' for b in backend.split('''_and_'''    )    ) + ''']'''
    __SCREAMING_SNAKE_CASE     :					Optional[int]			=						'''# This file is autogenerated by the command `make fix-copies`, do not edit.\n'''
    dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
    dummy_file += "\n".join([create_dummy_object(snake_case   ,	snake_case    ) for o in objects]    )
    __SCREAMING_SNAKE_CASE     :					List[Any]			=						dummy_file

  return dummy_files


def     a__			(    snake_case=False    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						create_dummy_files()
  # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
  __SCREAMING_SNAKE_CASE     :					str			=						{'''torch''': '''pt'''}

  # Locate actual dummy modules and read their content.
  __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						os.path.join(snake_case   ,	'''utils'''    )
  __SCREAMING_SNAKE_CASE     :					List[Any]			=						{
      backend: os.path.join(snake_case   ,	F'''dummy_{short_names.get(snake_case   ,	snake_case    )}_objects.py'''    )
      for backend in dummy_files.keys()
  }

  __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						{}
  for backend, file_path in dummy_file_paths.items():
    if os.path.isfile(snake_case    ):
      with open(snake_case   ,	'''r'''   ,	encoding='''utf-8'''   ,	newline='''\n'''    ) as f:
        __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						f.read()
    else:
      __SCREAMING_SNAKE_CASE     :					Any			=						''''''

  for backend in dummy_files.keys():
    if dummy_files[backend] != actual_dummies[backend]:
      if overwrite:
        print(
            F'''Updating transformers.utils.dummy_{short_names.get(snake_case   ,	snake_case    )}_objects.py as the main '''
            '''__init__ has new objects.'''    )
        with open(dummy_file_paths[backend]   ,	'''w'''   ,	encoding='''utf-8'''   ,	newline='''\n'''    ) as f:
          f.write(dummy_files[backend]    )
      else:
        raise ValueError(
            '''The main __init__ has objects that are not present in '''
            F'''transformers.utils.dummy_{short_names.get(snake_case   ,	snake_case    )}_objects.py. Run `make fix-copies` '''
            '''to fix this.'''    )


if __name__ == "__main__":
   lowercase_											=	argparse.ArgumentParser()
   parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
   lowercase_											=	parser.parse_args()

   check_dummies(args.fix_and_overwrite)
