

"""simple docstring"""


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     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  """token-classification"""

				def __init__(     self							:    int					,  UpperCamelCase__							:    Tuple)->       Tuple:



						'''simple docstring'''


						if type(UpperCamelCase__) == dict:
								__lowerCAmelCase:	Optional[Any]   				=      Namespace(**UpperCamelCase__)
						__lowerCAmelCase:	Any   				=      import_module("tasks")
						try:
								__lowerCAmelCase:	Optional[int]   				=      getattr(UpperCamelCase__					,  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:	List[str]   				=      self.token_classification_task.get_labels(hparams.labels)
						__lowerCAmelCase:	Any   				=      CrossEntropyLoss().ignore_index
						super().__init__(UpperCamelCase__					,  len(self.labels)					,  self.mode)

				def 							lowercase_					(     self							:    Union[str, Any]					,  **UpperCamelCase__							:    Optional[int])->       List[Any]:



						'''simple docstring'''


						return self.model(**UpperCamelCase__)

				def 							lowercase_					(     self							:    Union[str, Any]					,  UpperCamelCase__							:    Optional[int]					,  UpperCamelCase__							:    List[str])->       str:



						'''simple docstring'''


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

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

				def 							lowercase_					(     self							:    str)->       Any:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      self.hparams
						for mode in ["train", "dev", "test"]:
								__lowerCAmelCase:	Dict   				=      self._feature_file(UpperCamelCase__)
								if os.path.exists(UpperCamelCase__) and not args.overwrite_cache:
										logger.info("Loading features from cached file %s"					,  UpperCamelCase__)
										__lowerCAmelCase:	List[str]   				=      torch.load(UpperCamelCase__)
								else:
										logger.info("Creating features from dataset file at %s"					,  args.data_dir)
										__lowerCAmelCase:	Any   				=      self.token_classification_task.read_examples_from_file(args.data_dir					,  UpperCamelCase__)
										__lowerCAmelCase:	Any   				=      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 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    bool = False)->       DataLoader:



						'''simple docstring'''


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

				def 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    Optional[int])->       Union[str, Any]:



						'''simple docstring'''


						"""Compute validation""" ""
						__lowerCAmelCase:	str   				=      {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
						if self.config.model_type != "distilbert":
								__lowerCAmelCase:	Dict   				=      (
								    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(**UpperCamelCase__)
						__lowerCAmelCase				,			__lowerCAmelCase:	Any   				=      outputs[:2]
						__lowerCAmelCase:	Union[str, Any]   				=      logits.detach().cpu().numpy()
						__lowerCAmelCase:	Optional[Any]   				=      inputs["labels"].detach().cpu().numpy()
						return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}

				def 							lowercase_					(     self							:    Optional[Any]					,  UpperCamelCase__							:    int)->       Any:



						'''simple docstring'''


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

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

						__lowerCAmelCase:	Tuple   				=      {
						    "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:	str   				=      dict(results.items())
						__lowerCAmelCase:	List[str]   				=      results
						return ret, preds_list, out_label_list

				def 							lowercase_					(     self							:    Tuple					,  UpperCamelCase__							:    Optional[Any])->       Union[str, Any]:



						'''simple docstring'''


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

				def 							lowercase_					(     self							:    Union[str, Any]					,  UpperCamelCase__							:    List[Any])->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase				,			__lowerCAmelCase				,			__lowerCAmelCase:	List[str]   				=      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:	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 							lowercase_					(     UpperCamelCase__							:    List[Any]					,  UpperCamelCase__							:    Optional[int])->       Tuple:



						'''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_2_8					,  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)





"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A						       =      logging.get_logger(__name__)

__A						       =      {
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  """data2vec-vision"""

				def __init__(     self							:    Optional[int]					,  UpperCamelCase__							:    str=7_6_8					,  UpperCamelCase__							:    Any=1_2					,  UpperCamelCase__							:    str=1_2					,  UpperCamelCase__							:    Optional[Any]=3_0_7_2					,  UpperCamelCase__							:    Union[str, Any]="gelu"					,  UpperCamelCase__							:    List[Any]=0.0					,  UpperCamelCase__							:    Dict=0.0					,  UpperCamelCase__							:    Union[str, Any]=0.02					,  UpperCamelCase__							:    str=1e-12					,  UpperCamelCase__							:    Dict=2_2_4					,  UpperCamelCase__							:    List[Any]=1_6					,  UpperCamelCase__							:    Optional[Any]=3					,  UpperCamelCase__							:    List[str]=False					,  UpperCamelCase__							:    List[Any]=False					,  UpperCamelCase__							:    str=False					,  UpperCamelCase__							:    List[str]=False					,  UpperCamelCase__							:    int=0.1					,  UpperCamelCase__							:    List[Any]=0.1					,  UpperCamelCase__							:    Optional[Any]=True					,  UpperCamelCase__							:    List[Any]=[3, 5, 7, 1_1]					,  UpperCamelCase__							:    List[str]=[1, 2, 3, 6]					,  UpperCamelCase__							:    Union[str, Any]=True					,  UpperCamelCase__							:    Any=0.4					,  UpperCamelCase__							:    Union[str, Any]=2_5_6					,  UpperCamelCase__							:    List[Any]=1					,  UpperCamelCase__							:    str=False					,  UpperCamelCase__							:    Optional[int]=2_5_5					,  **UpperCamelCase__							:    Dict					,  )->       List[str]:



						'''simple docstring'''


						super().__init__(**UpperCamelCase__)

						__lowerCAmelCase:	List[str]   				=      hidden_size
						__lowerCAmelCase:	Union[str, Any]   				=      num_hidden_layers
						__lowerCAmelCase:	Dict   				=      num_attention_heads
						__lowerCAmelCase:	Optional[int]   				=      intermediate_size
						__lowerCAmelCase:	int   				=      hidden_act
						__lowerCAmelCase:	Union[str, Any]   				=      hidden_dropout_prob
						__lowerCAmelCase:	Any   				=      attention_probs_dropout_prob
						__lowerCAmelCase:	Dict   				=      initializer_range
						__lowerCAmelCase:	Any   				=      layer_norm_eps

						__lowerCAmelCase:	Union[str, Any]   				=      image_size
						__lowerCAmelCase:	Tuple   				=      patch_size
						__lowerCAmelCase:	List[str]   				=      num_channels
						__lowerCAmelCase:	Optional[Any]   				=      use_mask_token
						__lowerCAmelCase:	str   				=      use_absolute_position_embeddings
						__lowerCAmelCase:	Optional[int]   				=      use_relative_position_bias
						__lowerCAmelCase:	str   				=      use_shared_relative_position_bias
						__lowerCAmelCase:	Union[str, Any]   				=      layer_scale_init_value
						__lowerCAmelCase:	Any   				=      drop_path_rate
						__lowerCAmelCase:	Dict   				=      use_mean_pooling
						# decode head attributes (semantic segmentation)
						__lowerCAmelCase:	int   				=      out_indices
						__lowerCAmelCase:	Any   				=      pool_scales
						# auxiliary head attributes (semantic segmentation)
						__lowerCAmelCase:	List[Any]   				=      use_auxiliary_head
						__lowerCAmelCase:	int   				=      auxiliary_loss_weight
						__lowerCAmelCase:	Dict   				=      auxiliary_channels
						__lowerCAmelCase:	Any   				=      auxiliary_num_convs
						__lowerCAmelCase:	Any   				=      auxiliary_concat_input
						__lowerCAmelCase:	Any   				=      semantic_loss_ignore_index





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Dict       				=  version.parse("""1.11"""  )

				@property
				def 							lowercase_					(     self							:    Optional[Any])->       Mapping[str, Mapping[int, str]]:



						'''simple docstring'''


						return OrderedDict(
						    [
						        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
						    ])




				@property
				def 							lowercase_					(     self							:    Any)->       float:



						'''simple docstring'''


						return 1e-4


