

"""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_   :      Dict         =  """token-classification"""

				def __init__(	self							:				str     ,  UpperCamelCase__							:				Dict)->   List[Any]:



						'''simple docstring'''


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

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



						'''simple docstring'''


						return self.model(**UpperCamelCase__)

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



						'''simple docstring'''


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

				def 							lowercase_     (	self							:				Any)->   int:



						'''simple docstring'''


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



						'''simple docstring'''


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



						'''simple docstring'''


						"""Compute validation""" ""
						__lowerCAmelCase:       Union[str, Any]          =			{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
						if self.config.model_type != "distilbert":
								__lowerCAmelCase:       Optional[Any]          =			(
								    batch[2] if self.config.model_type in ["bert", "xlnet"] else None
								)  # XLM and RoBERTa don"t use token_type_ids
						__lowerCAmelCase:       Optional[int]          =			self(**UpperCamelCase__)
						__lowerCAmelCase      , __lowerCAmelCase:       Optional[Any]          =			outputs[:2]
						__lowerCAmelCase:       List[Any]          =			logits.detach().cpu().numpy()
						__lowerCAmelCase:       Dict          =			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__							:				Any)->   Union[str, Any]:



						'''simple docstring'''


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

						__lowerCAmelCase:       Optional[int]          =			dict(enumerate(self.labels))
						__lowerCAmelCase:       List[Any]          =			[[] for _ in range(out_label_ids.shape[0])]
						__lowerCAmelCase:       List[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]])

						__lowerCAmelCase:       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__),
						}

						__lowerCAmelCase:       Optional[int]          =			dict(results.items())
						__lowerCAmelCase:       Optional[Any]          =			results
						return ret, preds_list, out_label_list

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



						'''simple docstring'''


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

				def 							lowercase_     (	self							:				Tuple     ,  UpperCamelCase__							:				Dict)->   Dict:



						'''simple docstring'''


						__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase:       Optional[int]          =			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:       Optional[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__							:				Union[str, Any]     ,  UpperCamelCase__							:				str)->   Any:



						'''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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__A						   =			logging.get_logger(__name__)

__A						   =			{
    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Union[str, Any]         =  """switch_transformers"""
				SCREAMING_SNAKE_CASE_   :      Tuple         =  ["""past_key_values"""]
				SCREAMING_SNAKE_CASE_   :      Any         =  {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

				def __init__(	self							:				List[str]     ,  UpperCamelCase__							:				List[str]=3_2_1_2_8     ,  UpperCamelCase__							:				str=7_6_8     ,  UpperCamelCase__							:				Union[str, Any]=6_4     ,  UpperCamelCase__							:				Optional[int]=2_0_4_8     ,  UpperCamelCase__							:				Dict=6_4     ,  UpperCamelCase__							:				List[str]=1_2     ,  UpperCamelCase__							:				Dict=3     ,  UpperCamelCase__							:				Any=1_2     ,  UpperCamelCase__							:				Dict=3     ,  UpperCamelCase__							:				Dict=1_2     ,  UpperCamelCase__							:				List[str]=8     ,  UpperCamelCase__							:				Optional[Any]=False     ,  UpperCamelCase__							:				Optional[int]=0.01     ,  UpperCamelCase__							:				Optional[int]="float32"     ,  UpperCamelCase__							:				Any=False     ,  UpperCamelCase__							:				Dict=3_2     ,  UpperCamelCase__							:				Union[str, Any]=1_2_8     ,  UpperCamelCase__							:				Union[str, Any]=0.1     ,  UpperCamelCase__							:				Optional[int]=1e-6     ,  UpperCamelCase__							:				Optional[Any]=0.001     ,  UpperCamelCase__							:				Dict=0.001     ,  UpperCamelCase__							:				int=1.0     ,  UpperCamelCase__							:				str="relu"     ,  UpperCamelCase__							:				int=True     ,  UpperCamelCase__							:				Any=False     ,  UpperCamelCase__							:				str=True     ,  UpperCamelCase__							:				Tuple=0     ,  UpperCamelCase__							:				str=1     ,  **UpperCamelCase__							:				Tuple     ,  )->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			vocab_size
						__lowerCAmelCase:       str          =			d_model
						__lowerCAmelCase:       str          =			d_kv
						__lowerCAmelCase:       str          =			d_ff

						__lowerCAmelCase:       List[str]          =			num_sparse_encoder_layers

						__lowerCAmelCase:       List[Any]          =			num_layers
						__lowerCAmelCase:       Optional[Any]          =			(
						    num_decoder_layers if num_decoder_layers is not None else self.num_layers
						)  # default = symmetry
						__lowerCAmelCase:       Tuple          =			num_sparse_decoder_layers

						# This tells us, each how many encoder layer we'll have to set a sparse layer.
						if self.num_sparse_encoder_layers > 0:
								__lowerCAmelCase:       int          =			self.num_layers // self.num_sparse_encoder_layers
						else:
								__lowerCAmelCase:       Union[str, Any]          =			self.num_layers  # HACK: this will create 0 sparse layers

						# This tells us, each how many encoder layer we'll have to set a sparse layer.
						if self.num_sparse_decoder_layers > 0:
								__lowerCAmelCase:       Dict          =			self.num_decoder_layers // self.num_sparse_decoder_layers
						else:
								__lowerCAmelCase:       Any          =			self.num_decoder_layers  # HACK: this will create 0 sparse layers

						__lowerCAmelCase:       Dict          =			num_heads
						__lowerCAmelCase:       Dict          =			num_experts
						__lowerCAmelCase:       Any          =			expert_capacity
						__lowerCAmelCase:       List[Any]          =			router_bias
						__lowerCAmelCase:       int          =			router_jitter_noise
						if router_dtype not in ["float32", "float16", "bfloat16"]:
								raise ValueError(f"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}")
						__lowerCAmelCase:       Dict          =			router_dtype

						__lowerCAmelCase:       Optional[Any]          =			router_ignore_padding_tokens
						__lowerCAmelCase:       Union[str, Any]          =			relative_attention_num_buckets
						__lowerCAmelCase:       str          =			relative_attention_max_distance

						__lowerCAmelCase:       Optional[int]          =			dropout_rate
						__lowerCAmelCase:       Optional[Any]          =			layer_norm_epsilon
						__lowerCAmelCase:       int          =			initializer_factor
						__lowerCAmelCase:       Tuple          =			feed_forward_proj
						__lowerCAmelCase:       int          =			use_cache
						__lowerCAmelCase:       int          =			add_router_probs

						__lowerCAmelCase:       Optional[Any]          =			router_z_loss_coef
						__lowerCAmelCase:       Dict          =			router_aux_loss_coef

						__lowerCAmelCase:       Union[str, Any]          =			self.feed_forward_proj.split("-")
						__lowerCAmelCase:       Tuple          =			act_info[-1]
						__lowerCAmelCase:       str          =			act_info[0] == "gated"

						if len(UpperCamelCase__) > 1 and act_info[0] != "gated" or len(UpperCamelCase__) > 2:
								raise ValueError(
								    f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
								    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
								    "'gated-gelu' or 'relu'")

						# for backwards compatibility
						if feed_forward_proj == "gated-gelu":
								__lowerCAmelCase:       List[str]          =			"gelu_new"

						super().__init__(
						    pad_token_id=UpperCamelCase__     ,  eos_token_id=UpperCamelCase__     ,  is_encoder_decoder=UpperCamelCase__     ,  **UpperCamelCase__     ,  )


