

"""simple docstring"""




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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__A     :    int    =		logging.get_logger(__name__)

__A     :    Tuple    =		{
    'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}





class 			__UpperCamelCase (   _A						):
		SCREAMING_SNAKE_CASE								=    "mbart"
		SCREAMING_SNAKE_CASE								=    ["past_key_values"]
		SCREAMING_SNAKE_CASE								=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


		def __init__(self :		List[str]     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=5_0_2_6_5     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=1_0_2_4     ,     __SCREAMING_SNAKE_CASE :		Tuple=1_2     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]=4_0_9_6     ,     __SCREAMING_SNAKE_CASE :		List[Any]=1_6     ,     __SCREAMING_SNAKE_CASE :		Tuple=1_2     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=4_0_9_6     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=1_6     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=0.0     ,     __SCREAMING_SNAKE_CASE :		List[str]=0.0     ,     __SCREAMING_SNAKE_CASE :		List[Any]=True     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=True     ,     __SCREAMING_SNAKE_CASE :		Optional[int]="gelu"     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]=1_0_2_4     ,     __SCREAMING_SNAKE_CASE :		List[str]=0.1     ,     __SCREAMING_SNAKE_CASE :		str=0.0     ,     __SCREAMING_SNAKE_CASE :		Dict=0.0     ,     __SCREAMING_SNAKE_CASE :		int=0.0_2     ,     __SCREAMING_SNAKE_CASE :		str=0.0     ,     __SCREAMING_SNAKE_CASE :		Any=False     ,     __SCREAMING_SNAKE_CASE :		Any=1     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=0     ,     __SCREAMING_SNAKE_CASE :		List[Any]=2     ,     __SCREAMING_SNAKE_CASE :		List[Any]=2     ,     **__SCREAMING_SNAKE_CASE :		List[str]     ,     ):
							A			=					vocab_size
							A			=					max_position_embeddings
							A			=					d_model
							A			=					encoder_ffn_dim
							A			=					encoder_layers
							A			=					encoder_attention_heads
							A			=					decoder_ffn_dim
							A			=					decoder_layers
							A			=					decoder_attention_heads
							A			=					dropout
							A			=					attention_dropout
							A			=					activation_dropout
							A			=					activation_function
							A			=					init_std
							A			=					encoder_layerdrop
							A			=					decoder_layerdrop
							A			=					classifier_dropout
							A			=					use_cache
							A			=					encoder_layers
							A			=					scale_embedding  # scale factor will be sqrt(d_model) if True
							super().__init__(
							    pad_token_id=__SCREAMING_SNAKE_CASE     ,     bos_token_id=__SCREAMING_SNAKE_CASE     ,     eos_token_id=__SCREAMING_SNAKE_CASE     ,     is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,     forced_eos_token_id=__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE     ,     )





class 			__UpperCamelCase (   _A						):


		@property
		def 					SCREAMING_SNAKE_CASE__   (self :		int):
							if self.task in ["default", "seq2seq-lm"]:
												A			=					OrderedDict(
												    [
												        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
												        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
												    ])

												if self.use_past:
																	A			=					{0: "batch"}
																	A			=					{0: "batch", 1: "past_decoder_sequence + sequence"}
												else:
																	A			=					{0: "batch", 1: "decoder_sequence"}
																	A			=					{0: "batch", 1: "decoder_sequence"}

												if self.use_past:
																	self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE     ,     direction="inputs")
							elif self.task == "causal-lm":
												# TODO: figure this case out.
												A			=					OrderedDict(
												    [
												        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
												        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
												    ])
												if self.use_past:
																	A			,    A			=					self.num_layers
																	for i in range(__SCREAMING_SNAKE_CASE):
																						A			=					{0: "batch", 2: "past_sequence + sequence"}
																						A			=					{0: "batch", 2: "past_sequence + sequence"}
							else:
												A			=					OrderedDict(
												    [
												        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
												        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
												        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
												        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
												    ])

							return common_inputs


		@property
		def 					SCREAMING_SNAKE_CASE__   (self :		List[Any]):
							if self.task in ["default", "seq2seq-lm"]:
												A			=					super().outputs
							else:
												A			=					super(__SCREAMING_SNAKE_CASE     ,     self).outputs
												if self.use_past:
																	A			,    A			=					self.num_layers
																	for i in range(__SCREAMING_SNAKE_CASE):
																						A			=					{0: "batch", 2: "past_sequence + sequence"}
																						A			=					{0: "batch", 2: "past_sequence + sequence"}
							return common_outputs


		def 					SCREAMING_SNAKE_CASE__   (self :		Tuple     ,     __SCREAMING_SNAKE_CASE :		PreTrainedTokenizer     ,     __SCREAMING_SNAKE_CASE :		int = -1     ,     __SCREAMING_SNAKE_CASE :		int = -1     ,     __SCREAMING_SNAKE_CASE :		bool = False     ,     __SCREAMING_SNAKE_CASE :		Optional[TensorType] = None     ,     ):
							A			=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)

							# Generate decoder inputs
							A			=					seq_length if not self.use_past else 1
							A			=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
							A			=					{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
							A			=					dict(**__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE)

							if self.use_past:
												if not is_torch_available():
																	raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
												else:
																	import torch
												A			,    A			=					common_inputs["input_ids"].shape
												A			=					common_inputs["decoder_input_ids"].shape[1]
												A			,    A			=					self.num_attention_heads
												A			=					(
												    batch,
												    num_encoder_attention_heads,
												    encoder_seq_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)
												A			=					decoder_seq_length + 3
												A			=					(
												    batch,
												    num_decoder_attention_heads,
												    decoder_past_length,
												    self._config.hidden_size // num_decoder_attention_heads,
												)

												A			=					torch.cat(
												    [common_inputs["decoder_attention_mask"], torch.ones(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)]     ,     dim=1)

												A			=					[]
												# If the number of encoder and decoder layers are present in the model configuration, both are considered
												A			,    A			=					self.num_layers
												A			=					min(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
												A			=					max(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE) - min_num_layers
												A			=					"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

												for _ in range(__SCREAMING_SNAKE_CASE):
																	common_inputs["past_key_values"].append(
																	    (
																	        torch.zeros(__SCREAMING_SNAKE_CASE),
																	        torch.zeros(__SCREAMING_SNAKE_CASE),
																	        torch.zeros(__SCREAMING_SNAKE_CASE),
																	        torch.zeros(__SCREAMING_SNAKE_CASE),
																	    ))
												# TODO: test this.
												A			=					encoder_shape if remaining_side_name == "encoder" else decoder_shape
												for _ in range(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):
																	common_inputs["past_key_values"].append((torch.zeros(__SCREAMING_SNAKE_CASE), torch.zeros(__SCREAMING_SNAKE_CASE)))
							return common_inputs


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[Any]     ,     __SCREAMING_SNAKE_CASE :		PreTrainedTokenizer     ,     __SCREAMING_SNAKE_CASE :		int = -1     ,     __SCREAMING_SNAKE_CASE :		int = -1     ,     __SCREAMING_SNAKE_CASE :		bool = False     ,     __SCREAMING_SNAKE_CASE :		Optional[TensorType] = None     ,     ):
							A			=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)

							if self.use_past:
												if not is_torch_available():
																	raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
												else:
																	import torch
												A			,    A			=					common_inputs["input_ids"].shape
												# Not using the same length for past_key_values
												A			=					seqlen + 2
												A			,    A			=					self.num_layers
												A			,    A			=					self.num_attention_heads
												A			=					(
												    batch,
												    num_encoder_attention_heads,
												    past_key_values_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)

												A			=					common_inputs["attention_mask"].dtype
												A			=					torch.cat(
												    [common_inputs["attention_mask"], torch.ones(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     dtype=__SCREAMING_SNAKE_CASE)]     ,     dim=1)
												A			=					[
												    (torch.zeros(__SCREAMING_SNAKE_CASE), torch.zeros(__SCREAMING_SNAKE_CASE)) for _ in range(__SCREAMING_SNAKE_CASE)
												]
							return common_inputs


		def 					SCREAMING_SNAKE_CASE__   (self :		List[Any]     ,     __SCREAMING_SNAKE_CASE :		PreTrainedTokenizer     ,     __SCREAMING_SNAKE_CASE :		int = -1     ,     __SCREAMING_SNAKE_CASE :		int = -1     ,     __SCREAMING_SNAKE_CASE :		bool = False     ,     __SCREAMING_SNAKE_CASE :		Optional[TensorType] = None     ,     ):
							# Copied from OnnxConfig.generate_dummy_inputs
							# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							A			=					compute_effective_axis_dimension(
							    __SCREAMING_SNAKE_CASE     ,     fixed_dimension=OnnxConfig.default_fixed_batch     ,     num_token_to_add=0)

							# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
							A			=					tokenizer.num_special_tokens_to_add(__SCREAMING_SNAKE_CASE)
							A			=					compute_effective_axis_dimension(
							    __SCREAMING_SNAKE_CASE     ,     fixed_dimension=OnnxConfig.default_fixed_sequence     ,     num_token_to_add=__SCREAMING_SNAKE_CASE)

							# Generate dummy inputs according to compute batch and sequence
							A			=					[" ".join([tokenizer.unk_token]) * seq_length] * batch_size
							A			=					dict(tokenizer(__SCREAMING_SNAKE_CASE     ,     return_tensors=__SCREAMING_SNAKE_CASE))
							return common_inputs


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[Any]     ,     __SCREAMING_SNAKE_CASE :		PreTrainedTokenizer     ,     __SCREAMING_SNAKE_CASE :		int = -1     ,     __SCREAMING_SNAKE_CASE :		int = -1     ,     __SCREAMING_SNAKE_CASE :		bool = False     ,     __SCREAMING_SNAKE_CASE :		Optional[TensorType] = None     ,     ):
							if self.task in ["default", "seq2seq-lm"]:
												A			=					self._generate_dummy_inputs_for_default_and_seqaseq_lm(
												    __SCREAMING_SNAKE_CASE     ,     batch_size=__SCREAMING_SNAKE_CASE     ,     seq_length=__SCREAMING_SNAKE_CASE     ,     is_pair=__SCREAMING_SNAKE_CASE     ,     framework=__SCREAMING_SNAKE_CASE)

							elif self.task == "causal-lm":
												A			=					self._generate_dummy_inputs_for_causal_lm(
												    __SCREAMING_SNAKE_CASE     ,     batch_size=__SCREAMING_SNAKE_CASE     ,     seq_length=__SCREAMING_SNAKE_CASE     ,     is_pair=__SCREAMING_SNAKE_CASE     ,     framework=__SCREAMING_SNAKE_CASE)
							else:
												A			=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    __SCREAMING_SNAKE_CASE     ,     batch_size=__SCREAMING_SNAKE_CASE     ,     seq_length=__SCREAMING_SNAKE_CASE     ,     is_pair=__SCREAMING_SNAKE_CASE     ,     framework=__SCREAMING_SNAKE_CASE)

							return common_inputs


		def 					SCREAMING_SNAKE_CASE__   (self :		Dict     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]     ,     __SCREAMING_SNAKE_CASE :		Any):
							if self.task in ["default", "seq2seq-lm"]:
												A			=					super()._flatten_past_key_values_(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
							else:
												A			=					super(__SCREAMING_SNAKE_CASE     ,     self)._flatten_past_key_values_(
												    __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)












'''simple docstring'''







import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


_a					: str      =    logging.getLogger(__name__)





@dataclass
class     _UpperCAmelCase							:
		a   :   str     =field(
		    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}   )
		a   :   Optional[str]     =field(
		    default=lowerCAmelCase_	,					metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}   )
		a   :   Optional[str]     =field(
		    default="""NER"""	,					metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}   )
		a   :   Optional[str]     =field(
		    default=lowerCAmelCase_	,					metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}   )
		a   :   bool     =field(default=lowerCAmelCase_	,					metadata={"""help""": """Set this flag to use fast tokenization."""}   )
		# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
		# or just modify its tokenizer_config.json.
		a   :   Optional[str]     =field(
		    default=lowerCAmelCase_	,					metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}	,					)





@dataclass
class     _UpperCAmelCase							:
		a   :   str     =field(
		    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}   )
		a   :   Optional[str]     =field(
		    metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}   )
		a   :   int     =field(
		    default=1_28	,					metadata={
		        """help""": (
		            """The maximum total input sequence length after tokenization. Sequences longer """
		            """than this will be truncated, sequences shorter will be padded."""
		        )
		    }	,					)
		a   :   bool     =field(
		    default=lowerCAmelCase_	,					metadata={"""help""": """Overwrite the cached training and evaluation sets"""}   )





def    _lowerCAmelCase						(						)					->					List[str]:
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.
			__lowerCAmelCase											=				HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
			__lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase											=				parser.parse_args_into_dataclasses()

			if (
			    os.path.exists(training_args.output_dir	)
			    and os.listdir(training_args.output_dir	)
			    and training_args.do_train
			    and not training_args.overwrite_output_dir
			):
						raise ValueError(
						    f'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
						    """ --overwrite_output_dir to overcome."""	)

			__lowerCAmelCase											=				import_module("""tasks"""	)

			try:
						__lowerCAmelCase											=				getattr(lowercase					,	model_args.task_type	)
						__lowerCAmelCase											=				token_classification_task_clazz()
			except AttributeError:
						raise ValueError(
						    f'Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
						    f'Available tasks classes are: {TokenClassificationTask.__subclasses__()}'	)

			# Setup logging
			logging.basicConfig(
			    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""					,	datefmt="""%m/%d/%Y %H:%M:%S"""					,	level=logging.INFO					,	)
			logger.info(
			    """n_replicas: %s, distributed training: %s, 16-bits training: %s"""					,	training_args.n_replicas					,	bool(training_args.n_replicas > 1	)					,	training_args.fpaa					,	)
			logger.info("""Training/evaluation parameters %s"""					,	lowercase	)

			# Prepare Token Classification task
			__lowerCAmelCase											=				token_classification_task.get_labels(data_args.labels	)
			__lowerCAmelCase											=				dict(enumerate(lowercase	)	)
			__lowerCAmelCase											=				len(lowercase	)

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.

			__lowerCAmelCase											=				AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path					,	num_labels=lowercase					,	idalabel=lowercase					,	labelaid={label: i for i, label in enumerate(lowercase	)}					,	cache_dir=model_args.cache_dir					,	)
			__lowerCAmelCase											=				AutoTokenizer.from_pretrained(
			    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					,	cache_dir=model_args.cache_dir					,	use_fast=model_args.use_fast					,	)

			with training_args.strategy.scope():
						__lowerCAmelCase											=				TFAutoModelForTokenClassification.from_pretrained(
						    model_args.model_name_or_path					,	from_pt=bool(""".bin""" in model_args.model_name_or_path	)					,	config=lowercase					,	cache_dir=model_args.cache_dir					,	)

			# Get datasets
			__lowerCAmelCase											=				(
			    TFTokenClassificationDataset(
			        token_classification_task=lowercase					,	data_dir=data_args.data_dir					,	tokenizer=lowercase					,	labels=lowercase					,	model_type=config.model_type					,	max_seq_length=data_args.max_seq_length					,	overwrite_cache=data_args.overwrite_cache					,	mode=Split.train					,	)
			    if training_args.do_train
			    else None
			)
			__lowerCAmelCase											=				(
			    TFTokenClassificationDataset(
			        token_classification_task=lowercase					,	data_dir=data_args.data_dir					,	tokenizer=lowercase					,	labels=lowercase					,	model_type=config.model_type					,	max_seq_length=data_args.max_seq_length					,	overwrite_cache=data_args.overwrite_cache					,	mode=Split.dev					,	)
			    if training_args.do_eval
			    else None
			)

			def align_predictions(lowercase					,	lowercase	) -> Tuple[List[int], List[int]]:
						__lowerCAmelCase											=				np.argmax(lowercase					,	axis=2	)
						__lowerCAmelCase						,       __lowerCAmelCase											=				preds.shape
						__lowerCAmelCase											=				[[] for _ in range(lowercase	)]
						__lowerCAmelCase											=				[[] for _ in range(lowercase	)]

						for i in range(lowercase	):
									for j in range(lowercase	):
												if label_ids[i, j] != -100:
															out_label_list[i].append(label_map[label_ids[i][j]]	)
															preds_list[i].append(label_map[preds[i][j]]	)

						return preds_list, out_label_list

			def compute_metrics(lowercase	) -> Dict:
						__lowerCAmelCase						,       __lowerCAmelCase											=				align_predictions(p.predictions					,	p.label_ids	)

						return {
						    "precision": precision_score(lowercase					,	lowercase	),
						    "recall": recall_score(lowercase					,	lowercase	),
						    "f1": fa_score(lowercase					,	lowercase	),
						}

			# Initialize our Trainer
			__lowerCAmelCase											=				TFTrainer(
			    model=lowercase					,	args=lowercase					,	train_dataset=train_dataset.get_dataset() if train_dataset else None					,	eval_dataset=eval_dataset.get_dataset() if eval_dataset else None					,	compute_metrics=lowercase					,	)

			# Training
			if training_args.do_train:
						trainer.train()
						trainer.save_model()
						tokenizer.save_pretrained(training_args.output_dir	)

			# Evaluation
			__lowerCAmelCase											=				{}
			if training_args.do_eval:
						logger.info("""*** Evaluate ***"""	)

						__lowerCAmelCase											=				trainer.evaluate()
						__lowerCAmelCase											=				os.path.join(training_args.output_dir					,	"""eval_results.txt"""	)

						with open(lowercase					,	"""w"""	) as writer:
									logger.info("""***** Eval results *****"""	)

									for key, value in result.items():
												logger.info("""  %s = %s"""					,	lowercase					,	lowercase	)
												writer.write("""%s = %s\n""" % (key, value)	)

									results.update(lowercase	)

    # Predict
			if training_args.do_predict:
						__lowerCAmelCase											=				TFTokenClassificationDataset(
						    token_classification_task=lowercase					,	data_dir=data_args.data_dir					,	tokenizer=lowercase					,	labels=lowercase					,	model_type=config.model_type					,	max_seq_length=data_args.max_seq_length					,	overwrite_cache=data_args.overwrite_cache					,	mode=Split.test					,	)

						__lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase											=				trainer.predict(test_dataset.get_dataset()	)
						__lowerCAmelCase						,       __lowerCAmelCase											=				align_predictions(lowercase					,	lowercase	)
						__lowerCAmelCase											=				classification_report(lowercase					,	lowercase	)

						logger.info("""\n%s"""					,	lowercase	)

						__lowerCAmelCase											=				os.path.join(training_args.output_dir					,	"""test_results.txt"""	)

						with open(lowercase					,	"""w"""	) as writer:
									writer.write("""%s\n""" % report	)

						# Save predictions
						__lowerCAmelCase											=				os.path.join(training_args.output_dir					,	"""test_predictions.txt"""	)

						with open(lowercase					,	"""w"""	) as writer:
									with open(os.path.join(data_args.data_dir					,	"""test.txt"""	)					,	"""r"""	) as f:
												__lowerCAmelCase											=				0

												for line in f:
															if line.startswith("""-DOCSTART-"""	) or line == "" or line == "\n":
																		writer.write(lowercase	)

																		if not preds_list[example_id]:
																					example_id += 1
															elif preds_list[example_id]:
																		__lowerCAmelCase											=				line.split()[0] + """ """ + preds_list[example_id].pop(0	) + """\n"""

																		writer.write(lowercase	)
															else:
																		logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""					,	line.split()[0]	)

			return results


if __name__ == "__main__":
	main()

