





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			:



      """simple docstring"""






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




@dataclass
class 					_lowerCamelCase			:



      """simple docstring"""






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







def    lowerCAmelCase__(						)			->     str:
  # 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.

  __snake_case :					int          =							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 :					List[Any]          =							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
  else:
    __snake_case :					int          =							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 :					Tuple          =							getattr(UpperCamelCase__ ,  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" ,  UpperCamelCase__					)

  # Set seed
  set_seed(training_args.seed					)

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

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

  __snake_case :					str          =							AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path ,  num_labels=UpperCamelCase__ ,  idalabel=UpperCamelCase__ ,  labelaid={label: i for i, label in enumerate(UpperCamelCase__					)} ,  cache_dir=model_args.cache_dir ,  )
  __snake_case :					Tuple          =							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 :					str          =							AutoModelForTokenClassification.from_pretrained(
      model_args.model_name_or_path ,  from_tf=bool(".ckpt" in model_args.model_name_or_path					) ,  config=UpperCamelCase__ ,  cache_dir=model_args.cache_dir ,  )

  # Get datasets
  __snake_case :					Optional[Any]          =							(
      TokenClassificationDataset(
          token_classification_task=UpperCamelCase__ ,  data_dir=data_args.data_dir ,  tokenizer=UpperCamelCase__ ,  labels=UpperCamelCase__ ,  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 :					Dict          =							(
      TokenClassificationDataset(
          token_classification_task=UpperCamelCase__ ,  data_dir=data_args.data_dir ,  tokenizer=UpperCamelCase__ ,  labels=UpperCamelCase__ ,  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]]:
    __snake_case :					Optional[Any]          =							np.argmax(UpperCamelCase__ ,  axis=2					)

    __snake_case :					List[str]          =							preds.shape

    __snake_case :					Any          =							[[] for _ in range(UpperCamelCase__					)]
    __snake_case :					Optional[Any]          =							[[] for _ in range(UpperCamelCase__					)]

    for i in range(UpperCamelCase__					):
      for j in range(UpperCamelCase__					):
        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:
    __snake_case :					Tuple          =							align_predictions(p.predictions ,  p.label_ids					)
    return {
        "accuracy_score": accuracy_score(UpperCamelCase__ ,  UpperCamelCase__					),
        "precision": precision_score(UpperCamelCase__ ,  UpperCamelCase__					),
        "recall": recall_score(UpperCamelCase__ ,  UpperCamelCase__					),
        "f1": fa_score(UpperCamelCase__ ,  UpperCamelCase__					),
    }

  # Data collator
  __snake_case :					int          =							DataCollatorWithPadding(UpperCamelCase__ ,  pad_to_multiple_of=8					) if training_args.fpaa else None

  # Initialize our Trainer
  __snake_case :					Any          =							Trainer(
      model=UpperCamelCase__ ,  args=UpperCamelCase__ ,  train_dataset=UpperCamelCase__ ,  eval_dataset=UpperCamelCase__ ,  compute_metrics=UpperCamelCase__ ,  data_collator=UpperCamelCase__ ,  )

  # 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 :					Optional[Any]          =							{}
  if training_args.do_eval:
    logger.info("*** Evaluate ***"					)

    __snake_case :					Optional[Any]          =							trainer.evaluate()

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

      results.update(UpperCamelCase__					)

       # Predict
  if training_args.do_predict:
    __snake_case :					Union[str, Any]          =							TokenClassificationDataset(
        token_classification_task=UpperCamelCase__ ,  data_dir=data_args.data_dir ,  tokenizer=UpperCamelCase__ ,  labels=UpperCamelCase__ ,  model_type=config.model_type ,  max_seq_length=data_args.max_seq_length ,  overwrite_cache=data_args.overwrite_cache ,  mode=Split.test ,  )

    __snake_case :					Optional[Any]          =							trainer.predict(UpperCamelCase__					)
    __snake_case :					List[Any]          =							align_predictions(UpperCamelCase__ ,  UpperCamelCase__					)

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

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

  return results







def    lowerCAmelCase__(						lowercase							:     List[str]					)			->     Union[str, Any]:
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
     main()





'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
 from PIL import Image

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		int     				=    '''dandelin/vilt-b32-finetuned-vqa'''
 __lowercase					:		int     				=    (
     '''This is a tool that answers a question about an image. It takes an input named `image` which should be the '''
     '''image containing the information, as well as a `question` which should be the question in English. It '''
     '''returns a text that is the answer to the question.'''
 )
 __lowercase					:		Union[str, Any]     				=    '''image_qa'''
 __lowercase					:		Optional[Any]     				=    AutoProcessor
 __lowercase					:		Tuple     				=    AutoModelForVisualQuestionAnswering

 __lowercase					:		Tuple     				=    ['''image''', '''text''']
 __lowercase					:		List[str]     				=    ['''text''']


 def __init__(   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  requires_backends(self   ,   ['vision']    )
  super().__init__(*__lowercase   ,   **__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  return self.pre_processor(__lowercase   ,   __lowercase   ,   return_tensors='pt'    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  with torch.no_grad():
   return self.model(**__lowercase    ).logits







 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		List[str]	  =      outputs.argmax(-1    ).item()
  return self.model.config.idalabel[idx]






