






'''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


__lowerCamelCase   :					str   							=					logging.getLogger(__name__)







@dataclass
class    A_	:
							"""simple docstring"""




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







@dataclass
class    A_	:
							"""simple docstring"""




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

def 	__UpperCAmelCase       ( )->    List[str]:



			"""simple docstring"""



			snake_case_				:					Optional[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_				:					str   =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
			else:
						snake_case_				:					List[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."						)

			snake_case_				:					str   =       import_module("tasks"						)
			try:
						snake_case_				:					Dict   =       getattr(__magic_name__     ,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"     ,__magic_name__						)

			# Set seed
			set_seed(training_args.seed						)

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

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

			snake_case_				:					List[Any]   =       AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path     ,num_labels=__magic_name__     ,idalabel=__magic_name__     ,labelaid={label: i for i, label in enumerate(__magic_name__						)}     ,cache_dir=model_args.cache_dir     ,)
			snake_case_				:					Optional[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     ,)
			snake_case_				:					Any   =       AutoModelForTokenClassification.from_pretrained(
			    model_args.model_name_or_path     ,from_tf=bool(".ckpt" in model_args.model_name_or_path						)     ,config=__magic_name__     ,cache_dir=model_args.cache_dir     ,)

			# Get datasets
			snake_case_				:					str   =       (
			    TokenClassificationDataset(
			        token_classification_task=__magic_name__     ,data_dir=data_args.data_dir     ,tokenizer=__magic_name__     ,labels=__magic_name__     ,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_				:					List[str]   =       (
			    TokenClassificationDataset(
			        token_classification_task=__magic_name__     ,data_dir=data_args.data_dir     ,tokenizer=__magic_name__     ,labels=__magic_name__     ,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(__magic_name__     ,__magic_name__						) -> Tuple[List[int], List[int]]:
						snake_case_				:					Dict   =       np.argmax(__magic_name__     ,axis=2						)

						snake_case_				:					Dict   =       preds.shape

						snake_case_				:					Tuple   =       [[] for _ in range(__magic_name__						)]
						snake_case_				:					Tuple   =       [[] for _ in range(__magic_name__						)]

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

			# Data collator
			snake_case_				:					Optional[int]   =       DataCollatorWithPadding(__magic_name__     ,pad_to_multiple_of=8						) if training_args.fpaa else None

			# Initialize our Trainer
			snake_case_				:					Tuple   =       Trainer(
			    model=__magic_name__     ,args=__magic_name__     ,train_dataset=__magic_name__     ,eval_dataset=__magic_name__     ,compute_metrics=__magic_name__     ,data_collator=__magic_name__     ,)

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

						snake_case_				:					Tuple   =       trainer.evaluate()

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

									results.update(__magic_name__						)

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

						snake_case_				:					str   =       trainer.predict(__magic_name__						)
						snake_case_				:					List[Any]   =       align_predictions(__magic_name__     ,__magic_name__						)

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

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

			return results

def 	__UpperCAmelCase       ( __magic_name__						)->    Dict:



			"""simple docstring"""



			main()


if __name__ == "__main__":
						main()












'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase   :					List[str]   							=					logging.get_logger(__name__)

__lowerCamelCase   :					int   							=					{
    '''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}







class    A_	(a_							):
							"""simple docstring"""




							a__   		=	'''cvt'''

							def __init__(		self			:List[Any]       ,							lowerCAmelCase__			:Optional[int]=3       ,							lowerCAmelCase__			:Any=[7, 3, 3]       ,							lowerCAmelCase__			:Dict=[4, 2, 2]       ,							lowerCAmelCase__			:Union[str, Any]=[2, 1, 1]       ,							lowerCAmelCase__			:Any=[64, 192, 384]       ,							lowerCAmelCase__			:List[str]=[1, 3, 6]       ,							lowerCAmelCase__			:str=[1, 2, 10]       ,							lowerCAmelCase__			:Any=[4.0, 4.0, 4.0]       ,							lowerCAmelCase__			:int=[0.0, 0.0, 0.0]       ,							lowerCAmelCase__			:Optional[Any]=[0.0, 0.0, 0.0]       ,							lowerCAmelCase__			:Dict=[0.0, 0.0, 0.1]       ,							lowerCAmelCase__			:List[Any]=[True, True, True]       ,							lowerCAmelCase__			:List[Any]=[False, False, True]       ,							lowerCAmelCase__			:Dict=["dw_bn", "dw_bn", "dw_bn"]       ,							lowerCAmelCase__			:Any=[3, 3, 3]       ,							lowerCAmelCase__			:Tuple=[1, 1, 1]       ,							lowerCAmelCase__			:Optional[int]=[2, 2, 2]       ,							lowerCAmelCase__			:Union[str, Any]=[1, 1, 1]       ,							lowerCAmelCase__			:Any=[1, 1, 1]       ,							lowerCAmelCase__			:List[str]=0.0_2       ,							lowerCAmelCase__			:Dict=1E-1_2       ,							**lowerCAmelCase__			:Optional[Any]       ,							)				->  str:


										'''simple docstring'''




										super().__init__(**lowerCAmelCase__				)
										snake_case_				:					int   =       num_channels
										snake_case_				:					int   =       patch_sizes
										snake_case_				:					Optional[Any]   =       patch_stride
										snake_case_				:					Dict   =       patch_padding
										snake_case_				:					Tuple   =       embed_dim
										snake_case_				:					Optional[int]   =       num_heads
										snake_case_				:					Union[str, Any]   =       depth
										snake_case_				:					Optional[int]   =       mlp_ratio
										snake_case_				:					Tuple   =       attention_drop_rate
										snake_case_				:					str   =       drop_rate
										snake_case_				:					Tuple   =       drop_path_rate
										snake_case_				:					Any   =       qkv_bias
										snake_case_				:					Union[str, Any]   =       cls_token
										snake_case_				:					int   =       qkv_projection_method
										snake_case_				:					Any   =       kernel_qkv
										snake_case_				:					Union[str, Any]   =       padding_kv
										snake_case_				:					str   =       stride_kv
										snake_case_				:					Dict   =       padding_q
										snake_case_				:					Tuple   =       stride_q
										snake_case_				:					Any   =       initializer_range
										snake_case_				:					Any   =       layer_norm_eps




