



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import VivitImageProcessor
class 				__A					( unittest.TestCase      ):





			def __init__(self							,    __magic_name__							,    __magic_name__=7							,    __magic_name__=3							,    __magic_name__=10							,    __magic_name__=18							,    __magic_name__=30							,    __magic_name__=400							,    __magic_name__=True							,    __magic_name__=None							,    __magic_name__=True							,    __magic_name__=[0.5, 0.5, 0.5]							,    __magic_name__=[0.5, 0.5, 0.5]							,    __magic_name__=None							,    ):
						lowerCamelCase__ :      List[str]								=		size if size is not None else {"""shortest_edge""": 18}
						lowerCamelCase__ :      Union[str, Any]								=		crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

						lowerCamelCase__ :      Dict								=		parent
						lowerCamelCase__ :      List[Any]								=		batch_size
						lowerCamelCase__ :      Union[str, Any]								=		num_channels
						lowerCamelCase__ :      str								=		num_frames
						lowerCamelCase__ :      Tuple								=		image_size
						lowerCamelCase__ :      List[str]								=		min_resolution
						lowerCamelCase__ :      Tuple								=		max_resolution
						lowerCamelCase__ :      List[str]								=		do_resize
						lowerCamelCase__ :      Union[str, Any]								=		size
						lowerCamelCase__ :      List[str]								=		do_normalize
						lowerCamelCase__ :      Optional[int]								=		image_mean
						lowerCamelCase__ :      List[str]								=		image_std
						lowerCamelCase__ :      int								=		crop_size





			def        _snake_case    (self				):
						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "crop_size": self.crop_size,
						}







@require_torch
@require_vision
class 				__A					( A_					, unittest.TestCase      ):
			UpperCamelCase					:Union[str, Any]     =			VivitImageProcessor if is_vision_available() else None





			def        _snake_case    (self				):
						lowerCamelCase__ :      Dict								=		VivitImageProcessingTester(self				)





			@property
			def        _snake_case    (self				):
						return self.image_processor_tester.prepare_image_processor_dict()





			def        _snake_case    (self				):
						lowerCamelCase__ :      str								=		self.image_processing_class(**self.image_processor_dict				)
						self.assertTrue(hasattr(__magic_name__							,    """image_mean"""				)				)
						self.assertTrue(hasattr(__magic_name__							,    """image_std"""				)				)
						self.assertTrue(hasattr(__magic_name__							,    """do_normalize"""				)				)
						self.assertTrue(hasattr(__magic_name__							,    """do_resize"""				)				)
						self.assertTrue(hasattr(__magic_name__							,    """do_center_crop"""				)				)
						self.assertTrue(hasattr(__magic_name__							,    """size"""				)				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Tuple								=		self.image_processing_class.from_dict(self.image_processor_dict				)
						self.assertEqual(image_processor.size							,    {"""shortest_edge""": 18}				)
						self.assertEqual(image_processor.crop_size							,    {"""height""": 18, """width""": 18}				)

						lowerCamelCase__ :      int								=		self.image_processing_class.from_dict(self.image_processor_dict							,    size=42							,    crop_size=84				)
						self.assertEqual(image_processor.size							,    {"""shortest_edge""": 42}				)
						self.assertEqual(image_processor.crop_size							,    {"""height""": 84, """width""": 84}				)





			def        _snake_case    (self				):
						# Initialize image_processing
						lowerCamelCase__ :      Optional[int]								=		self.image_processing_class(**self.image_processor_dict				)
						# create random PIL videos
						lowerCamelCase__ :      Tuple								=		prepare_video_inputs(self.image_processor_tester							,    equal_resolution=__magic_name__				)
						for video in video_inputs:
									self.assertIsInstance(__magic_name__							,    __magic_name__				)
									self.assertIsInstance(video[0]							,    Image.Image				)

						# Test not batched input
						lowerCamelCase__ :      Tuple								=		image_processing(video_inputs[0]							,    return_tensors="""pt"""				).pixel_values
						self.assertEqual(
						    encoded_videos.shape							,    (
						        1,
						        self.image_processor_tester.num_frames,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )							,    )

						# Test batched
						lowerCamelCase__ :      str								=		image_processing(__magic_name__							,    return_tensors="""pt"""				).pixel_values
						self.assertEqual(
						    encoded_videos.shape							,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_frames,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )							,    )





			def        _snake_case    (self				):
						# Initialize image_processing
						lowerCamelCase__ :      Dict								=		self.image_processing_class(**self.image_processor_dict				)
						# create random numpy tensors
						lowerCamelCase__ :      Any								=		prepare_video_inputs(self.image_processor_tester							,    equal_resolution=__magic_name__							,    numpify=__magic_name__				)
						for video in video_inputs:
									self.assertIsInstance(__magic_name__							,    __magic_name__				)
									self.assertIsInstance(video[0]							,    np.ndarray				)

						# Test not batched input
						lowerCamelCase__ :      str								=		image_processing(video_inputs[0]							,    return_tensors="""pt"""				).pixel_values
						self.assertEqual(
						    encoded_videos.shape							,    (
						        1,
						        self.image_processor_tester.num_frames,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )							,    )

						# Test batched
						lowerCamelCase__ :      Any								=		image_processing(__magic_name__							,    return_tensors="""pt"""				).pixel_values
						self.assertEqual(
						    encoded_videos.shape							,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_frames,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )							,    )





			def        _snake_case    (self				):
						# Initialize image_processing
						lowerCamelCase__ :      Any								=		self.image_processing_class(**self.image_processor_dict				)
						# create random PyTorch tensors
						lowerCamelCase__ :      int								=		prepare_video_inputs(self.image_processor_tester							,    equal_resolution=__magic_name__							,    torchify=__magic_name__				)
						for video in video_inputs:
									self.assertIsInstance(__magic_name__							,    __magic_name__				)
									self.assertIsInstance(video[0]							,    torch.Tensor				)

						# Test not batched input
						lowerCamelCase__ :      int								=		image_processing(video_inputs[0]							,    return_tensors="""pt"""				).pixel_values
						self.assertEqual(
						    encoded_videos.shape							,    (
						        1,
						        self.image_processor_tester.num_frames,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )							,    )

						# Test batched
						lowerCamelCase__ :      List[str]								=		image_processing(__magic_name__							,    return_tensors="""pt"""				).pixel_values
						self.assertEqual(
						    encoded_videos.shape							,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_frames,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )							,    )








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


_lowercase     							=						logging.getLogger(__name__)
class 				__A					( A_      ):
			UpperCamelCase					:Optional[int]     =			'''token-classification'''





			def __init__(self							,    __magic_name__				):
						if type(__magic_name__				) == dict:
									lowerCamelCase__ :      Any								=		Namespace(**__magic_name__				)
						lowerCamelCase__ :      str								=		import_module("""tasks"""				)
						try:
									lowerCamelCase__ :      Optional[Any]								=		getattr(__magic_name__							,    hparams.task_type				)
									lowerCamelCase__ :      TokenClassificationTask								=		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__()}"				)
						lowerCamelCase__ :      Any								=		self.token_classification_task.get_labels(hparams.labels				)
						lowerCamelCase__ :      Tuple								=		CrossEntropyLoss().ignore_index
						super().__init__(__magic_name__							,    len(self.labels				)							,    self.mode				)





			def        _snake_case    (self							,    **__magic_name__				):
						return self.model(**__magic_name__				)





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

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





			def        _snake_case    (self				):
						lowerCamelCase__ :      Dict								=		self.hparams
						for mode in ["train", "dev", "test"]:
									lowerCamelCase__ :      List[str]								=		self._feature_file(__magic_name__				)
									if os.path.exists(__magic_name__				) and not args.overwrite_cache:
												logger.info("""Loading features from cached file %s"""							,    __magic_name__				)
												lowerCamelCase__ :      Union[str, Any]								=		torch.load(__magic_name__				)
									else:
												logger.info("""Creating features from dataset file at %s"""							,    args.data_dir				)
												lowerCamelCase__ :      int								=		self.token_classification_task.read_examples_from_file(args.data_dir							,    __magic_name__				)
												lowerCamelCase__ :      Tuple								=		self.token_classification_task.convert_examples_to_features(
												    __magic_name__							,    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=__magic_name__							,    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"""							,    __magic_name__				)
												torch.save(__magic_name__							,    __magic_name__				)





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





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





			def        _snake_case    (self							,    __magic_name__				):
						lowerCamelCase__ :      List[str]								=		torch.stack([x["""val_loss"""] for x in outputs]				).mean()
						lowerCamelCase__ :      Optional[int]								=		np.concatenate([x["""pred"""] for x in outputs]							,    axis=0				)
						lowerCamelCase__ :      List[str]								=		np.argmax(__magic_name__							,    axis=2				)
						lowerCamelCase__ :      Optional[Any]								=		np.concatenate([x["""target"""] for x in outputs]							,    axis=0				)

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

						lowerCamelCase__ :      Tuple								=		{
						    """val_loss""": val_loss_mean,
						    """accuracy_score""": accuracy_score(__magic_name__							,    __magic_name__				),
						    """precision""": precision_score(__magic_name__							,    __magic_name__				),
						    """recall""": recall_score(__magic_name__							,    __magic_name__				),
						    """f1""": fa_score(__magic_name__							,    __magic_name__				),
						}

						lowerCamelCase__ :      Dict								=		dict(results.items()				)
						lowerCamelCase__ :      str								=		results
						return ret, preds_list, out_label_list





			def        _snake_case    (self							,    __magic_name__				):
						# when stable
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      str								=		self._eval_end(__magic_name__				)
						lowerCamelCase__ :      Union[str, Any]								=		ret["""log"""]
						return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





			def        _snake_case    (self							,    __magic_name__				):
						# updating to test_epoch_end instead of deprecated test_end
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Union[str, Any]								=		self._eval_end(__magic_name__				)

						# Converting to the dict required by pl
						# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
						# pytorch_lightning/trainer/logging.py#L139
						lowerCamelCase__ :      List[Any]								=		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        _snake_case    (__magic_name__							,    __magic_name__				):
						# Add NER specific options
						BaseTransformer.add_model_specific_args(__magic_name__							,    __magic_name__				)
						parser.add_argument(
						    """--task_type"""							,    default="""NER"""							,    type=__magic_name__							,    help="""Task type to fine tune in training (e.g. NER, POS, etc)"""				)
						parser.add_argument(
						    """--max_seq_length"""							,    default=128							,    type=__magic_name__							,    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=__magic_name__							,    help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""							,    )
						parser.add_argument(
						    """--gpus"""							,    default=0							,    type=__magic_name__							,    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__":
				_lowercase     							=						argparse.ArgumentParser()
				add_generic_args(parser, os.getcwd())
				_lowercase     							=						NERTransformer.add_model_specific_args(parser, os.getcwd())
				_lowercase     							=						parser.parse_args()
				_lowercase     							=						NERTransformer(args)
				_lowercase     							=						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
								_lowercase     							=						sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
								_lowercase     							=						model.load_from_checkpoint(checkpoints[-1])
								trainer.test(model)



