

'''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


UpperCAmelCase					 =	logging.getLogger(__name__)


@dataclass
class 	lowerCAmelCase  :
       lowerCAmelCase_												=			field(
           metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
       lowerCAmelCase_												=			field(
           default=A ,      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=A ,      metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
       lowerCAmelCase_												=			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_												=			field(
           default=A ,      metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} ,      )






@dataclass
class 	lowerCAmelCase  :
       lowerCAmelCase_												=			field(
           metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."} )
       lowerCAmelCase_												=			field(
           default=A ,      metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."} ,      )
       lowerCAmelCase_												=			field(
           default=1_2_8 ,      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=A ,      metadata={"help": "Overwrite the cached training and evaluation sets"} )






def 					__UpperCamelCase				(						):






     '''simple docstring'''





     __lowercase      		=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.
          __lowercase		,  __lowercase		,  __lowercase      		=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
     else:
          __lowercase		,  __lowercase		,  __lowercase      		=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.'			)

     __lowercase      		=import_module('tasks'			)
     try:
          __lowercase      		=getattr(lowercase__,       model_args.task_type			)
          __lowercase      		=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',       lowercase__			)

     # Set seed
     set_seed(training_args.seed			)

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

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

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

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

          __lowercase		,  __lowercase      		=preds.shape

          __lowercase      		=[[] for _ in range(lowercase__			)]
          __lowercase      		=[[] for _ in range(lowercase__			)]

          for i in range(lowercase__			):
               for j in range(lowercase__			):
                    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(lowercase__    :	EvalPrediction			) -> Dict:
          __lowercase		,  __lowercase      		=align_predictions(p.predictions,       p.label_ids			)
          return {
              "accuracy_score": accuracy_score(lowercase__,       lowercase__			),
              "precision": precision_score(lowercase__,       lowercase__			),
              "recall": recall_score(lowercase__,       lowercase__			),
              "f1": fa_score(lowercase__,       lowercase__			),
          }

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

     # Initialize our Trainer
     __lowercase      		=Trainer(
         model=lowercase__,       args=lowercase__,       train_dataset=lowercase__,       eval_dataset=lowercase__,       compute_metrics=lowercase__,       data_collator=lowercase__,       )

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

          __lowercase      		=trainer.evaluate()

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

               results.update(lowercase__			)

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

          __lowercase		,  __lowercase		,  __lowercase      		=trainer.predict(lowercase__			)
          __lowercase		,  __lowercase      		=align_predictions(lowercase__,       lowercase__			)

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

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

     return results

def 					__UpperCamelCase				(						lowercase__    :	Union[str, Any]			):






     '''simple docstring'''





     main()


if __name__ == "__main__":
       main()





'''simple docstring'''

import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
       import tensorflow as tf

       from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
       from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

UpperCAmelCase					 =	logging.get_logger(__name__)


class 	lowerCAmelCase  (   enum.Enum ):
       lowerCAmelCase_												=			0
       lowerCAmelCase_												=			1






@add_end_docstrings(A )
class 	lowerCAmelCase  (   A ):
       lowerCAmelCase_												=			"generated"







       def __init__(				self      :			Tuple    ,   *__lowercase      :			Tuple    ,   **__lowercase      :			Optional[int]   ):







            """simple docstring"""







            super().__init__(*__lowercase    ,   **__lowercase   )

            self.check_model_type(
                TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
                if self.framework == 'tf'
                else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING   )







       def       snake_case (				self      :			Dict    ,   __lowercase      :			Any=None    ,   __lowercase      :			int=None    ,   __lowercase      :			List[str]=None    ,   __lowercase      :			int=None    ,   __lowercase      :			str=None    ,   __lowercase      :			List[Any]=None    ,   **__lowercase      :			Union[str, Any]    ,   ):







            """simple docstring"""







            __lowercase      		={}
            if truncation is not None:
                 __lowercase      		=truncation

            __lowercase      		=generate_kwargs

            __lowercase      		={}
            if return_tensors is not None and return_type is None:
                 __lowercase      		=ReturnType.TENSORS if return_tensors else ReturnType.TEXT
            if return_type is not None:
                 __lowercase      		=return_type

            if clean_up_tokenization_spaces is not None:
                 __lowercase      		=clean_up_tokenization_spaces

            if stop_sequence is not None:
                 __lowercase      		=self.tokenizer.encode(__lowercase    ,   add_special_tokens=__lowercase   )
                 if len(__lowercase   ) > 1:
                      warnings.warn(
                          'Stopping on a multiple token sequence is not yet supported on transformers. The first token of'
                          ' the stop sequence will be used as the stop sequence string in the interim.'   )
                 __lowercase      		=stop_sequence_ids[0]

            return preprocess_params, forward_params, postprocess_params







       def       snake_case (				self      :			List[Any]    ,   __lowercase      :			int    ,   __lowercase      :			int    ,   __lowercase      :			int   ):







            """simple docstring"""







            return True







       def       snake_case (				self      :			Optional[Any]    ,   *__lowercase      :			Optional[int]    ,   __lowercase      :			Union[str, Any]   ):







            """simple docstring"""







            __lowercase      		=self.model.config.prefix if self.model.config.prefix is not None else ''
            if isinstance(args[0]    ,   __lowercase   ):
                 if self.tokenizer.pad_token_id is None:
                      raise ValueError('Please make sure that the tokenizer has a pad_token_id when using a batch input'   )
                 __lowercase      		=([prefix + arg for arg in args[0]],)
                 __lowercase      		=True

            elif isinstance(args[0]    ,   __lowercase   ):
                 __lowercase      		=(prefix + args[0],)
                 __lowercase      		=False
            else:
                 raise ValueError(
                     f''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'''   )
            __lowercase      		=self.tokenizer(*__lowercase    ,   padding=__lowercase    ,   truncation=__lowercase    ,   return_tensors=self.framework   )
            # This is produced by tokenizers but is an invalid generate kwargs
            if "token_type_ids" in inputs:
                 del inputs["token_type_ids"]
            return inputs







       def __call__(				self      :			Any    ,   *__lowercase      :			int    ,   **__lowercase      :			Optional[Any]   ):







            """simple docstring"""







            __lowercase      		=super().__call__(*__lowercase    ,   **__lowercase   )
            if (
                isinstance(args[0]    ,   __lowercase   )
                and all(isinstance(__lowercase    ,   __lowercase   ) for el in args[0]   )
                and all(len(__lowercase   ) == 1 for res in result   )
            ):
                 return [res[0] for res in result]
            return result







       def       snake_case (				self      :			List[str]    ,   __lowercase      :			Tuple    ,   __lowercase      :			List[Any]=TruncationStrategy.DO_NOT_TRUNCATE    ,   **__lowercase      :			Dict   ):







            """simple docstring"""







            __lowercase      		=self._parse_and_tokenize(__lowercase    ,   truncation=__lowercase    ,   **__lowercase   )
            return inputs







       def       snake_case (				self      :			Optional[int]    ,   __lowercase      :			Tuple    ,   **__lowercase      :			Tuple   ):







            """simple docstring"""







            if self.framework == "pt":
                 __lowercase		,  __lowercase      		=model_inputs['input_ids'].shape
            elif self.framework == "tf":
                 __lowercase		,  __lowercase      		=tf.shape(model_inputs['input_ids']   ).numpy()

            __lowercase      		=generate_kwargs.get('min_length'    ,   self.model.config.min_length   )
            __lowercase      		=generate_kwargs.get('max_length'    ,   self.model.config.max_length   )
            self.check_inputs(__lowercase    ,   generate_kwargs['min_length']    ,   generate_kwargs['max_length']   )
            __lowercase      		=self.model.generate(**__lowercase    ,   **__lowercase   )
            __lowercase      		=output_ids.shape[0]
            if self.framework == "pt":
                 __lowercase      		=output_ids.reshape(__lowercase    ,   out_b // in_b    ,   *output_ids.shape[1:]   )
            elif self.framework == "tf":
                 __lowercase      		=tf.reshape(__lowercase    ,   (in_b, out_b // in_b, *output_ids.shape[1:])   )
            return {"output_ids": output_ids}







       def       snake_case (				self      :			str    ,   __lowercase      :			Optional[Any]    ,   __lowercase      :			Optional[int]=ReturnType.TEXT    ,   __lowercase      :			Union[str, Any]=False   ):







            """simple docstring"""







            __lowercase      		=[]
            for output_ids in model_outputs["output_ids"][0]:
                 if return_type == ReturnType.TENSORS:
                      __lowercase      		={f'''{self.return_name}_token_ids''': output_ids}
                 elif return_type == ReturnType.TEXT:
                      __lowercase      		={
                          f'''{self.return_name}_text''': self.tokenizer.decode(
                              __lowercase    ,   skip_special_tokens=__lowercase    ,   clean_up_tokenization_spaces=__lowercase    ,   )
                      }
                 records.append(__lowercase   )
            return records






@add_end_docstrings(A )
class 	lowerCAmelCase  (   A ):
       lowerCAmelCase_												=			"summary"







       def __call__(				self      :			Dict    ,   *__lowercase      :			Dict    ,   **__lowercase      :			Dict   ):







            """simple docstring"""







            return super().__call__(*__lowercase    ,   **__lowercase   )







       def       snake_case (				self      :			str    ,   __lowercase      :			int    ,   __lowercase      :			int    ,   __lowercase      :			int   ):







            """simple docstring"""







            if max_length < min_length:
                 logger.warning(f'''Your min_length={min_length} must be inferior than your max_length={max_length}.'''   )

            if input_length < max_length:
                 logger.warning(
                     f'''Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '''
                     'a summarization task, where outputs shorter than the input are typically wanted, you might '
                     f'''consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})'''   )






@add_end_docstrings(A )
class 	lowerCAmelCase  (   A ):
       lowerCAmelCase_												=			"translation"







       def       snake_case (				self      :			int    ,   __lowercase      :			int    ,   __lowercase      :			int    ,   __lowercase      :			int   ):







            """simple docstring"""







            if input_length > 0.9 * max_length:
                 logger.warning(
                     f'''Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '''
                     'increasing your max_length manually, e.g. translator(\'...\', max_length=400)'   )
            return True







       def       snake_case (				self      :			int    ,   *__lowercase      :			str    ,   __lowercase      :			List[str]=TruncationStrategy.DO_NOT_TRUNCATE    ,   __lowercase      :			List[Any]=None    ,   __lowercase      :			Optional[int]=None   ):







            """simple docstring"""







            if getattr(self.tokenizer    ,   '_build_translation_inputs'    ,   __lowercase   ):
                 return self.tokenizer._build_translation_inputs(
                     *__lowercase    ,   return_tensors=self.framework    ,   truncation=__lowercase    ,   src_lang=__lowercase    ,   tgt_lang=__lowercase   )
            else:
                 return super()._parse_and_tokenize(*__lowercase    ,   truncation=__lowercase   )







       def       snake_case (				self      :			Optional[int]    ,   __lowercase      :			List[str]=None    ,   __lowercase      :			Optional[Any]=None    ,   **__lowercase      :			int   ):







            """simple docstring"""







            __lowercase		,  __lowercase		,  __lowercase      		=super()._sanitize_parameters(**__lowercase   )
            if src_lang is not None:
                 __lowercase      		=src_lang
            if tgt_lang is not None:
                 __lowercase      		=tgt_lang
            if src_lang is None and tgt_lang is None:
                 # Backward compatibility, direct arguments use is preferred.
                 __lowercase      		=kwargs.get('task'    ,   self.task   )
                 __lowercase      		=task.split('_'   )
                 if task and len(__lowercase   ) == 4:
                      # translation, XX, to YY
                      __lowercase      		=items[1]
                      __lowercase      		=items[3]
            return preprocess_params, forward_params, postprocess_params







       def __call__(				self      :			Optional[Any]    ,   *__lowercase      :			Any    ,   **__lowercase      :			List[Any]   ):







            """simple docstring"""







            return super().__call__(*__lowercase    ,   **__lowercase   )


