

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
       import os

       # The slow tests are often failing with OOM error on GPU
       # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
       # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
       A_		:      Dict					    = 'platform'

       import jax
       import jax.numpy as jnp

       from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
           FlaxBlenderbotSmallForConditionalGeneration,
           FlaxBlenderbotSmallModel,
           shift_tokens_right,
       )


def    __a							( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE=None							, SCREAMING_SNAKE_CASE=None							, SCREAMING_SNAKE_CASE=None							, SCREAMING_SNAKE_CASE=None							, SCREAMING_SNAKE_CASE=None							, SCREAMING_SNAKE_CASE=None							, )   ->  Dict:






    '''simple docstring'''



    if attention_mask is None:
        __UpperCAmelCase					       =      np.where(input_ids != config.pad_token_id							, 1							, 0   )
    if decoder_attention_mask is None:
        __UpperCAmelCase					       =      np.where(decoder_input_ids != config.pad_token_id							, 1							, 0   )
    if head_mask is None:
        __UpperCAmelCase					       =      np.ones((config.encoder_layers, config.encoder_attention_heads)   )
    if decoder_head_mask is None:
        __UpperCAmelCase					       =      np.ones((config.decoder_layers, config.decoder_attention_heads)   )
    if cross_attn_head_mask is None:
        __UpperCAmelCase					       =      np.ones((config.decoder_layers, config.decoder_attention_heads)   )
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": attention_mask,
    }
class        A_ :


    '''simple docstring'''






    def __init__(self  ,   lowercase__  ,   lowercase__=13  ,   lowercase__=7  ,   lowercase__=True  ,   lowercase__=False  ,   lowercase__=99  ,   lowercase__=16  ,   lowercase__=2  ,   lowercase__=4  ,   lowercase__=4  ,   lowercase__="gelu"  ,   lowercase__=0.1  ,   lowercase__=0.1  ,   lowercase__=32  ,   lowercase__=2  ,   lowercase__=1  ,   lowercase__=0  ,   lowercase__=0.02  ,   )  ->   List[Any]:
        __UpperCAmelCase					       =      parent
        __UpperCAmelCase					       =      batch_size
        __UpperCAmelCase					       =      seq_length
        __UpperCAmelCase					       =      is_training
        __UpperCAmelCase					       =      use_labels
        __UpperCAmelCase					       =      vocab_size
        __UpperCAmelCase					       =      hidden_size
        __UpperCAmelCase					       =      num_hidden_layers
        __UpperCAmelCase					       =      num_attention_heads
        __UpperCAmelCase					       =      intermediate_size
        __UpperCAmelCase					       =      hidden_act
        __UpperCAmelCase					       =      hidden_dropout_prob
        __UpperCAmelCase					       =      attention_probs_dropout_prob
        __UpperCAmelCase					       =      max_position_embeddings
        __UpperCAmelCase					       =      eos_token_id
        __UpperCAmelCase					       =      pad_token_id
        __UpperCAmelCase					       =      bos_token_id
        __UpperCAmelCase					       =      initializer_range


    def   lowerCAmelCase_    (self	)  ->   Tuple:
        __UpperCAmelCase					       =      np.clip(ids_tensor([self.batch_size, self.seq_length - 1]  ,   self.vocab_size	)  ,   3  ,   self.vocab_size	)
        __UpperCAmelCase					       =      np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)  ,   dtype=np.intaa	))  ,   -1	)

        __UpperCAmelCase					       =      shift_tokens_right(__a  ,   1  ,   2	)

        __UpperCAmelCase					       =      BlenderbotSmallConfig(
            vocab_size=self.vocab_size  ,   d_model=self.hidden_size  ,   encoder_layers=self.num_hidden_layers  ,   decoder_layers=self.num_hidden_layers  ,   encoder_attention_heads=self.num_attention_heads  ,   decoder_attention_heads=self.num_attention_heads  ,   encoder_ffn_dim=self.intermediate_size  ,   decoder_ffn_dim=self.intermediate_size  ,   dropout=self.hidden_dropout_prob  ,   attention_dropout=self.attention_probs_dropout_prob  ,   max_position_embeddings=self.max_position_embeddings  ,   eos_token_id=self.eos_token_id  ,   bos_token_id=self.bos_token_id  ,   pad_token_id=self.pad_token_id  ,   initializer_range=self.initializer_range  ,   use_cache=__a  ,   )
        __UpperCAmelCase					       =      prepare_blenderbot_inputs_dict(__a  ,   __a  ,   __a	)
        return config, inputs_dict


    def   lowerCAmelCase_    (self	)  ->   Optional[int]:
        __UpperCAmelCase       ,  __UpperCAmelCase					       =      self.prepare_config_and_inputs()
        return config, inputs_dict


    def   lowerCAmelCase_    (self  ,   lowercase__  ,   lowercase__  ,   lowercase__	)  ->   Tuple:
        __UpperCAmelCase					       =      20
        __UpperCAmelCase					       =      model_class_name(__a	)

        __UpperCAmelCase					       =      model.encode(inputs_dict['''input_ids''']	)

        __UpperCAmelCase       ,  __UpperCAmelCase					       =      (
            inputs_dict['''decoder_input_ids'''],
            inputs_dict['''decoder_attention_mask'''],
        )

        __UpperCAmelCase					       =      model.init_cache(decoder_input_ids.shape[0]  ,   __a  ,   __a	)
        __UpperCAmelCase					       =      jnp.ones((decoder_input_ids.shape[0], max_decoder_length)  ,   dtype='''i4'''	)

        __UpperCAmelCase					       =      jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]  ,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,   )
        __UpperCAmelCase					       =      model.decode(
            decoder_input_ids[:, :-1]  ,   __a  ,   decoder_attention_mask=__a  ,   past_key_values=__a  ,   decoder_position_ids=__a  ,   )

        __UpperCAmelCase					       =      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,   dtype='''i4'''	)
        __UpperCAmelCase					       =      model.decode(
            decoder_input_ids[:, -1:]  ,   __a  ,   decoder_attention_mask=__a  ,   past_key_values=outputs_cache.past_key_values  ,   decoder_position_ids=__a  ,   )

        __UpperCAmelCase					       =      model.decode(__a  ,   __a	)

        __UpperCAmelCase					       =      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
        self.parent.assertTrue(diff < 1E-3  ,   msg=F'''Max diff is {diff}'''	)






    def   lowerCAmelCase_    (self  ,   lowercase__  ,   lowercase__  ,   lowercase__	)  ->   List[Any]:
        __UpperCAmelCase					       =      20
        __UpperCAmelCase					       =      model_class_name(__a	)

        __UpperCAmelCase					       =      model.encode(inputs_dict['''input_ids''']	)

        __UpperCAmelCase       ,  __UpperCAmelCase					       =      (
            inputs_dict['''decoder_input_ids'''],
            inputs_dict['''decoder_attention_mask'''],
        )

        __UpperCAmelCase					       =      jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])	),
            ]  ,   axis=-1  ,   )

        __UpperCAmelCase					       =      model.init_cache(decoder_input_ids.shape[0]  ,   __a  ,   __a	)
        __UpperCAmelCase					       =      jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]  ,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,   )

        __UpperCAmelCase					       =      model.decode(
            decoder_input_ids[:, :-1]  ,   __a  ,   decoder_attention_mask=__a  ,   past_key_values=__a  ,   decoder_position_ids=__a  ,   )
        __UpperCAmelCase					       =      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,   dtype='''i4'''	)
        __UpperCAmelCase					       =      model.decode(
            decoder_input_ids[:, -1:]  ,   __a  ,   past_key_values=outputs_cache.past_key_values  ,   decoder_attention_mask=__a  ,   decoder_position_ids=__a  ,   )

        __UpperCAmelCase					       =      model.decode(__a  ,   __a  ,   decoder_attention_mask=__a	)

        __UpperCAmelCase					       =      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
        self.parent.assertTrue(diff < 1E-3  ,   msg=F'''Max diff is {diff}'''	)






@require_flax
class        A_ (   unittest.TestCase    ):


    '''simple docstring'''






    a__  				=							99


    def   lowerCAmelCase_    (self	)  ->   Tuple:
        __UpperCAmelCase					       =      np.array(
            [
                [71, 82, 18, 33, 46, 91, 2],
                [68, 34, 26, 58, 30, 82, 2],
                [5, 97, 17, 39, 94, 40, 2],
                [76, 83, 94, 25, 70, 78, 2],
                [87, 59, 41, 35, 48, 66, 2],
                [55, 13, 16, 58, 5, 2, 1],  # note padding
                [64, 27, 31, 51, 12, 75, 2],
                [52, 64, 86, 17, 83, 39, 2],
                [48, 61, 9, 24, 71, 82, 2],
                [26, 1, 60, 48, 22, 13, 2],
                [21, 5, 62, 28, 14, 76, 2],
                [45, 98, 37, 86, 59, 48, 2],
                [70, 70, 50, 9, 28, 0, 2],
            ]  ,   dtype=np.intaa  ,   )

        __UpperCAmelCase					       =      input_ids.shape[0]
        __UpperCAmelCase					       =      BlenderbotSmallConfig(
            vocab_size=self.vocab_size  ,   d_model=24  ,   encoder_layers=2  ,   decoder_layers=2  ,   encoder_attention_heads=2  ,   decoder_attention_heads=2  ,   encoder_ffn_dim=32  ,   decoder_ffn_dim=32  ,   max_position_embeddings=48  ,   eos_token_id=2  ,   pad_token_id=1  ,   bos_token_id=0  ,   )
        return config, input_ids, batch_size


    def   lowerCAmelCase_    (self	)  ->   Union[str, Any]:
        __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase					       =      self._get_config_and_data()
        __UpperCAmelCase					       =      FlaxBlenderbotSmallForConditionalGeneration(__a	)
        __UpperCAmelCase					       =      lm_model(input_ids=__a	)
        __UpperCAmelCase					       =      (batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs['''logits'''].shape  ,   __a	)


    def   lowerCAmelCase_    (self	)  ->   List[str]:
        __UpperCAmelCase					       =      BlenderbotSmallConfig(
            vocab_size=self.vocab_size  ,   d_model=14  ,   encoder_layers=2  ,   decoder_layers=2  ,   encoder_attention_heads=2  ,   decoder_attention_heads=2  ,   encoder_ffn_dim=8  ,   decoder_ffn_dim=8  ,   max_position_embeddings=48  ,   )
        __UpperCAmelCase					       =      FlaxBlenderbotSmallForConditionalGeneration(__a	)
        __UpperCAmelCase					       =      np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]  ,   dtype=np.intaa	)
        __UpperCAmelCase					       =      np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]  ,   dtype=np.intaa	)
        __UpperCAmelCase					       =      lm_model(input_ids=__a  ,   decoder_input_ids=__a	)
        __UpperCAmelCase					       =      (*summary.shape, config.vocab_size)
        self.assertEqual(outputs['''logits'''].shape  ,   __a	)






    def   lowerCAmelCase_    (self	)  ->   int:
        __UpperCAmelCase					       =      np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]  ,   dtype=np.intaa	)
        __UpperCAmelCase					       =      shift_tokens_right(__a  ,   1  ,   2	)
        __UpperCAmelCase					       =      np.equal(__a  ,   1	).astype(np.floataa	).sum()
        __UpperCAmelCase					       =      np.equal(__a  ,   1	).astype(np.floataa	).sum()
        self.assertEqual(shifted.shape  ,   input_ids.shape	)
        self.assertEqual(__a  ,   n_pad_before - 1	)
        self.assertTrue(np.equal(shifted[:, 0]  ,   2	).all()	)






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


    '''simple docstring'''






    a__  				=							True
    a__  				=							(
        (
            FlaxBlenderbotSmallModel,
            FlaxBlenderbotSmallForConditionalGeneration,
        )
        if is_flax_available()
        else ()
    )
    a__  				=							(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


    def   lowerCAmelCase_    (self	)  ->   List[str]:
        __UpperCAmelCase					       =      FlaxBlenderbotSmallModelTester(self	)


    def   lowerCAmelCase_    (self	)  ->   Tuple:
        __UpperCAmelCase       ,  __UpperCAmelCase					       =      self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward(__a  ,   __a  ,   __a	)


    def   lowerCAmelCase_    (self	)  ->   Optional[Any]:
        __UpperCAmelCase       ,  __UpperCAmelCase					       =      self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward_with_attn_mask(__a  ,   __a  ,   __a	)


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

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__	):
                __UpperCAmelCase					       =      self._prepare_for_class(__a  ,   __a	)
                __UpperCAmelCase					       =      model_class(__a	)

                @jax.jit
                def encode_jitted(lowercase__  ,   lowercase__=None  ,   **lowercase__	):
                    return model.encode(input_ids=__a  ,   attention_mask=__a	)

                with self.subTest('''JIT Enabled'''	):
                    __UpperCAmelCase					       =      encode_jitted(**__a	).to_tuple()

                with self.subTest('''JIT Disabled'''	):
                    with jax.disable_jit():
                        __UpperCAmelCase					       =      encode_jitted(**__a	).to_tuple()

                self.assertEqual(len(__a	)  ,   len(__a	)	)
                for jitted_output, output in zip(__a  ,   __a	):
                    self.assertEqual(jitted_output.shape  ,   output.shape	)


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

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__	):
                __UpperCAmelCase					       =      model_class(__a	)
                __UpperCAmelCase					       =      model.encode(inputs_dict['''input_ids''']  ,   inputs_dict['''attention_mask''']	)

                __UpperCAmelCase					       =      {
                    '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                    '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                    '''encoder_outputs''': encoder_outputs,
                }

                @jax.jit
                def decode_jitted(lowercase__  ,   lowercase__  ,   lowercase__	):
                    return model.decode(
                        decoder_input_ids=__a  ,   decoder_attention_mask=__a  ,   encoder_outputs=__a  ,   )

                with self.subTest('''JIT Enabled'''	):
                    __UpperCAmelCase					       =      decode_jitted(**__a	).to_tuple()

                with self.subTest('''JIT Disabled'''	):
                    with jax.disable_jit():
                        __UpperCAmelCase					       =      decode_jitted(**__a	).to_tuple()

                self.assertEqual(len(__a	)  ,   len(__a	)	)
                for jitted_output, output in zip(__a  ,   __a	):
                    self.assertEqual(jitted_output.shape  ,   output.shape	)






    @slow
    def   lowerCAmelCase_    (self	)  ->   Union[str, Any]:



        for model_class_name in self.all_model_classes:
            __UpperCAmelCase					       =      model_class_name.from_pretrained('''facebook/blenderbot_small-90M'''	)
            # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
            __UpperCAmelCase					       =      np.ones((1, 1)	) * model.config.eos_token_id
            __UpperCAmelCase					       =      model(__a	)
            self.assertIsNotNone(__a	)









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()