


'''simple docstring'''



import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
	import numpy as np
	import torch

	from . import (
	    AlbertForPreTraining,
	    BartForConditionalGeneration,
	    BertForPreTraining,
	    BertForQuestionAnswering,
	    BertForSequenceClassification,
	    CamembertForMaskedLM,
	    CTRLLMHeadModel,
	    DistilBertForMaskedLM,
	    DistilBertForQuestionAnswering,
	    DPRContextEncoder,
	    DPRQuestionEncoder,
	    DPRReader,
	    ElectraForPreTraining,
	    FlaubertWithLMHeadModel,
	    GPTaLMHeadModel,
	    LayoutLMForMaskedLM,
	    LxmertForPreTraining,
	    LxmertVisualFeatureEncoder,
	    OpenAIGPTLMHeadModel,
	    RobertaForMaskedLM,
	    RobertaForSequenceClassification,
	    TaForConditionalGeneration,
	    TransfoXLLMHeadModel,
	    XLMRobertaForMaskedLM,
	    XLMWithLMHeadModel,
	    XLNetLMHeadModel,
	)


logging.set_verbosity_info()

_lowercase     :     Optional[int]        =	{
    'bart': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'bert': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-base-cased-finetuned-mrpc': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'dpr': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'gpt2': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlnet': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm-roberta': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'transfo-xl': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'openai-gpt': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'roberta': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'layoutlm': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'roberta-large-mnli': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'camembert': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'flaubert': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert-base-distilled-squad': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert-visual-feature-encoder': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'ctrl': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'albert': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    't5': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'electra': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'wav2vec2': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}


def  lowerCamelCase__ (			A  :     str ,  A  :     str ,  A  :     Union[str, Any] ,  A  :     List[str] ,  A  :     Optional[int]=False ,  A  :     Optional[Any]=True	):




						'''simple docstring'''






						if model_type not in MODEL_CLASSES:
												raise ValueError(f"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()	)}."""	)

						UpperCAmelCase							,							UpperCAmelCase							,							UpperCAmelCase							,							UpperCAmelCase								=      MODEL_CLASSES[model_type]

						# Initialise TF model
						if config_file in aws_config_map:
												UpperCAmelCase								=      cached_file(A ,  A ,  force_download=not use_cached_models	)
						UpperCAmelCase								=      config_class.from_json_file(A	)
						UpperCAmelCase								=      True
						UpperCAmelCase								=      True
						print(f"""Building TensorFlow model from configuration: {config}"""	)
						UpperCAmelCase								=      model_class(A	)

						# Load weights from tf checkpoint
						if pytorch_checkpoint_path in aws_config_map.keys():
												UpperCAmelCase								=      cached_file(
												    A ,  A ,  force_download=not use_cached_models	)
						# Load PyTorch checkpoint in tf2 model:
						UpperCAmelCase								=      load_pytorch_checkpoint_in_tfa_model(A ,  A	)

						if compare_with_pt_model:
												UpperCAmelCase								=      tf_model(tf_model.dummy_inputs ,  training=A	)  # build the network

												UpperCAmelCase								=      torch.load(A ,  map_location='''cpu'''	)
												UpperCAmelCase								=      pt_model_class.from_pretrained(
												    pretrained_model_name_or_path=A ,  config=A ,  state_dict=A	)

												with torch.no_grad():
																		UpperCAmelCase								=      pt_model(**pt_model.dummy_inputs	)

												UpperCAmelCase								=      pto[0].numpy()
												UpperCAmelCase								=      tfo[0].numpy()
												UpperCAmelCase								=      np.amax(np.abs(np_pt - np_tf	)	)
												print(f"""Max absolute difference between models outputs {diff}"""	)
												assert diff <= 2E-2, f"""Error, model absolute difference is >2e-2: {diff}"""

						# Save pytorch-model
						print(f"""Save TensorFlow model to {tf_dump_path}"""	)
						tf_model.save_weights(A ,  save_format='''h5'''	)


def  lowerCamelCase__ (			A  :     Any ,  A  :     Dict ,  A  :     List[Any]=None ,  A  :     int=None ,  A  :     List[Any]=False ,  A  :     Optional[Any]=False ,  A  :     str=False ,  A  :     Optional[int]=False ,  ):




						'''simple docstring'''






						if args_model_type is None:
												UpperCAmelCase								=      list(MODEL_CLASSES.keys()	)
						else:
												UpperCAmelCase								=      [args_model_type]

						for j, model_type in enumerate(A ,  start=1	):
												print('''=''' * 1_00	)
												print(f""" Converting model type {j}/{len(A	)}: {model_type}"""	)
												print('''=''' * 1_00	)
												if model_type not in MODEL_CLASSES:
																		raise ValueError(f"""Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()	)}."""	)

												UpperCAmelCase							,							UpperCAmelCase							,							UpperCAmelCase							,							UpperCAmelCase							,							UpperCAmelCase								=      MODEL_CLASSES[model_type]

												if model_shortcut_names_or_path is None:
																		UpperCAmelCase								=      list(aws_model_maps.keys()	)
												if config_shortcut_names_or_path is None:
																		UpperCAmelCase								=      model_shortcut_names_or_path

												for i, (model_shortcut_name, config_shortcut_name) in enumerate(
												    zip(A ,  A	) ,  start=1	):
																		print('''-''' * 1_00	)
																		if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
																								if not only_convert_finetuned_models:
																														print(f"""    Skipping finetuned checkpoint {model_shortcut_name}"""	)
																														continue
																								UpperCAmelCase								=      model_shortcut_name
																		elif only_convert_finetuned_models:
																								print(f"""    Skipping not finetuned checkpoint {model_shortcut_name}"""	)
																								continue
																		print(
																		    f"""    Converting checkpoint {i}/{len(A	)}: {model_shortcut_name} - model_type {model_type}"""	)
																		print('''-''' * 1_00	)

																		if config_shortcut_name in aws_config_map:
																								UpperCAmelCase								=      cached_file(A ,  A ,  force_download=not use_cached_models	)
																		else:
																								UpperCAmelCase								=      config_shortcut_name

																		if model_shortcut_name in aws_model_maps:
																								UpperCAmelCase								=      cached_file(A ,  A ,  force_download=not use_cached_models	)
																		else:
																								UpperCAmelCase								=      model_shortcut_name

																		if os.path.isfile(A	):
																								UpperCAmelCase								=      '''converted_model'''

																		convert_pt_checkpoint_to_tf(
																		    model_type=A ,  pytorch_checkpoint_path=A ,  config_file=A ,  tf_dump_path=os.path.join(A ,  model_shortcut_name + '''-tf_model.h5'''	) ,  compare_with_pt_model=A ,  )
																		if remove_cached_files:
																								os.remove(A	)
																								os.remove(A	)


if __name__ == "__main__":
	_lowercase     :     int        =	argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--tf_dump_path""", default=None, type=str, required=True, help="""Path to the output Tensorflow dump file."""
	)
	parser.add_argument(
	    """--model_type""",
	    default=None,
	    type=str,
	    help=(
	        F"""Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and """
	        """convert all the models from AWS."""
	    ),
	)
	parser.add_argument(
	    """--pytorch_checkpoint_path""",
	    default=None,
	    type=str,
	    help=(
	        """Path to the PyTorch checkpoint path or shortcut name to download from AWS. """
	        """If not given, will download and convert all the checkpoints from AWS."""
	    ),
	)
	parser.add_argument(
	    """--config_file""",
	    default=None,
	    type=str,
	    help=(
	        """The config json file corresponding to the pre-trained model. \n"""
	        """This specifies the model architecture. If not given and """
	        """--pytorch_checkpoint_path is not given or is a shortcut name """
	        """use the configuration associated to the shortcut name on the AWS"""
	    ),
	)
	parser.add_argument(
	    """--compare_with_pt_model""", action="""store_true""", help="""Compare Tensorflow and PyTorch model predictions."""
	)
	parser.add_argument(
	    """--use_cached_models""",
	    action="""store_true""",
	    help="""Use cached models if possible instead of updating to latest checkpoint versions.""",
	)
	parser.add_argument(
	    """--remove_cached_files""",
	    action="""store_true""",
	    help="""Remove pytorch models after conversion (save memory when converting in batches).""",
	)
	parser.add_argument("""--only_convert_finetuned_models""", action="""store_true""", help="""Only convert finetuned models.""")
	_lowercase     :     int        =	parser.parse_args()

	# if args.pytorch_checkpoint_path is not None:
	#     convert_pt_checkpoint_to_tf(args.model_type.lower(),
	#                                 args.pytorch_checkpoint_path,
	#                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
	#                                 args.tf_dump_path,
	#                                 compare_with_pt_model=args.compare_with_pt_model,
	#                                 use_cached_models=args.use_cached_models)
	# else:
	convert_all_pt_checkpoints_to_tf(
	    args.model_type.lower() if args.model_type is not None else None,
	    args.tf_dump_path,
	    model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
	    if args.pytorch_checkpoint_path is not None
	    else None,
	    config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
	    compare_with_pt_model=args.compare_with_pt_model,
	    use_cached_models=args.use_cached_models,
	    remove_cached_files=args.remove_cached_files,
	    only_convert_finetuned_models=args.only_convert_finetuned_models,
	)








'''simple docstring'''




import logging
import os
import sys
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 accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_a					:			List[Any]							=    logging.getLogger(__name__)



@dataclass
class 							lowercase_						:

  '''simple docstring'''





  __lowerCAmelCase       :			str     	= field(
      metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}			)
  __lowerCAmelCase       :			Optional[str]     	= field(
      default=a			,							metadata={"help": "Pretrained config name or path if not the same as model_name"}			)
  __lowerCAmelCase       :			Optional[str]     	= field(
      default="NER"			,							metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}			)
  __lowerCAmelCase       :			Optional[str]     	= field(
      default=a			,							metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}			)
  __lowerCAmelCase       :			bool     	= field(default=a			,							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       :			Optional[str]     	= field(
      default=a			,							metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}			,							)




@dataclass
class 							lowercase_						:

  '''simple docstring'''





  __lowerCAmelCase       :			str     	= field(
      metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}			)
  __lowerCAmelCase       :			Optional[str]     	= field(
      default=a			,							metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}			,							)
  __lowerCAmelCase       :			int     	= 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       :			bool     	= field(
      default=a			,							metadata={"help": "Overwrite the cached training and evaluation sets"}			)




def    lowerCamelCase__		(    ):
  # 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.

  UpperCAmelCase    				=						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
  if len(sys.argv    ) == 2 and sys.argv[1].endswith('.json'    ):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase    				=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
  else:
    UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase    				=						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.'    )

  UpperCAmelCase    				=						import_module('tasks'    )
  try:
    UpperCAmelCase    				=						getattr(SCREAMING_SNAKE_CASE							, model_args.task_type    )
    UpperCAmelCase    				=						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 if training_args.local_rank in [-1, 0] else logging.WARN							, )
  logger.warning(
      'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'							, training_args.local_rank							, training_args.device							, training_args.n_gpu							, bool(training_args.local_rank != -1    )							, training_args.fpaa							, )
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank    ):
    transformers.utils.logging.set_verbosity_info()
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()
  logger.info('Training/evaluation parameters %s'							, SCREAMING_SNAKE_CASE    )

  # Set seed
  set_seed(training_args.seed    )

  # Prepare CONLL-2003 task
  UpperCAmelCase    				=						token_classification_task.get_labels(data_args.labels    )
  UpperCAmelCase    				=						dict(enumerate(SCREAMING_SNAKE_CASE    )    )
  UpperCAmelCase    				=						len(SCREAMING_SNAKE_CASE    )

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

  UpperCAmelCase    				=						AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path							, num_labels=SCREAMING_SNAKE_CASE							, idalabel=SCREAMING_SNAKE_CASE							, labelaid={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE    )}							, cache_dir=model_args.cache_dir							, )
  UpperCAmelCase    				=						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							, )
  UpperCAmelCase    				=						AutoModelForTokenClassification.from_pretrained(
      model_args.model_name_or_path							, from_tf=bool('.ckpt' in model_args.model_name_or_path    )							, config=SCREAMING_SNAKE_CASE							, cache_dir=model_args.cache_dir							, )

  # Get datasets
  UpperCAmelCase    				=						(
      TokenClassificationDataset(
          token_classification_task=SCREAMING_SNAKE_CASE							, data_dir=data_args.data_dir							, tokenizer=SCREAMING_SNAKE_CASE							, labels=SCREAMING_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
  )
  UpperCAmelCase    				=						(
      TokenClassificationDataset(
          token_classification_task=SCREAMING_SNAKE_CASE							, data_dir=data_args.data_dir							, tokenizer=SCREAMING_SNAKE_CASE							, labels=SCREAMING_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(SCREAMING_SNAKE_CASE   : np.ndarray							, SCREAMING_SNAKE_CASE   : np.ndarray    ) -> Tuple[List[int], List[int]]:
    UpperCAmelCase    				=						np.argmax(SCREAMING_SNAKE_CASE							, axis=2    )

    UpperCAmelCase				,   UpperCAmelCase    				=						preds.shape

    UpperCAmelCase    				=						[[] for _ in range(SCREAMING_SNAKE_CASE    )]
    UpperCAmelCase    				=						[[] for _ in range(SCREAMING_SNAKE_CASE    )]

    for i in range(SCREAMING_SNAKE_CASE    ):
      for j in range(SCREAMING_SNAKE_CASE    ):
        if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
          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(SCREAMING_SNAKE_CASE   : EvalPrediction    ) -> Dict:
    UpperCAmelCase				,   UpperCAmelCase    				=						align_predictions(p.predictions							, p.label_ids    )
    return {
        "accuracy_score": accuracy_score(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    ),
        "precision": precision_score(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    ),
        "recall": recall_score(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    ),
        "f1": fa_score(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    ),
    }

  # Data collator
  UpperCAmelCase    				=						DataCollatorWithPadding(SCREAMING_SNAKE_CASE							, pad_to_multiple_of=8    ) if training_args.fpaa else None

  # Initialize our Trainer
  UpperCAmelCase    				=						Trainer(
      model=SCREAMING_SNAKE_CASE							, args=SCREAMING_SNAKE_CASE							, train_dataset=SCREAMING_SNAKE_CASE							, eval_dataset=SCREAMING_SNAKE_CASE							, compute_metrics=SCREAMING_SNAKE_CASE							, data_collator=SCREAMING_SNAKE_CASE							, )

  # Training
  if training_args.do_train:
    trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path    ) else None    )
    trainer.save_model()
    # For convenience, we also re-save the tokenizer to the same directory,
    # so that you can share your model easily on huggingface.co/models =)
    if trainer.is_world_process_zero():
      tokenizer.save_pretrained(training_args.output_dir    )

    # Evaluation
  UpperCAmelCase    				=						{}
  if training_args.do_eval:
    logger.info('*** Evaluate ***'    )

    UpperCAmelCase    				=						trainer.evaluate()

    UpperCAmelCase    				=						os.path.join(training_args.output_dir							, 'eval_results.txt'    )
    if trainer.is_world_process_zero():
      with open(SCREAMING_SNAKE_CASE							, 'w'    ) as writer:
        logger.info('***** Eval results *****'    )
        for key, value in result.items():
          logger.info('  %s = %s'							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
          writer.write('%s = %s\n' % (key, value)    )

      results.update(SCREAMING_SNAKE_CASE    )

    # Predict
  if training_args.do_predict:
    UpperCAmelCase    				=						TokenClassificationDataset(
        token_classification_task=SCREAMING_SNAKE_CASE							, data_dir=data_args.data_dir							, tokenizer=SCREAMING_SNAKE_CASE							, labels=SCREAMING_SNAKE_CASE							, model_type=config.model_type							, max_seq_length=data_args.max_seq_length							, overwrite_cache=data_args.overwrite_cache							, mode=Split.test							, )

    UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase    				=						trainer.predict(SCREAMING_SNAKE_CASE    )
    UpperCAmelCase				,   UpperCAmelCase    				=						align_predictions(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )

    UpperCAmelCase    				=						os.path.join(training_args.output_dir							, 'test_results.txt'    )
    if trainer.is_world_process_zero():
      with open(SCREAMING_SNAKE_CASE							, 'w'    ) as writer:
        for key, value in metrics.items():
          logger.info('  %s = %s'							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
          writer.write('%s = %s\n' % (key, value)    )

        # Save predictions
    UpperCAmelCase    				=						os.path.join(training_args.output_dir							, 'test_predictions.txt'    )
    if trainer.is_world_process_zero():
      with open(SCREAMING_SNAKE_CASE							, 'w'    ) as writer:
        with open(os.path.join(data_args.data_dir							, 'test.txt'    )							, 'r'    ) as f:
          token_classification_task.write_predictions_to_file(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )

  return results


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Dict    ):
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
  main()


