





"""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


A			      =	logging.getLogger(__name__)




@dataclass
class 					__lowercase      :







					'''simple docstring'''




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






@dataclass
class 					__lowercase      :







					'''simple docstring'''




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






def      __A   (      )		->		Optional[Any]:
				# 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.

				__a			:    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.
								__a			:    List[Any]      							=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
				else:
								__a			:    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.''')

				__a			:    Tuple      							=	import_module('''tasks''')
				try:
								__a			:    List[Any]      							=	getattr(__lowerCAmelCase ,							model_args.task_type)
								__a			:    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
				__a			:    Optional[Any]      							=	token_classification_task.get_labels(data_args.labels)
				__a			:    Dict[int, str]      							=	dict(enumerate(__lowerCAmelCase))
				__a			:    List[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.

				__a			:    Optional[Any]      							=	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 ,							)
				__a			:    Optional[int]      							=	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 ,							)
				__a			:    str      							=	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
				__a			:    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
				)
				__a			:    List[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(a_    :np.ndarray ,							a_    :np.ndarray) -> Tuple[List[int], List[int]]:
								__a			:    Dict      							=	np.argmax(__lowerCAmelCase ,							axis=2)

								__a			:    Tuple      							=	preds.shape

								__a			:    Tuple      							=	[[] for _ in range(__lowerCAmelCase)]
								__a			:    Union[str, 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(a_    :EvalPrediction) -> Dict:
								__a			:    List[str]      							=	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
				__a			:    Any      							=	DataCollatorWithPadding(__lowerCAmelCase ,							pad_to_multiple_of=8) if training_args.fpaa else None

				# Initialize our Trainer
				__a			:    str      							=	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
				__a			:    Tuple      							=	{}
				if training_args.do_eval:
								logger.info('''*** Evaluate ***''')

								__a			:    int      							=	trainer.evaluate()

								__a			:    str      							=	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:
								__a			:    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.test ,							)

								__a			:    List[Any]      							=	trainer.predict(__lowerCAmelCase)
								__a			:    List[str]      							=	align_predictions(__lowerCAmelCase ,							__lowerCAmelCase)

								__a			:    int      							=	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
								__a			:    Optional[Any]      							=	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      __A   (      a_    :Optional[int])		->		str:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
		main()







from __future__ import annotations

from typing import Any







class 		_A						:

  def __init__(    self						:	List[Any]   ,				lowerCamelCase__						:	int   ,				lowerCamelCase__						:	int   ,				lowerCamelCase__						:	float = 0			):
     """simple docstring"""

     __UpperCamelCase	,     __UpperCamelCase	:			Tuple					=					row, column
     __UpperCamelCase	:			Tuple					=					[[default_value for c in range(lowerCamelCase__			)] for r in range(lowerCamelCase__			)]

  def __str__(    self						:	List[Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					f'Matrix consist of {self.row} rows and {self.column} columns\n'

     # Make string identifier
     __UpperCamelCase	:			Optional[int]					=					0
     for row_vector in self.array:
        for obj in row_vector:
           __UpperCamelCase	:			int					=					max(lowerCamelCase__   ,				len(str(lowerCamelCase__			)			)			)
     __UpperCamelCase	:			Union[str, Any]					=					f'%{max_element_length}s'

     # Make string and return
     def single_line(lowerCamelCase__						:	list[float]			) -> str:
        nonlocal string_format_identifier
        __UpperCamelCase	:			List[Any]					=					"""["""
        line += ", ".join(string_format_identifier % (obj,) for obj in row_vector			)
        line += "]"
        return line

     s += "\n".join(single_line(lowerCamelCase__			) for row_vector in self.array			)
     return s

  def __repr__(    self						:	Any			):
     """simple docstring"""

     return str(self			)

  def    a					(    self						:	str   ,				lowerCamelCase__						:	tuple[int, int]			):
     """simple docstring"""

     if not (isinstance(lowerCamelCase__   ,				(list, tuple)			) and len(lowerCamelCase__			) == 2):
        return False
     elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
        return False
     else:
        return True

  def __getitem__(    self						:	Union[str, Any]   ,				lowerCamelCase__						:	tuple[int, int]			):
     """simple docstring"""

     assert self.validate_indicies(lowerCamelCase__			)
     return self.array[loc[0]][loc[1]]

  def __setitem__(    self						:	Union[str, Any]   ,				lowerCamelCase__						:	tuple[int, int]   ,				lowerCamelCase__						:	float			):
     """simple docstring"""

     assert self.validate_indicies(lowerCamelCase__			)
     __UpperCamelCase	:			str					=					value

  def __add__(    self						:	int   ,				lowerCamelCase__						:	Matrix			):
     """simple docstring"""

     assert isinstance(lowerCamelCase__   ,				lowerCamelCase__			)
     assert self.row == another.row and self.column == another.column

     # Add
     __UpperCamelCase	:			Tuple					=					Matrix(self.row   ,				self.column			)
     for r in range(self.row			):
        for c in range(self.column			):
           __UpperCamelCase	:			Optional[int]					=					self[r, c] + another[r, c]
     return result

  def __neg__(    self						:	Optional[int]			):
     """simple docstring"""

     __UpperCamelCase	:			str					=					Matrix(self.row   ,				self.column			)
     for r in range(self.row			):
        for c in range(self.column			):
           __UpperCamelCase	:			Any					=					-self[r, c]
     return result

  def __sub__(    self						:	Tuple   ,				lowerCamelCase__						:	Matrix			):
     """simple docstring"""

     return self + (-another)

  def __mul__(    self						:	Tuple   ,				lowerCamelCase__						:	int | float | Matrix			):
     """simple docstring"""

     if isinstance(lowerCamelCase__   ,				(int, float)			):  # Scalar multiplication
        __UpperCamelCase	:			List[str]					=					Matrix(self.row   ,				self.column			)
        for r in range(self.row			):
           for c in range(self.column			):
              __UpperCamelCase	:			Tuple					=					self[r, c] * another
        return result
     elif isinstance(lowerCamelCase__   ,				lowerCamelCase__			):  # Matrix multiplication
        assert self.column == another.row
        __UpperCamelCase	:			List[Any]					=					Matrix(self.row   ,				another.column			)
        for r in range(self.row			):
           for c in range(another.column			):
              for i in range(self.column			):
                 result[r, c] += self[r, i] * another[i, c]
        return result
     else:
        __UpperCamelCase	:			Any					=					f'Unsupported type given for another ({type(lowerCamelCase__			)})'
        raise TypeError(lowerCamelCase__			)

  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					Matrix(self.column   ,				self.row			)
     for r in range(self.row			):
        for c in range(self.column			):
           __UpperCamelCase	:			str					=					self[r, c]
     return result

  def    a					(    self						:	Any   ,				lowerCamelCase__						:	Matrix   ,				lowerCamelCase__						:	Matrix			):
     """simple docstring"""

     assert isinstance(lowerCamelCase__   ,				lowerCamelCase__			) and isinstance(lowerCamelCase__   ,				lowerCamelCase__			)
     assert self.row == self.column == u.row == v.row  # u, v should be column vector
     assert u.column == v.column == 1  # u, v should be column vector

     # Calculate
     __UpperCamelCase	:			Optional[int]					=					v.transpose()
     __UpperCamelCase	:			Tuple					=					(v_t * self * u)[0, 0] + 1
     if numerator_factor == 0:
        return None  # It's not invertable
     return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":


       def 				__lowerCamelCase     (							)     ->  None:
          # a^(-1)
          __UpperCamelCase	:			str					=					Matrix(3					,							3					,							0							)
          for i in range(3							):
             __UpperCamelCase	:			List[str]					=					1
          print(f'a^(-1) is {ainv}'							)
          # u, v
          __UpperCamelCase	:			Any					=					Matrix(3					,							1					,							0							)
          __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	:			Any					=					1, 2, -3
          __UpperCamelCase	:			int					=					Matrix(3					,							1					,							0							)
          __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	:			Dict					=					4, -2, 5
          print(f'u is {u}'							)
          print(f'v is {v}'							)
          print(f'uv^T is {u * v.transpose()}'							)
          # Sherman Morrison
          print(f'(a + uv^T)^(-1) is {ainv.sherman_morrison(__lowerCAmelCase					,							__lowerCAmelCase							)}'							)


       def 				__lowerCamelCase     (							)     ->  None:
          import doctest

          doctest.testmod()



       testa()



