
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()


__magic_name__  :  Union[str, Any]     			=   logging.getLogger(__name__)


@dataclass
class 							lowerCamelCase       :



  """simple docstring"""





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



@dataclass
class 							lowerCamelCase       :



  """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=128			,  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=__snake_case			,  metadata={"""help""": """Overwrite the cached training and evaluation sets"""}					)





def        lowerCAmelCase       (				)->			Optional[Any]:
    # 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.
    A_								=					HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
    A_	,	A_	,	A_								=					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." )

    A_								=					import_module("tasks" )

    try:
        A_								=					getattr(snake_case__				,					model_args.task_type )
        A_								=					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
    A_								=					token_classification_task.get_labels(data_args.labels )
    A_								=					dict(enumerate(snake_case__ ) )
    A_								=					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.

    A_								=					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				,					)
    A_								=					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():
        A_								=					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
    A_								=					(
        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
    )
    A_								=					(
        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__   :      np.ndarray				,					snake_case__   :      np.ndarray ) -> Tuple[List[int], List[int]]:
        A_								=					np.argmax(snake_case__				,					axis=2 )
        A_	,	A_								=					preds.shape
        A_								=					[[] for _ in range(snake_case__ )]
        A_								=					[[] 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__   :      EvalPrediction ) -> Dict:
        A_	,	A_								=					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
    A_								=					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
    A_								=					{}
    if training_args.do_eval:
        logger.info("*** Evaluate ***" )

        A_								=					trainer.evaluate()
        A_								=					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:
        A_								=					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				,					)

        A_	,	A_	,	A_								=					trainer.predict(test_dataset.get_dataset() )
        A_	,	A_								=					align_predictions(snake_case__				,					snake_case__ )
        A_								=					classification_report(snake_case__				,					snake_case__ )

        logger.info("\n%s"				,					snake_case__ )

        A_								=					os.path.join(training_args.output_dir				,					"test_results.txt" )

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

        # Save predictions
        A_								=					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:
                A_								=					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]:
                        A_								=					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 unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
     from PIL import Image


class 							lowerCamelCase       (		unittest.TestCase			,  __snake_case					):



  """simple docstring"""





  def 					lowercase_						(					self					):
      A_								=					load_tool("image-question-answering"					)
      self.tool.setup()
      A_								=					load_tool("image-question-answering"						,    remote=__UpperCamelCase					)

  def 					lowercase_						(					self					):
      A_								=					Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
      A_								=					self.tool(__UpperCamelCase						,    "How many cats are sleeping on the couch?"					)
      self.assertEqual(__UpperCamelCase						,    "2"					)

  def 					lowercase_						(					self					):
      A_								=					Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
      A_								=					self.remote_tool(__UpperCamelCase						,    "How many cats are sleeping on the couch?"					)
      self.assertEqual(__UpperCamelCase						,    "2"					)

  def 					lowercase_						(					self					):
      A_								=					Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
      A_								=					self.tool(image=__UpperCamelCase						,    question="How many cats are sleeping on the couch?"					)
      self.assertEqual(__UpperCamelCase						,    "2"					)

  def 					lowercase_						(					self					):
      A_								=					Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
      A_								=					self.remote_tool(image=__UpperCamelCase						,    question="How many cats are sleeping on the couch?"					)
      self.assertEqual(__UpperCamelCase						,    "2"					)






