



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()


UpperCamelCase_									=   logging.getLogger(__name__)







@dataclass
class       _snake_case				:






   '''simple docstring'''






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







@dataclass
class       _snake_case				:






   '''simple docstring'''






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







def       lowerCamelCase_  (    ):



    '''simple docstring'''




    UpperCAmelCase_     :       List[Any]  		=					HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)    )
    UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_     :       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."""    )

    UpperCAmelCase_     :       int  		=					import_module("""tasks"""    )

    try:
        UpperCAmelCase_     :       Optional[Any]  		=					getattr(_a   ,		model_args.task_type    )
        UpperCAmelCase_     :       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   ,		)
    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"""   ,		_a    )

    # Prepare Token Classification task
    UpperCAmelCase_     :       Union[str, Any]  		=					token_classification_task.get_labels(data_args.labels    )
    UpperCAmelCase_     :       Dict[int, str]  		=					dict(enumerate(_a    )    )
    UpperCAmelCase_     :       Tuple  		=					len(_a    )

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

    UpperCAmelCase_     :       List[Any]  		=					AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,		num_labels=_a   ,		idalabel=_a   ,		labelaid={label: i for i, label in enumerate(_a    )}   ,		cache_dir=model_args.cache_dir   ,		)
    UpperCAmelCase_     :       int  		=					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():
        UpperCAmelCase_     :       Dict  		=					TFAutoModelForTokenClassification.from_pretrained(
            model_args.model_name_or_path   ,		from_pt=bool(""".bin""" in model_args.model_name_or_path    )   ,		config=_a   ,		cache_dir=model_args.cache_dir   ,		)

    # Get datasets
    UpperCAmelCase_     :       Dict  		=					(
        TFTokenClassificationDataset(
            token_classification_task=_a   ,		data_dir=data_args.data_dir   ,		tokenizer=_a   ,		labels=_a   ,		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
    )
    UpperCAmelCase_     :       Tuple  		=					(
        TFTokenClassificationDataset(
            token_classification_task=_a   ,		data_dir=data_args.data_dir   ,		tokenizer=_a   ,		labels=_a   ,		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(_a       :						np.ndarray   ,		_a       :						np.ndarray    ) -> Tuple[List[int], List[int]]:
        UpperCAmelCase_     :       str  		=					np.argmax(_a   ,		axis=2    )
        UpperCAmelCase_		, UpperCAmelCase_     :       Tuple  		=					preds.shape
        UpperCAmelCase_     :       Union[str, Any]  		=					[[] for _ in range(_a    )]
        UpperCAmelCase_     :       Union[str, Any]  		=					[[] for _ in range(_a    )]

        for i in range(_a    ):
            for j in range(_a    ):
                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(_a       :						EvalPrediction    ) -> Dict:
        UpperCAmelCase_		, UpperCAmelCase_     :       Optional[int]  		=					align_predictions(p.predictions   ,		p.label_ids    )

        return {
            "precision": precision_score(_a   ,		_a    ),
            "recall": recall_score(_a   ,		_a    ),
            "f1": fa_score(_a   ,		_a    ),
        }

    # Initialize our Trainer
    UpperCAmelCase_     :       Dict  		=					TFTrainer(
        model=_a   ,		args=_a   ,		train_dataset=train_dataset.get_dataset() if train_dataset else None   ,		eval_dataset=eval_dataset.get_dataset() if eval_dataset else None   ,		compute_metrics=_a   ,		)

    # Training
    if training_args.do_train:
        trainer.train()
        trainer.save_model()
        tokenizer.save_pretrained(training_args.output_dir    )

    # Evaluation
    UpperCAmelCase_     :       Union[str, Any]  		=					{}
    if training_args.do_eval:
        logger.info("""*** Evaluate ***"""    )

        UpperCAmelCase_     :       Dict  		=					trainer.evaluate()
        UpperCAmelCase_     :       Dict  		=					os.path.join(training_args.output_dir   ,		"""eval_results.txt"""    )

        with open(_a   ,		"""w"""    ) as writer:
            logger.info("""***** Eval results *****"""    )

            for key, value in result.items():
                logger.info("""  %s = %s"""   ,		_a   ,		_a    )
                writer.write("""%s = %s\n""" % (key, value)    )

            results.update(_a    )

    # Predict
    if training_args.do_predict:
        UpperCAmelCase_     :       Tuple  		=					TFTokenClassificationDataset(
            token_classification_task=_a   ,		data_dir=data_args.data_dir   ,		tokenizer=_a   ,		labels=_a   ,		model_type=config.model_type   ,		max_seq_length=data_args.max_seq_length   ,		overwrite_cache=data_args.overwrite_cache   ,		mode=Split.test   ,		)

        UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_     :       str  		=					trainer.predict(test_dataset.get_dataset()    )
        UpperCAmelCase_		, UpperCAmelCase_     :       List[str]  		=					align_predictions(_a   ,		_a    )
        UpperCAmelCase_     :       int  		=					classification_report(_a   ,		_a    )

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

        UpperCAmelCase_     :       List[Any]  		=					os.path.join(training_args.output_dir   ,		"""test_results.txt"""    )

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

        # Save predictions
        UpperCAmelCase_     :       Optional[Any]  		=					os.path.join(training_args.output_dir   ,		"""test_predictions.txt"""    )

        with open(_a   ,		"""w"""    ) as writer:
            with open(os.path.join(data_args.data_dir   ,		"""test.txt"""    )   ,		"""r"""    ) as f:
                UpperCAmelCase_     :       Dict  		=					0

                for line in f:
                    if line.startswith("""-DOCSTART-"""    ) or line == "" or line == "\n":
                        writer.write(_a    )

                        if not preds_list[example_id]:
                            example_id += 1
                    elif preds_list[example_id]:
                        UpperCAmelCase_     :       Union[str, Any]  		=					line.split()[0] + """ """ + preds_list[example_id].pop(0    ) + """\n"""

                        writer.write(_a    )
                    else:
                        logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""   ,		line.split()[0]    )

    return results


if __name__ == "__main__":
      main()





from math import cos, sin







def       lowerCamelCase_  (    _a       :						float    ):



    '''simple docstring'''




    UpperCAmelCase_     :       str  		=					float(_a    )
    return [[scaling_factor * int(x == y    ) for x in range(2    )] for y in range(2    )]







def       lowerCamelCase_  (    _a       :						float    ):



    '''simple docstring'''




    UpperCAmelCase_		, UpperCAmelCase_     :       List[str]  		=					cos(_a    ), sin(_a    )
    return [[c, -s], [s, c]]







def       lowerCamelCase_  (    _a       :						float    ):



    '''simple docstring'''




    UpperCAmelCase_		, UpperCAmelCase_     :       Optional[Any]  		=					cos(_a    ), sin(_a    )
    UpperCAmelCase_     :       Dict  		=					c * s
    return [[c * c, cs], [cs, s * s]]







def       lowerCamelCase_  (    _a       :						float    ):



    '''simple docstring'''




    UpperCAmelCase_		, UpperCAmelCase_     :       List[Any]  		=					cos(_a    ), sin(_a    )
    UpperCAmelCase_     :       Optional[Any]  		=					c * s
    return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(F"    {scaling(5) = }")
print(F"  {rotation(45) = }")
print(F"{projection(45) = }")






print(F"{reflection(45) = }")
