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







@dataclass
class 					lowerCamelCase		:
     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=A_							,			metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}							,			)
     UpperCAmelCase__    :							int									=		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."
             )
         }							,			)
     UpperCAmelCase__    :							bool									=		field(
         default=A_							,			metadata={"help": "Overwrite the cached training and evaluation sets"}  )



def 						lowercase_      (       )							->							str:
    """simple docstring"""

    snake_case								= 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					,							snake_case					,							snake_case								= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
    else:
        snake_case					,							snake_case					,							snake_case								= 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								= import_module("tasks"		)
    try:
        snake_case								= getattr(A__			,		model_args.task_type		)
        snake_case								= 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"			,		A__		)

    # Set seed
    set_seed(training_args.seed		)

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

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

    snake_case								= 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			,		)
    snake_case								= 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								= AutoModelForTokenClassification.from_pretrained(
        model_args.model_name_or_path			,		from_tf=bool(".ckpt" in model_args.model_name_or_path		)			,		config=A__			,		cache_dir=model_args.cache_dir			,		)

    # Get datasets
    snake_case								= (
        TokenClassificationDataset(
            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
    )
    snake_case								= (
        TokenClassificationDataset(
            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__			,		A__		) -> Tuple[List[int], List[int]]:
        snake_case								= np.argmax(A__			,		axis=2		)

        snake_case					,							snake_case								= preds.shape

        snake_case								= [[] for _ in range(A__		)]
        snake_case								= [[] for _ in range(A__		)]

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

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

    # Initialize our Trainer
    snake_case								= Trainer(
        model=A__			,		args=A__			,		train_dataset=A__			,		eval_dataset=A__			,		compute_metrics=A__			,		data_collator=A__			,		)

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

        snake_case								= trainer.evaluate()

        snake_case								= os.path.join(training_args.output_dir			,		"eval_results.txt"		)
        if trainer.is_world_process_zero():
            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:
        snake_case								= TokenClassificationDataset(
            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			,		)

        snake_case					,							snake_case					,							snake_case								= trainer.predict(A__		)
        snake_case					,							snake_case								= align_predictions(A__			,		A__		)

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

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

    return results



def 						lowercase_      (       A__		)							->							Union[str, Any]:
    """simple docstring"""

    main()


if __name__ == "__main__":
   main()



from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap



def 						lowercase_      (       A__		)							->							str:
    """simple docstring"""

    return getitem, k



def 						lowercase_      (       A__			,		A__		)							->							str:
    """simple docstring"""

    return setitem, k, v



def 						lowercase_      (       A__		)							->							List[Any]:
    """simple docstring"""

    return delitem, k



def 						lowercase_      (       A__			,		A__			,		*A__		)							->							str:
    """simple docstring"""

    try:
        return fun(A__			,		*A__		), None
    except Exception as e:
        return None, e


_A   =			(
    _set("key_a", "val_a"),
    _set("key_b", "val_b"),
)

_A   =			[
    _set("key_a", "val_a"),
    _set("key_a", "val_b"),
]

_A   =			[
    _set("key_a", "val_a"),
    _set("key_b", "val_b"),
    _del("key_a"),
    _del("key_b"),
    _set("key_a", "val_a"),
    _del("key_a"),
]

_A   =			[
    _get("key_a"),
    _del("key_a"),
    _set("key_a", "val_a"),
    _del("key_a"),
    _del("key_a"),
    _get("key_a"),
]

_A   =			[
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

_A   =			[
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set("key_a", "val_b"),
]



@pytest.mark.parametrize(
    "operations"			,		(
        pytest.param(_add_items			,		id="add items"		),
        pytest.param(_overwrite_items			,		id="overwrite items"		),
        pytest.param(_delete_items			,		id="delete items"		),
        pytest.param(_access_absent_items			,		id="access absent items"		),
        pytest.param(_add_with_resize_up			,		id="add with resize up"		),
        pytest.param(_add_with_resize_down			,		id="add with resize down"		),
    )			,		)
def 						lowercase_      (       A__		)							->							List[Any]:
    """simple docstring"""

    snake_case								= HashMap(initial_block_size=4		)
    snake_case								= {}
    for _, (fun, *args) in enumerate(A__		):
        snake_case					,							snake_case								= _run_operation(A__			,		A__			,		*A__		)
        snake_case					,							snake_case								= _run_operation(A__			,		A__			,		*A__		)
        assert my_res == py_res
        assert str(A__		) == str(A__		)
        assert set(A__		) == set(A__		)
        assert len(A__		) == len(A__		)
        assert set(my.items()		) == set(py.items()		)





def 						lowercase_      (       )							->							Optional[int]:
    """simple docstring"""

    def is_public(A__		) -> bool:
        return not name.startswith("_"		)

    snake_case								= {name for name in dir({}		) if is_public(A__		)}
    snake_case								= {name for name in dir(HashMap()		) if is_public(A__		)}

    assert dict_public_names > hash_public_names

