


from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


lowerCAmelCase       =							logging.get_logger(__name__)
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		try:
				with open(SCREAMING_SNAKE_CASE						,				'''rb'''     ) as flax_state_f:
						lowercase__            =      from_bytes(SCREAMING_SNAKE_CASE						,				flax_state_f.read()     )
		except UnpicklingError as e:
				try:
						with open(SCREAMING_SNAKE_CASE     ) as f:
								if f.read().startswith('''version'''     ):
										raise OSError(
										    '''You seem to have cloned a repository without having git-lfs installed. Please'''
										    ''' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'''
										    ''' folder you cloned.'''     )
								else:
										raise ValueError from e
				except (UnicodeDecodeError, ValueError):
						raise EnvironmentError(f'Unable to convert {model_file} to Flax deserializable object. '     )

		return load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		try:
				import torch  # noqa: F401
		except ImportError:
				logger.error(
				    '''Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'''
				    ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
				    ''' instructions.'''     )
				raise

		# check if we have bf16 weights
		lowercase__            =      flatten_dict(jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE     : x.dtype == jnp.bfloataa						,				SCREAMING_SNAKE_CASE     )     ).values()
		if any(SCREAMING_SNAKE_CASE     ):
				# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

				# and bf16 is not fully supported in PT yet.
				logger.warning(
				    '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
				    '''before loading those in PyTorch model.'''     )
				lowercase__            =      jax.tree_util.tree_map(
				    lambda SCREAMING_SNAKE_CASE     : params.astype(np.floataa     ) if params.dtype == jnp.bfloataa else params						,				SCREAMING_SNAKE_CASE     )

		lowercase__            =      ''''''

		lowercase__            =      flatten_dict(SCREAMING_SNAKE_CASE						,				sep='''.'''     )
		lowercase__            =      pt_model.state_dict()

		# keep track of unexpected & missing keys
		lowercase__            =      []
		lowercase__            =      set(pt_model_dict.keys()     )

		for flax_key_tuple, flax_tensor in flax_state_dict.items():
				lowercase__            =      flax_key_tuple.split('''.'''     )

				if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
						lowercase__            =      flax_key_tuple_array[:-1] + ['''weight''']
						lowercase__            =      jnp.transpose(SCREAMING_SNAKE_CASE						,				(3, 2, 0, 1)     )
				elif flax_key_tuple_array[-1] == "kernel":
						lowercase__            =      flax_key_tuple_array[:-1] + ['''weight''']
						lowercase__            =      flax_tensor.T
				elif flax_key_tuple_array[-1] == "scale":
						lowercase__            =      flax_key_tuple_array[:-1] + ['''weight''']

				if "time_embedding" not in flax_key_tuple_array:
						for i, flax_key_tuple_string in enumerate(SCREAMING_SNAKE_CASE     ):
								lowercase__            =      (
								    flax_key_tuple_string.replace('''_0'''						,				'''.0'''     )
								    .replace('''_1'''						,				'''.1'''     )
								    .replace('''_2'''						,				'''.2'''     )
								    .replace('''_3'''						,				'''.3'''     )
								    .replace('''_4'''						,				'''.4'''     )
								    .replace('''_5'''						,				'''.5'''     )
								    .replace('''_6'''						,				'''.6'''     )
								    .replace('''_7'''						,				'''.7'''     )
								    .replace('''_8'''						,				'''.8'''     )
								    .replace('''_9'''						,				'''.9'''     )
								)

				lowercase__            =      '''.'''.join(SCREAMING_SNAKE_CASE     )

				if flax_key in pt_model_dict:
						if flax_tensor.shape != pt_model_dict[flax_key].shape:
								raise ValueError(
								    f'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
								    f'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'     )
						else:
								# add weight to pytorch dict
								lowercase__            =      np.asarray(SCREAMING_SNAKE_CASE     ) if not isinstance(SCREAMING_SNAKE_CASE						,				np.ndarray     ) else flax_tensor
								lowercase__            =      torch.from_numpy(SCREAMING_SNAKE_CASE     )
								# remove from missing keys
								missing_keys.remove(SCREAMING_SNAKE_CASE     )
				else:
						# weight is not expected by PyTorch model
						unexpected_keys.append(SCREAMING_SNAKE_CASE     )

		pt_model.load_state_dict(SCREAMING_SNAKE_CASE     )

		# re-transform missing_keys to list
		lowercase__            =      list(SCREAMING_SNAKE_CASE     )

		if len(SCREAMING_SNAKE_CASE     ) > 0:
				logger.warning(
				    '''Some weights of the Flax model were not used when initializing the PyTorch model'''
				    f' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
				    f' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
				    ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
				    f' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
				    ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
				    ''' FlaxBertForSequenceClassification model).'''     )
		if len(SCREAMING_SNAKE_CASE     ) > 0:
				logger.warning(
				    f'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
				    f' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
				    ''' use it for predictions and inference.'''     )

		return pt_model






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       =							logging.getLogger(__name__)


class        _a							(			UpperCamelCase__			):
							_lowercase	:			Union[str, Any]        = '''token-classification'''

							def __init__(							self:	int    ,						UpperCamelCase_:	Optional[Any]							)			->   Dict:





									"""simple docstring"""







									if type(UpperCamelCase_							) == dict:
											lowercase__            =      Namespace(**UpperCamelCase_							)
									lowercase__            =      import_module('''tasks'''							)
									try:
											lowercase__            =      getattr(UpperCamelCase_    ,						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__(UpperCamelCase_    ,						len(self.labels							)    ,						self.mode							)

							def       lowerCamelCase_ (							self:	Tuple    ,						**UpperCamelCase_:	Optional[int]							)			->   str:





									"""simple docstring"""







									return self.model(**UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	List[str]    ,						UpperCamelCase_:	List[Any]    ,						UpperCamelCase_:	int							)			->   int:





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

							def       lowerCamelCase_ (							self:	Optional[Any]							)			->   Optional[int]:





									"""simple docstring"""







									lowercase__            =      self.hparams
									for mode in ["train", "dev", "test"]:
											lowercase__            =      self._feature_file(UpperCamelCase_							)
											if os.path.exists(UpperCamelCase_							) and not args.overwrite_cache:
													logger.info('''Loading features from cached file %s'''    ,						UpperCamelCase_							)
													lowercase__            =      torch.load(UpperCamelCase_							)
											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    ,						UpperCamelCase_							)
													lowercase__            =      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       lowerCamelCase_ (							self:	Union[str, Any]    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	bool = False							)			->   DataLoader:





									"""simple docstring"""







									lowercase__            =      self._feature_file(UpperCamelCase_							)
									logger.info('''Loading features from cached file %s'''    ,						UpperCamelCase_							)
									lowercase__            =      torch.load(UpperCamelCase_							)
									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(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_							)    ,						batch_size=UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	Any    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	List[Any]							)			->   Union[str, 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(**UpperCamelCase_							)
									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       lowerCamelCase_ (							self:	Dict    ,						UpperCamelCase_:	List[str]							)			->   int:





									"""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(UpperCamelCase_    ,						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(UpperCamelCase_    ,						UpperCamelCase_							),
									    '''precision''': precision_score(UpperCamelCase_    ,						UpperCamelCase_							),
									    '''recall''': recall_score(UpperCamelCase_    ,						UpperCamelCase_							),
									    '''f1''': fa_score(UpperCamelCase_    ,						UpperCamelCase_							),
									}

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

							def       lowerCamelCase_ (							self:	List[Any]    ,						UpperCamelCase_:	List[Any]							)			->   Dict:





									"""simple docstring"""







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

							def       lowerCamelCase_ (							self:	List[str]    ,						UpperCamelCase_:	Dict							)			->   Dict:





									"""simple docstring"""







									lowercase__     ,		lowercase__     ,		lowercase__            =      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
									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       lowerCamelCase_ (							UpperCamelCase_:	Optional[Any]    ,						UpperCamelCase_:	str							)			->   Optional[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=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__":
			lowerCAmelCase       =							argparse.ArgumentParser()
			add_generic_args(parser, os.getcwd())
			lowerCAmelCase       =							NERTransformer.add_model_specific_args(parser, os.getcwd())
			lowerCAmelCase       =							parser.parse_args()
			lowerCAmelCase       =							NERTransformer(args)
			lowerCAmelCase       =							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       =							sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
						lowerCAmelCase       =							model.load_from_checkpoint(checkpoints[-1])
						trainer.test(model)


