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


SCREAMING_SNAKE_CASE		:     List[Any]         =	logging.getLogger(__name__)

class        UpperCamelCase						(				__a				):
						a__		:Optional[int]      =			'''token-classification'''





						def __init__(self			,     __UpperCamelCase						)				->				Union[str, Any]:
												if type(__UpperCamelCase						) == dict:
																		UpperCamelCase_		:							Optional[Any]  =		Namespace(**__UpperCamelCase						)
												UpperCamelCase_		:							Tuple  =		import_module("""tasks"""						)
												try:
																		UpperCamelCase_		:							str  =		getattr(__UpperCamelCase			,     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_		:							List[Any]  =		self.token_classification_task.get_labels(hparams.labels						)
												UpperCamelCase_		:							Optional[int]  =		CrossEntropyLoss().ignore_index
												super().__init__(__UpperCamelCase			,     len(self.labels						)			,     self.mode						)





						def        A_							(self			,     **__UpperCamelCase						)				->				Optional[int]:
												return self.model(**__UpperCamelCase						)





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

												UpperCamelCase_		:							str  =		self(**__UpperCamelCase						)
												UpperCamelCase_		:							Optional[int]  =		outputs[0]
												# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
												return {"loss": loss}





						def        A_							(self						)				->				int:
												UpperCamelCase_		:							str  =		self.hparams
												for mode in ["train", "dev", "test"]:
																		UpperCamelCase_		:							int  =		self._feature_file(__UpperCamelCase						)
																		if os.path.exists(__UpperCamelCase						) and not args.overwrite_cache:
																								logger.info("""Loading features from cached file %s"""			,     __UpperCamelCase						)
																								UpperCamelCase_		:							Union[str, Any]  =		torch.load(__UpperCamelCase						)
																		else:
																								logger.info("""Creating features from dataset file at %s"""			,     args.data_dir						)
																								UpperCamelCase_		:							int  =		self.token_classification_task.read_examples_from_file(args.data_dir			,     __UpperCamelCase						)
																								UpperCamelCase_		:							str  =		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        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase = False						)				->				DataLoader:
												UpperCamelCase_		:							List[Any]  =		self._feature_file(__UpperCamelCase						)
												logger.info("""Loading features from cached file %s"""			,     __UpperCamelCase						)
												UpperCamelCase_		:							Optional[Any]  =		torch.load(__UpperCamelCase						)
												UpperCamelCase_		:							Any  =		torch.tensor([f.input_ids for f in features]			,     dtype=torch.long						)
												UpperCamelCase_		:							int  =		torch.tensor([f.attention_mask for f in features]			,     dtype=torch.long						)
												if features[0].token_type_ids is not None:
																		UpperCamelCase_		:							Optional[Any]  =		torch.tensor([f.token_type_ids for f in features]			,     dtype=torch.long						)
												else:
																		UpperCamelCase_		:							List[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(__UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase						)			,     batch_size=__UpperCamelCase						)





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





						def        A_							(self			,     __UpperCamelCase						)				->				List[str]:
												UpperCamelCase_		:							Tuple  =		torch.stack([x["""val_loss"""] for x in outputs]						).mean()
												UpperCamelCase_		:							Tuple  =		np.concatenate([x["""pred"""] for x in outputs]			,     axis=0						)
												UpperCamelCase_		:							Optional[int]  =		np.argmax(__UpperCamelCase			,     axis=2						)
												UpperCamelCase_		:							int  =		np.concatenate([x["""target"""] for x in outputs]			,     axis=0						)

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

												UpperCamelCase_		:							str  =		dict(results.items()						)
												UpperCamelCase_		:							Any  =		results
												return ret, preds_list, out_label_list





						def        A_							(self			,     __UpperCamelCase						)				->				Any:
												# when stable
												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		self._eval_end(__UpperCamelCase						)
												UpperCamelCase_		:							Dict  =		ret["""log"""]
												return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





						def        A_							(self			,     __UpperCamelCase						)				->				Tuple:
												# updating to test_epoch_end instead of deprecated test_end
												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							List[Any]  =		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
												UpperCamelCase_		:							Optional[int]  =		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_							(__UpperCamelCase			,     __UpperCamelCase						)				->				List[Any]:
												# Add NER specific options
												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__":
	SCREAMING_SNAKE_CASE		:     str         =	argparse.ArgumentParser()
	add_generic_args(parser, os.getcwd())
	SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	NERTransformer.add_model_specific_args(parser, os.getcwd())
	SCREAMING_SNAKE_CASE		:     Any         =	parser.parse_args()
	SCREAMING_SNAKE_CASE		:     Optional[Any]         =	NERTransformer(args)
	SCREAMING_SNAKE_CASE		:     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
		SCREAMING_SNAKE_CASE		:     Any         =	sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
		SCREAMING_SNAKE_CASE		:     str         =	model.load_from_checkpoint(checkpoints[-1])
		trainer.test(model)







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
	from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


SCREAMING_SNAKE_CASE		:     List[str]         =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:     str         =	{
    "bigscience/bloom": "https://huggingface.co/bigscience/bloom/resolve/main/config.json",
    "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/config.json",
    "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json",
    "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json",
    "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/config.json",
    "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json",
}

class        UpperCamelCase						(				__a				):
						a__		:Any      =			'''bloom'''
						a__		:Optional[int]      =			['''past_key_values''']
						a__		:Tuple      =			{
						    '''num_hidden_layers''': '''n_layer''',
						    '''num_attention_heads''': '''n_head''',
						}





						def __init__(self			,     __UpperCamelCase=250_880			,     __UpperCamelCase=64			,     __UpperCamelCase=2			,     __UpperCamelCase=8			,     __UpperCamelCase=1E-5			,     __UpperCamelCase=0.02			,     __UpperCamelCase=True			,     __UpperCamelCase=1			,     __UpperCamelCase=2			,     __UpperCamelCase=False			,     __UpperCamelCase=0.0			,     __UpperCamelCase=0.0			,     __UpperCamelCase=1			,     __UpperCamelCase=False			,     **__UpperCamelCase			,     )				->				List[Any]:
												UpperCamelCase_		:							Dict  =		vocab_size
												# Backward compatibility with n_embed kwarg
												UpperCamelCase_		:							Optional[Any]  =		kwargs.pop("""n_embed"""			,     __UpperCamelCase						)
												UpperCamelCase_		:							List[Any]  =		hidden_size if n_embed is None else n_embed
												UpperCamelCase_		:							List[Any]  =		n_layer
												UpperCamelCase_		:							Optional[Any]  =		n_head
												UpperCamelCase_		:							List[str]  =		layer_norm_epsilon
												UpperCamelCase_		:							int  =		initializer_range
												UpperCamelCase_		:							List[str]  =		use_cache
												UpperCamelCase_		:							List[Any]  =		pretraining_tp
												UpperCamelCase_		:							Optional[int]  =		apply_residual_connection_post_layernorm
												UpperCamelCase_		:							Any  =		hidden_dropout
												UpperCamelCase_		:							Optional[Any]  =		attention_dropout

												UpperCamelCase_		:							List[str]  =		bos_token_id
												UpperCamelCase_		:							Optional[Any]  =		eos_token_id
												UpperCamelCase_		:							str  =		slow_but_exact

												super().__init__(bos_token_id=__UpperCamelCase			,     eos_token_id=__UpperCamelCase			,     **__UpperCamelCase						)


class        UpperCamelCase						(				__a				):
						a__		:Dict      =			version.parse('''1.12'''				)





						def __init__(self			,     __UpperCamelCase			,     __UpperCamelCase = "default"			,     __UpperCamelCase = None			,     __UpperCamelCase = False			,     )				->				List[str]:
												super().__init__(__UpperCamelCase			,     task=__UpperCamelCase			,     patching_specs=__UpperCamelCase			,     use_past=__UpperCamelCase						)
												if not getattr(self._config			,     """pad_token_id"""			,     __UpperCamelCase						):
																		# TODO: how to do that better?
																		UpperCamelCase_		:							Optional[Any]  =		0





						@property
						def        A_							(self						)				->				Mapping[str, Mapping[int, str]]:
												UpperCamelCase_		:							str  =		OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}						)
												if self.use_past:
																		# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
																		self.fill_with_past_key_values_(__UpperCamelCase			,     direction="""inputs"""			,     inverted_values_shape=__UpperCamelCase						)
																		UpperCamelCase_		:							int  =		{0: """batch""", 1: """past_sequence + sequence"""}
												else:
																		UpperCamelCase_		:							Dict  =		{0: """batch""", 1: """sequence"""}

												return common_inputs





						@property
						def        A_							(self						)				->				int:
												return self._config.n_layer





						@property
						def        A_							(self						)				->				int:
												return self._config.n_head





						@property
						def        A_							(self						)				->				float:
												return 1E-3





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = -1			,     __UpperCamelCase = -1			,     __UpperCamelCase = False			,     __UpperCamelCase = None			,     )				->				Mapping[str, Any]:
												UpperCamelCase_		:							Union[str, Any]  =		super(__UpperCamelCase			,     self						).generate_dummy_inputs(
												    __UpperCamelCase			,     batch_size=__UpperCamelCase			,     seq_length=__UpperCamelCase			,     is_pair=__UpperCamelCase			,     framework=__UpperCamelCase						)

												# We need to order the input in the way they appears in the forward()
												UpperCamelCase_		:							List[Any]  =		OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}						)

												# Need to add the past_keys
												if self.use_past:
																		if not is_torch_available():
																								raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""						)
																		else:
																								import torch

																								UpperCamelCase_,UpperCamelCase_		:							List[str]  =		common_inputs["""input_ids"""].shape
																								# Not using the same length for past_key_values
																								UpperCamelCase_		:							List[str]  =		seqlen + 2
																								UpperCamelCase_		:							Tuple  =		self._config.hidden_size // self.num_attention_heads
																								UpperCamelCase_		:							List[Any]  =		(
																								    batch * self.num_attention_heads,
																								    head_dim,
																								    past_key_values_length,
																								)
																								UpperCamelCase_		:							str  =		(
																								    batch * self.num_attention_heads,
																								    past_key_values_length,
																								    head_dim,
																								)
																								UpperCamelCase_		:							Any  =		[
																								    (torch.zeros(__UpperCamelCase						), torch.zeros(__UpperCamelCase						)) for _ in range(self.num_layers						)
																								]

												UpperCamelCase_		:							int  =		common_inputs["""attention_mask"""]
												if self.use_past:
																		UpperCamelCase_		:							List[str]  =		ordered_inputs["""attention_mask"""].dtype
																		UpperCamelCase_		:							Any  =		torch.cat(
																		    [ordered_inputs["""attention_mask"""], torch.ones(__UpperCamelCase			,     __UpperCamelCase			,     dtype=__UpperCamelCase						)]			,     dim=1						)

												return ordered_inputs





						@property
						def        A_							(self						)				->				int:
												return 13






