
from __future__ import annotations

import unittest

from transformers import OpenAIGPTConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
				import tensorflow as tf

				from transformers.models.openai.modeling_tf_openai import (
				    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFOpenAIGPTDoubleHeadsModel,
				    TFOpenAIGPTForSequenceClassification,
				    TFOpenAIGPTLMHeadModel,
				    TFOpenAIGPTModel,
				)
class    _UpperCamelCase:

						def __init__(					self		:			str	,						_lowerCamelCase		:			Union[str, Any]	,						):
													_UpperCAmelCase  :			Any	       =		parent
													_UpperCAmelCase  :			int	       =		13
													_UpperCAmelCase  :			Union[str, Any]	       =		7
													_UpperCAmelCase  :			Any	       =		True
													_UpperCAmelCase  :			str	       =		True
													_UpperCAmelCase  :			List[str]	       =		True
													_UpperCAmelCase  :			List[Any]	       =		True
													_UpperCAmelCase  :			List[str]	       =		True
													_UpperCAmelCase  :			Optional[int]	       =		99
													_UpperCAmelCase  :			Any	       =		32
													_UpperCAmelCase  :			List[Any]	       =		2
													_UpperCAmelCase  :			Optional[Any]	       =		4
													_UpperCAmelCase  :			Union[str, Any]	       =		37
													_UpperCAmelCase  :			Dict	       =		"gelu"
													_UpperCAmelCase  :			List[str]	       =		0.1
													_UpperCAmelCase  :			Any	       =		0.1
													_UpperCAmelCase  :			List[str]	       =		5_12
													_UpperCAmelCase  :			Any	       =		16
													_UpperCAmelCase  :			Optional[int]	       =		2
													_UpperCAmelCase  :			Union[str, Any]	       =		0.02
													_UpperCAmelCase  :			Any	       =		3
													_UpperCAmelCase  :			List[str]	       =		4
													_UpperCAmelCase  :			List[str]	       =		None
													_UpperCAmelCase  :			List[Any]	       =		self.vocab_size - 1

						def 						a__ (					self		:			int			):
													_UpperCAmelCase  :			List[Any]	       =		ids_tensor([self.batch_size, self.seq_length]	,						self.vocab_size			)

													_UpperCAmelCase  :			List[str]	       =		None
													if self.use_input_mask:
																				_UpperCAmelCase  :			Any	       =		random_attention_mask([self.batch_size, self.seq_length]			)

													_UpperCAmelCase  :			Any	       =		None
													if self.use_token_type_ids:
																				_UpperCAmelCase  :			Optional[int]	       =		ids_tensor([self.batch_size, self.seq_length]	,						self.type_vocab_size			)

													_UpperCAmelCase  :			Optional[Any]	       =		None
													if self.use_mc_token_ids:
																				_UpperCAmelCase  :			Union[str, Any]	       =		ids_tensor([self.batch_size, self.num_choices]	,						self.seq_length			)

													_UpperCAmelCase  :			Tuple	       =		None
													_UpperCAmelCase  :			str	       =		None
													_UpperCAmelCase  :			int	       =		None
													if self.use_labels:
																				_UpperCAmelCase  :			Optional[int]	       =		ids_tensor([self.batch_size]	,						self.type_sequence_label_size			)
																				_UpperCAmelCase  :			Optional[Any]	       =		ids_tensor([self.batch_size, self.seq_length]	,						self.num_labels			)
																				_UpperCAmelCase  :			Optional[Any]	       =		ids_tensor([self.batch_size]	,						self.num_choices			)

													_UpperCAmelCase  :			List[Any]	       =		OpenAIGPTConfig(
													    vocab_size=self.vocab_size	,						n_embd=self.hidden_size	,						n_layer=self.num_hidden_layers	,						n_head=self.num_attention_heads	,						n_positions=self.max_position_embeddings	,						pad_token_id=self.pad_token_id	,						)

													_UpperCAmelCase  :			Optional[Any]	       =		ids_tensor([self.num_hidden_layers, self.num_attention_heads]	,						2			)

													return (
													    config,
													    input_ids,
													    input_mask,
													    head_mask,
													    token_type_ids,
													    mc_token_ids,
													    sequence_labels,
													    token_labels,
													    choice_labels,
													)

						def 						a__ (					self		:			Dict	,						_lowerCamelCase		:			Tuple	,						_lowerCamelCase		:			Optional[int]	,						_lowerCamelCase		:			Union[str, Any]	,						_lowerCamelCase		:			str	,						_lowerCamelCase		:			List[Any]	,						*_lowerCamelCase		:			Any			):
													_UpperCAmelCase  :			Optional[int]	       =		TFOpenAIGPTModel(config=_UpperCAmelCase			)
													_UpperCAmelCase  :			Optional[int]	       =		{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
													_UpperCAmelCase  :			Tuple	       =		model(_UpperCAmelCase			)

													_UpperCAmelCase  :			List[Any]	       =		[input_ids, input_mask]
													_UpperCAmelCase  :			Tuple	       =		model(_UpperCAmelCase			)

													_UpperCAmelCase  :			Optional[Any]	       =		model(_UpperCAmelCase			)

													self.parent.assertEqual(result.last_hidden_state.shape	,						(self.batch_size, self.seq_length, self.hidden_size)			)

						def 						a__ (					self		:			Tuple	,						_lowerCamelCase		:			Optional[Any]	,						_lowerCamelCase		:			Optional[int]	,						_lowerCamelCase		:			List[Any]	,						_lowerCamelCase		:			Dict	,						_lowerCamelCase		:			Optional[int]	,						*_lowerCamelCase		:			Dict			):
													_UpperCAmelCase  :			Optional[Any]	       =		TFOpenAIGPTLMHeadModel(config=_UpperCAmelCase			)
													_UpperCAmelCase  :			Any	       =		{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
													_UpperCAmelCase  :			Optional[Any]	       =		model(_UpperCAmelCase			)
													self.parent.assertEqual(result.logits.shape	,						(self.batch_size, self.seq_length, self.vocab_size)			)

						def 						a__ (					self		:			List[str]	,						_lowerCamelCase		:			Any	,						_lowerCamelCase		:			List[Any]	,						_lowerCamelCase		:			Tuple	,						_lowerCamelCase		:			Dict	,						_lowerCamelCase		:			Optional[int]	,						_lowerCamelCase		:			Tuple	,						*_lowerCamelCase		:			List[str]			):
													_UpperCAmelCase  :			Any	       =		TFOpenAIGPTDoubleHeadsModel(config=_UpperCAmelCase			)

													_UpperCAmelCase  :			List[str]	       =		tf.tile(tf.expand_dims(_UpperCAmelCase	,						1			)	,						(1, self.num_choices, 1)			)
													_UpperCAmelCase  :			Tuple	       =		tf.tile(tf.expand_dims(_UpperCAmelCase	,						1			)	,						(1, self.num_choices, 1)			)
													_UpperCAmelCase  :			Tuple	       =		tf.tile(tf.expand_dims(_UpperCAmelCase	,						1			)	,						(1, self.num_choices, 1)			)

													_UpperCAmelCase  :			Dict	       =		{
													    "input_ids": multiple_choice_inputs_ids,
													    "mc_token_ids": mc_token_ids,
													    "attention_mask": multiple_choice_input_mask,
													    "token_type_ids": multiple_choice_token_type_ids,
													}
													_UpperCAmelCase  :			str	       =		model(_UpperCAmelCase			)
													self.parent.assertEqual(
													    result.logits.shape	,						(self.batch_size, self.num_choices, self.seq_length, self.vocab_size)			)
													self.parent.assertEqual(result.mc_logits.shape	,						(self.batch_size, self.num_choices)			)

						def 						a__ (					self		:			Tuple	,						_lowerCamelCase		:			List[str]	,						_lowerCamelCase		:			List[str]	,						_lowerCamelCase		:			Optional[int]	,						_lowerCamelCase		:			List[Any]	,						_lowerCamelCase		:			List[Any]	,						*_lowerCamelCase		:			Dict			):
													_UpperCAmelCase  :			Optional[Any]	       =		self.num_labels
													_UpperCAmelCase  :			List[str]	       =		ids_tensor([self.batch_size]	,						self.type_sequence_label_size			)
													_UpperCAmelCase  :			Any	       =		{
													    "input_ids": input_ids,
													    "attention_mask": input_mask,
													    "token_type_ids": token_type_ids,
													    "labels": sequence_labels,
													}
													_UpperCAmelCase  :			Tuple	       =		TFOpenAIGPTForSequenceClassification(_UpperCAmelCase			)
													_UpperCAmelCase  :			Union[str, Any]	       =		model(_UpperCAmelCase			)
													self.parent.assertEqual(result.logits.shape	,						(self.batch_size, self.num_labels)			)






						def 						a__ (					self		:			Tuple			):
													_UpperCAmelCase  :			str	       =		self.prepare_config_and_inputs()

													(
													    (
													    _UpperCAmelCase   
													)       ,(
													    _UpperCAmelCase   
													)       ,(
													    _UpperCAmelCase   
													)       ,(
													    _UpperCAmelCase   
													)       ,(
													    _UpperCAmelCase   
													)       ,(
													    _UpperCAmelCase   
													)       ,(
													    _UpperCAmelCase   
													)       ,(
													    _UpperCAmelCase   
													)       ,(
													    _UpperCAmelCase   
													)       ,   
													)  :			List[str]	       =		config_and_inputs

													_UpperCAmelCase  :			List[str]	       =		{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
													return config, inputs_dict







@require_tf
class    _UpperCamelCase(   lowerCAmelCase__		,						lowerCAmelCase__		,						unittest.TestCase				):
						__A:						str				   =      (
						    (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
						    if is_tf_available()
						    else ()
						)
						__A:						Optional[int]				   =      (
						    (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
						)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
						__A:						Optional[Any]				   =      (
						    {
						        "feature-extraction": TFOpenAIGPTModel,
						        "text-classification": TFOpenAIGPTForSequenceClassification,
						        "text-generation": TFOpenAIGPTLMHeadModel,
						        "zero-shot": TFOpenAIGPTForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						__A:						Any				   =      False
						__A:						Dict				   =      False

						def 						a__ (					self		:			List[str]	,						_lowerCamelCase		:			Union[str, Any]	,						_lowerCamelCase		:			Any	,						_lowerCamelCase		:			int	,						_lowerCamelCase		:			str	,						_lowerCamelCase		:			int			):


													if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
																				# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
																				# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
																				# tiny config could not be created.
																				return True

													return False

						def 						a__ (					self		:			Optional[int]			):
													_UpperCAmelCase  :			str	       =		TFOpenAIGPTModelTester(self			)
													_UpperCAmelCase  :			Tuple	       =		ConfigTester(self	,						config_class=_UpperCAmelCase	,						n_embd=37			)

						def 						a__ (					self		:			Optional[Any]			):
													self.config_tester.run_common_tests()

						def 						a__ (					self		:			int			):
													_UpperCAmelCase  :			Tuple	       =		self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_openai_gpt_model(*_UpperCAmelCase			)

						def 						a__ (					self		:			Optional[int]			):
													_UpperCAmelCase  :			List[str]	       =		self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_openai_gpt_lm_head(*_UpperCAmelCase			)

						def 						a__ (					self		:			int			):
													_UpperCAmelCase  :			Tuple	       =		self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_openai_gpt_double_head(*_UpperCAmelCase			)

						def 						a__ (					self		:			Union[str, Any]			):
													_UpperCAmelCase  :			int	       =		self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_UpperCAmelCase			)






						@slow
						def 						a__ (					self		:			List[Any]			):


													for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				_UpperCAmelCase  :			List[str]	       =		TFOpenAIGPTModel.from_pretrained(_UpperCAmelCase			)
																				self.assertIsNotNone(_UpperCAmelCase			)







@require_tf
class    _UpperCamelCase(   unittest.TestCase				):

						@slow
						def 						a__ (					self		:			str			):
													_UpperCAmelCase  :			Optional[Any]	       =		TFOpenAIGPTLMHeadModel.from_pretrained("openai-gpt"			)
													_UpperCAmelCase  :			str	       =		tf.convert_to_tensor([[4_81, 47_35, 5_44]]	,						dtype=tf.intaa			)  # the president is
													_UpperCAmelCase  :			str	       =		[
													    4_81,
													    47_35,
													    5_44,
													    2_46,
													    9_63,
													    8_70,
													    7_62,
													    2_39,
													    2_44,
													    4_04_77,
													    2_44,
													    2_49,
													    7_19,
													    8_81,
													    4_87,
													    5_44,
													    2_40,
													    2_44,
													    6_03,
													    4_81,
													]  # the president is a very good man. " \n " i\'m sure he is, " said the

													_UpperCAmelCase  :			Optional[int]	       =		model.generate(_UpperCAmelCase	,						do_sample=_UpperCAmelCase			)
													self.assertListEqual(output_ids[0].numpy().tolist()	,						_UpperCAmelCase			)






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()


SCREAMING_SNAKE_CASE__				=							logging.getLogger(__name__)
@dataclass
class       A__  :
							lowerCAmelCase__						:		str            =				field(
							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}							)
							lowerCAmelCase__						:		Optional[str]            =				field(
							    default=lowerCAmelCase__       ,		metadata={"help": "Pretrained config name or path if not the same as model_name"}							)
							lowerCAmelCase__						:		Optional[str]            =				field(
							    default="NER"       ,		metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}							)
							lowerCAmelCase__						:		Optional[str]            =				field(
							    default=lowerCAmelCase__       ,		metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}							)
							lowerCAmelCase__						:		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.
							lowerCAmelCase__						:		Optional[str]            =				field(
							    default=lowerCAmelCase__       ,		metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}       ,		)






@dataclass
class       A__  :
							lowerCAmelCase__						:		str            =				field(
							    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}							)
							lowerCAmelCase__						:		Optional[str]            =				field(
							    metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}							)
							lowerCAmelCase__						:		int            =				field(
							    default=128       ,		metadata={
							        "help": (
							            "The maximum total input sequence length after tokenization. Sequences longer "
							            "than this will be truncated, sequences shorter will be padded."
							        )
							    }       ,		)
							lowerCAmelCase__						:		bool            =				field(
							    default=lowerCAmelCase__       ,		metadata={"help": "Overwrite the cached training and evaluation sets"}							)






def 				__SCREAMING_SNAKE_CASE  (						)	->		int:
				# 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.
				__lowercase								=   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)     )
				__lowercase						,   __lowercase						,   __lowercase								=   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.'     )

				__lowercase								=   import_module('tasks'     )

				try:
								__lowercase								=   getattr(SCREAMING_SNAKE_CASE       ,				model_args.task_type     )
								__lowercase								=   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'       ,				SCREAMING_SNAKE_CASE     )

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

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

				__lowercase								=   AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,				num_labels=SCREAMING_SNAKE_CASE       ,				idalabel=SCREAMING_SNAKE_CASE       ,				labelaid={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE     )}       ,				cache_dir=model_args.cache_dir       ,				)
				__lowercase								=   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():
								__lowercase								=   TFAutoModelForTokenClassification.from_pretrained(
								    model_args.model_name_or_path       ,				from_pt=bool('.bin' in model_args.model_name_or_path     )       ,				config=SCREAMING_SNAKE_CASE       ,				cache_dir=model_args.cache_dir       ,				)

				# Get datasets
				__lowercase								=   (
				    TFTokenClassificationDataset(
				        token_classification_task=SCREAMING_SNAKE_CASE       ,				data_dir=data_args.data_dir       ,				tokenizer=SCREAMING_SNAKE_CASE       ,				labels=SCREAMING_SNAKE_CASE       ,				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
				)
				__lowercase								=   (
				    TFTokenClassificationDataset(
				        token_classification_task=SCREAMING_SNAKE_CASE       ,				data_dir=data_args.data_dir       ,				tokenizer=SCREAMING_SNAKE_CASE       ,				labels=SCREAMING_SNAKE_CASE       ,				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(SCREAMING_SNAKE_CASE			: np.ndarray       ,				SCREAMING_SNAKE_CASE			: np.ndarray     ) -> Tuple[List[int], List[int]]:
								__lowercase								=   np.argmax(SCREAMING_SNAKE_CASE       ,				axis=2     )
								__lowercase						,   __lowercase								=   preds.shape
								__lowercase								=   [[] for _ in range(SCREAMING_SNAKE_CASE     )]
								__lowercase								=   [[] for _ in range(SCREAMING_SNAKE_CASE     )]

								for i in range(SCREAMING_SNAKE_CASE     ):
												for j in range(SCREAMING_SNAKE_CASE     ):
																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(SCREAMING_SNAKE_CASE			: EvalPrediction     ) -> Dict:
								__lowercase						,   __lowercase								=   align_predictions(p.predictions       ,				p.label_ids     )

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

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

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

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

								__lowercase								=   trainer.evaluate()
								__lowercase								=   os.path.join(training_args.output_dir       ,				'eval_results.txt'     )

								with open(SCREAMING_SNAKE_CASE       ,				'w'     ) as writer:
												logger.info('***** Eval results *****'     )

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

												results.update(SCREAMING_SNAKE_CASE     )

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

								__lowercase						,   __lowercase						,   __lowercase								=   trainer.predict(test_dataset.get_dataset()     )
								__lowercase						,   __lowercase								=   align_predictions(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
								__lowercase								=   classification_report(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )

								logger.info('\n%s'       ,				SCREAMING_SNAKE_CASE     )

								__lowercase								=   os.path.join(training_args.output_dir       ,				'test_results.txt'     )

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

								# Save predictions
								__lowercase								=   os.path.join(training_args.output_dir       ,				'test_predictions.txt'     )

								with open(SCREAMING_SNAKE_CASE       ,				'w'     ) as writer:
												with open(os.path.join(data_args.data_dir       ,				'test.txt'     )       ,				'r'     ) as f:
																__lowercase								=   0

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

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

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

				return results


if __name__ == "__main__":
							main()





