



import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


a__			=		logging.getLogger(__name__)







class 						snake_case  (		_lowerCamelCase     ):



   '''simple docstring'''






   snake_case_    :       int								=						"""token-classification"""

   def __init__(    self							:	Optional[int] ,						lowerCAmelCase							:	Tuple) -> int:

    """simple docstring"""







    if type(lowerCAmelCase_) == dict:
     _snake_case			:     List[Any] 		=    Namespace(**lowerCAmelCase_)
    _snake_case			:     int 		=    import_module("""tasks""")
    try:
     _snake_case			:     int 		=    getattr(lowerCAmelCase_ ,						hparams.task_type)
     _snake_case			:     int 		=    token_classification_task_clazz()
    except AttributeError:
     raise ValueError(
         F'''Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
         F'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}''')
    _snake_case			:     Optional[int] 		=    self.token_classification_task.get_labels(hparams.labels)
    _snake_case			:     Dict 		=    CrossEntropyLoss().ignore_index
    super().__init__(lowerCAmelCase_ ,						len(self.labels) ,						self.mode)

   def    UpperCamelCase_     (    self							:	Dict ,						**lowerCAmelCase							:	Optional[Any]) -> List[Any]:

    """simple docstring"""







    return self.model(**lowerCAmelCase_)

   def    UpperCamelCase_     (    self							:	List[str] ,						lowerCAmelCase							:	str ,						lowerCAmelCase							:	List[Any]) -> Any:

    """simple docstring"""







    _snake_case			:     Any 		=    {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
    if self.config.model_type != "distilbert":
     _snake_case			:     Any 		=    (
         batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
     )  # XLM and RoBERTa don"t use token_type_ids

    _snake_case			:     Optional[int] 		=    self(**lowerCAmelCase_)
    _snake_case			:     Tuple 		=    outputs[0]
    # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
    return {"loss": loss}

   def    UpperCamelCase_     (    self							:	Optional[Any]) -> Optional[int]:

    """simple docstring"""







    _snake_case			:     Optional[int] 		=    self.hparams
    for mode in ["train", "dev", "test"]:
     _snake_case			:     Tuple 		=    self._feature_file(lowerCAmelCase_)
     if os.path.exists(lowerCAmelCase_) and not args.overwrite_cache:
      logger.info("""Loading features from cached file %s""" ,						lowerCAmelCase_)
      _snake_case			:     int 		=    torch.load(lowerCAmelCase_)
     else:
      logger.info("""Creating features from dataset file at %s""" ,						args.data_dir)
      _snake_case			:     Optional[Any] 		=    self.token_classification_task.read_examples_from_file(args.data_dir ,						lowerCAmelCase_)
      _snake_case			:     List[str] 		=    self.token_classification_task.convert_examples_to_features(
          lowerCAmelCase_ ,						self.labels ,						args.max_seq_length ,						self.tokenizer ,						cls_token_at_end=bool(self.config.model_type in ["""xlnet"""]) ,						cls_token=self.tokenizer.cls_token ,						cls_token_segment_id=2 if self.config.model_type in ["""xlnet"""] else 0 ,						sep_token=self.tokenizer.sep_token ,						sep_token_extra=lowerCAmelCase_ ,						pad_on_left=bool(self.config.model_type in ["""xlnet"""]) ,						pad_token=self.tokenizer.pad_token_id ,						pad_token_segment_id=self.tokenizer.pad_token_type_id ,						pad_token_label_id=self.pad_token_label_id ,						)
      logger.info("""Saving features into cached file %s""" ,						lowerCAmelCase_)
      torch.save(lowerCAmelCase_ ,						lowerCAmelCase_)

   def    UpperCamelCase_     (    self							:	List[str] ,						lowerCAmelCase							:	Any ,						lowerCAmelCase							:	Optional[int] ,						lowerCAmelCase							:	Optional[int] = False) -> Optional[int]:

    """simple docstring"""







    _snake_case			:     int 		=    self._feature_file(lowerCAmelCase_)
    logger.info("""Loading features from cached file %s""" ,						lowerCAmelCase_)
    _snake_case			:     Union[str, Any] 		=    torch.load(lowerCAmelCase_)
    _snake_case			:     Optional[int] 		=    torch.tensor([f.input_ids for f in features] ,						dtype=torch.long)
    _snake_case			:     int 		=    torch.tensor([f.attention_mask for f in features] ,						dtype=torch.long)
    if features[0].token_type_ids is not None:
     _snake_case			:     Any 		=    torch.tensor([f.token_type_ids for f in features] ,						dtype=torch.long)
    else:
     _snake_case			:     Union[str, Any] 		=    torch.tensor([0 for f in features] ,						dtype=torch.long)
     # HACK(we will not use this anymore soon)
    _snake_case			:     Union[str, Any] 		=    torch.tensor([f.label_ids for f in features] ,						dtype=torch.long)
    return DataLoader(
        TensorDataset(lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_) ,						batch_size=lowerCAmelCase_)

   def    UpperCamelCase_     (    self							:	Any ,						lowerCAmelCase							:	List[Any] ,						lowerCAmelCase							:	Tuple) -> Tuple:

    """simple docstring"""







    """Compute validation""" ""
    _snake_case			:     Optional[int] 		=    {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
    if self.config.model_type != "distilbert":
     _snake_case			:     List[Any] 		=    (
         batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
     )  # XLM and RoBERTa don"t use token_type_ids
    _snake_case			:     Dict 		=    self(**lowerCAmelCase_)
    _snake_case		,			_snake_case			:     int 		=    outputs[:2]
    _snake_case			:     List[str] 		=    logits.detach().cpu().numpy()
    _snake_case			:     int 		=    inputs["""labels"""].detach().cpu().numpy()
    return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}

   def    UpperCamelCase_     (    self							:	List[Any] ,						lowerCAmelCase							:	int) -> Optional[int]:

    """simple docstring"""







    _snake_case			:     List[Any] 		=    torch.stack([x["""val_loss"""] for x in outputs]).mean()
    _snake_case			:     Tuple 		=    np.concatenate([x["""pred"""] for x in outputs] ,						axis=0)
    _snake_case			:     Dict 		=    np.argmax(lowerCAmelCase_ ,						axis=2)
    _snake_case			:     List[Any] 		=    np.concatenate([x["""target"""] for x in outputs] ,						axis=0)

    _snake_case			:     Dict 		=    dict(enumerate(self.labels))
    _snake_case			:     int 		=    [[] for _ in range(out_label_ids.shape[0])]
    _snake_case			:     List[str] 		=    [[] for _ in range(out_label_ids.shape[0])]

    for i in range(out_label_ids.shape[0]):
     for j in range(out_label_ids.shape[1]):
      if out_label_ids[i, j] != self.pad_token_label_id:
       out_label_list[i].append(label_map[out_label_ids[i][j]])
       preds_list[i].append(label_map[preds[i][j]])

    _snake_case			:     Dict 		=    {
        """val_loss""": val_loss_mean,
        """accuracy_score""": accuracy_score(lowerCAmelCase_ ,						lowerCAmelCase_),
        """precision""": precision_score(lowerCAmelCase_ ,						lowerCAmelCase_),
        """recall""": recall_score(lowerCAmelCase_ ,						lowerCAmelCase_),
        """f1""": fa_score(lowerCAmelCase_ ,						lowerCAmelCase_),
    }

    _snake_case			:     Union[str, Any] 		=    dict(results.items())
    _snake_case			:     Optional[Any] 		=    results
    return ret, preds_list, out_label_list

   def    UpperCamelCase_     (    self							:	Optional[Any] ,						lowerCAmelCase							:	Union[str, Any]) -> str:

    """simple docstring"""







    _snake_case		,			_snake_case		,			_snake_case			:     List[Any] 		=    self._eval_end(lowerCAmelCase_)
    _snake_case			:     int 		=    ret["""log"""]
    return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

   def    UpperCamelCase_     (    self							:	Union[str, Any] ,						lowerCAmelCase							:	Dict) -> int:

    """simple docstring"""







    _snake_case		,			_snake_case		,			_snake_case			:     int 		=    self._eval_end(lowerCAmelCase_)

    # Converting to the dict required by pl
    # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
    # pytorch_lightning/trainer/logging.py#L139
    _snake_case			:     int 		=    ret["""log"""]
    # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
    return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


   @staticmethod
   def    UpperCamelCase_     (    lowerCAmelCase							:	int ,						lowerCAmelCase							:	str) -> int:

    """simple docstring"""







    BaseTransformer.add_model_specific_args(lowerCAmelCase_ ,						lowerCAmelCase_)
    parser.add_argument(
        """--task_type""" ,						default="""NER""" ,						type=lowerCAmelCase_ ,						help="""Task type to fine tune in training (e.g. NER, POS, etc)""")
    parser.add_argument(
        """--max_seq_length""" ,						default=128 ,						type=lowerCAmelCase_ ,						help=(
            """The maximum total input sequence length after tokenization. Sequences longer """
            """than this will be truncated, sequences shorter will be padded."""
        ) ,						)

    parser.add_argument(
        """--labels""" ,						default="""""" ,						type=lowerCAmelCase_ ,						help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.""" ,						)
    parser.add_argument(
        """--gpus""" ,						default=0 ,						type=lowerCAmelCase_ ,						help="""The number of GPUs allocated for this, it is by default 0 meaning none""" ,						)

    parser.add_argument(
        """--overwrite_cache""" ,						action="""store_true""" ,						help="""Overwrite the cached training and evaluation sets""")

    return parser


if __name__ == "__main__":
  a__			=		argparse.ArgumentParser()
  add_generic_args(parser, os.getcwd())
  a__			=		NERTransformer.add_model_specific_args(parser, os.getcwd())
  a__			=		parser.parse_args()
  a__			=		NERTransformer(args)
  a__			=		generic_train(model, args)

  if args.do_predict:
    # See https://github.com/huggingface/transformers/issues/3159
    # pl use this default format to create a checkpoint:
    # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
    # /pytorch_lightning/callbacks/model_checkpoint.py#L322
    a__			=		sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
    a__			=		model.load_from_checkpoint(checkpoints[-1])
    trainer.test(model)


'''simple docstring'''







from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np


class 						__UpperCAmelCase      (					_lowerCamelCase						):


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								return 0.0







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	tuple[int | float, int | float]:
	_snake_case   						=		min([-20, np.min(fft_results[1 : samplerate // 2 - 1]	)]	)
	_snake_case   						=		max([20, np.max(fft_results[1 : samplerate // 2 - 1]	)]	)
	return lowest, highest







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	None:
	_snake_case   						=		512
	_snake_case   						=		[1] + [0] * (size - 1)
	_snake_case   						=		[filter_type.process(__A	) for item in inputs]

	_snake_case   						=		[0] * (samplerate - size)  # zero-padding
	outputs += filler
	_snake_case   						=		np.abs(np.fft.fft(__A	)	)
	_snake_case   						=		20 * np.logaa(__A	)

	# Frequencies on log scale from 24 to nyquist frequency
	plt.xlim(24			,				samplerate / 2 - 1	)
	plt.xlabel('Frequency (Hz)'	)
	plt.xscale('log'	)

	# Display within reasonable bounds
	_snake_case   						=		get_bounds(__A			,				__A	)
	plt.ylim(max([-80, bounds[0]]	)			,				min([80, bounds[1]]	)	)
	plt.ylabel('Gain (dB)'	)

	plt.plot(__A	)
	plt.show()







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	None:
	_snake_case   						=		512
	_snake_case   						=		[1] + [0] * (size - 1)
	_snake_case   						=		[filter_type.process(__A	) for item in inputs]

	_snake_case   						=		[0] * (samplerate - size)  # zero-padding
	outputs += filler
	_snake_case   						=		np.angle(np.fft.fft(__A	)	)

	# Frequencies on log scale from 24 to nyquist frequency
	plt.xlim(24			,				samplerate / 2 - 1	)
	plt.xlabel('Frequency (Hz)'	)
	plt.xscale('log'	)

	plt.ylim(-2 * pi			,				2 * pi	)
	plt.ylabel('Phase shift (Radians)'	)
	plt.plot(np.unwrap(__A			,				-2 * pi	)	)
	plt.show()

