

import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
       from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor
class        A_ :


    '''simple docstring'''






    def __init__(self  ,   lowercase__  ,   lowercase__=13  ,   lowercase__=32  ,   lowercase__=2  ,   lowercase__=3  ,   lowercase__=16  ,   lowercase__=[1, 2, 1]  ,   lowercase__=[2, 2, 4]  ,   lowercase__=2  ,   lowercase__=2.0  ,   lowercase__=True  ,   lowercase__=0.0  ,   lowercase__=0.0  ,   lowercase__=0.1  ,   lowercase__="gelu"  ,   lowercase__=False  ,   lowercase__=True  ,   lowercase__=0.02  ,   lowercase__=1E-5  ,   lowercase__=True  ,   lowercase__=None  ,   lowercase__=True  ,   lowercase__=10  ,   lowercase__=8  ,   )  ->   List[Any]:
        __UpperCAmelCase					       =      parent
        __UpperCAmelCase					       =      batch_size
        __UpperCAmelCase					       =      image_size
        __UpperCAmelCase					       =      patch_size
        __UpperCAmelCase					       =      num_channels
        __UpperCAmelCase					       =      embed_dim
        __UpperCAmelCase					       =      depths
        __UpperCAmelCase					       =      num_heads
        __UpperCAmelCase					       =      window_size
        __UpperCAmelCase					       =      mlp_ratio
        __UpperCAmelCase					       =      qkv_bias
        __UpperCAmelCase					       =      hidden_dropout_prob
        __UpperCAmelCase					       =      attention_probs_dropout_prob
        __UpperCAmelCase					       =      drop_path_rate
        __UpperCAmelCase					       =      hidden_act
        __UpperCAmelCase					       =      use_absolute_embeddings
        __UpperCAmelCase					       =      patch_norm
        __UpperCAmelCase					       =      layer_norm_eps
        __UpperCAmelCase					       =      initializer_range
        __UpperCAmelCase					       =      is_training
        __UpperCAmelCase					       =      scope
        __UpperCAmelCase					       =      use_labels
        __UpperCAmelCase					       =      type_sequence_label_size
        __UpperCAmelCase					       =      encoder_stride


    def   lowerCAmelCase_    (self	)  ->   Dict:
        __UpperCAmelCase					       =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

        __UpperCAmelCase					       =      None
        if self.use_labels:
            __UpperCAmelCase					       =      ids_tensor([self.batch_size]  ,   self.type_sequence_label_size	)

        __UpperCAmelCase					       =      self.get_config()

        return config, pixel_values, labels


    def   lowerCAmelCase_    (self	)  ->   List[str]:
        return SwinvaConfig(
            image_size=self.image_size  ,   patch_size=self.patch_size  ,   num_channels=self.num_channels  ,   embed_dim=self.embed_dim  ,   depths=self.depths  ,   num_heads=self.num_heads  ,   window_size=self.window_size  ,   mlp_ratio=self.mlp_ratio  ,   qkv_bias=self.qkv_bias  ,   hidden_dropout_prob=self.hidden_dropout_prob  ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,   drop_path_rate=self.drop_path_rate  ,   hidden_act=self.hidden_act  ,   use_absolute_embeddings=self.use_absolute_embeddings  ,   path_norm=self.patch_norm  ,   layer_norm_eps=self.layer_norm_eps  ,   initializer_range=self.initializer_range  ,   encoder_stride=self.encoder_stride  ,   )


    def   lowerCAmelCase_    (self  ,   lowercase__  ,   lowercase__  ,   lowercase__	)  ->   Any:
        __UpperCAmelCase					       =      SwinvaModel(config=__a	)
        model.to(__a	)
        model.eval()
        __UpperCAmelCase					       =      model(__a	)

        __UpperCAmelCase					       =      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths	) - 1))
        __UpperCAmelCase					       =      int(config.embed_dim * 2 ** (len(config.depths	) - 1)	)

        self.parent.assertEqual(result.last_hidden_state.shape  ,   (self.batch_size, expected_seq_len, expected_dim)	)


    def   lowerCAmelCase_    (self  ,   lowercase__  ,   lowercase__  ,   lowercase__	)  ->   Dict:
        __UpperCAmelCase					       =      SwinvaForMaskedImageModeling(config=__a	)
        model.to(__a	)
        model.eval()
        __UpperCAmelCase					       =      model(__a	)
        self.parent.assertEqual(
            result.logits.shape  ,   (self.batch_size, self.num_channels, self.image_size, self.image_size)	)

        # test greyscale images
        __UpperCAmelCase					       =      1
        __UpperCAmelCase					       =      SwinvaForMaskedImageModeling(__a	)
        model.to(__a	)
        model.eval()

        __UpperCAmelCase					       =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
        __UpperCAmelCase					       =      model(__a	)
        self.parent.assertEqual(result.logits.shape  ,   (self.batch_size, 1, self.image_size, self.image_size)	)


    def   lowerCAmelCase_    (self  ,   lowercase__  ,   lowercase__  ,   lowercase__	)  ->   str:
        __UpperCAmelCase					       =      self.type_sequence_label_size
        __UpperCAmelCase					       =      SwinvaForImageClassification(__a	)
        model.to(__a	)
        model.eval()
        __UpperCAmelCase					       =      model(__a  ,   labels=__a	)
        self.parent.assertEqual(result.logits.shape  ,   (self.batch_size, self.type_sequence_label_size)	)






    def   lowerCAmelCase_    (self	)  ->   Tuple:
        __UpperCAmelCase					       =      self.prepare_config_and_inputs()
        __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase					       =      config_and_inputs
        __UpperCAmelCase					       =      {'''pixel_values''': pixel_values}
        return config, inputs_dict






@require_torch
class        A_ (   UpperCAmelCase_			,  UpperCAmelCase_			,  unittest.TestCase    ):


    '''simple docstring'''






    a__  				=							(
        (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
    )
    a__  				=							(
        {"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
        if is_torch_available()
        else {}
    )

    a__  				=							False
    a__  				=							False
    a__  				=							False
    a__  				=							False


    def   lowerCAmelCase_    (self	)  ->   Any:
        __UpperCAmelCase					       =      SwinvaModelTester(self	)
        __UpperCAmelCase					       =      ConfigTester(self  ,   config_class=__a  ,   embed_dim=37	)


    def   lowerCAmelCase_    (self	)  ->   Optional[int]:
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()


    def   lowerCAmelCase_    (self	)  ->   int:
        __UpperCAmelCase					       =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__a	)


    @unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.'''	)
    def   lowerCAmelCase_    (self	)  ->   List[str]:
        pass


    @unittest.skip(reason='''Swinv2 does not use inputs_embeds'''	)
    def   lowerCAmelCase_    (self	)  ->   List[Any]:
        pass


    def   lowerCAmelCase_    (self	)  ->   Optional[Any]:
        __UpperCAmelCase       ,  __UpperCAmelCase					       =      self.model_tester.prepare_config_and_inputs_for_common()

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


    def   lowerCAmelCase_    (self	)  ->   int:
        __UpperCAmelCase       ,  __UpperCAmelCase					       =      self.model_tester.prepare_config_and_inputs_for_common()

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

            __UpperCAmelCase					       =      ['''pixel_values''']
            self.assertListEqual(arg_names[:1]  ,   __a	)


    def   lowerCAmelCase_    (self	)  ->   str:
        __UpperCAmelCase       ,  __UpperCAmelCase					       =      self.model_tester.prepare_config_and_inputs_for_common()
        __UpperCAmelCase					       =      True

        for model_class in self.all_model_classes:
            __UpperCAmelCase					       =      True
            __UpperCAmelCase					       =      False
            __UpperCAmelCase					       =      True
            __UpperCAmelCase					       =      model_class(__a	)
            model.to(__a	)
            model.eval()
            with torch.no_grad():
                __UpperCAmelCase					       =      model(**self._prepare_for_class(__a  ,   __a	)	)
            __UpperCAmelCase					       =      outputs.attentions
            __UpperCAmelCase					       =      len(self.model_tester.depths	)
            self.assertEqual(len(__a	)  ,   __a	)

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            __UpperCAmelCase					       =      True
            __UpperCAmelCase					       =      config.window_size**2
            __UpperCAmelCase					       =      model_class(__a	)
            model.to(__a	)
            model.eval()
            with torch.no_grad():
                __UpperCAmelCase					       =      model(**self._prepare_for_class(__a  ,   __a	)	)
            __UpperCAmelCase					       =      outputs.attentions
            self.assertEqual(len(__a	)  ,   __a	)

            self.assertListEqual(
                list(attentions[0].shape[-3:]	)  ,   [self.model_tester.num_heads[0], window_size_squared, window_size_squared]  ,   )
            __UpperCAmelCase					       =      len(__a	)

            # Check attention is always last and order is fine
            __UpperCAmelCase					       =      True
            __UpperCAmelCase					       =      True
            __UpperCAmelCase					       =      model_class(__a	)
            model.to(__a	)
            model.eval()
            with torch.no_grad():
                __UpperCAmelCase					       =      model(**self._prepare_for_class(__a  ,   __a	)	)

            if hasattr(self.model_tester  ,   '''num_hidden_states_types'''	):
                __UpperCAmelCase					       =      self.model_tester.num_hidden_states_types
            else:
                # also another +1 for reshaped_hidden_states
                __UpperCAmelCase					       =      2
            self.assertEqual(out_len + added_hidden_states  ,   len(__a	)	)

            __UpperCAmelCase					       =      outputs.attentions

            self.assertEqual(len(__a	)  ,   __a	)

            self.assertListEqual(
                list(self_attentions[0].shape[-3:]	)  ,   [self.model_tester.num_heads[0], window_size_squared, window_size_squared]  ,   )


    def   lowerCAmelCase_    (self  ,   lowercase__  ,   lowercase__  ,   lowercase__  ,   lowercase__	)  ->   List[Any]:
        __UpperCAmelCase					       =      model_class(__a	)
        model.to(__a	)
        model.eval()

        with torch.no_grad():
            __UpperCAmelCase					       =      model(**self._prepare_for_class(__a  ,   __a	)	)

        __UpperCAmelCase					       =      outputs.hidden_states

        __UpperCAmelCase					       =      getattr(
            self.model_tester  ,   '''expected_num_hidden_layers'''  ,   len(self.model_tester.depths	) + 1	)
        self.assertEqual(len(__a	)  ,   __a	)

        # Swinv2 has a different seq_length
        __UpperCAmelCase					       =      (
            config.patch_size
            if isinstance(config.patch_size  ,   collections.abc.Iterable	)
            else (config.patch_size, config.patch_size)
        )

        __UpperCAmelCase					       =      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

        self.assertListEqual(
            list(hidden_states[0].shape[-2:]	)  ,   [num_patches, self.model_tester.embed_dim]  ,   )

        __UpperCAmelCase					       =      outputs.reshaped_hidden_states
        self.assertEqual(len(__a	)  ,   __a	)

        __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase					       =      reshaped_hidden_states[0].shape
        __UpperCAmelCase					       =      (
            reshaped_hidden_states[0].view(__a  ,   __a  ,   height * width	).permute(0  ,   2  ,   1	)
        )
        self.assertListEqual(
            list(reshaped_hidden_states.shape[-2:]	)  ,   [num_patches, self.model_tester.embed_dim]  ,   )


    def   lowerCAmelCase_    (self	)  ->   Optional[Any]:
        __UpperCAmelCase       ,  __UpperCAmelCase					       =      self.model_tester.prepare_config_and_inputs_for_common()

        __UpperCAmelCase					       =      (
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size  ,   collections.abc.Iterable	)
            else (self.model_tester.image_size, self.model_tester.image_size)
        )

        for model_class in self.all_model_classes:
            __UpperCAmelCase					       =      True
            self.check_hidden_states_output(__a  ,   __a  ,   __a  ,   __a	)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            __UpperCAmelCase					       =      True

            self.check_hidden_states_output(__a  ,   __a  ,   __a  ,   __a	)


    def   lowerCAmelCase_    (self	)  ->   Optional[Any]:
        __UpperCAmelCase       ,  __UpperCAmelCase					       =      self.model_tester.prepare_config_and_inputs_for_common()
        __UpperCAmelCase					       =      3

        __UpperCAmelCase					       =      (
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size  ,   collections.abc.Iterable	)
            else (self.model_tester.image_size, self.model_tester.image_size)
        )
        __UpperCAmelCase					       =      (
            config.patch_size
            if isinstance(config.patch_size  ,   collections.abc.Iterable	)
            else (config.patch_size, config.patch_size)
        )

        __UpperCAmelCase					       =      image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
        __UpperCAmelCase					       =      image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

        for model_class in self.all_model_classes:
            __UpperCAmelCase					       =      True
            self.check_hidden_states_output(__a  ,   __a  ,   __a  ,   (padded_height, padded_width)	)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            __UpperCAmelCase					       =      True
            self.check_hidden_states_output(__a  ,   __a  ,   __a  ,   (padded_height, padded_width)	)


    def   lowerCAmelCase_    (self	)  ->   List[str]:
        __UpperCAmelCase					       =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*__a	)


    def   lowerCAmelCase_    (self	)  ->   Any:
        __UpperCAmelCase					       =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__a	)


    @slow
    def   lowerCAmelCase_    (self	)  ->   Any:



        for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __UpperCAmelCase					       =      SwinvaModel.from_pretrained(__a	)
            self.assertIsNotNone(__a	)






    def   lowerCAmelCase_    (self	)  ->   Union[str, Any]:
        __UpperCAmelCase       ,  __UpperCAmelCase					       =      self.model_tester.prepare_config_and_inputs_for_common()

        __UpperCAmelCase					       =      _config_zero_init(__a	)
        for model_class in self.all_model_classes:
            __UpperCAmelCase					       =      model_class(config=__a	)
            for name, param in model.named_parameters():
                if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
                    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'''  ,   )






@require_vision
@require_torch
class        A_ (   unittest.TestCase    ):


    '''simple docstring'''






    @cached_property
    def   lowerCAmelCase_    (self	)  ->   Any:
        return (
            AutoImageProcessor.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''	)
            if is_vision_available()
            else None
        )






    @slow
    def   lowerCAmelCase_    (self	)  ->   str:
        __UpperCAmelCase					       =      SwinvaForImageClassification.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''	).to(
            __a	)
        __UpperCAmelCase					       =      self.default_image_processor

        __UpperCAmelCase					       =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
        __UpperCAmelCase					       =      image_processor(images=__a  ,   return_tensors='''pt'''	).to(__a	)

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

        # verify the logits
        __UpperCAmelCase					       =      torch.Size((1, 1_000)	)
        self.assertEqual(outputs.logits.shape  ,   __a	)
        __UpperCAmelCase					       =      torch.tensor([-0.3947, -0.4306, 0.0026]	).to(__a	)
        self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,   __a  ,   atol=1E-4	)	)









import logging
import os
import sys
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 accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


a_    :  Optional[int]						  =		logging.getLogger(__name__)





@dataclass
class  lowerCamelCase__	:
     """simple docstring"""



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





@dataclass
class  lowerCamelCase__	:
     """simple docstring"""



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



def      __lowercase(						):


      """simple docstring"""



      lowerCamelCase             =		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
      if len(sys.argv			) == 2 and sys.argv[1].endswith(".json"			):
            # If we pass only one argument to the script and it's the path to a json file,
            # let's parse it to get our arguments.
            lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase             =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
      else:
            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(UpperCAmelCase__ , 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 if training_args.local_rank in [-1, 0] else logging.WARN , )
      logger.warning(
          "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1			) , training_args.fpaa , )
      # Set the verbosity to info of the Transformers logger (on main process only):
      if is_main_process(training_args.local_rank			):
            transformers.utils.logging.set_verbosity_info()
            transformers.utils.logging.enable_default_handler()
            transformers.utils.logging.enable_explicit_format()
      logger.info("Training/evaluation parameters %s" , UpperCAmelCase__			)

      # Set seed
      set_seed(training_args.seed			)

      # Prepare CONLL-2003 task
      lowerCamelCase             =		token_classification_task.get_labels(data_args.labels			)
      lowerCamelCase             =		dict(enumerate(UpperCAmelCase__			)			)
      lowerCamelCase             =		len(UpperCAmelCase__			)

      # 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=UpperCAmelCase__ , idalabel=UpperCAmelCase__ , labelaid={label: i for i, label in enumerate(UpperCAmelCase__			)} , 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 , )
      lowerCamelCase             =		AutoModelForTokenClassification.from_pretrained(
          model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path			) , config=UpperCAmelCase__ , cache_dir=model_args.cache_dir , )

      # Get datasets
      lowerCamelCase             =		(
          TokenClassificationDataset(
              token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , 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             =		(
          TokenClassificationDataset(
              token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , 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(UpperCAmelCase__ , UpperCAmelCase__			) -> Tuple[List[int], List[int]]:
            lowerCamelCase             =		np.argmax(UpperCAmelCase__ , axis=2			)

            lowerCamelCase      ,				lowerCamelCase             =		preds.shape

            lowerCamelCase             =		[[] for _ in range(UpperCAmelCase__			)]
            lowerCamelCase             =		[[] for _ in range(UpperCAmelCase__			)]

            for i in range(UpperCAmelCase__			):
                  for j in range(UpperCAmelCase__			):
                        if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
                              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(UpperCAmelCase__			) -> Dict:
            lowerCamelCase      ,				lowerCamelCase             =		align_predictions(p.predictions , p.label_ids			)
            return {
                "accuracy_score": accuracy_score(UpperCAmelCase__ , UpperCAmelCase__			),
                "precision": precision_score(UpperCAmelCase__ , UpperCAmelCase__			),
                "recall": recall_score(UpperCAmelCase__ , UpperCAmelCase__			),
                "f1": fa_score(UpperCAmelCase__ , UpperCAmelCase__			),
            }

      # Data collator
      lowerCamelCase             =		DataCollatorWithPadding(UpperCAmelCase__ , pad_to_multiple_of=8			) if training_args.fpaa else None

      # Initialize our Trainer
      lowerCamelCase             =		Trainer(
          model=UpperCAmelCase__ , args=UpperCAmelCase__ , train_dataset=UpperCAmelCase__ , eval_dataset=UpperCAmelCase__ , compute_metrics=UpperCAmelCase__ , data_collator=UpperCAmelCase__ , )

      # Training
      if training_args.do_train:
            trainer.train(
                model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
            trainer.save_model()
            # For convenience, we also re-save the tokenizer to the same directory,
            # so that you can share your model easily on huggingface.co/models =)
            if trainer.is_world_process_zero():
                  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"			)
            if trainer.is_world_process_zero():
                  with open(UpperCAmelCase__ , "w"			) as writer:
                        logger.info("***** Eval results *****"			)
                        for key, value in result.items():
                              logger.info("  %s = %s" , UpperCAmelCase__ , UpperCAmelCase__			)
                              writer.write("%s = %s\n" % (key, value)			)

                  results.update(UpperCAmelCase__			)

    # Predict
      if training_args.do_predict:
            lowerCamelCase             =		TokenClassificationDataset(
                token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , 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(UpperCAmelCase__			)
            lowerCamelCase      ,				lowerCamelCase             =		align_predictions(UpperCAmelCase__ , UpperCAmelCase__			)

            lowerCamelCase             =		os.path.join(training_args.output_dir , "test_results.txt"			)
            if trainer.is_world_process_zero():
                  with open(UpperCAmelCase__ , "w"			) as writer:
                        for key, value in metrics.items():
                              logger.info("  %s = %s" , UpperCAmelCase__ , UpperCAmelCase__			)
                              writer.write("%s = %s\n" % (key, value)			)

        # Save predictions
            lowerCamelCase             =		os.path.join(training_args.output_dir , "test_predictions.txt"			)
            if trainer.is_world_process_zero():
                  with open(UpperCAmelCase__ , "w"			) as writer:
                        with open(os.path.join(data_args.data_dir , "test.txt"			) , "r"			) as f:
                              token_classification_task.write_predictions_to_file(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			)

      return results

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      main()


if __name__ == "__main__":
     main()