





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:	Dict = logging.getLogger(__name__)


@dataclass
class       __lowerCAmelCase					:


	'''simple docstring'''





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


@dataclass
class       __lowerCAmelCase					:


	'''simple docstring'''





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


def       SCREAMING_SNAKE_CASE__  (       )   ->							Union[str, Any]:




		'''simple docstring'''
		# 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.

		lowercase__					:      str      =		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.
				lowercase__   ,				lowercase__   ,				lowercase__					:      str      =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
		else:
				lowercase__   ,				lowercase__   ,				lowercase__					:      Union[str, Any]      =		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.'			)

		lowercase__					:      List[str]      =		import_module('tasks'			)
		try:
				lowercase__					:      str      =		getattr(_lowercase   ,    model_args.task_type			)
				lowercase__					:      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'   ,    _lowercase			)

		# Set seed
		set_seed(training_args.seed			)

		# Prepare CONLL-2003 task
		lowercase__					:      Tuple      =		token_classification_task.get_labels(data_args.labels			)
		lowercase__					:      Dict[int, str]      =		dict(enumerate(_lowercase			)			)
		lowercase__					:      Optional[int]      =		len(_lowercase			)

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

		lowercase__					:      Dict      =		AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,    num_labels=_lowercase   ,    idalabel=_lowercase   ,    labelaid={label: i for i, label in enumerate(_lowercase			)}   ,    cache_dir=model_args.cache_dir   ,    )
		lowercase__					:      Dict      =		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   ,    )
		lowercase__					:      Any      =		AutoModelForTokenClassification.from_pretrained(
		    model_args.model_name_or_path   ,    from_tf=bool('.ckpt' in model_args.model_name_or_path			)   ,    config=_lowercase   ,    cache_dir=model_args.cache_dir   ,    )

		# Get datasets
		lowercase__					:      int      =		(
		    TokenClassificationDataset(
		        token_classification_task=_lowercase   ,    data_dir=data_args.data_dir   ,    tokenizer=_lowercase   ,    labels=_lowercase   ,    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
		)
		lowercase__					:      List[Any]      =		(
		    TokenClassificationDataset(
		        token_classification_task=_lowercase   ,    data_dir=data_args.data_dir   ,    tokenizer=_lowercase   ,    labels=_lowercase   ,    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]]:
				lowercase__					:      str      =		np.argmax(_lowercase   ,    axis=2			)

				lowercase__   ,				lowercase__					:      Tuple      =		preds.shape

				lowercase__					:      Tuple      =		[[] for _ in range(_lowercase			)]
				lowercase__					:      Dict      =		[[] for _ in range(_lowercase			)]

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

		# Data collator
		lowercase__					:      List[str]      =		DataCollatorWithPadding(_lowercase   ,    pad_to_multiple_of=8			) if training_args.fpaa else None

		# Initialize our Trainer
		lowercase__					:      List[str]      =		Trainer(
		    model=_lowercase   ,    args=_lowercase   ,    train_dataset=_lowercase   ,    eval_dataset=_lowercase   ,    compute_metrics=_lowercase   ,    data_collator=_lowercase   ,    )

		# 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
		lowercase__					:      List[str]      =		{}
		if training_args.do_eval:
				logger.info('*** Evaluate ***'			)

				lowercase__					:      Union[str, Any]      =		trainer.evaluate()

				lowercase__					:      Tuple      =		os.path.join(training_args.output_dir   ,    'eval_results.txt'			)
				if trainer.is_world_process_zero():
						with open(_lowercase   ,    'w'			) as writer:
								logger.info('***** Eval results *****'			)
								for key, value in result.items():
										logger.info('  %s = %s'   ,    _lowercase   ,    _lowercase			)
										writer.write('%s = %s\n' % (key, value)			)

						results.update(_lowercase			)

    # Predict
		if training_args.do_predict:
				lowercase__					:      List[Any]      =		TokenClassificationDataset(
				    token_classification_task=_lowercase   ,    data_dir=data_args.data_dir   ,    tokenizer=_lowercase   ,    labels=_lowercase   ,    model_type=config.model_type   ,    max_seq_length=data_args.max_seq_length   ,    overwrite_cache=data_args.overwrite_cache   ,    mode=Split.test   ,    )

				lowercase__   ,				lowercase__   ,				lowercase__					:      Any      =		trainer.predict(_lowercase			)
				lowercase__   ,				lowercase__					:      Optional[int]      =		align_predictions(_lowercase   ,    _lowercase			)

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

        # Save predictions
				lowercase__					:      Optional[int]      =		os.path.join(training_args.output_dir   ,    'test_predictions.txt'			)
				if trainer.is_world_process_zero():
						with open(_lowercase   ,    'w'			) as writer:
								with open(os.path.join(data_args.data_dir   ,    'test.txt'			)   ,    'r'			) as f:
										token_classification_task.write_predictions_to_file(_lowercase   ,    _lowercase   ,    _lowercase			)

		return results


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[int]			)   ->							Tuple:




		'''simple docstring'''
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
			main()









from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase:	Optional[int] = logging.get_logger(__name__)

__UpperCamelCase:	Optional[int] = {
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				"mobilenet_v1"

	def __init__(						self:						Dict,					lowerCamelCase_:						Any=3,					lowerCamelCase_:						Dict=224,					lowerCamelCase_:						int=1.0,					lowerCamelCase_:						List[Any]=8,					lowerCamelCase_:						int="relu6",					lowerCamelCase_:						Optional[int]=True,					lowerCamelCase_:						Any=0.9_9_9,					lowerCamelCase_:						List[str]=0.0_2,					lowerCamelCase_:						Optional[int]=0.0_0_1,					**lowerCamelCase_:						Tuple,					):
			super().__init__(**lowerCamelCase_			)

			if depth_multiplier <= 0:
					raise ValueError('depth_multiplier must be greater than zero.'			)

			lowercase__					:      Optional[int]      =		num_channels
			lowercase__					:      List[Any]      =		image_size
			lowercase__					:      str      =		depth_multiplier
			lowercase__					:      Optional[Any]      =		min_depth
			lowercase__					:      int      =		hidden_act
			lowercase__					:      Dict      =		tf_padding
			lowercase__					:      Optional[Any]      =		classifier_dropout_prob
			lowercase__					:      int      =		initializer_range
			lowercase__					:      Any      =		layer_norm_eps


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				version.parse("1.11"      )

	@property
	def    snake_case__(						self:						Optional[int]			):
			return OrderedDict([('pixel_values', {0: 'batch'})]			)

	@property
	def    snake_case__(						self:						Dict			):
			if self.task == "image-classification":
					return OrderedDict([('logits', {0: 'batch'})]			)
			else:
					return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]			)





	@property
	def    snake_case__(						self:						List[Any]			):
			return 1E-4


