


import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


__a       =					logging.get_logger(__name__)

__a       =					{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__a       =					{
    '''vocab_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
__a       =					{
    '''vocab_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
__a       =					{
    '''vocab_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}

__a       =					{
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_12,
}
__a       =					{
    '''facebook/dpr-question_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-question_encoder-multiset-base''': 5_12,
}
__a       =					{
    '''facebook/dpr-reader-single-nq-base''': 5_12,
    '''facebook/dpr-reader-multiset-base''': 5_12,
}


__a       =					{
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
__a       =					{
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
__a       =					{
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}







class  __SCREAMING_SNAKE_CASE      (	__lowerCamelCase      ):
      A :							int										=   VOCAB_FILES_NAMES
      A :							List[Any]										=   CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      A :							int										=   CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A :							Optional[int]										=   CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class  __SCREAMING_SNAKE_CASE      (	__lowerCamelCase      ):
      A :							Any										=   VOCAB_FILES_NAMES
      A :							List[Any]										=   QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      A :							Tuple										=   QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A :							Optional[int]										=   QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION





__a       =					collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

__a       =					collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


__a       =					r'''
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    ```
    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
    ```

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `\'tf\'`: Return TensorFlow `tf.constant` objects.
                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.
                - `\'np\'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer\'s default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Returns:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    '''







@add_start_docstrings(__lowerCamelCase      )
class  __SCREAMING_SNAKE_CASE      :


      def __call__(							self							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__ = None							,  SCREAMING_SNAKE_CASE__ = None							,  SCREAMING_SNAKE_CASE__ = False							,  SCREAMING_SNAKE_CASE__ = False							,  SCREAMING_SNAKE_CASE__ = None							,  SCREAMING_SNAKE_CASE__ = None							,  SCREAMING_SNAKE_CASE__ = None							,  **SCREAMING_SNAKE_CASE__							,  ):
            if titles is None and texts is None:
                  return super().__call__(
                      __lowercase							,  padding=__lowercase							,  truncation=__lowercase							,  max_length=__lowercase							,  return_tensors=__lowercase							,  return_attention_mask=__lowercase							,  **__lowercase							,  )
            elif titles is None or texts is None:
                  lowercase	:      Dict          =						titles if texts is None else texts
                  return super().__call__(
                      __lowercase							,  __lowercase							,  padding=__lowercase							,  truncation=__lowercase							,  max_length=__lowercase							,  return_tensors=__lowercase							,  return_attention_mask=__lowercase							,  **__lowercase							,  )
            lowercase	:      Tuple          =						titles if not isinstance(__lowercase							,  __lowercase						) else [titles]
            lowercase	:      Optional[Any]          =						texts if not isinstance(__lowercase							,  __lowercase						) else [texts]
            lowercase	:      str          =						len(__lowercase						)
            lowercase	:      List[str]          =						questions if not isinstance(__lowercase							,  __lowercase						) else [questions] * n_passages
            if len(__lowercase						) != len(__lowercase						):
                  raise ValueError(
                      f"""There should be as many titles than texts but got {len(__lowercase						)} titles and {len(__lowercase						)} texts."""						)
            lowercase	:      Optional[int]          =						super().__call__(__lowercase							,  __lowercase							,  padding=__lowercase							,  truncation=__lowercase						)['''input_ids''']
            lowercase	:      Optional[Any]          =						super().__call__(__lowercase							,  add_special_tokens=__lowercase							,  padding=__lowercase							,  truncation=__lowercase						)['''input_ids''']
            lowercase	:      Dict          =						{
                '''input_ids''': [
                    (encoded_question_and_title + encoded_text)[:max_length]
                    if max_length is not None and truncation
                    else encoded_question_and_title + encoded_text
                    for encoded_question_and_title, encoded_text in zip(__lowercase							,  __lowercase						)
                ]
            }
            if return_attention_mask is not False:
                  lowercase	:      int          =						[]
                  for input_ids in encoded_inputs["input_ids"]:
                        attention_mask.append([int(input_id != self.pad_token_id						) for input_id in input_ids]						)
                  lowercase	:      List[Any]          =						attention_mask
            return self.pad(__lowercase							,  padding=__lowercase							,  max_length=__lowercase							,  return_tensors=__lowercase						)


      def 				__lowerCamelCase	(							self							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__ = 16							,  SCREAMING_SNAKE_CASE__ = 64							,  SCREAMING_SNAKE_CASE__ = 4							,  ):
            lowercase	:      Union[str, Any]          =						reader_input['''input_ids''']
            lowercase	:      Optional[Any]          =						reader_output[:3]
            lowercase	:      Any          =						len(__lowercase						)
            lowercase	:      int          =						sorted(range(__lowercase						)							,  reverse=__lowercase							,  key=relevance_logits.__getitem__						)
            lowercase	:      List[DPRReaderOutput]          =						[]
            for doc_id in sorted_docs:
                  lowercase	:      str          =						list(input_ids[doc_id]						)
                  # assuming question & title information is at the beginning of the sequence
                  lowercase	:      List[Any]          =						sequence_ids.index(self.sep_token_id							,  2						) + 1  # second sep id
                  if sequence_ids[-1] == self.pad_token_id:
                        lowercase	:      int          =						sequence_ids.index(self.pad_token_id						)
                  else:
                        lowercase	:      Any          =						len(__lowercase						)

                  lowercase	:      Union[str, Any]          =						self._get_best_spans(
                      start_logits=start_logits[doc_id][passage_offset:sequence_len]							,  end_logits=end_logits[doc_id][passage_offset:sequence_len]							,  max_answer_length=__lowercase							,  top_spans=__lowercase							,  )
                  for start_index, end_index in best_spans:
                        start_index += passage_offset
                        end_index += passage_offset
                        nbest_spans_predictions.append(
                            DPRSpanPrediction(
                                span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]							,  relevance_score=relevance_logits[doc_id]							,  doc_id=__lowercase							,  start_index=__lowercase							,  end_index=__lowercase							,  text=self.decode(sequence_ids[start_index : end_index + 1]						)							,  )						)
                  if len(__lowercase						) >= num_spans:
                        break
            return nbest_spans_predictions[:num_spans]






      def 				__lowerCamelCase	(							self							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  ):
            lowercase	:      List[str]          =						[]
            for start_index, start_score in enumerate(__lowercase						):
                  for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]						):
                        scores.append(((start_index, start_index + answer_length), start_score + end_score)						)
            lowercase	:      Dict          =						sorted(__lowercase							,  key=lambda SCREAMING_SNAKE_CASE__						: x[1]							,  reverse=__lowercase						)
            lowercase	:      List[Any]          =						[]
            for (start_index, end_index), score in scores:
                  if start_index > end_index:
                        raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]"""						)
                  lowercase	:      Tuple          =						end_index - start_index + 1
                  if length > max_answer_length:
                        raise ValueError(f"""Span is too long: {length} > {max_answer_length}"""						)
                  if any(
                      start_index <= prev_start_index <= prev_end_index <= end_index
                      or prev_start_index <= start_index <= end_index <= prev_end_index
                      for (prev_start_index, prev_end_index) in chosen_span_intervals						):
                        continue
                  chosen_span_intervals.append((start_index, end_index)						)

                  if len(__lowercase						) == top_spans:
                        break
            return chosen_span_intervals







@add_end_docstrings(__lowerCamelCase      )
class  __SCREAMING_SNAKE_CASE      (	__lowerCamelCase     ,  __lowerCamelCase      ):
      A :							Union[str, Any]										=   VOCAB_FILES_NAMES
      A :							Union[str, Any]										=   READER_PRETRAINED_VOCAB_FILES_MAP
      A :							Optional[Any]										=   READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A :							Optional[Any]										=   READER_PRETRAINED_INIT_CONFIGURATION
      A :							Tuple										=   ["""input_ids""", """attention_mask"""]






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__								=						logging.getLogger(__name__)


@dataclass
class       a							:
		__lowerCAmelCase	:    str											=					field(
		    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}				)
		__lowerCAmelCase	:    Optional[str]											=					field(
		    default=__lowerCamelCase  ,							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=__lowerCamelCase  ,							metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}				)
		__lowerCAmelCase	:    bool											=					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	:    Optional[str]											=					field(
		    default=__lowerCamelCase  ,							metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}  ,							)


@dataclass
class       a							:
		__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=__lowerCamelCase  ,							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=__lowerCamelCase  ,							metadata={"""help""": """Overwrite the cached training and evaluation sets"""}				)



def 			_lowerCAmelCase       (					)   ->					Dict:


				"""simple docstring"""

				snake_case__	:      Any     		=	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.
								snake_case__		, snake_case__		, snake_case__	:      str     		=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
				else:
								snake_case__		, snake_case__		, snake_case__	:      List[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.'''      )

				snake_case__	:      int     		=	import_module('''tasks'''      )
				try:
								snake_case__	:      Optional[int]     		=	getattr(__lowerCAmelCase  ,  model_args.task_type      )
								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 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'''  ,  __lowerCAmelCase      )

				# Set seed
				set_seed(training_args.seed      )

				# Prepare CONLL-2003 task
				snake_case__	:      Optional[int]     		=	token_classification_task.get_labels(data_args.labels      )
				snake_case__	:      Dict[int, str]     		=	dict(enumerate(__lowerCAmelCase      )      )
				snake_case__	:      Optional[Any]     		=	len(__lowerCAmelCase      )

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

				snake_case__	:      List[str]     		=	AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,  num_labels=__lowerCAmelCase  ,  idalabel=__lowerCAmelCase  ,  labelaid={label: i for i, label in enumerate(__lowerCAmelCase      )}  ,  cache_dir=model_args.cache_dir  ,  )
				snake_case__	:      List[str]     		=	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  ,  )
				snake_case__	:      Tuple     		=	AutoModelForTokenClassification.from_pretrained(
				    model_args.model_name_or_path  ,  from_tf=bool('''.ckpt''' in model_args.model_name_or_path      )  ,  config=__lowerCAmelCase  ,  cache_dir=model_args.cache_dir  ,  )

				# Get datasets
				snake_case__	:      Dict     		=	(
				    TokenClassificationDataset(
				        token_classification_task=__lowerCAmelCase  ,  data_dir=data_args.data_dir  ,  tokenizer=__lowerCAmelCase  ,  labels=__lowerCAmelCase  ,  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
				)
				snake_case__	:      Optional[Any]     		=	(
				    TokenClassificationDataset(
				        token_classification_task=__lowerCAmelCase  ,  data_dir=data_args.data_dir  ,  tokenizer=__lowerCAmelCase  ,  labels=__lowerCAmelCase  ,  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(__lowerCAmelCase  ,  __lowerCAmelCase      ) -> Tuple[List[int], List[int]]:
								snake_case__	:      Any     		=	np.argmax(__lowerCAmelCase  ,  axis=2      )

								snake_case__		, snake_case__	:      List[Any]     		=	preds.shape

								snake_case__	:      List[Any]     		=	[[] for _ in range(__lowerCAmelCase      )]
								snake_case__	:      int     		=	[[] for _ in range(__lowerCAmelCase      )]

								for i in range(__lowerCAmelCase      ):
												for j in range(__lowerCAmelCase      ):
																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(__lowerCAmelCase      ) -> Dict:
								snake_case__		, snake_case__	:      List[str]     		=	align_predictions(p.predictions  ,  p.label_ids      )
								return {
								    "accuracy_score": accuracy_score(__lowerCAmelCase  ,  __lowerCAmelCase      ),
								    "precision": precision_score(__lowerCAmelCase  ,  __lowerCAmelCase      ),
								    "recall": recall_score(__lowerCAmelCase  ,  __lowerCAmelCase      ),
								    "f1": fa_score(__lowerCAmelCase  ,  __lowerCAmelCase      ),
								}

				# Data collator
				snake_case__	:      Any     		=	DataCollatorWithPadding(__lowerCAmelCase  ,  pad_to_multiple_of=8      ) if training_args.fpaa else None

				# Initialize our Trainer
				snake_case__	:      Tuple     		=	Trainer(
				    model=__lowerCAmelCase  ,  args=__lowerCAmelCase  ,  train_dataset=__lowerCAmelCase  ,  eval_dataset=__lowerCAmelCase  ,  compute_metrics=__lowerCAmelCase  ,  data_collator=__lowerCAmelCase  ,  )

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

								snake_case__	:      Tuple     		=	trainer.evaluate()

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

												results.update(__lowerCAmelCase      )

    # Predict
				if training_args.do_predict:
								snake_case__	:      Optional[Any]     		=	TokenClassificationDataset(
								    token_classification_task=__lowerCAmelCase  ,  data_dir=data_args.data_dir  ,  tokenizer=__lowerCAmelCase  ,  labels=__lowerCAmelCase  ,  model_type=config.model_type  ,  max_seq_length=data_args.max_seq_length  ,  overwrite_cache=data_args.overwrite_cache  ,  mode=Split.test  ,  )

								snake_case__		, snake_case__		, snake_case__	:      List[str]     		=	trainer.predict(__lowerCAmelCase      )
								snake_case__		, snake_case__	:      int     		=	align_predictions(__lowerCAmelCase  ,  __lowerCAmelCase      )

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

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

				return results



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					Union[str, Any]:


				"""simple docstring"""

				main()


if __name__ == "__main__":
				main()

