

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 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Optional[Any]       				=  "token-classification"







	def __init__(   self: Tuple  , __UpperCamelCase: Tuple      ):

						'''simple docstring'''





						if type(__UpperCamelCase      ) == dict:
											__magic_name__							=    Namespace(**__UpperCamelCase      )
						__magic_name__							=    import_module('tasks'      )
						try:
											__magic_name__							=    getattr(__UpperCamelCase  , hparams.task_type      )
											__magic_name__							=    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__()}'      )
						__magic_name__							=    self.token_classification_task.get_labels(hparams.labels      )
						__magic_name__							=    CrossEntropyLoss().ignore_index
						super().__init__(__UpperCamelCase  , len(self.labels      )  , self.mode      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , **__UpperCamelCase: Optional[Any]      ):

						'''simple docstring'''





						return self.model(**__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: str  , __UpperCamelCase: int  , __UpperCamelCase: str      ):

						'''simple docstring'''





						__magic_name__							=    {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
						if self.config.model_type != "distilbert":
											__magic_name__							=    (
											    batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
											)  # XLM and RoBERTa don"t use token_type_ids

						__magic_name__							=    self(**__UpperCamelCase      )
						__magic_name__							=    outputs[0]
						# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
						return {"loss": loss}







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						__magic_name__							=    self.hparams
						for mode in ["train", "dev", "test"]:
											__magic_name__							=    self._feature_file(__UpperCamelCase      )
											if os.path.exists(__UpperCamelCase      ) and not args.overwrite_cache:
																logger.info('Loading features from cached file %s'  , __UpperCamelCase      )
																__magic_name__							=    torch.load(__UpperCamelCase      )
											else:
																logger.info('Creating features from dataset file at %s'  , args.data_dir      )
																__magic_name__							=    self.token_classification_task.read_examples_from_file(args.data_dir  , __UpperCamelCase      )
																__magic_name__							=    self.token_classification_task.convert_examples_to_features(
																    __UpperCamelCase  , 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=__UpperCamelCase  , 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'  , __UpperCamelCase      )
																torch.save(__UpperCamelCase  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , __UpperCamelCase: int  , __UpperCamelCase: int  , __UpperCamelCase: bool = False      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]  , __UpperCamelCase: Optional[int]  , __UpperCamelCase: Any      ):

						'''simple docstring'''





						"""Compute validation""" ""
						__magic_name__							=    {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
						if self.config.model_type != "distilbert":
											__magic_name__							=    (
											    batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
											)  # XLM and RoBERTa don"t use token_type_ids
						__magic_name__							=    self(**__UpperCamelCase      )
						__magic_name__,	__magic_name__							=    outputs[:2]
						__magic_name__							=    logits.detach().cpu().numpy()
						__magic_name__							=    inputs['labels'].detach().cpu().numpy()
						return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]  , __UpperCamelCase: Optional[int]      ):

						'''simple docstring'''





						__magic_name__							=    torch.stack([x['val_loss'] for x in outputs]      ).mean()
						__magic_name__							=    np.concatenate([x['pred'] for x in outputs]  , axis=0      )
						__magic_name__							=    np.argmax(__UpperCamelCase  , axis=2      )
						__magic_name__							=    np.concatenate([x['target'] for x in outputs]  , axis=0      )

						__magic_name__							=    dict(enumerate(self.labels      )      )
						__magic_name__							=    [[] for _ in range(out_label_ids.shape[0]      )]
						__magic_name__							=    [[] 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]]      )

						__magic_name__							=    {
						    'val_loss': val_loss_mean,
						    'accuracy_score': accuracy_score(__UpperCamelCase  , __UpperCamelCase      ),
						    'precision': precision_score(__UpperCamelCase  , __UpperCamelCase      ),
						    'recall': recall_score(__UpperCamelCase  , __UpperCamelCase      ),
						    'f1': fa_score(__UpperCamelCase  , __UpperCamelCase      ),
						}

						__magic_name__							=    dict(results.items()      )
						__magic_name__							=    results
						return ret, preds_list, out_label_list







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , __UpperCamelCase: Dict      ):

						'''simple docstring'''





						__magic_name__,	__magic_name__,	__magic_name__							=    self._eval_end(__UpperCamelCase      )
						__magic_name__							=    ret['log']
						return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]  , __UpperCamelCase: List[str]      ):

						'''simple docstring'''





						__magic_name__,	__magic_name__,	__magic_name__							=    self._eval_end(__UpperCamelCase      )

						# Converting to the dict required by pl
						# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
						# pytorch_lightning/trainer/logging.py#L139
						__magic_name__							=    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 	_SCREAMING_SNAKE_CASE							(   __UpperCamelCase: List[Any]  , __UpperCamelCase: str      ):

						'''simple docstring'''





						BaseTransformer.add_model_specific_args(__UpperCamelCase  , __UpperCamelCase      )
						parser.add_argument(
						    '--task_type'  , default='NER'  , type=__UpperCamelCase  , help='Task type to fine tune in training (e.g. NER, POS, etc)'      )
						parser.add_argument(
						    '--max_seq_length'  , default=1_28  , type=__UpperCamelCase  , 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=__UpperCamelCase  , help='Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'  , )
						parser.add_argument(
						    '--gpus'  , default=0  , type=__UpperCamelCase  , 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)







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


A__						=							logging.get_logger(__name__)

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Tuple       				=  ["pixel_values"]







	def __init__(   self: Optional[Any]  , __UpperCamelCase: bool = True  , __UpperCamelCase: Dict[str, int] = None  , __UpperCamelCase: int = 0.9  , __UpperCamelCase: PILImageResampling = PILImageResampling.BICUBIC  , __UpperCamelCase: bool = True  , __UpperCamelCase: Dict[str, int] = None  , __UpperCamelCase: Union[int, float] = 1 / 2_55  , __UpperCamelCase: bool = True  , __UpperCamelCase: bool = True  , __UpperCamelCase: Optional[Union[float, List[float]]] = None  , __UpperCamelCase: Optional[Union[float, List[float]]] = None  , **__UpperCamelCase: List[str]  , ):

						'''simple docstring'''





						super().__init__(**__UpperCamelCase      )
						__magic_name__							=    size if size is not None else {'shortest_edge': 2_24}
						__magic_name__							=    get_size_dict(__UpperCamelCase  , default_to_square=__UpperCamelCase      )
						__magic_name__							=    crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
						__magic_name__							=    get_size_dict(__UpperCamelCase  , param_name='crop_size'      )

						__magic_name__							=    do_resize
						__magic_name__							=    size
						__magic_name__							=    crop_pct
						__magic_name__							=    resample
						__magic_name__							=    do_center_crop
						__magic_name__							=    crop_size
						__magic_name__							=    do_rescale
						__magic_name__							=    rescale_factor
						__magic_name__							=    do_normalize
						__magic_name__							=    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
						__magic_name__							=    image_std if image_std is not None else IMAGENET_DEFAULT_STD







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]  , __UpperCamelCase: np.ndarray  , __UpperCamelCase: Dict[str, int]  , __UpperCamelCase: Optional[float] = None  , __UpperCamelCase: PILImageResampling = PILImageResampling.BICUBIC  , __UpperCamelCase: Optional[Union[str, ChannelDimension]] = None  , **__UpperCamelCase: Optional[Any]  , ):

						'''simple docstring'''





						__magic_name__							=    get_size_dict(__UpperCamelCase  , default_to_square=__UpperCamelCase      )
						if "shortest_edge" not in size and ("height" not in size or "width" not in size):
											raise ValueError(F'size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'      )
						if crop_pct is not None:
											if "shortest_edge" in size:
																__magic_name__							=    int(size['shortest_edge'] / crop_pct      )
											elif "height" in size and "width" in size:
																if size["height"] == size["width"]:
																					__magic_name__							=    int(size['height'] / crop_pct      )
																else:
																					__magic_name__							=    (int(size['height'] / crop_pct      ), int(size['width'] / crop_pct      ))
											else:
																raise ValueError('Invalid size for resize: {}'.format(__UpperCamelCase      )      )

											__magic_name__							=    get_resize_output_image_size(__UpperCamelCase  , size=__UpperCamelCase  , default_to_square=__UpperCamelCase      )
						else:
											if "shortest_edge" in size:
																__magic_name__							=    get_resize_output_image_size(__UpperCamelCase  , size=size['shortest_edge']  , default_to_square=__UpperCamelCase      )
											elif "height" in size and "width" in size:
																__magic_name__							=    (size['height'], size['width'])
											else:
																raise ValueError('Invalid size for resize: {}'.format(__UpperCamelCase      )      )

						return resize(__UpperCamelCase  , size=__UpperCamelCase  , resample=__UpperCamelCase  , data_format=__UpperCamelCase  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Any  , __UpperCamelCase: np.ndarray  , __UpperCamelCase: Dict[str, int]  , __UpperCamelCase: Optional[Union[str, ChannelDimension]] = None  , **__UpperCamelCase: List[str]  , ):

						'''simple docstring'''





						__magic_name__							=    get_size_dict(__UpperCamelCase      )
						if "height" not in size or "width" not in size:
											raise ValueError(F'size must contain \'height\' and \'width\' as keys. Got {size.keys()}'      )
						return center_crop(__UpperCamelCase  , size=(size['height'], size['width'])  , data_format=__UpperCamelCase  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]  , __UpperCamelCase: np.ndarray  , __UpperCamelCase: Union[int, float]  , __UpperCamelCase: Optional[Union[str, ChannelDimension]] = None  , **__UpperCamelCase: Optional[Any]  , ):

						'''simple docstring'''





						return rescale(__UpperCamelCase  , scale=__UpperCamelCase  , data_format=__UpperCamelCase  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict  , __UpperCamelCase: np.ndarray  , __UpperCamelCase: Union[float, List[float]]  , __UpperCamelCase: Union[float, List[float]]  , __UpperCamelCase: Optional[Union[str, ChannelDimension]] = None  , **__UpperCamelCase: Optional[int]  , ):

						'''simple docstring'''





						return normalize(__UpperCamelCase  , mean=__UpperCamelCase  , std=__UpperCamelCase  , data_format=__UpperCamelCase  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , __UpperCamelCase: ImageInput  , __UpperCamelCase: bool = None  , __UpperCamelCase: Dict[str, int] = None  , __UpperCamelCase: int = None  , __UpperCamelCase: PILImageResampling = None  , __UpperCamelCase: bool = None  , __UpperCamelCase: Dict[str, int] = None  , __UpperCamelCase: bool = None  , __UpperCamelCase: float = None  , __UpperCamelCase: bool = None  , __UpperCamelCase: Optional[Union[float, List[float]]] = None  , __UpperCamelCase: Optional[Union[float, List[float]]] = None  , __UpperCamelCase: Optional[Union[str, TensorType]] = None  , __UpperCamelCase: ChannelDimension = ChannelDimension.FIRST  , **__UpperCamelCase: Any  , ):

						'''simple docstring'''





						__magic_name__							=    do_resize if do_resize is not None else self.do_resize
						__magic_name__							=    crop_pct if crop_pct is not None else self.crop_pct
						__magic_name__							=    resample if resample is not None else self.resample
						__magic_name__							=    do_center_crop if do_center_crop is not None else self.do_center_crop
						__magic_name__							=    do_rescale if do_rescale is not None else self.do_rescale
						__magic_name__							=    rescale_factor if rescale_factor is not None else self.rescale_factor
						__magic_name__							=    do_normalize if do_normalize is not None else self.do_normalize
						__magic_name__							=    image_mean if image_mean is not None else self.image_mean
						__magic_name__							=    image_std if image_std is not None else self.image_std

						__magic_name__							=    size if size is not None else self.size
						__magic_name__							=    get_size_dict(__UpperCamelCase  , default_to_square=__UpperCamelCase      )
						__magic_name__							=    crop_size if crop_size is not None else self.crop_size
						__magic_name__							=    get_size_dict(__UpperCamelCase  , param_name='crop_size'      )

						__magic_name__							=    make_list_of_images(__UpperCamelCase      )

						if not valid_images(__UpperCamelCase      ):
											raise ValueError(
											    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
											    'torch.Tensor, tf.Tensor or jax.ndarray.'      )

						if do_resize and size is None or resample is None:
											raise ValueError('Size and resample must be specified if do_resize is True.'      )

						if do_center_crop and crop_pct is None:
											raise ValueError('Crop_pct must be specified if do_center_crop is True.'      )

						if do_rescale and rescale_factor is None:
											raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

						if do_normalize and (image_mean is None or image_std is None):
											raise ValueError('Image mean and std must be specified if do_normalize is True.'      )

						# All transformations expect numpy arrays.
						__magic_name__							=    [to_numpy_array(__UpperCamelCase      ) for image in images]

						if do_resize:
											__magic_name__							=    [self.resize(image=__UpperCamelCase  , size=__UpperCamelCase  , crop_pct=__UpperCamelCase  , resample=__UpperCamelCase      ) for image in images]

						if do_center_crop:
											__magic_name__							=    [self.center_crop(image=__UpperCamelCase  , size=__UpperCamelCase      ) for image in images]

						if do_rescale:
											__magic_name__							=    [self.rescale(image=__UpperCamelCase  , scale=__UpperCamelCase      ) for image in images]

						if do_normalize:
											__magic_name__							=    [self.normalize(image=__UpperCamelCase  , mean=__UpperCamelCase  , std=__UpperCamelCase      ) for image in images]

						__magic_name__							=    [to_channel_dimension_format(__UpperCamelCase  , __UpperCamelCase      ) for image in images]

						__magic_name__							=    {'pixel_values': images}
						return BatchFeature(data=__UpperCamelCase  , tensor_type=__UpperCamelCase      )




