



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


snake_case__					:     Optional[Any]    		=   logging.getLogger(__name__)

class   _A   ( _lowercase	):


						'''simple docstring'''





						_snake_case			:     str      							=      """token-classification"""



						def __init__(	self	:							Optional[Any]							,      lowerCamelCase	:							Dict	):






							'''simple docstring'''



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



						def     _snake_case	(	self	:							Union[str, Any]							,      **lowerCamelCase	:							str	):






							'''simple docstring'''



							return self.model(**lowerCamelCase	)



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							Any							,      lowerCamelCase	:							Optional[Any]	):






							'''simple docstring'''



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

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



						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



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



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							int							,      lowerCamelCase	:							int							,      lowerCamelCase	:							bool = False	):






							'''simple docstring'''



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



						def     _snake_case	(	self	:							List[Any]							,      lowerCamelCase	:							Optional[Any]							,      lowerCamelCase	:							Any	):






							'''simple docstring'''



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



						def     _snake_case	(	self	:							Tuple							,      lowerCamelCase	:							List[Any]	):






							'''simple docstring'''



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

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

							__lowercase		       =			{
							    "val_loss": val_loss_mean,
							    "accuracy_score": accuracy_score(lowerCamelCase							,      lowerCamelCase	),
							    "precision": precision_score(lowerCamelCase							,      lowerCamelCase	),
							    "recall": recall_score(lowerCamelCase							,      lowerCamelCase	),
							    "f1": fa_score(lowerCamelCase							,      lowerCamelCase	),
							}

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



						def     _snake_case	(	self	:							Optional[int]							,      lowerCamelCase	:							Tuple	):






							'''simple docstring'''



							__lowercase       ,    __lowercase       ,    __lowercase		       =			self._eval_end(lowerCamelCase	)
							__lowercase		       =			ret["log"]
							return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



						def     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							Union[str, Any]	):






							'''simple docstring'''



							__lowercase       ,    __lowercase       ,    __lowercase		       =			self._eval_end(lowerCamelCase	)

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






							'''simple docstring'''



							BaseTransformer.add_model_specific_args(lowerCamelCase							,      lowerCamelCase	)
							parser.add_argument(
							    "--task_type"							,      default="NER"							,      type=lowerCamelCase							,      help="Task type to fine tune in training (e.g. NER, POS, etc)"	)
							parser.add_argument(
							    "--max_seq_length"							,      default=128							,      type=lowerCamelCase							,      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=lowerCamelCase							,      help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."							,      )
							parser.add_argument(
							    "--gpus"							,      default=0							,      type=lowerCamelCase							,      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__":
	snake_case__					:     Union[str, Any]    		=   argparse.ArgumentParser()
	add_generic_args(parser, os.getcwd())
	snake_case__					:     int    		=   NERTransformer.add_model_specific_args(parser, os.getcwd())
	snake_case__					:     int    		=   parser.parse_args()
	snake_case__					:     Dict    		=   NERTransformer(args)
	snake_case__					:     List[Any]    		=   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
		snake_case__					:     Optional[int]    		=   sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
		snake_case__					:     Dict    		=   model.load_from_checkpoint(checkpoints[-1])
		trainer.test(model)








import copy
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
from ..auto import CONFIG_MAPPING


snake_case__					:     int    		=   logging.get_logger(__name__)

snake_case__					:     Optional[int]    		=   {
    """microsoft/conditional-detr-resnet-50""": (
        """https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
    ),
}

class   _A   ( _lowercase	):


						'''simple docstring'''





						_snake_case			:     Dict      							=      """conditional_detr"""
						_snake_case			:     Union[str, Any]      							=      ["""past_key_values"""]
						_snake_case			:     Optional[int]      							=      {
						    """hidden_size""": """d_model""",
						    """num_attention_heads""": """encoder_attention_heads""",
						}



						def __init__(	self	:							Optional[Any]							,      lowerCamelCase	:							int=True							,      lowerCamelCase	:							Tuple=None							,      lowerCamelCase	:							Optional[int]=3							,      lowerCamelCase	:							Optional[int]=300							,      lowerCamelCase	:							List[Any]=6							,      lowerCamelCase	:							str=2_048							,      lowerCamelCase	:							Any=8							,      lowerCamelCase	:							List[str]=6							,      lowerCamelCase	:							Any=2_048							,      lowerCamelCase	:							List[Any]=8							,      lowerCamelCase	:							Optional[Any]=0.0							,      lowerCamelCase	:							List[str]=0.0							,      lowerCamelCase	:							List[Any]=True							,      lowerCamelCase	:							str="relu"							,      lowerCamelCase	:							int=256							,      lowerCamelCase	:							Dict=0.1							,      lowerCamelCase	:							Optional[Any]=0.0							,      lowerCamelCase	:							Dict=0.0							,      lowerCamelCase	:							Tuple=0.02							,      lowerCamelCase	:							int=1.0							,      lowerCamelCase	:							Tuple=False							,      lowerCamelCase	:							List[str]="sine"							,      lowerCamelCase	:							List[Any]="resnet50"							,      lowerCamelCase	:							Any=True							,      lowerCamelCase	:							Any=False							,      lowerCamelCase	:							List[Any]=2							,      lowerCamelCase	:							List[Any]=5							,      lowerCamelCase	:							str=2							,      lowerCamelCase	:							Dict=1							,      lowerCamelCase	:							List[str]=1							,      lowerCamelCase	:							Union[str, Any]=2							,      lowerCamelCase	:							Dict=5							,      lowerCamelCase	:							List[Any]=2							,      lowerCamelCase	:							Tuple=0.25							,      **lowerCamelCase	:							List[str]							,      ):






							'''simple docstring'''



							if backbone_config is not None and use_timm_backbone:
								raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."	)

							if not use_timm_backbone:
								if backbone_config is None:
									logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."	)
									__lowercase		       =			CONFIG_MAPPING["resnet"](out_features=["stage4"]	)
								elif isinstance(lowerCamelCase							,      lowerCamelCase	):
									__lowercase		       =			backbone_config.get("model_type"	)
									__lowercase		       =			CONFIG_MAPPING[backbone_model_type]
									__lowercase		       =			config_class.from_dict(lowerCamelCase	)

							__lowercase		       =			use_timm_backbone
							__lowercase		       =			backbone_config
							__lowercase		       =			num_channels
							__lowercase		       =			num_queries
							__lowercase		       =			d_model
							__lowercase		       =			encoder_ffn_dim
							__lowercase		       =			encoder_layers
							__lowercase		       =			encoder_attention_heads
							__lowercase		       =			decoder_ffn_dim
							__lowercase		       =			decoder_layers
							__lowercase		       =			decoder_attention_heads
							__lowercase		       =			dropout
							__lowercase		       =			attention_dropout
							__lowercase		       =			activation_dropout
							__lowercase		       =			activation_function
							__lowercase		       =			init_std
							__lowercase		       =			init_xavier_std
							__lowercase		       =			encoder_layerdrop
							__lowercase		       =			decoder_layerdrop
							__lowercase		       =			encoder_layers
							__lowercase		       =			auxiliary_loss
							__lowercase		       =			position_embedding_type
							__lowercase		       =			backbone
							__lowercase		       =			use_pretrained_backbone
							__lowercase		       =			dilation
							# Hungarian matcher
							__lowercase		       =			class_cost
							__lowercase		       =			bbox_cost
							__lowercase		       =			giou_cost
							# Loss coefficients
							__lowercase		       =			mask_loss_coefficient
							__lowercase		       =			dice_loss_coefficient
							__lowercase		       =			cls_loss_coefficient
							__lowercase		       =			bbox_loss_coefficient
							__lowercase		       =			giou_loss_coefficient
							__lowercase		       =			focal_alpha
							super().__init__(is_encoder_decoder=lowerCamelCase							,      **lowerCamelCase	)



						@property
						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							return self.encoder_attention_heads



						@property
						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							return self.d_model



						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



							__lowercase		       =			copy.deepcopy(self.__dict__	)
							if self.backbone_config is not None:
								__lowercase		       =			self.backbone_config.to_dict()
							__lowercase		       =			self.__class__.model_type
							return output

class   _A   ( _lowercase	):


						'''simple docstring'''





						_snake_case			:     Any      							=      version.parse("""1.11"""	)



						@property
						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



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



						@property
						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							return 1e-5



						@property
						def     _snake_case	(	self	:							Optional[Any]	):






							'''simple docstring'''



							return 12



