
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


lowerCamelCase_						: Tuple         =  logging.getLogger(__name__)

class        _UpperCamelCase				(					_A  ):
			__UpperCamelCase      :						Any					      = """token-classification"""

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

			def       lowerCAmelCase__	(       self :       List[Any]						,	**snake_case_ :       str      ):
							return self.model(**snake_case_      )

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

							UpperCamelCase_:							Union[str, Any]	=						self(**snake_case_      )
							UpperCamelCase_:							List[Any]	=						outputs[0]
							# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
							return {"loss": loss}

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

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

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

			def       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       str      ):
							UpperCamelCase_:							int	=						torch.stack([x["""val_loss"""] for x in outputs]      ).mean()
							UpperCamelCase_:							List[Any]	=						np.concatenate([x["""pred"""] for x in outputs]						,	axis=0      )
							UpperCamelCase_:							List[Any]	=						np.argmax(snake_case_						,	axis=2      )
							UpperCamelCase_:							List[Any]	=						np.concatenate([x["""target"""] for x in outputs]						,	axis=0      )

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

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

							UpperCamelCase_:							List[str]	=						dict(results.items()      )
							UpperCamelCase_:							List[str]	=						results
							return ret, preds_list, out_label_list

			def       lowerCAmelCase__	(       self :       Tuple						,	snake_case_ :       Optional[Any]      ):
							# when stable
							UpperCamelCase_:							Dict	=						self._eval_end(snake_case_      )
							UpperCamelCase_:							Tuple	=						ret["""log"""]
							return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

			def       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       Union[str, Any]      ):
							# updating to test_epoch_end instead of deprecated test_end
							UpperCamelCase_:							Union[str, Any]	=						self._eval_end(snake_case_      )

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








# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_						: List[str]         =  {
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Optional[Any]         =  [
							    """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MgpstrModel""",
							    """MgpstrPreTrainedModel""",
							    """MgpstrForSceneTextRecognition""",
							]

if TYPE_CHECKING:
							from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
							from .processing_mgp_str import MgpstrProcessor
							from .tokenization_mgp_str import MgpstrTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mgp_str import (
														    MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MgpstrForSceneTextRecognition,
														    MgpstrModel,
														    MgpstrPreTrainedModel,
														)
else:
							import sys

							lowerCamelCase_						: int         =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






