



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


__snake_case    :		int  =logging.getLogger(__name__)



@dataclass
class 	lowerCamelCase__      :




		'''simple docstring'''



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



@dataclass
class 	lowerCamelCase__      :




		'''simple docstring'''



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






def      lowerCAmelCase__		(   ):





				'''simple docstring'''

				lowerCAmelCase__					:    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.
								lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__					:    Optional[int]												=					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
				else:
								lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__					:    List[str]												=					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.''')

				lowerCAmelCase__					:    Optional[Any]												=					import_module('''tasks''')
				try:
								lowerCAmelCase__					:    Any												=					getattr(lowerCamelCase_	,model_args.task_type)
								lowerCAmelCase__					:    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'''	,lowerCamelCase_)

				# Set seed
				set_seed(training_args.seed)

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

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

				lowerCAmelCase__					:    List[str]												=					AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,num_labels=lowerCamelCase_	,idalabel=lowerCamelCase_	,labelaid={label: i for i, label in enumerate(lowerCamelCase_)}	,cache_dir=model_args.cache_dir	,)
				lowerCAmelCase__					:    str												=					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	,)
				lowerCAmelCase__					:    Union[str, Any]												=					AutoModelForTokenClassification.from_pretrained(
				    model_args.model_name_or_path	,from_tf=bool('''.ckpt''' in model_args.model_name_or_path)	,config=lowerCamelCase_	,cache_dir=model_args.cache_dir	,)

				# Get datasets
				lowerCAmelCase__					:    List[str]												=					(
				    TokenClassificationDataset(
				        token_classification_task=lowerCamelCase_	,data_dir=data_args.data_dir	,tokenizer=lowerCamelCase_	,labels=lowerCamelCase_	,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
				)
				lowerCAmelCase__					:    Any												=					(
				    TokenClassificationDataset(
				        token_classification_task=lowerCamelCase_	,data_dir=data_args.data_dir	,tokenizer=lowerCamelCase_	,labels=lowerCamelCase_	,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(lowerCamelCase_		:		np.ndarray	,lowerCamelCase_		:		np.ndarray) -> Tuple[List[int], List[int]]:
								lowerCAmelCase__					:    List[str]												=					np.argmax(lowerCamelCase_	,axis=2)

								lowerCAmelCase__						,		lowerCAmelCase__					:    Tuple												=					preds.shape

								lowerCAmelCase__					:    Tuple												=					[[] for _ in range(lowerCamelCase_)]
								lowerCAmelCase__					:    Optional[Any]												=					[[] for _ in range(lowerCamelCase_)]

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

				# Data collator
				lowerCAmelCase__					:    Union[str, Any]												=					DataCollatorWithPadding(lowerCamelCase_	,pad_to_multiple_of=8) if training_args.fpaa else None

				# Initialize our Trainer
				lowerCAmelCase__					:    Union[str, Any]												=					Trainer(
				    model=lowerCamelCase_	,args=lowerCamelCase_	,train_dataset=lowerCamelCase_	,eval_dataset=lowerCamelCase_	,compute_metrics=lowerCamelCase_	,data_collator=lowerCamelCase_	,)

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

								lowerCAmelCase__					:    Any												=					trainer.evaluate()

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

												results.update(lowerCamelCase_)

    # Predict
				if training_args.do_predict:
								lowerCAmelCase__					:    Dict												=					TokenClassificationDataset(
								    token_classification_task=lowerCamelCase_	,data_dir=data_args.data_dir	,tokenizer=lowerCamelCase_	,labels=lowerCamelCase_	,model_type=config.model_type	,max_seq_length=data_args.max_seq_length	,overwrite_cache=data_args.overwrite_cache	,mode=Split.test	,)

								lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__					:    Dict												=					trainer.predict(lowerCamelCase_)
								lowerCAmelCase__						,		lowerCAmelCase__					:    Tuple												=					align_predictions(lowerCamelCase_	,lowerCamelCase_)

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

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

				return results






def      lowerCAmelCase__		(   lowerCamelCase_		:		Optional[int]):





				'''simple docstring'''

				main()


if __name__ == "__main__":
							main()






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


__snake_case    :		Any  =logging.get_logger(__name__)

__snake_case    :		Tuple  ={
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     ="""vit_msn"""



		def __init__(self		,__lowerCamelCase=7_68		,__lowerCamelCase=12		,__lowerCamelCase=12		,__lowerCamelCase=30_72		,__lowerCamelCase="gelu"		,__lowerCamelCase=0.0		,__lowerCamelCase=0.0		,__lowerCamelCase=0.02		,__lowerCamelCase=1e-06		,__lowerCamelCase=2_24		,__lowerCamelCase=16		,__lowerCamelCase=3		,__lowerCamelCase=True		,**__lowerCamelCase		,)					->      Any:

						"""simple docstring"""






						super().__init__(**__lowerCamelCase			)

						lowerCAmelCase__					:    List[Any]												=					hidden_size
						lowerCAmelCase__					:    str												=					num_hidden_layers
						lowerCAmelCase__					:    List[str]												=					num_attention_heads
						lowerCAmelCase__					:    Optional[int]												=					intermediate_size
						lowerCAmelCase__					:    List[Any]												=					hidden_act
						lowerCAmelCase__					:    Tuple												=					hidden_dropout_prob
						lowerCAmelCase__					:    List[Any]												=					attention_probs_dropout_prob
						lowerCAmelCase__					:    int												=					initializer_range
						lowerCAmelCase__					:    Union[str, Any]												=					layer_norm_eps
						lowerCAmelCase__					:    List[str]												=					image_size
						lowerCAmelCase__					:    str												=					patch_size
						lowerCAmelCase__					:    Optional[int]												=					num_channels
						lowerCAmelCase__					:    int												=					qkv_bias

