
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       :				Optional[int]       =			logging.getLogger(__name__)




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		List[Any]       =					"token-classification"



	def __init__(  self	:    Optional[Any]			,  lowercase	:    Union[str, Any]	):


				'''simple docstring'''


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



	def  A							(  self	:    Tuple			,  **lowercase	:    Tuple	):


				'''simple docstring'''


				return self.model(**lowercase	)



	def  A							(  self	:    Optional[Any]			,  lowercase	:    Optional[int]			,  lowercase	:    Optional[int]	):


				'''simple docstring'''


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

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



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


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



	def  A							(  self	:    Optional[int]			,  lowercase	:    int			,  lowercase	:    int			,  lowercase	:    bool = False	):


				'''simple docstring'''


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



	def  A							(  self	:    Dict			,  lowercase	:    List[Any]			,  lowercase	:    Tuple	):


				'''simple docstring'''


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



	def  A							(  self	:    Tuple			,  lowercase	:    Union[str, Any]	):


				'''simple docstring'''


				_snake_case		=			torch.stack([x['val_loss'] for x in outputs]	).mean()
				_snake_case		=			np.concatenate([x['pred'] for x in outputs]			,  axis=0	)
				_snake_case		=			np.argmax(lowercase			,  axis=2	)
				_snake_case		=			np.concatenate([x['target'] for x in outputs]			,  axis=0	)

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

				_snake_case		=			{
				    'val_loss': val_loss_mean,
				    'accuracy_score': accuracy_score(lowercase			,  lowercase	),
				    'precision': precision_score(lowercase			,  lowercase	),
				    'recall': recall_score(lowercase			,  lowercase	),
				    'f1': fa_score(lowercase			,  lowercase	),
				}

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



	def  A							(  self	:    Optional[int]			,  lowercase	:    Tuple	):


				'''simple docstring'''


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



	def  A							(  self	:    Union[str, Any]			,  lowercase	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case	,					_snake_case	,					_snake_case		=			self._eval_end(lowercase	)

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


				'''simple docstring'''


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

from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class    SCREAMING_SNAKE_CASE__     (	nn.Module					):





	'''simple docstring'''



	def __init__(  self	:    Optional[int]			,  lowercase	:    int = 16			,  lowercase	:    int = 88			,  lowercase	:    Optional[int] = None			,  lowercase	:    int = 1			,  lowercase	:    float = 0.0			,  lowercase	:    int = 32			,  lowercase	:    Optional[int] = None			,  lowercase	:    bool = False			,  lowercase	:    Optional[int] = None			,  lowercase	:    Optional[int] = None			,  lowercase	:    str = "geglu"			,  lowercase	:    Optional[int] = None			,  ):


				'''simple docstring'''


				super().__init__()
				_snake_case		=			nn.ModuleList(
				    [
				        TransformeraDModel(
				            num_attention_heads=lowercase			,  attention_head_dim=lowercase			,  in_channels=lowercase			,  num_layers=lowercase			,  dropout=lowercase			,  norm_num_groups=lowercase			,  cross_attention_dim=lowercase			,  attention_bias=lowercase			,  sample_size=lowercase			,  num_vector_embeds=lowercase			,  activation_fn=lowercase			,  num_embeds_ada_norm=lowercase			,  )
				        for _ in range(2	)
				    ]	)

				# Variables that can be set by a pipeline:

				# The ratio of transformer1 to transformer2's output states to be combined during inference
				_snake_case		=			0.5

				# The shape of `encoder_hidden_states` is expected to be
				# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
				_snake_case		=			[77, 257]

				# Which transformer to use to encode which condition.
				# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
				_snake_case		=			[1, 0]





	def  A							(  self	:    Optional[int]			,  lowercase	:    Optional[int]			,  lowercase	:    List[Any]			,  lowercase	:    List[str]=None			,  lowercase	:    Tuple=None			,  lowercase	:    Dict=None			,  lowercase	:    bool = True			,  ):


				'''simple docstring'''


				_snake_case		=			hidden_states

				_snake_case		=			[]
				_snake_case		=			0
				# attention_mask is not used yet
				for i in range(2	):
							# for each of the two transformers, pass the corresponding condition tokens
							_snake_case		=			encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
							_snake_case		=			self.transformer_index_for_condition[i]
							_snake_case		=			self.transformers[transformer_index](
							    lowercase			,  encoder_hidden_states=lowercase			,  timestep=lowercase			,  cross_attention_kwargs=lowercase			,  return_dict=lowercase			,  )[0]
							encoded_states.append(encoded_state - input_states	)
							tokens_start += self.condition_lengths[i]

				_snake_case		=			encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
				_snake_case		=			output_states + input_states

				if not return_dict:
							return (output_states,)

				return TransformeraDModelOutput(sample=lowercase	)