"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel






@require_tf
class   __UpperCAmelCase		:
				A__ :  str    				=			MBartConfig
				A__ :  Any    				=			{}
				A__ :  List[Any]    				=			"""gelu"""

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase=13      ,					_lowerCamelCase=7      ,					_lowerCamelCase=True      ,					_lowerCamelCase=False      ,					_lowerCamelCase=99      ,					_lowerCamelCase=32      ,					_lowerCamelCase=2      ,					_lowerCamelCase=4      ,					_lowerCamelCase=37      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=20      ,					_lowerCamelCase=2      ,					_lowerCamelCase=1      ,					_lowerCamelCase=0      ,					):
										lowerCamelCase__   =parent
										lowerCamelCase__   =batch_size
										lowerCamelCase__   =seq_length
										lowerCamelCase__   =is_training
										lowerCamelCase__   =use_labels
										lowerCamelCase__   =vocab_size
										lowerCamelCase__   =hidden_size
										lowerCamelCase__   =num_hidden_layers
										lowerCamelCase__   =num_attention_heads
										lowerCamelCase__   =intermediate_size
										lowerCamelCase__   =hidden_dropout_prob
										lowerCamelCase__   =attention_probs_dropout_prob
										lowerCamelCase__   =max_position_embeddings
										lowerCamelCase__   =eos_token_id
										lowerCamelCase__   =pad_token_id
										lowerCamelCase__   =bos_token_id

				def 						_a      ( self ):
										lowerCamelCase__   =ids_tensor([self.batch_size, self.seq_length - 1]      ,					self.vocab_size )
										lowerCamelCase__   =tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size )      ,					1 )
										lowerCamelCase__   =tf.concat([input_ids, eos_tensor]      ,					axis=1 )

										lowerCamelCase__   =ids_tensor([self.batch_size, self.seq_length]      ,					self.vocab_size )

										lowerCamelCase__   =self.config_cls(
										    vocab_size=self.vocab_size      ,					d_model=self.hidden_size      ,					encoder_layers=self.num_hidden_layers      ,					decoder_layers=self.num_hidden_layers      ,					encoder_attention_heads=self.num_attention_heads      ,					decoder_attention_heads=self.num_attention_heads      ,					encoder_ffn_dim=self.intermediate_size      ,					decoder_ffn_dim=self.intermediate_size      ,					dropout=self.hidden_dropout_prob      ,					attention_dropout=self.attention_probs_dropout_prob      ,					max_position_embeddings=self.max_position_embeddings      ,					eos_token_ids=[2]      ,					bos_token_id=self.bos_token_id      ,					pad_token_id=self.pad_token_id      ,					decoder_start_token_id=self.pad_token_id      ,					**self.config_updates      ,					)
										lowerCamelCase__   =prepare_mbart_inputs_dict(A_      ,					A_      ,					A_ )
										return config, inputs_dict






				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   =TFMBartModel(config=A_ ).get_decoder()
										lowerCamelCase__   =inputs_dict["input_ids"]

										lowerCamelCase__   =input_ids[:1, :]
										lowerCamelCase__   =inputs_dict["attention_mask"][:1, :]
										lowerCamelCase__   =inputs_dict["head_mask"]
										lowerCamelCase__   =1

										# first forward pass
										lowerCamelCase__   =model(A_      ,					attention_mask=A_      ,					head_mask=A_      ,					use_cache=A_ )

										lowerCamelCase__   , lowerCamelCase__   =outputs.to_tuple()
										lowerCamelCase__   =past_key_values[1]





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase=None  ,       __lowerCAmelCase=None  ,       __lowerCAmelCase=None  ,       __lowerCAmelCase=None  ,       __lowerCAmelCase=None  ,       )				->   Dict:




						'''simple docstring'''



						if attention_mask is None:
												lowerCamelCase__   =tf.cast(tf.math.not_equal(lowerCAmelCase_  ,       config.pad_token_id							)  ,       tf.inta							)
						if decoder_attention_mask is None:
												lowerCamelCase__   =tf.concat(
												    [
												        tf.ones(decoder_input_ids[:, :1].shape  ,       dtype=tf.inta							),
												        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]  ,       config.pad_token_id							)  ,       tf.inta							),
												    ]  ,       axis=-1  ,       )
						if head_mask is None:
												lowerCamelCase__   =tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
						if decoder_head_mask is None:
												lowerCamelCase__   =tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
						if cross_attn_head_mask is None:
												lowerCamelCase__   =tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": decoder_attention_mask,
						    "head_mask": head_mask,
						    "decoder_head_mask": decoder_head_mask,
						    "cross_attn_head_mask": cross_attn_head_mask,
						}






@require_tf
class   __UpperCAmelCase		(      __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase      ):
				A__ :  Tuple    				=			(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
				A__ :  List[str]    				=			(TFMBartForConditionalGeneration,) if is_tf_available() else ()
				A__ :  Any    				=			(
				    {
				        """conversational""": TFMBartForConditionalGeneration,
				        """feature-extraction""": TFMBartModel,
				        """summarization""": TFMBartForConditionalGeneration,
				        """text2text-generation""": TFMBartForConditionalGeneration,
				        """translation""": TFMBartForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				A__ :  List[str]    				=			True
				A__ :  Tuple    				=			False
				A__ :  Union[str, Any]    				=			False

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase ):

										if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
																# Exception encountered when calling layer '...'
																return True

										return False

				def 						_a      ( self ):
										lowerCamelCase__   =TFMBartModelTester(self )
										lowerCamelCase__   =ConfigTester(self      ,					config_class=A_ )

				def 						_a      ( self ):
										self.config_tester.run_common_tests()






				def 						_a      ( self ):
										lowerCamelCase__   =self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*A_ )






@require_sentencepiece
@require_tokenizers
@require_tf
class   __UpperCAmelCase		(      unittest.TestCase      ):
				A__ :  Tuple    				=			[
				    """ UN Chief Says There Is No Military Solution in Syria""",
				]
				A__ :  Dict    				=			[
				    """Şeful ONU declară că nu există o soluţie militară în Siria""",
				]
				A__ :  Optional[int]    				=			"""facebook/mbart-large-en-ro"""

				@cached_property
				def 						_a      ( self ):
										return AutoTokenizer.from_pretrained(self.model_name )

				@cached_property
				def 						_a      ( self ):
										lowerCamelCase__   =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
										return model

				def 						_a      ( self      ,					**_lowerCamelCase ):
										lowerCamelCase__   =self.translate_src_text(**A_ )
										self.assertListEqual(self.expected_text      ,					A_ )

				def 						_a      ( self      ,					**_lowerCamelCase ):
										lowerCamelCase__   =self.tokenizer(self.src_text      ,					**A_      ,					return_tensors="tf" )
										lowerCamelCase__   =self.model.generate(
										    model_inputs.input_ids      ,					attention_mask=model_inputs.attention_mask      ,					num_beams=2 )
										lowerCamelCase__   =self.tokenizer.batch_decode(A_      ,					skip_special_tokens=A_ )
										return generated_words






				@slow
				def 						_a      ( self ):
										self._assert_generated_batch_equal_expected()









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					:				List[str]				=			logging.getLogger(__name__)



class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		Dict				=	"""token-classification"""



    def __init__(       self   ,				A_     ):





        '''simple docstring'''

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



    def  lowercase_					(       self   ,				**A_     ):





        '''simple docstring'''

        return self.model(**A_     )



    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

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

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

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



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_ = False     ):





        '''simple docstring'''

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



    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

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



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

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

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

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

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



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

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



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self._eval_end(A_     )

        # Converting to the dict required by pl
        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
        # pytorch_lightning/trainer/logging.py#L139
        SCREAMING_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  lowercase_					(       A_   ,				A_     ):





        '''simple docstring'''

        BaseTransformer.add_model_specific_args(A_   ,				A_     )
        parser.add_argument(
            '''--task_type'''   ,				default='''NER'''   ,				type=A_   ,				help='''Task type to fine tune in training (e.g. NER, POS, etc)'''     )
        parser.add_argument(
            '''--max_seq_length'''   ,				default=1_28   ,				type=A_   ,				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=A_   ,				help='''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''   ,				)
        parser.add_argument(
            '''--gpus'''   ,				default=0   ,				type=A_   ,				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					:				List[str]				=			argparse.ArgumentParser()
  add_generic_args(parser, os.getcwd())
  _A					:				List[Any]				=			NERTransformer.add_model_specific_args(parser, os.getcwd())
  _A					:				Optional[Any]				=			parser.parse_args()
  _A					:				Any				=			NERTransformer(args)
  _A					:				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
    _A					:				Optional[int]				=			sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
    _A					:				Optional[int]				=			model.load_from_checkpoint(checkpoints[-1])
    trainer.test(model)
