





"""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_ =			logging.getLogger(__name__)






@dataclass
class 		__snake_case					:





 """simple docstring"""


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






@dataclass
class 		__snake_case					:





 """simple docstring"""


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







def 		__lowercase     (   )   ->List[str]:



    '''simple docstring'''







    __A    :						Optional[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.
        __A ,       __A ,       __A    :						Dict						 =     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
    else:
        __A ,       __A ,       __A    :						Tuple						 =     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    :						Optional[int]						 =     import_module('''tasks'''						)
    try:
        __A    :						int						 =     getattr(snake_case_				,model_args.task_type						)
        __A    :						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'''				,snake_case_						)

    # Set seed
    set_seed(training_args.seed						)

    # Prepare CONLL-2003 task
    __A    :						List[Any]						 =     token_classification_task.get_labels(data_args.labels						)
    __A    :						Dict[int, str]						 =     dict(enumerate(snake_case_						)						)
    __A    :						Optional[Any]						 =     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    :						Optional[Any]						 =     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    :						List[Any]						 =     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				,)
    __A    :						int						 =     AutoModelForTokenClassification.from_pretrained(
        model_args.model_name_or_path				,from_tf=bool('''.ckpt''' in model_args.model_name_or_path						)				,config=snake_case_				,cache_dir=model_args.cache_dir				,)

    # Get datasets
    __A    :						Dict						 =     (
        TokenClassificationDataset(
            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    :						List[Any]						 =     (
        TokenClassificationDataset(
            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    :						Dict						 =     np.argmax(snake_case_				,axis=2						)

        __A ,       __A    :						Tuple						 =     preds.shape

        __A    :						Optional[Any]						 =     [[] for _ in range(snake_case_						)]
        __A    :						Dict						 =     [[] for _ in range(snake_case_						)]

        for i in range(snake_case_						):
            for j in range(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(snake_case_  :  EvalPrediction						) -> Dict:
        __A ,       __A    :						Optional[int]						 =     align_predictions(p.predictions				,p.label_ids						)
        return {
            "accuracy_score": accuracy_score(snake_case_				,snake_case_						),
            "precision": precision_score(snake_case_				,snake_case_						),
            "recall": recall_score(snake_case_				,snake_case_						),
            "f1": fa_score(snake_case_				,snake_case_						),
        }

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

    # Initialize our Trainer
    __A    :						Dict						 =     Trainer(
        model=snake_case_				,args=snake_case_				,train_dataset=snake_case_				,eval_dataset=snake_case_				,compute_metrics=snake_case_				,data_collator=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
    __A    :						Optional[int]						 =     {}
    if training_args.do_eval:
        logger.info('''*** Evaluate ***'''						)

        __A    :						List[Any]						 =     trainer.evaluate()

        __A    :						int						 =     os.path.join(training_args.output_dir				,'''eval_results.txt'''						)
        if trainer.is_world_process_zero():
            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    :						Union[str, Any]						 =     TokenClassificationDataset(
            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    :						Optional[int]						 =     trainer.predict(snake_case_						)
        __A ,       __A    :						Any						 =     align_predictions(snake_case_				,snake_case_						)

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

        # Save predictions
        __A    :						str						 =     os.path.join(training_args.output_dir				,'''test_predictions.txt'''						)
        if trainer.is_world_process_zero():
            with open(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(snake_case_				,snake_case_				,snake_case_						)

    return results







def 		__lowercase     (   snake_case_  :  Optional[int]						)   ->Tuple:



    '''simple docstring'''







    main()


if __name__ == "__main__":
       main()











"""simple docstring"""



import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

       from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
       a_ =			False

if is_vision_available():
       from PIL import Image

       from transformers import PixaStructImageProcessor






class 		__snake_case					(		unittest.TestCase     ):





 """simple docstring"""


 def __init__(    self					,						__lowerCamelCase					,						__lowerCamelCase=7					,						__lowerCamelCase=3					,						__lowerCamelCase=18					,						__lowerCamelCase=30					,						__lowerCamelCase=400					,						__lowerCamelCase=None					,						__lowerCamelCase=True					,						__lowerCamelCase=True					,						__lowerCamelCase=None					,						):



     '''simple docstring'''




     __A    :						str						 =     size if size is not None else {'''height''': 20, '''width''': 20}
     __A    :						Dict						 =     parent
     __A    :						Union[str, Any]						 =     batch_size
     __A    :						List[Any]						 =     num_channels
     __A    :						Union[str, Any]						 =     image_size
     __A    :						Any						 =     min_resolution
     __A    :						str						 =     max_resolution
     __A    :						Any						 =     size
     __A    :						Dict						 =     do_normalize
     __A    :						str						 =     do_convert_rgb
     __A    :						List[Any]						 =     [512, 1024, 2048, 4096]
     __A    :						Dict						 =     patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[str]						 =     '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
     __A    :						Optional[Any]						 =     Image.open(requests.get(__lowerCamelCase					,						stream=__lowerCamelCase  ).raw  ).convert('''RGB'''  )
     return raw_image






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11							,							reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""							,							)
@require_torch
@require_vision
class 		__snake_case					(		SCREAMING_SNAKE_CASE__							,							unittest.TestCase     ):





 """simple docstring"""


 _lowerCamelCase        =     PixaStructImageProcessor if is_vision_available() else None

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						str						 =     PixaStructImageProcessingTester(self  )

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return self.image_processor_tester.prepare_image_processor_dict()

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Any						 =     self.image_processing_class(**self.image_processor_dict  )
     self.assertTrue(hasattr(__lowerCamelCase					,						'''do_normalize'''  )  )
     self.assertTrue(hasattr(__lowerCamelCase					,						'''do_convert_rgb'''  )  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Tuple						 =     self.image_processor_tester.prepare_dummy_image()

     __A    :						Optional[int]						 =     self.image_processing_class(**self.image_processor_dict  )
     __A    :						Dict						 =     2048

     __A    :						Any						 =     image_processor(__lowerCamelCase					,						return_tensors='''pt'''					,						max_patches=__lowerCamelCase  )
     self.assertTrue(torch.allclose(inputs.flattened_patches.mean()					,						torch.tensor(0.0_6_0_6  )					,						atol=1e-3					,						rtol=1e-3  )  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[Any]						 =     self.image_processing_class(**self.image_processor_dict  )
     # create random PIL images
     __A    :						str						 =     prepare_image_inputs(self.image_processor_tester					,						equal_resolution=__lowerCamelCase  )
     for image in image_inputs:
         self.assertIsInstance(__lowerCamelCase					,						Image.Image  )

     # Test not batched input
     __A    :						List[str]						 =     (
         (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
         * self.image_processor_tester.num_channels
     ) + 2

     for max_patch in self.image_processor_tester.max_patches:
         # Test not batched input
         __A    :						Dict						 =     image_processor(
             image_inputs[0]					,						return_tensors='''pt'''					,						max_patches=__lowerCamelCase  ).flattened_patches
         self.assertEqual(
             encoded_images.shape					,						(1, max_patch, expected_hidden_dim)					,						)

         # Test batched
         __A    :						Tuple						 =     image_processor(
             __lowerCamelCase					,						return_tensors='''pt'''					,						max_patches=__lowerCamelCase  ).flattened_patches
         self.assertEqual(
             encoded_images.shape					,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,						)

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[str]						 =     self.image_processing_class(**self.image_processor_dict  )
     # create random PIL images
     __A    :						Union[str, Any]						 =     prepare_image_inputs(self.image_processor_tester					,						equal_resolution=__lowerCamelCase  )
     for image in image_inputs:
         self.assertIsInstance(__lowerCamelCase					,						Image.Image  )

     # Test not batched input
     __A    :						Tuple						 =     (
         (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
         * self.image_processor_tester.num_channels
     ) + 2

     __A    :						str						 =     True

     for max_patch in self.image_processor_tester.max_patches:
         # Test not batched input
         with self.assertRaises(__lowerCamelCase  ):
             __A    :						List[Any]						 =     image_processor(
                 image_inputs[0]					,						return_tensors='''pt'''					,						max_patches=__lowerCamelCase  ).flattened_patches

         __A    :						Any						 =     '''Hello'''

         __A    :						Tuple						 =     image_processor(
             image_inputs[0]					,						return_tensors='''pt'''					,						max_patches=__lowerCamelCase					,						header_text=__lowerCamelCase  ).flattened_patches
         self.assertEqual(
             encoded_images.shape					,						(1, max_patch, expected_hidden_dim)					,						)

         # Test batched
         __A    :						Union[str, Any]						 =     image_processor(
             __lowerCamelCase					,						return_tensors='''pt'''					,						max_patches=__lowerCamelCase					,						header_text=__lowerCamelCase  ).flattened_patches
         self.assertEqual(
             encoded_images.shape					,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,						)

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						int						 =     self.image_processing_class(**self.image_processor_dict  )
     # create random numpy tensors
     __A    :						Dict						 =     prepare_image_inputs(self.image_processor_tester					,						equal_resolution=__lowerCamelCase					,						numpify=__lowerCamelCase  )
     for image in image_inputs:
         self.assertIsInstance(__lowerCamelCase					,						np.ndarray  )

     __A    :						List[Any]						 =     (
         (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
         * self.image_processor_tester.num_channels
     ) + 2

     for max_patch in self.image_processor_tester.max_patches:
         # Test not batched input
         __A    :						List[str]						 =     image_processor(
             image_inputs[0]					,						return_tensors='''pt'''					,						max_patches=__lowerCamelCase  ).flattened_patches
         self.assertEqual(
             encoded_images.shape					,						(1, max_patch, expected_hidden_dim)					,						)

         # Test batched
         __A    :						Union[str, Any]						 =     image_processor(
             __lowerCamelCase					,						return_tensors='''pt'''					,						max_patches=__lowerCamelCase  ).flattened_patches
         self.assertEqual(
             encoded_images.shape					,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,						)







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						int						 =     self.image_processing_class(**self.image_processor_dict  )
     # create random PyTorch tensors
     __A    :						Optional[Any]						 =     prepare_image_inputs(self.image_processor_tester					,						equal_resolution=__lowerCamelCase					,						torchify=__lowerCamelCase  )
     for image in image_inputs:
         self.assertIsInstance(__lowerCamelCase					,						torch.Tensor  )

     # Test not batched input
     __A    :						int						 =     (
         (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
         * self.image_processor_tester.num_channels
     ) + 2

     for max_patch in self.image_processor_tester.max_patches:
         # Test not batched input
         __A    :						List[Any]						 =     image_processor(
             image_inputs[0]					,						return_tensors='''pt'''					,						max_patches=__lowerCamelCase  ).flattened_patches
         self.assertEqual(
             encoded_images.shape					,						(1, max_patch, expected_hidden_dim)					,						)

         # Test batched
         __A    :						Dict						 =     image_processor(
             __lowerCamelCase					,						return_tensors='''pt'''					,						max_patches=__lowerCamelCase  ).flattened_patches
         self.assertEqual(
             encoded_images.shape					,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,						)






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11							,							reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""							,							)
@require_torch
@require_vision
class 		__snake_case					(		SCREAMING_SNAKE_CASE__							,							unittest.TestCase     ):





 """simple docstring"""


 _lowerCamelCase        =     PixaStructImageProcessor if is_vision_available() else None

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						int						 =     PixaStructImageProcessingTester(self					,						num_channels=4  )
     __A    :						Any						 =     3

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return self.image_processor_tester.prepare_image_processor_dict()

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Union[str, Any]						 =     self.image_processing_class(**self.image_processor_dict  )
     self.assertTrue(hasattr(__lowerCamelCase					,						'''do_normalize'''  )  )
     self.assertTrue(hasattr(__lowerCamelCase					,						'''do_convert_rgb'''  )  )







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Optional[int]						 =     self.image_processing_class(**self.image_processor_dict  )
     # create random PIL images
     __A    :						Dict						 =     prepare_image_inputs(self.image_processor_tester					,						equal_resolution=__lowerCamelCase  )
     for image in image_inputs:
         self.assertIsInstance(__lowerCamelCase					,						Image.Image  )

     # Test not batched input
     __A    :						List[Any]						 =     (
         (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
         * (self.image_processor_tester.num_channels - 1)
     ) + 2

     for max_patch in self.image_processor_tester.max_patches:
         # Test not batched input
         __A    :						Any						 =     image_processor(
             image_inputs[0]					,						return_tensors='''pt'''					,						max_patches=__lowerCamelCase  ).flattened_patches
         self.assertEqual(
             encoded_images.shape					,						(1, max_patch, expected_hidden_dim)					,						)

         # Test batched
         __A    :						int						 =     image_processor(
             __lowerCamelCase					,						return_tensors='''pt'''					,						max_patches=__lowerCamelCase  ).flattened_patches
         self.assertEqual(
             encoded_images.shape					,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,						)




