


'''simple docstring'''

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_						:       List[Any]      			=				logging.getLogger(__name__)





@dataclass
class   snake_case					:





  """simple docstring"""




  _lowerCamelCase						=						field(
      metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}    )
  _lowerCamelCase						=						field(
      default=lowercase    ,		metadata={"help": "Pretrained config name or path if not the same as model_name"}    )
  _lowerCamelCase						=						field(
      default="NER"    ,		metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}    )
  _lowerCamelCase						=						field(
      default=lowercase    ,		metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}    )
  _lowerCamelCase						=						field(default=lowercase    ,		metadata={"help": "Set this flag to use fast tokenization."}    )
  # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
  # or just modify its tokenizer_config.json.
  _lowerCamelCase						=						field(
      default=lowercase    ,		metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}    ,		)





@dataclass
class   snake_case					:





  """simple docstring"""




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





def __snake_case      (   ):
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.

 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_		:		np.ndarray      ,       UpperCAmelCase_		:		np.ndarray	) -> 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_		:		EvalPrediction	) -> 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 __snake_case      (   UpperCAmelCase_		:		List[Any]	):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
     main()







'''simple docstring'''

import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
     import torch

     from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin





class   snake_case					:





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase="resnet50"    ,						UpperCamelCase=3    ,						UpperCamelCase=32    ,						UpperCamelCase=3    ,						UpperCamelCase=True    ,						UpperCamelCase=True    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						parent
   lowerCamelCase_  				=						out_indices if out_indices is not None else [4]
   lowerCamelCase_  				=						stage_names
   lowerCamelCase_  				=						out_features
   lowerCamelCase_  				=						backbone
   lowerCamelCase_  				=						batch_size
   lowerCamelCase_  				=						image_size
   lowerCamelCase_  				=						num_channels
   lowerCamelCase_  				=						use_pretrained_backbone
   lowerCamelCase_  				=						is_training






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )
   lowerCamelCase_  				=						self.get_config()

   return config, pixel_values






  def 						snake_case	(					self  ):







   """simple docstring"""
   return TimmBackboneConfig(
       image_size=self.image_size    ,						num_channels=self.num_channels    ,						out_features=self.out_features    ,						out_indices=self.out_indices    ,						stage_names=self.stage_names    ,						use_pretrained_backbone=self.use_pretrained_backbone    ,						backbone=self.backbone    ,						)






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						TimmBackbone(config=UpperCamelCase  )
   model.to(UpperCamelCase  )
   model.eval()
   with torch.no_grad():
    lowerCamelCase_  				=						model(UpperCamelCase  )
   self.parent.assertEqual(
       result.feature_map[-1].shape    ,						(self.batch_size, model.channels[-1], 14, 14)    ,						)







  def 						snake_case	(					self  ):







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





@require_torch
@require_timm
class   snake_case					(					lowercase    ,		lowercase    ,		lowercase    ,		unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase						=						(TimmBackbone,) if is_torch_available() else ()
  _lowerCamelCase						=						{"feature-extraction": TimmBackbone} if is_torch_available() else {}
  _lowerCamelCase						=						False
  _lowerCamelCase						=						False
  _lowerCamelCase						=						False
  _lowerCamelCase						=						False






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						TimmBackboneModelTester(self  )
   lowerCamelCase_  				=						ConfigTester(self    ,						config_class=UpperCamelCase    ,						has_text_modality=UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   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 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"resnet18"
   lowerCamelCase_  				=						"microsoft/resnet-18"

   lowerCamelCase_  				=						AutoBackbone.from_pretrained(UpperCamelCase    ,						use_timm_backbone=UpperCamelCase  )
   lowerCamelCase_  				=						AutoBackbone.from_pretrained(UpperCamelCase  )

   self.assertEqual(len(timm_model.out_features  )    ,						len(transformers_model.out_features  )  )
   self.assertEqual(len(timm_model.stage_names  )    ,						len(transformers_model.stage_names  )  )
   self.assertEqual(timm_model.channels    ,						transformers_model.channels  )
   # Out indices are set to the last layer by default. For timm models, we don't know
   # the number of layers in advance, so we set it to (-1,), whereas for transformers
   # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
   self.assertEqual(timm_model.out_indices    ,						(-1,)  )
   self.assertEqual(transformers_model.out_indices    ,						[len(timm_model.stage_names  ) - 1]  )

   lowerCamelCase_  				=						AutoBackbone.from_pretrained(UpperCamelCase    ,						use_timm_backbone=UpperCamelCase    ,						out_indices=[1, 2, 3]  )
   lowerCamelCase_  				=						AutoBackbone.from_pretrained(UpperCamelCase    ,						out_indices=[1, 2, 3]  )

   self.assertEqual(timm_model.out_indices    ,						transformers_model.out_indices  )
   self.assertEqual(len(timm_model.out_features  )    ,						len(transformers_model.out_features  )  )
   self.assertEqual(timm_model.channels    ,						transformers_model.channels  )






  @unittest.skip("TimmBackbone doesn't support feed forward chunking"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone initialization is managed on the timm side"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone models doesn't have inputs_embeds"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone models doesn't have inputs_embeds"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("model weights aren't tied in TimmBackbone."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("model weights aren't tied in TimmBackbone."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone doesn't have hidden size info in its configuration."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone doesn't support output_attentions."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("Safetensors is not supported by timm."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






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







   """simple docstring"""
   pass






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_				,lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs_for_common()

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

    lowerCamelCase_  				=						["pixel_values"]
    self.assertListEqual(arg_names[:1]    ,						UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_				,lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs_for_common()
   lowerCamelCase_  				=						True
   lowerCamelCase_  				=						self.has_attentions

   # no need to test all models as different heads yield the same functionality
   lowerCamelCase_  				=						self.all_model_classes[0]
   lowerCamelCase_  				=						model_class(UpperCamelCase  )
   model.to(UpperCamelCase  )

   lowerCamelCase_  				=						self._prepare_for_class(UpperCamelCase    ,						UpperCamelCase  )
   lowerCamelCase_  				=						model(**UpperCamelCase  )
   lowerCamelCase_  				=						outputs[0][-1]

   # Encoder-/Decoder-only models
   lowerCamelCase_  				=						outputs.hidden_states[0]
   hidden_states.retain_grad()

   if self.has_attentions:
    lowerCamelCase_  				=						outputs.attentions[0]
    attentions.retain_grad()

   output.flatten()[0].backward(retain_graph=UpperCamelCase  )

   self.assertIsNotNone(hidden_states.grad  )

   if self.has_attentions:
    self.assertIsNotNone(attentions.grad  )







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_				,lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    lowerCamelCase_  				=						model_class(UpperCamelCase  )
    model.to(UpperCamelCase  )
    model.eval()
    lowerCamelCase_  				=						model(**UpperCamelCase  )

    self.assertEqual(len(result.feature_maps  )    ,						len(config.out_indices  )  )
    self.assertEqual(len(model.channels  )    ,						len(config.out_indices  )  )

    # Check output of last stage is taken if out_features=None, out_indices=None
    lowerCamelCase_  				=						copy.deepcopy(UpperCamelCase  )
    lowerCamelCase_  				=						None
    lowerCamelCase_  				=						model_class(UpperCamelCase  )
    model.to(UpperCamelCase  )
    model.eval()
    lowerCamelCase_  				=						model(**UpperCamelCase  )

    self.assertEqual(len(result.feature_maps  )    ,						1  )
    self.assertEqual(len(model.channels  )    ,						1  )

    # Check backbone can be initialized with fresh weights
    lowerCamelCase_  				=						copy.deepcopy(UpperCamelCase  )
    lowerCamelCase_  				=						False
    lowerCamelCase_  				=						model_class(UpperCamelCase  )
    model.to(UpperCamelCase  )
    model.eval()
    lowerCamelCase_  				=						model(**UpperCamelCase  )



