"""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_:					Tuple     =					logging.getLogger(__name__)






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






@dataclass
class    a__    :
			snake_case_							=      field(
			    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}	)
			snake_case_							=      field(
			    default=_a					,					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=_a					,					metadata={"help": "Overwrite the cached training and evaluation sets"}	)







def 					__a   (		):






					'''simple docstring'''



					lowercase__				 =							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__				 =							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
					else:
										lowercase__ ,						lowercase__ ,						lowercase__				 =							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__				 =							import_module("tasks"      )
					try:
										lowercase__				 =							getattr(A    ,				model_args.task_type      )
										lowercase__				 =							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
					lowercase__				 =							token_classification_task.get_labels(data_args.labels      )
					lowercase__				 =							dict(enumerate(A      )      )
					lowercase__				 =							len(A      )

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

					lowercase__				 =							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    ,				)
					lowercase__				 =							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__				 =							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
					lowercase__				 =							(
					    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
					)
					lowercase__				 =							(
					    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]]:
										lowercase__				 =							np.argmax(A    ,				axis=2      )

										lowercase__ ,						lowercase__				 =							preds.shape

										lowercase__				 =							[[] for _ in range(A      )]
										lowercase__				 =							[[] 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:
										lowercase__ ,						lowercase__				 =							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
					lowercase__				 =							DataCollatorWithPadding(A    ,				pad_to_multiple_of=8      ) if training_args.fpaa else None

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

										lowercase__				 =							trainer.evaluate()

										lowercase__				 =							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:
										lowercase__				 =							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    ,				)

										lowercase__ ,						lowercase__ ,						lowercase__				 =							trainer.predict(A      )
										lowercase__ ,						lowercase__				 =							align_predictions(A    ,				A      )

										lowercase__				 =							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
										lowercase__				 =							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 					__a   (		A      ):






					'''simple docstring'''



					main()


if __name__ == "__main__":
	main()





"""simple docstring"""


from typing import Any

import numpy as np







def 					__a   (		A      ):






     '''simple docstring'''



     return np.array_equal(A    ,				matrix.conjugate().T      )







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							v.conjugate().T
     lowercase__				 =							v_star.dot(A      )
     assert isinstance(A    ,				np.ndarray      )
     return (v_star_dot.dot(A      )) / (v_star.dot(A      ))







def 					__a   (		):






     '''simple docstring'''



     lowercase__				 =							np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]      )
     lowercase__				 =							np.array([[1], [2], [3]]      )
     assert is_hermitian(A      ), f'''{a} is not hermitian.'''
     print(rayleigh_quotient(A    ,				A      )      )

     lowercase__				 =							np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]      )
     assert is_hermitian(A      ), f'''{a} is not hermitian.'''
     assert rayleigh_quotient(A    ,				A      ) == float(3      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 tests()




