






'''simple docstring'''




import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
    from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import DPTImageProcessor

class      _UpperCAmelCase    :




   """simple docstring"""

   def __init__(	self				:			str   ,   __UpperCAmelCase				:			Optional[Any]   ,   __UpperCAmelCase				:			Tuple=2   ,   __UpperCAmelCase				:			Any=32   ,   __UpperCAmelCase				:			Optional[Any]=16   ,   __UpperCAmelCase				:			int=3   ,   __UpperCAmelCase				:			Optional[int]=True   ,   __UpperCAmelCase				:			Any=True   ,   __UpperCAmelCase				:			List[str]=32   ,   __UpperCAmelCase				:			List[str]=4   ,   __UpperCAmelCase				:			Optional[Any]=[0, 1, 2, 3]   ,   __UpperCAmelCase				:			Optional[Any]=4   ,   __UpperCAmelCase				:			Union[str, Any]=37   ,   __UpperCAmelCase				:			str="gelu"   ,   __UpperCAmelCase				:			Union[str, Any]=0.1   ,   __UpperCAmelCase				:			Any=0.1   ,   __UpperCAmelCase				:			Tuple=0.02   ,   __UpperCAmelCase				:			Tuple=3   ,   __UpperCAmelCase				:			Optional[int]=False   ,   __UpperCAmelCase				:			Union[str, Any]=None   ,   ):





         '''simple docstring'''





         _A      =			parent
         _A      =			batch_size
         _A      =			image_size
         _A      =			patch_size
         _A      =			num_channels
         _A      =			is_training
         _A      =			use_labels
         _A      =			hidden_size
         _A      =			num_hidden_layers
         _A      =			backbone_out_indices
         _A      =			num_attention_heads
         _A      =			intermediate_size
         _A      =			hidden_act
         _A      =			hidden_dropout_prob
         _A      =			attention_probs_dropout_prob
         _A      =			initializer_range
         _A      =			num_labels
         _A      =			scope
         _A      =			is_hybrid
         # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
         _A      =			(image_size // patch_size) ** 2
         _A      =			num_patches + 1

   def 		lowerCAmelCase				(	self				:			Any	):





         '''simple docstring'''





         _A      =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

         _A      =			None
         if self.use_labels:
               _A      =			ids_tensor([self.batch_size, self.image_size, self.image_size]   ,   self.num_labels	)

         _A      =			self.get_config()

         return config, pixel_values, labels

   def 		lowerCAmelCase				(	self				:			Dict	):





         '''simple docstring'''





         return DPTConfig(
             image_size=self.image_size   ,   patch_size=self.patch_size   ,   num_channels=self.num_channels   ,   hidden_size=self.hidden_size   ,   num_hidden_layers=self.num_hidden_layers   ,   backbone_out_indices=self.backbone_out_indices   ,   num_attention_heads=self.num_attention_heads   ,   intermediate_size=self.intermediate_size   ,   hidden_act=self.hidden_act   ,   hidden_dropout_prob=self.hidden_dropout_prob   ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,   is_decoder=__UpperCAmelCase   ,   initializer_range=self.initializer_range   ,   is_hybrid=self.is_hybrid   ,   )

   def 		lowerCAmelCase				(	self				:			List[str]   ,   __UpperCAmelCase				:			str   ,   __UpperCAmelCase				:			Optional[int]   ,   __UpperCAmelCase				:			Optional[Any]	):





         '''simple docstring'''





         _A      =			DPTModel(config=__UpperCAmelCase	)
         model.to(__UpperCAmelCase	)
         model.eval()
         _A      =			model(__UpperCAmelCase	)
         self.parent.assertEqual(result.last_hidden_state.shape   ,   (self.batch_size, self.seq_length, self.hidden_size)	)

   def 		lowerCAmelCase				(	self				:			str   ,   __UpperCAmelCase				:			Union[str, Any]   ,   __UpperCAmelCase				:			Union[str, Any]   ,   __UpperCAmelCase				:			Optional[int]	):





         '''simple docstring'''





         _A      =			self.num_labels
         _A      =			DPTForDepthEstimation(__UpperCAmelCase	)
         model.to(__UpperCAmelCase	)
         model.eval()
         _A      =			model(__UpperCAmelCase	)
         self.parent.assertEqual(result.predicted_depth.shape   ,   (self.batch_size, self.image_size, self.image_size)	)

   def 		lowerCAmelCase				(	self				:			Tuple   ,   __UpperCAmelCase				:			Optional[Any]   ,   __UpperCAmelCase				:			str   ,   __UpperCAmelCase				:			List[str]	):





         '''simple docstring'''





         _A      =			self.num_labels
         _A      =			DPTForSemanticSegmentation(__UpperCAmelCase	)
         model.to(__UpperCAmelCase	)
         model.eval()
         _A      =			model(__UpperCAmelCase   ,   labels=__UpperCAmelCase	)
         self.parent.assertEqual(
             result.logits.shape   ,   (self.batch_size, self.num_labels, self.image_size, self.image_size)	)

   def 		lowerCAmelCase				(	self				:			Optional[int]	):





         '''simple docstring'''





         _A      =			self.prepare_config_and_inputs()
         _A  ,			_A  ,			_A      =			config_and_inputs
         _A      =			{"pixel_values": pixel_values}
         return config, inputs_dict





@require_torch
class      _UpperCAmelCase    (  snake_case_							,					snake_case_							,					unittest.TestCase		):




   """simple docstring"""

   snake_case					       =		(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
   snake_case					       =		(
       {
           '''depth-estimation''': DPTForDepthEstimation,
           '''feature-extraction''': DPTModel,
           '''image-segmentation''': DPTForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   snake_case					       =		False
   snake_case					       =		False
   snake_case					       =		False

   def 		lowerCAmelCase				(	self				:			Optional[Any]	):





         '''simple docstring'''





         _A      =			DPTModelTester(self	)
         _A      =			ConfigTester(self   ,   config_class=__UpperCAmelCase   ,   has_text_modality=__UpperCAmelCase   ,   hidden_size=37	)

   def 		lowerCAmelCase				(	self				:			int	):





         '''simple docstring'''





         self.config_tester.run_common_tests()

   @unittest.skip(reason="DPT does not use inputs_embeds"	)
   def 		lowerCAmelCase				(	self				:			Optional[Any]	):





         '''simple docstring'''





         pass

   def 		lowerCAmelCase				(	self				:			Dict	):





         '''simple docstring'''





         _A  ,			_A      =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               _A      =			model_class(__UpperCAmelCase	)
               self.assertIsInstance(model.get_input_embeddings()   ,   (nn.Module)	)
               _A      =			model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(__UpperCAmelCase   ,   nn.Linear	)	)

   def 		lowerCAmelCase				(	self				:			Union[str, Any]	):





         '''simple docstring'''





         _A  ,			_A      =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               _A      =			model_class(__UpperCAmelCase	)
               _A      =			inspect.signature(model.forward	)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               _A      =			[*signature.parameters.keys()]

               _A      =			["pixel_values"]
               self.assertListEqual(arg_names[:1]   ,   __UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			Union[str, Any]	):





         '''simple docstring'''





         _A      =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			Tuple	):





         '''simple docstring'''





         _A      =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_depth_estimation(*__UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			List[Any]	):





         '''simple docstring'''





         _A      =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			Optional[int]	):





         '''simple docstring'''





         for model_class in self.all_model_classes:
               if model_class.__name__ == "DPTForDepthEstimation":
                     continue

               _A  ,			_A      =			self.model_tester.prepare_config_and_inputs_for_common()
               _A      =			True

               if model_class in get_values(__UpperCAmelCase	):
                     continue

               _A      =			model_class(__UpperCAmelCase	)
               model.to(__UpperCAmelCase	)
               model.train()
               _A      =			self._prepare_for_class(__UpperCAmelCase   ,   __UpperCAmelCase   ,   return_labels=__UpperCAmelCase	)
               _A      =			model(**__UpperCAmelCase	).loss
               loss.backward()

   def 		lowerCAmelCase				(	self				:			Union[str, Any]	):





         '''simple docstring'''





         for model_class in self.all_model_classes:
               if model_class.__name__ == "DPTForDepthEstimation":
                     continue

               _A  ,			_A      =			self.model_tester.prepare_config_and_inputs_for_common()
               _A      =			False
               _A      =			True

               if model_class in get_values(__UpperCAmelCase	) or not model_class.supports_gradient_checkpointing:
                     continue
               _A      =			model_class(__UpperCAmelCase	)
               model.to(__UpperCAmelCase	)
               model.gradient_checkpointing_enable()
               model.train()
               _A      =			self._prepare_for_class(__UpperCAmelCase   ,   __UpperCAmelCase   ,   return_labels=__UpperCAmelCase	)
               _A      =			model(**__UpperCAmelCase	).loss
               loss.backward()

   def 		lowerCAmelCase				(	self				:			List[Any]	):





         '''simple docstring'''





         _A  ,			_A      =			self.model_tester.prepare_config_and_inputs_for_common()

         _A      =			_config_zero_init(__UpperCAmelCase	)
         for model_class in self.all_model_classes:
               _A      =			model_class(config=__UpperCAmelCase	)
               # Skip the check for the backbone
               _A      =			[]
               for name, module in model.named_modules():
                     if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                           _A      =			[f'''{name}.{key}''' for key in module.state_dict().keys()]
                           break

               for name, param in model.named_parameters():
                     if param.requires_grad:
                           if name in backbone_params:
                                 continue
                           self.assertIn(
                               ((param.data.mean() * 1E9).round() / 1E9).item()   ,   [0.0, 1.0]   ,   msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''   ,   )

   @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."	)
   def 		lowerCAmelCase				(	self				:			List[Any]	):





         '''simple docstring'''





         pass

   @slow
   def 		lowerCAmelCase				(	self				:			Tuple	):





         '''simple docstring'''





         for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _A      =			DPTModel.from_pretrained(__UpperCAmelCase	)
               self.assertIsNotNone(__UpperCAmelCase	)





def       __lowercase		(    ) ->		Optional[int]:

      '''simple docstring'''


      _A      =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
      return image


@require_torch
@require_vision
@slow
class      _UpperCAmelCase    (  unittest.TestCase		):




   """simple docstring"""

   def 		lowerCAmelCase				(	self				:			Optional[Any]	):





         '''simple docstring'''





         _A      =			DPTImageProcessor.from_pretrained("Intel/dpt-large"	)
         _A      =			DPTForDepthEstimation.from_pretrained("Intel/dpt-large"	).to(__UpperCAmelCase	)

         _A      =			prepare_img()
         _A      =			image_processor(images=__UpperCAmelCase   ,   return_tensors="pt"	).to(__UpperCAmelCase	)

         # forward pass
         with torch.no_grad():
               _A      =			model(**__UpperCAmelCase	)
               _A      =			outputs.predicted_depth

         # verify the predicted depth
         _A      =			torch.Size((1, 384, 384)	)
         self.assertEqual(predicted_depth.shape   ,   __UpperCAmelCase	)

         _A      =			torch.tensor(
             [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]	).to(__UpperCAmelCase	)

         self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]   ,   __UpperCAmelCase   ,   atol=1E-4	)	)

   def 		lowerCAmelCase				(	self				:			str	):





         '''simple docstring'''





         _A      =			DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"	)
         _A      =			DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"	).to(__UpperCAmelCase	)

         _A      =			prepare_img()
         _A      =			image_processor(images=__UpperCAmelCase   ,   return_tensors="pt"	).to(__UpperCAmelCase	)

         # forward pass
         with torch.no_grad():
               _A      =			model(**__UpperCAmelCase	)

         # verify the logits
         _A      =			torch.Size((1, 150, 480, 480)	)
         self.assertEqual(outputs.logits.shape   ,   __UpperCAmelCase	)

         _A      =			torch.tensor(
             [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]	).to(__UpperCAmelCase	)

         self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]   ,   __UpperCAmelCase   ,   atol=1E-4	)	)

   def 		lowerCAmelCase				(	self				:			Optional[Any]	):





         '''simple docstring'''





         _A      =			DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"	)
         _A      =			DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"	).to(__UpperCAmelCase	)

         _A      =			prepare_img()
         _A      =			image_processor(images=__UpperCAmelCase   ,   return_tensors="pt"	).to(__UpperCAmelCase	)

         # forward pass
         with torch.no_grad():
               _A      =			model(**__UpperCAmelCase	)

         _A      =			outputs.logits.detach().cpu()

         _A      =			image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase   ,   target_sizes=[(500, 300)]	)
         _A      =			torch.Size((500, 300)	)
         self.assertEqual(segmentation[0].shape   ,   __UpperCAmelCase	)

         _A      =			image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase	)
         _A      =			torch.Size((480, 480)	)
         self.assertEqual(segmentation[0].shape   ,   __UpperCAmelCase	)








'''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()


lowerCamelCase_							      =      logging.getLogger(__name__)

@dataclass
class      _UpperCAmelCase    :




   """simple docstring"""

   snake_case					       =		field(
       metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}		)
   snake_case					       =		field(
       default=snake_case_							,					metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}		)
   snake_case					       =		field(
       default='''NER'''							,					metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}		)
   snake_case					       =		field(
       default=snake_case_							,					metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}		)
   snake_case					       =		field(default=snake_case_							,					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.
   snake_case					       =		field(
       default=snake_case_							,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}							,					)





@dataclass
class      _UpperCAmelCase    :




   """simple docstring"""

   snake_case					       =		field(
       metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}		)
   snake_case					       =		field(
       metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}		)
   snake_case					       =		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.'''
           )
       }							,					)
   snake_case					       =		field(
       default=snake_case_							,					metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}		)





def       __lowercase		(    ) ->		Any:

      '''simple docstring'''


      _A      =			HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
      _A  ,			_A  ,			_A      =			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." )

      _A      =			import_module("tasks" )

      try:
            _A      =			getattr(__lowercase     ,						model_args.task_type )
            _A      =			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
      _A      =			token_classification_task.get_labels(data_args.labels )
      _A      =			dict(enumerate(__lowercase ) )
      _A      =			len(__lowercase )

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

      _A      =			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     ,						)
      _A      =			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():
            _A      =			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
      _A      =			(
          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
      )
      _A      =			(
          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]]:
            _A      =			np.argmax(__lowercase     ,						axis=2 )
            _A  ,			_A      =			preds.shape
            _A      =			[[] for _ in range(__lowercase )]
            _A      =			[[] 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:
            _A  ,			_A      =			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
      _A      =			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
      _A      =			{}
      if training_args.do_eval:
            logger.info("*** Evaluate ***" )

            _A      =			trainer.evaluate()
            _A      =			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:
            _A      =			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     ,						)

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

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

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

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

            # Save predictions
            _A      =			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:
                        _A      =			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]:
                                    _A      =			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()





