



import logging
import os
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 classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


_lowerCAmelCase:	List[Any]      							=					logging.getLogger(__name__)




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







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







def      _lowercase(				):
							# 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__     						=HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)    )
							a__						,    a__						,    a__     						=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__     						=import_module('tasks'    )

							try:
														a__     						=getattr(lowercase__						,    model_args.task_type    )
														a__     						=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						,    )
							logger.info(
							    'n_replicas: %s, distributed training: %s, 16-bits training: %s'						,    training_args.n_replicas						,    bool(training_args.n_replicas > 1    )						,    training_args.fpaa						,    )
							logger.info('Training/evaluation parameters %s'						,    lowercase__    )

							# Prepare Token Classification task
							a__     						=token_classification_task.get_labels(data_args.labels    )
							a__     						=dict(enumerate(lowercase__    )    )
							a__     						=len(lowercase__    )

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

							a__     						=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						,    )
							a__     						=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						,    )

							with training_args.strategy.scope():
														a__     						=TFAutoModelForTokenClassification.from_pretrained(
														    model_args.model_name_or_path						,    from_pt=bool('.bin' in model_args.model_name_or_path    )						,    config=lowercase__						,    cache_dir=model_args.cache_dir						,    )

							# Get datasets
							a__     						=(
							    TFTokenClassificationDataset(
							        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
							)
							a__     						=(
							    TFTokenClassificationDataset(
							        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(__a							:	np.ndarray						,    __a							:	np.ndarray    ) -> Tuple[List[int], List[int]]:
														a__     						=np.argmax(lowercase__						,    axis=2    )
														a__						,    a__     						=preds.shape
														a__     						=[[] for _ in range(lowercase__    )]
														a__     						=[[] for _ in range(lowercase__    )]

														for i in range(lowercase__    ):
																					for j in range(lowercase__    ):
																												if label_ids[i, j] != -100:
																																			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__						,    a__     						=align_predictions(p.predictions						,    p.label_ids    )

														return {
														    "precision": precision_score(lowercase__						,    lowercase__    ),
														    "recall": recall_score(lowercase__						,    lowercase__    ),
														    "f1": fa_score(lowercase__						,    lowercase__    ),
														}

							# Initialize our Trainer
							a__     						=TFTrainer(
							    model=lowercase__						,    args=lowercase__						,    train_dataset=train_dataset.get_dataset() if train_dataset else None						,    eval_dataset=eval_dataset.get_dataset() if eval_dataset else None						,    compute_metrics=lowercase__						,    )

							# Training
							if training_args.do_train:
														trainer.train()
														trainer.save_model()
														tokenizer.save_pretrained(training_args.output_dir    )

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

														a__     						=trainer.evaluate()
														a__     						=os.path.join(training_args.output_dir						,    'eval_results.txt'    )

														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:
														a__     						=TFTokenClassificationDataset(
														    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						,    )

														a__						,    a__						,    a__     						=trainer.predict(test_dataset.get_dataset()    )
														a__						,    a__     						=align_predictions(lowercase__						,    lowercase__    )
														a__     						=classification_report(lowercase__						,    lowercase__    )

														logger.info('\n%s'						,    lowercase__    )

														a__     						=os.path.join(training_args.output_dir						,    'test_results.txt'    )

														with open(lowercase__						,    'w'    ) as writer:
																					writer.write('%s\n' % report    )

														# Save predictions
														a__     						=os.path.join(training_args.output_dir						,    'test_predictions.txt'    )

														with open(lowercase__						,    'w'    ) as writer:
																					with open(os.path.join(data_args.data_dir						,    'test.txt'    )						,    'r'    ) as f:
																												a__     						=0

																												for line in f:
																																			if line.startswith('-DOCSTART-'    ) or line == "" or line == "\n":
																																										writer.write(lowercase__    )

																																										if not preds_list[example_id]:
																																																	example_id += 1
																																			elif preds_list[example_id]:
																																										a__     						=line.split()[0] + ' ' + preds_list[example_id].pop(0    ) + '\n'

																																										writer.write(lowercase__    )
																																			else:
																																										logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.'						,    line.split()[0]    )

							return results


if __name__ == "__main__":
	main()





_lowerCAmelCase:	List[str]      							=					[0.33, 0.44, 0.55, 0.44, 0.33]
_lowerCAmelCase:	Union[str, Any]      							=					3
_lowerCAmelCase:	Dict      							=					5

# Buffer Space (with Parameters Space)
_lowerCAmelCase:	list[float]      							=					[]
_lowerCAmelCase:	list[float]      							=					[]

# Machine Time
_lowerCAmelCase:	Optional[Any]      							=					0






def      _lowercase(				__a							:	Optional[Any]    ):
							global buffer_space, params_space, machine_time, K, m, t

							# Choosing Dynamical Systems (All)
							for key, value in enumerate(__a    ):
														# Evolution Parameter
														a__     						=float(seed / value    )

														# Control Theory: Orbit Change
														a__     						=(buffer_space[(key + 1) % m] + e) % 1

														# Control Theory: Trajectory Change
														a__     						=(params_space[key] + e) % 1 + 3

														# Modification (Transition Function) - Jumps
														a__     						=round(float(r * value * (1 - value)    )						,    10    )
														a__     						=r  # Saving to Parameters Space

							# Logistic Map
							assert max(__a    ) < 1
							assert max(__a    ) < 4

							# Machine Time
							machine_time += 1






def      _lowercase(				):
							global buffer_space, params_space, machine_time, K, m, t

							# PRNG (Xorshift by George Marsaglia)
							def xorshift(__a							:	str						,    __a							:	Dict    ):
														x ^= y >> 13
														y ^= x << 17
														x ^= y >> 5
														return x

							# Choosing Dynamical Systems (Increment)
							a__     						=machine_time % m

							# Evolution (Time Length)
							for _ in range(0						,    __a    ):
														# Variables (Position + Parameters)
														a__     						=params_space[key]
														a__     						=buffer_space[key]

														# Modification (Transition Function) - Flow
														a__     						=round(float(r * value * (1 - value)    )						,    10    )
														a__     						=(machine_time * 0.01 + r * 1.01) % 1 + 3

							# Choosing Chaotic Data
							a__     						=int(buffer_space[(key + 2) % m] * (10**10)    )
							a__     						=int(buffer_space[(key - 2) % m] * (10**10)    )

							# Machine Time
							machine_time += 1

							return xorshift(__a						,    __a    ) % 0Xffff_ffff






def      _lowercase(				):
							global buffer_space, params_space, machine_time, K, m, t

							a__     						=K
							a__     						=[0] * m
							a__     						=0


if __name__ == "__main__":
	# Initialization
	reset()

	# Pushing Data (Input)
	import random

	_lowerCAmelCase:	int      							=					random.sample(range(0xff_fff_fff), 100)
	for chunk in message:
		push(chunk)

	# for controlling
	_lowerCAmelCase:	List[Any]      							=					''

	# Pulling Data (Output)
	while inp in ("e", "E"):
		print(F"""{format(pull(), '#04x')}""")
		print(buffer_space)
		print(params_space)
		_lowerCAmelCase:	str      							=					input('(e)exit? ').strip()

