


import unittest

from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        FalconForCausalLM,
        FalconForQuestionAnswering,
        FalconForSequenceClassification,
        FalconForTokenClassification,
        FalconModel,
    )



class 	__lowerCamelCase			:







       def __init__(			self: Optional[Any],A_: int,A_: str=3,A_: List[Any]=7,A_: List[str]=True,A_: List[str]=True,A_: Any=False,A_: int=True,A_: str=99,A_: List[str]=32,A_: Optional[Any]=5,A_: Optional[int]=4,A_: List[Any]=37,A_: List[str]="gelu",A_: Tuple=0.1,A_: Any=0.1,A_: str=512,A_: Any=16,A_: List[str]=2,A_: Dict=0.0_2,A_: Tuple=3,A_: Union[str, Any]=4,A_: Union[str, Any]=None,):






            '''simple docstring'''




            __UpperCamelCase =			parent
            __UpperCamelCase =			batch_size
            __UpperCamelCase =			seq_length
            __UpperCamelCase =			is_training
            __UpperCamelCase =			use_input_mask
            __UpperCamelCase =			use_token_type_ids
            __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 =			type_vocab_size
            __UpperCamelCase =			type_sequence_label_size
            __UpperCamelCase =			initializer_range
            __UpperCamelCase =			num_labels
            __UpperCamelCase =			num_choices
            __UpperCamelCase =			scope







       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			ids_tensor([self.batch_size, self.seq_length],self.vocab_size					)

            __UpperCamelCase =			None
            if self.use_input_mask:
                 __UpperCamelCase =			random_attention_mask([self.batch_size, self.seq_length]					)

            __UpperCamelCase =			None

            __UpperCamelCase =			None
            __UpperCamelCase =			None
            __UpperCamelCase =			None
            if self.use_labels:
                 __UpperCamelCase =			ids_tensor([self.batch_size],self.type_sequence_label_size					)
                 __UpperCamelCase =			ids_tensor([self.batch_size, self.seq_length],self.num_labels					)
                 __UpperCamelCase =			ids_tensor([self.batch_size],self.num_choices					)

            __UpperCamelCase =			self.get_config()

            return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







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






            '''simple docstring'''




            return FalconConfig(
                vocab_size=self.vocab_size,hidden_size=self.hidden_size,num_hidden_layers=self.num_hidden_layers,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,max_position_embeddings=self.max_position_embeddings,type_vocab_size=self.type_vocab_size,is_decoder=A_,initializer_range=self.initializer_range,pad_token_id=1,new_decoder_architecture=A_,)







       def 		snake_case_	(			self: int,A_: Union[str, Any],A_: Union[str, Any],A_: int,A_: Any,A_: Tuple,A_: Any,A_: Tuple					):






            '''simple docstring'''




            __UpperCamelCase =			FalconModel(config=A_					)
            model.to(A_					)
            model.eval()
            __UpperCamelCase =			model(A_,attention_mask=A_					)
            __UpperCamelCase =			model(A_					)
            self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)					)







       def 		snake_case_	(			self: Dict,A_: Any,A_: Optional[Any],A_: List[str],A_: str,A_: Optional[Any],A_: Optional[Any],A_: Dict,A_: Optional[Any],A_: str,):






            '''simple docstring'''




            __UpperCamelCase =			True
            __UpperCamelCase =			FalconModel(A_					)
            model.to(A_					)
            model.eval()
            __UpperCamelCase =			model(
                A_,attention_mask=A_,encoder_hidden_states=A_,encoder_attention_mask=A_,)
            __UpperCamelCase =			model(
                A_,attention_mask=A_,encoder_hidden_states=A_,)
            __UpperCamelCase =			model(A_,attention_mask=A_					)
            self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)					)







       def 		snake_case_	(			self: Union[str, Any],A_: Union[str, Any],A_: List[str],A_: Tuple,A_: List[Any],A_: Tuple,A_: int,A_: int,A_: Tuple,A_: Tuple,):






            '''simple docstring'''




            __UpperCamelCase =			FalconForCausalLM(config=A_					)
            model.to(A_					)
            model.eval()
            __UpperCamelCase =			model(A_,attention_mask=A_,labels=A_					)
            self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.vocab_size)					)







       def 		snake_case_	(			self: str,A_: int,A_: str,A_: Optional[int],A_: Optional[int],A_: List[str],A_: str,A_: Union[str, Any],A_: Tuple,A_: Dict,):






            '''simple docstring'''




            __UpperCamelCase =			True
            __UpperCamelCase =			True
            __UpperCamelCase =			FalconForCausalLM(config=A_					)
            model.to(A_					)
            model.eval()

            # first forward pass
            __UpperCamelCase =			model(
                A_,attention_mask=A_,encoder_hidden_states=A_,encoder_attention_mask=A_,use_cache=A_,)
            __UpperCamelCase =			outputs.past_key_values

            # create hypothetical multiple next token and extent to next_input_ids
            __UpperCamelCase =			ids_tensor((self.batch_size, 3),config.vocab_size					)
            __UpperCamelCase =			ids_tensor((self.batch_size, 3),vocab_size=2					)

            # append to next input_ids and
            __UpperCamelCase =			torch.cat([input_ids, next_tokens],dim=-1					)
            __UpperCamelCase =			torch.cat([input_mask, next_mask],dim=-1					)

            __UpperCamelCase =			model(
                A_,attention_mask=A_,encoder_hidden_states=A_,encoder_attention_mask=A_,output_hidden_states=A_,)['hidden_states'][0]
            __UpperCamelCase =			model(
                A_,attention_mask=A_,encoder_hidden_states=A_,encoder_attention_mask=A_,past_key_values=A_,output_hidden_states=A_,)['hidden_states'][0]

            # select random slice
            __UpperCamelCase =			ids_tensor((1,),output_from_past.shape[-1]					).item()
            __UpperCamelCase =			output_from_no_past[:, -3:, random_slice_idx].detach()
            __UpperCamelCase =			output_from_past[:, :, random_slice_idx].detach()

            self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

            # test that outputs are equal for slice
            self.parent.assertTrue(torch.allclose(A_,A_,atol=1E-3					)					)







       def 		snake_case_	(			self: List[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			self.prepare_config_and_inputs()
            (
                (
                __UpperCamelCase
            ),    (
                __UpperCamelCase
            ),    (
                __UpperCamelCase
            ),    (
                __UpperCamelCase
            ),    (
                __UpperCamelCase
            ),    (
                __UpperCamelCase
            ),    (
                __UpperCamelCase
            ),    
            ) =			config_and_inputs
            __UpperCamelCase =			{'input_ids': input_ids, 'attention_mask': input_mask}
            return config, inputs_dict



@require_torch
class 	__lowerCamelCase			(_a				,      _a				,      _a				,      unittest.TestCase						):
       _lowercase											=     (
           (
               FalconModel,
               FalconForCausalLM,
               FalconForSequenceClassification,
               FalconForTokenClassification,
               FalconForQuestionAnswering,
           )
           if is_torch_available()
           else ()
       )
       _lowercase											=     (FalconForCausalLM,) if is_torch_available() else ()
       _lowercase											=     (
           {
               """feature-extraction""": FalconModel,
               """text-classification""": FalconForSequenceClassification,
               """text-generation""": FalconForCausalLM,
               """question-answering""": FalconForQuestionAnswering,
               """token-classification""": FalconForTokenClassification,
               """zero-shot""": FalconForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       _lowercase											=     False
       _lowercase											=     False







       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




            __UpperCamelCase =			FalconModelTester(self					)
            __UpperCamelCase =			ConfigTester(self,config_class=A_,hidden_size=37					)







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            self.config_tester.run_common_tests()







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*A_					)







       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            __UpperCamelCase,    *__UpperCamelCase =			self.model_tester.prepare_config_and_inputs()
            for alibi in [True, False]:
                 __UpperCamelCase =			alibi
                 self.model_tester.create_and_check_model(A_,*A_					)







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            __UpperCamelCase,    __UpperCamelCase =			self.model_tester.prepare_config_and_inputs_for_common()
            __UpperCamelCase =			3
            __UpperCamelCase =			input_dict['input_ids']
            __UpperCamelCase =			input_ids.ne(1					).to(A_					)
            __UpperCamelCase =			ids_tensor([self.model_tester.batch_size],self.model_tester.type_sequence_label_size					)
            __UpperCamelCase =			FalconForSequenceClassification(A_					)
            model.to(A_					)
            model.eval()
            __UpperCamelCase =			model(A_,attention_mask=A_,labels=A_					)
            self.assertEqual(result.logits.shape,(self.model_tester.batch_size, self.model_tester.num_labels)					)







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            __UpperCamelCase,    __UpperCamelCase =			self.model_tester.prepare_config_and_inputs_for_common()
            __UpperCamelCase =			3
            __UpperCamelCase =			'single_label_classification'
            __UpperCamelCase =			input_dict['input_ids']
            __UpperCamelCase =			input_ids.ne(1					).to(A_					)
            __UpperCamelCase =			ids_tensor([self.model_tester.batch_size],self.model_tester.type_sequence_label_size					)
            __UpperCamelCase =			FalconForSequenceClassification(A_					)
            model.to(A_					)
            model.eval()
            __UpperCamelCase =			model(A_,attention_mask=A_,labels=A_					)
            self.assertEqual(result.logits.shape,(self.model_tester.batch_size, self.model_tester.num_labels)					)







       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




            __UpperCamelCase,    __UpperCamelCase =			self.model_tester.prepare_config_and_inputs_for_common()
            __UpperCamelCase =			input_dict['input_ids']
            __UpperCamelCase =			FalconForCausalLM(A_					)
            model.to(A_					)
            model.eval()
            __UpperCamelCase =			model(A_,use_cache=A_					)
            __UpperCamelCase =			input_ids.shape[0]
            __UpperCamelCase =			model._convert_to_rw_cache(result.past_key_values					)
            __UpperCamelCase =			model._convert_cache_to_standard_format(A_,A_					)
            for layer in range(len(A_					)					):
                 for tensor_idx in range(2					):
                      self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3					)
                      self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4					)
                      self.assertTrue(
                          torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx]					)					)







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






            '''simple docstring'''




            __UpperCamelCase,    __UpperCamelCase =			self.model_tester.prepare_config_and_inputs_for_common()
            __UpperCamelCase =			3
            __UpperCamelCase =			'multi_label_classification'
            __UpperCamelCase =			input_dict['input_ids']
            __UpperCamelCase =			input_ids.ne(1					).to(A_					)
            __UpperCamelCase =			ids_tensor(
                [self.model_tester.batch_size, config.num_labels],self.model_tester.type_sequence_label_size					).to(torch.float					)
            __UpperCamelCase =			FalconForSequenceClassification(A_					)
            model.to(A_					)
            model.eval()
            __UpperCamelCase =			model(A_,attention_mask=A_,labels=A_					)
            self.assertEqual(result.logits.shape,(self.model_tester.batch_size, self.model_tester.num_labels)					)







       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




            for model_class in self.all_generative_model_classes:
                 __UpperCamelCase,    __UpperCamelCase =			self.model_tester.prepare_config_and_inputs_for_common()

                 # If it doesn't support cache, pass the test
                 if not hasattr(A_,'use_cache'					):
                      return

                 __UpperCamelCase =			model_class(A_					).to(A_					)
                 if "use_cache" not in inputs:
                      __UpperCamelCase =			True
                 __UpperCamelCase =			model(**A_					)

                 # If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
                 if "past_key_values" not in outputs:
                      return

                 __UpperCamelCase =			(
                     getattr(A_,'decoder_layers',A_					)
                     or getattr(A_,'num_decoder_layers',A_					)
                     or config.num_hidden_layers
                 )
                 __UpperCamelCase =			getattr(A_,'num_kv_heads',config.num_attention_heads					)
                 __UpperCamelCase =			getattr(A_,'d_model',config.hidden_size					)
                 __UpperCamelCase =			embed_dim // num_attention_heads

                 __UpperCamelCase =			outputs['past_key_values']
                 self.assertEqual(len(A_					),A_					)

                 __UpperCamelCase,    __UpperCamelCase =			inputs['input_ids'].shape
                 for i in range(A_					):
                      if config.new_decoder_architecture:
                           __UpperCamelCase =			config.num_attention_heads
                      elif config.multi_query:
                           __UpperCamelCase =			1
                      self.assertEqual(len(past_kv[0]					),2					)  # K V for the decoder = 2
                      self.assertEqual(
                          past_kv[i][0].shape,(batch_size, num_attention_heads, seq_length, per_head_embed_dim)					)
                      self.assertEqual(
                          past_kv[i][1].shape,(batch_size, num_attention_heads, seq_length, per_head_embed_dim)					)



@require_torch
class 	__lowerCamelCase			(unittest.TestCase						):







       @slow
       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




            __UpperCamelCase =			AutoTokenizer.from_pretrained('Rocketknight1/falcon-rw-1b'					)
            __UpperCamelCase =			FalconForCausalLM.from_pretrained('Rocketknight1/falcon-rw-1b'					)
            model.eval()
            model.to(A_					)
            __UpperCamelCase =			tokenizer('My favorite food is',return_tensors='pt'					).to(A_					)

            __UpperCamelCase =			(
                'My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday.'
            )

            __UpperCamelCase =			model.generate(**A_,do_sample=A_,max_new_tokens=19					)
            __UpperCamelCase =			tokenizer.batch_decode(A_					)[0]

            self.assertEqual(A_,A_					)







       @slow
       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
                 __UpperCamelCase =			AutoTokenizer.from_pretrained(A_					)
                 __UpperCamelCase =			FalconForCausalLM.from_pretrained(A_					)
                 model.eval()
                 model.to(A_					)
                 __UpperCamelCase =			tokenizer('My favorite food is',return_tensors='pt'					).to(A_					)

                 # We just test that these run without errors - the models are randomly initialized
                 # and so the actual text outputs will be garbage
                 model.generate(**A_,do_sample=A_,max_new_tokens=4					)
                 model.generate(**A_,do_sample=A_,max_new_tokens=4					)
                 model.generate(**A_,num_beams=2,max_new_tokens=4					)







       @slow
       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            with torch.no_grad():
                 for repo in [
                     "Rocketknight1/falcon-rw-1b",
                     "Rocketknight1/tiny-random-falcon-7b",
                     "Rocketknight1/tiny-random-falcon-40b",
                 ]:
                      __UpperCamelCase =			AutoTokenizer.from_pretrained(A_					)
                      __UpperCamelCase =			FalconForCausalLM.from_pretrained(A_					)
                      model.eval()
                      model.to(device=A_					)
                      __UpperCamelCase =			tokenizer('My favorite food is',return_tensors='pt'					).to(A_					)

                      # Test results are the same with and without cache
                      __UpperCamelCase =			model.generate(**A_,do_sample=A_,max_new_tokens=20,use_cache=A_					)
                      __UpperCamelCase =			model.generate(**A_,do_sample=A_,max_new_tokens=20,use_cache=A_					)
                      self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0					)






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


__snake_case   							=						logging.getLogger(__name__)



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



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






def 		_A		(		)					-> str:


     """simple docstring"""


     __UpperCamelCase =			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.
          __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
     else:
          __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			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.' )

     __UpperCamelCase =			import_module('tasks' )
     try:
          __UpperCamelCase =			getattr(_lowercase					,  model_args.task_type )
          __UpperCamelCase =			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'					,  _lowercase )

     # Set seed
     set_seed(training_args.seed )

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

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

     __UpperCamelCase =			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					,  )
     __UpperCamelCase =			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					,  )
     __UpperCamelCase =			AutoModelForTokenClassification.from_pretrained(
         model_args.model_name_or_path					,  from_tf=bool('.ckpt' in model_args.model_name_or_path )					,  config=_lowercase					,  cache_dir=model_args.cache_dir					,  )

     # Get datasets
     __UpperCamelCase =			(
         TokenClassificationDataset(
             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
     )
     __UpperCamelCase =			(
         TokenClassificationDataset(
             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]]:
          __UpperCamelCase =			np.argmax(_lowercase					,  axis=2 )

          __UpperCamelCase,    __UpperCamelCase =			preds.shape

          __UpperCamelCase =			[[] for _ in range(_lowercase )]
          __UpperCamelCase =			[[] for _ in range(_lowercase )]

          for i in range(_lowercase ):
               for j in range(_lowercase ):
                    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(_lowercase ) -> Dict:
          __UpperCamelCase,    __UpperCamelCase =			align_predictions(p.predictions					,  p.label_ids )
          return {
              "accuracy_score": accuracy_score(_lowercase					,  _lowercase ),
              "precision": precision_score(_lowercase					,  _lowercase ),
              "recall": recall_score(_lowercase					,  _lowercase ),
              "f1": fa_score(_lowercase					,  _lowercase ),
          }

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

     # Initialize our Trainer
     __UpperCamelCase =			Trainer(
         model=_lowercase					,  args=_lowercase					,  train_dataset=_lowercase					,  eval_dataset=_lowercase					,  compute_metrics=_lowercase					,  data_collator=_lowercase					,  )

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

          __UpperCamelCase =			trainer.evaluate()

          __UpperCamelCase =			os.path.join(training_args.output_dir					,  'eval_results.txt' )
          if trainer.is_world_process_zero():
               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:
          __UpperCamelCase =			TokenClassificationDataset(
              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					,  )

          __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			trainer.predict(_lowercase )
          __UpperCamelCase,    __UpperCamelCase =			align_predictions(_lowercase					,  _lowercase )

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

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

     return results






def 		_A		(		_lowercase )					-> Dict:


     """simple docstring"""


     main()


if __name__ == "__main__":
    main()


