
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


UpperCamelCase_   	=	logging.getLogger(__name__)

class       a		(   __lowerCamelCase					):
							lowercase_		:						str   							=  'token-classification'







							def __init__(						self  :  Optional[Any]      ,				snake_case__  :  Union[str, Any] ):
								"""simple docstring"""







								if type(UpperCAmelCase_ ) == dict:
									__lowerCAmelCase         =							Namespace(**UpperCAmelCase_ )
								__lowerCAmelCase         =							import_module("tasks" )
								try:
									__lowerCAmelCase         =							getattr(UpperCAmelCase_      ,				hparams.task_type )
									__lowerCAmelCase         =							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         =							self.token_classification_task.get_labels(hparams.labels )
								__lowerCAmelCase         =							CrossEntropyLoss().ignore_index
								super().__init__(UpperCAmelCase_      ,				len(self.labels )      ,				self.mode )







							def       UpperCAmelCase__					(						self  :  int      ,				**snake_case__  :  List[str] ):
								"""simple docstring"""







								return self.model(**UpperCAmelCase_ )







							def       UpperCAmelCase__					(						self  :  Any      ,				snake_case__  :  List[Any]      ,				snake_case__  :  str ):
								"""simple docstring"""







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

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







							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.hparams
								for mode in ["train", "dev", "test"]:
									__lowerCAmelCase         =							self._feature_file(UpperCAmelCase_ )
									if os.path.exists(UpperCAmelCase_ ) and not args.overwrite_cache:
										logger.info("Loading features from cached file %s"      ,				UpperCAmelCase_ )
										__lowerCAmelCase         =							torch.load(UpperCAmelCase_ )
									else:
										logger.info("Creating features from dataset file at %s"      ,				args.data_dir )
										__lowerCAmelCase         =							self.token_classification_task.read_examples_from_file(args.data_dir      ,				UpperCAmelCase_ )
										__lowerCAmelCase         =							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       UpperCAmelCase__					(						self  :  int      ,				snake_case__  :  int      ,				snake_case__  :  int      ,				snake_case__  :  bool = False ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  Dict      ,				snake_case__  :  Any      ,				snake_case__  :  Tuple ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  Dict      ,				snake_case__  :  Optional[int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							torch.stack([x["val_loss"] for x in outputs] ).mean()
								__lowerCAmelCase         =							np.concatenate([x["pred"] for x in outputs]      ,				axis=0 )
								__lowerCAmelCase         =							np.argmax(UpperCAmelCase_      ,				axis=2 )
								__lowerCAmelCase         =							np.concatenate([x["target"] for x in outputs]      ,				axis=0 )

								__lowerCAmelCase         =							dict(enumerate(self.labels ) )
								__lowerCAmelCase         =							[[] for _ in range(out_label_ids.shape[0] )]
								__lowerCAmelCase         =							[[] 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         =							{
								    '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_ ),
								}

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







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  Any ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							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
								__lowerCAmelCase         =							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__					(						snake_case__  :  Any      ,				snake_case__  :  Optional[int] ):
								"""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=128      ,				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__":
					UpperCamelCase_   	=	argparse.ArgumentParser()
					add_generic_args(parser, os.getcwd())
					UpperCamelCase_   	=	NERTransformer.add_model_specific_args(parser, os.getcwd())
					UpperCamelCase_   	=	parser.parse_args()
					UpperCamelCase_   	=	NERTransformer(args)
					UpperCamelCase_   	=	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
										UpperCamelCase_   	=	sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
										UpperCamelCase_   	=	model.load_from_checkpoint(checkpoints[-1])
										trainer.test(model)








from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
					import torch

if is_tf_available():
					import tensorflow as tf

class       a		(   __UpperCAmelCase					):
							lowercase_		:						Tuple   							=  ['image_processor']
							lowercase_		:						Tuple   							=  'SamImageProcessor'







							def __init__(						self  :  List[str]      ,				snake_case__  :  Optional[Any] ):
								"""simple docstring"""







								super().__init__(snake_case__ )
								__lowerCAmelCase         =							self.image_processor
								__lowerCAmelCase         =							-10
								__lowerCAmelCase         =							self.image_processor.size["longest_edge"]







							def __call__(						self  :  Any      ,				snake_case__  :  Optional[int]=None      ,				snake_case__  :  Optional[int]=None      ,				snake_case__  :  str=None      ,				snake_case__  :  List[str]=None      ,				snake_case__  :  Optional[Union[str, TensorType]] = None      ,				**snake_case__  :  Any      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							self.image_processor(
								    snake_case__      ,				return_tensors=snake_case__      ,				**snake_case__      ,				)

								# pop arguments that are not used in the foward but used nevertheless
								__lowerCAmelCase         =							encoding_image_processor["original_sizes"]

								if hasattr(snake_case__      ,				"numpy" ):  # Checks if Torch or TF tensor
									__lowerCAmelCase         =							original_sizes.numpy()

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							self._check_and_preprocess_points(
								    input_points=snake_case__      ,				input_labels=snake_case__      ,				input_boxes=snake_case__      ,				)

								__lowerCAmelCase         =							self._normalize_and_convert(
								    snake_case__      ,				snake_case__      ,				input_points=snake_case__      ,				input_labels=snake_case__      ,				input_boxes=snake_case__      ,				return_tensors=snake_case__      ,				)

								return encoding_image_processor







							def       UpperCAmelCase__					(						self  :  Tuple      ,				snake_case__  :  Any      ,				snake_case__  :  Optional[Any]      ,				snake_case__  :  str=None      ,				snake_case__  :  Any=None      ,				snake_case__  :  Any=None      ,				snake_case__  :  List[str]="pt"      ,				):
								"""simple docstring"""







								if input_points is not None:
									if len(snake_case__ ) != len(snake_case__ ):
										__lowerCAmelCase         =							[
										    self._normalize_coordinates(self.target_size      ,				snake_case__      ,				original_sizes[0] ) for point in input_points
										]
									else:
										__lowerCAmelCase         =							[
										    self._normalize_coordinates(self.target_size      ,				snake_case__      ,				snake_case__ )
										    for point, original_size in zip(snake_case__      ,				snake_case__ )
										]
									# check that all arrays have the same shape
									if not all(point.shape == input_points[0].shape for point in input_points ):
										if input_labels is not None:
											__lowerCAmelCase   ,						__lowerCAmelCase         =							self._pad_points_and_labels(snake_case__      ,				snake_case__ )

									__lowerCAmelCase         =							np.array(snake_case__ )

								if input_labels is not None:
									__lowerCAmelCase         =							np.array(snake_case__ )

								if input_boxes is not None:
									if len(snake_case__ ) != len(snake_case__ ):
										__lowerCAmelCase         =							[
										    self._normalize_coordinates(self.target_size      ,				snake_case__      ,				original_sizes[0]      ,				is_bounding_box=snake_case__ )
										    for box in input_boxes
										]
									else:
										__lowerCAmelCase         =							[
										    self._normalize_coordinates(self.target_size      ,				snake_case__      ,				snake_case__      ,				is_bounding_box=snake_case__ )
										    for box, original_size in zip(snake_case__      ,				snake_case__ )
										]
									__lowerCAmelCase         =							np.array(snake_case__ )

								if input_boxes is not None:
									if return_tensors == "pt":
										__lowerCAmelCase         =							torch.from_numpy(snake_case__ )
										# boxes batch size of 1 by default
										__lowerCAmelCase         =							input_boxes.unsqueeze(1 ) if len(input_boxes.shape ) != 3 else input_boxes
									elif return_tensors == "tf":
										__lowerCAmelCase         =							tf.convert_to_tensor(snake_case__ )
										# boxes batch size of 1 by default
										__lowerCAmelCase         =							tf.expand_dims(snake_case__      ,				1 ) if len(input_boxes.shape ) != 3 else input_boxes
									encoding_image_processor.update({"input_boxes": input_boxes} )
								if input_points is not None:
									if return_tensors == "pt":
										__lowerCAmelCase         =							torch.from_numpy(snake_case__ )
										# point batch size of 1 by default
										__lowerCAmelCase         =							input_points.unsqueeze(1 ) if len(input_points.shape ) != 4 else input_points
									elif return_tensors == "tf":
										__lowerCAmelCase         =							tf.convert_to_tensor(snake_case__ )
										# point batch size of 1 by default
										__lowerCAmelCase         =							tf.expand_dims(snake_case__      ,				1 ) if len(input_points.shape ) != 4 else input_points
									encoding_image_processor.update({"input_points": input_points} )
								if input_labels is not None:
									if return_tensors == "pt":
										__lowerCAmelCase         =							torch.from_numpy(snake_case__ )
										# point batch size of 1 by default
										__lowerCAmelCase         =							input_labels.unsqueeze(1 ) if len(input_labels.shape ) != 3 else input_labels
									elif return_tensors == "tf":
										__lowerCAmelCase         =							tf.convert_to_tensor(snake_case__ )
										# point batch size of 1 by default
										__lowerCAmelCase         =							tf.expand_dims(snake_case__      ,				1 ) if len(input_labels.shape ) != 3 else input_labels
									encoding_image_processor.update({"input_labels": input_labels} )

								return encoding_image_processor







							def       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  Union[str, Any]      ,				snake_case__  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							max([point.shape[0] for point in input_points] )
								__lowerCAmelCase         =							[]
								for i, point in enumerate(snake_case__ ):
									if point.shape[0] != expected_nb_points:
										__lowerCAmelCase         =							np.concatenate(
										    [point, np.zeros((expected_nb_points - point.shape[0], 2) ) + self.point_pad_value]      ,				axis=0 )
										__lowerCAmelCase         =							np.append(input_labels[i]      ,				[self.point_pad_value] )
									processed_input_points.append(snake_case__ )
								__lowerCAmelCase         =							processed_input_points
								return input_points, input_labels







							def       UpperCAmelCase__					(						self  :  Dict      ,				snake_case__  :  int      ,				snake_case__  :  np.ndarray      ,				snake_case__  :  List[str]      ,				snake_case__  :  Dict=False ):
								"""simple docstring"""







								__lowerCAmelCase   ,						__lowerCAmelCase         =							original_size
								__lowerCAmelCase   ,						__lowerCAmelCase         =							self.image_processor._get_preprocess_shape(snake_case__      ,				longest_edge=snake_case__ )
								__lowerCAmelCase         =							deepcopy(snake_case__ ).astype(snake_case__ )

								if is_bounding_box:
									__lowerCAmelCase         =							coords.reshape(-1      ,				2      ,				2 )

								__lowerCAmelCase         =							coords[..., 0] * (new_w / old_w)
								__lowerCAmelCase         =							coords[..., 1] * (new_h / old_h)

								if is_bounding_box:
									__lowerCAmelCase         =							coords.reshape(-1      ,				4 )

								return coords







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  Union[str, Any]=None      ,				snake_case__  :  int=None      ,				snake_case__  :  Any=None      ,				):
								"""simple docstring"""







								if input_points is not None:
									if hasattr(snake_case__      ,				"numpy" ):  # Checks for TF or Torch tensor
										__lowerCAmelCase         =							input_points.numpy().tolist()

									if not isinstance(snake_case__      ,				snake_case__ ) or not isinstance(input_points[0]      ,				snake_case__ ):
										raise ValueError("Input points must be a list of list of floating points." )
									__lowerCAmelCase         =							[np.array(snake_case__ ) for input_point in input_points]
								else:
									__lowerCAmelCase         =							None

								if input_labels is not None:
									if hasattr(snake_case__      ,				"numpy" ):
										__lowerCAmelCase         =							input_labels.numpy().tolist()

									if not isinstance(snake_case__      ,				snake_case__ ) or not isinstance(input_labels[0]      ,				snake_case__ ):
										raise ValueError("Input labels must be a list of list integers." )
									__lowerCAmelCase         =							[np.array(snake_case__ ) for label in input_labels]
								else:
									__lowerCAmelCase         =							None

								if input_boxes is not None:
									if hasattr(snake_case__      ,				"numpy" ):
										__lowerCAmelCase         =							input_boxes.numpy().tolist()

									if (
									    not isinstance(snake_case__      ,				snake_case__ )
									    or not isinstance(input_boxes[0]      ,				snake_case__ )
									    or not isinstance(input_boxes[0][0]      ,				snake_case__ )
									):
										raise ValueError("Input boxes must be a list of list of list of floating points." )
									__lowerCAmelCase         =							[np.array(snake_case__ ).astype(np.floataa ) for box in input_boxes]
								else:
									__lowerCAmelCase         =							None

								return input_points, input_labels, input_boxes







							@property
							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.image_processor.model_input_names
								return list(dict.fromkeys(snake_case__ ) )







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				*snake_case__  :  Dict      ,				**snake_case__  :  Union[str, Any] ):
								"""simple docstring"""







								return self.image_processor.post_process_masks(*snake_case__      ,				**snake_case__ )






