



import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


A_		:  int										=  logging.getLogger(__name__)



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



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


def 						UpperCAmelCase__   (   ):






 '''simple docstring'''

 a							=		HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)       )
 a		,	a		,	a							=		parser.parse_args_into_dataclasses()

 if (
     os.path.exists(training_args.output_dir       )
     and os.listdir(training_args.output_dir       )
     and training_args.do_train
     and not training_args.overwrite_output_dir
 ):
  raise ValueError(
      F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
      " --overwrite_output_dir to overcome."       )

 a							=		import_module("tasks"       )

 try:
  a							=		getattr(UpperCAmelCase__			,       model_args.task_type       )
  a							=		token_classification_task_clazz()
 except AttributeError:
  raise ValueError(
      F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
      F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""       )

 # Setup logging
 logging.basicConfig(
     format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"			,       datefmt="%m/%d/%Y %H:%M:%S"			,       level=logging.INFO			,       )
 logger.info(
     "n_replicas: %s, distributed training: %s, 16-bits training: %s"			,       training_args.n_replicas			,       bool(training_args.n_replicas > 1       )			,       training_args.fpaa			,       )
 logger.info("Training/evaluation parameters %s"			,       UpperCAmelCase__       )

 # Prepare Token Classification task
 a							=		token_classification_task.get_labels(data_args.labels       )
 a							=		dict(enumerate(UpperCAmelCase__       )       )
 a							=		len(UpperCAmelCase__       )

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

 a							=		AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path			,       num_labels=UpperCAmelCase__			,       idalabel=UpperCAmelCase__			,       labelaid={label: i for i, label in enumerate(UpperCAmelCase__       )}			,       cache_dir=model_args.cache_dir			,       )
 a							=		AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path			,       cache_dir=model_args.cache_dir			,       use_fast=model_args.use_fast			,       )

 with training_args.strategy.scope():
  a							=		TFAutoModelForTokenClassification.from_pretrained(
      model_args.model_name_or_path			,       from_pt=bool(".bin" in model_args.model_name_or_path       )			,       config=UpperCAmelCase__			,       cache_dir=model_args.cache_dir			,       )

 # Get datasets
 a							=		(
     TFTokenClassificationDataset(
         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
 )
 a							=		(
     TFTokenClassificationDataset(
         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]]:
  a							=		np.argmax(UpperCAmelCase__			,       axis=2       )
  a		,	a							=		preds.shape
  a							=		[[] for _ in range(UpperCAmelCase__       )]
  a							=		[[] for _ in range(UpperCAmelCase__       )]

  for i in range(UpperCAmelCase__       ):
   for j in range(UpperCAmelCase__       ):
    if label_ids[i, j] != -1_00:
     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:
  a		,	a							=		align_predictions(p.predictions			,       p.label_ids       )

  return {
      "precision": precision_score(UpperCAmelCase__			,       UpperCAmelCase__       ),
      "recall": recall_score(UpperCAmelCase__			,       UpperCAmelCase__       ),
      "f1": fa_score(UpperCAmelCase__			,       UpperCAmelCase__       ),
  }

 # Initialize our Trainer
 a							=		TFTrainer(
     model=UpperCAmelCase__			,       args=UpperCAmelCase__			,       train_dataset=train_dataset.get_dataset() if train_dataset else None			,       eval_dataset=eval_dataset.get_dataset() if eval_dataset else None			,       compute_metrics=UpperCAmelCase__			,       )

 # Training
 if training_args.do_train:
  trainer.train()
  trainer.save_model()
  tokenizer.save_pretrained(training_args.output_dir       )

 # Evaluation
 a							=		{}
 if training_args.do_eval:
  logger.info("*** Evaluate ***"       )

  a							=		trainer.evaluate()
  a							=		os.path.join(training_args.output_dir			,       "eval_results.txt"       )

  with open(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:
  a							=		TFTokenClassificationDataset(
      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			,       )

  a		,	a		,	a							=		trainer.predict(test_dataset.get_dataset()       )
  a		,	a							=		align_predictions(UpperCAmelCase__			,       UpperCAmelCase__       )
  a							=		classification_report(UpperCAmelCase__			,       UpperCAmelCase__       )

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

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

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

  # Save predictions
  a							=		os.path.join(training_args.output_dir			,       "test_predictions.txt"       )

  with open(UpperCAmelCase__			,       "w"       ) as writer:
   with open(os.path.join(data_args.data_dir			,       "test.txt"       )			,       "r"       ) as f:
    a							=		0

    for line in f:
     if line.startswith("-DOCSTART-"       ) or line == "" or line == "\n":
      writer.write(UpperCAmelCase__       )

      if not preds_list[example_id]:
       example_id += 1
     elif preds_list[example_id]:
      a							=		line.split()[0] + " " + preds_list[example_id].pop(0       ) + "\n"

      writer.write(UpperCAmelCase__       )
     else:
      logger.warning("Maximum sequence length exceeded: No prediction for '%s'."			,       line.split()[0]       )

 return results


if __name__ == "__main__":
     main()







from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
	from PIL import Image

	from transformers import ConvNextImageProcessor

class 		lowercase_       :




					def __init__(   self:		Any,     _lowercase:		Tuple,     _lowercase:		int=13,     _lowercase:		Dict=32,     _lowercase:		Any=3,     _lowercase:		int=4,     _lowercase:		Optional[Any]=[10, 20, 30, 40],     _lowercase:		Any=[2, 2, 3, 2],     _lowercase:		Optional[int]=True,     _lowercase:		List[Any]=True,     _lowercase:		List[Any]=37,     _lowercase:		List[str]="gelu",     _lowercase:		List[str]=10,     _lowercase:		Any=0.02,     _lowercase:		int=3,     _lowercase:		List[str]=None,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  parent
									__lowerCAmelCase					=  batch_size
									__lowerCAmelCase					=  image_size
									__lowerCAmelCase					=  num_channels
									__lowerCAmelCase					=  num_stages
									__lowerCAmelCase					=  hidden_sizes
									__lowerCAmelCase					=  depths
									__lowerCAmelCase					=  is_training
									__lowerCAmelCase					=  use_labels
									__lowerCAmelCase					=  intermediate_size
									__lowerCAmelCase					=  hidden_act
									__lowerCAmelCase					=  type_sequence_label_size
									__lowerCAmelCase					=  initializer_range
									__lowerCAmelCase					=  scope




					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


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

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

									__lowerCAmelCase					=  self.get_config()

									return config, pixel_values, labels




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									return ConvNextConfig(
									    num_channels=self.num_channels,     hidden_sizes=self.hidden_sizes,     depths=self.depths,     num_stages=self.num_stages,     hidden_act=self.hidden_act,     is_decoder=_lowercase,     initializer_range=self.initializer_range,     )




					def     _lowercase						(   self:		Optional[Any],     _lowercase:		List[Any],     _lowercase:		Optional[Any],     _lowercase:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  TFConvNextModel(config=_lowercase)
									__lowerCAmelCase					=  model(_lowercase,     training=_lowercase)
									# expected last hidden states: B, C, H // 32, W // 32
									self.parent.assertEqual(
									    result.last_hidden_state.shape,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),     )




					def     _lowercase						(   self:		Optional[Any],     _lowercase:		int,     _lowercase:		int,     _lowercase:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  self.type_sequence_label_size
									__lowerCAmelCase					=  TFConvNextForImageClassification(_lowercase)
									__lowerCAmelCase					=  model(_lowercase,     labels=_lowercase,     training=_lowercase)
									self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.type_sequence_label_size))







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


									'''simple docstring'''


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





@require_tf
class 		lowercase_       (	lowerCAmelCase__						,							lowerCAmelCase__						,							unittest.TestCase				):
					__UpperCamelCase     	= (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
					__UpperCamelCase     	= (
					    {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
					    if is_tf_available()
					    else {}
					)

					__UpperCamelCase     	= False
					__UpperCamelCase     	= False
					__UpperCamelCase     	= False
					__UpperCamelCase     	= False
					__UpperCamelCase     	= False




					def     _lowercase						(   self:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase					=  TFConvNextModelTester(self)
									__lowerCAmelCase					=  ConfigTester(
									    self,     config_class=_lowercase,     has_text_modality=_lowercase,     hidden_size=37,     )




					@unittest.skip(reason="""ConvNext does not use inputs_embeds""")
					def     _lowercase						(   self:		Any):


									'''simple docstring'''


									pass




					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices("""GPU""")) == 0,     reason="""TF does not support backprop for grouped convolutions on CPU.""",     )
					@slow
					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									super().test_keras_fit()




					@unittest.skip(reason="""ConvNext does not support input and output embeddings""")
					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									pass




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

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

													__lowerCAmelCase					=  ["""pixel_values"""]
													self.assertListEqual(arg_names[:1],     _lowercase)




					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_lowercase)




					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices("""GPU""")) == 0,     reason="""TF does not support backprop for grouped convolutions on CPU.""",     )
					def     _lowercase						(   self:		int):


									'''simple docstring'''


									super().test_dataset_conversion()




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									def check_hidden_states_output(_lowercase:		Any,     _lowercase:		Dict,     _lowercase:		Any):
													__lowerCAmelCase					=  model_class(_lowercase)

													__lowerCAmelCase					=  model(**self._prepare_for_class(_lowercase,     _lowercase))
													__lowerCAmelCase					=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													__lowerCAmelCase					=  self.model_tester.num_stages
													self.assertEqual(len(_lowercase),     expected_num_stages + 1)

													# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
													self.assertListEqual(
													    list(hidden_states[0].shape[-2:]),     [self.model_tester.image_size // 4, self.model_tester.image_size // 4],     )

									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													__lowerCAmelCase					=  True
													check_hidden_states_output(_lowercase,     _lowercase,     _lowercase)

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

													check_hidden_states_output(_lowercase,     _lowercase,     _lowercase)




					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

									def check_equivalence(_lowercase:		Tuple,     _lowercase:		Tuple,     _lowercase:		str,     _lowercase:		Dict={}):
													__lowerCAmelCase					=  model(_lowercase,     return_dict=_lowercase,     **_lowercase)
													__lowerCAmelCase					=  model(_lowercase,     return_dict=_lowercase,     **_lowercase).to_tuple()

													def recursive_check(_lowercase:		Union[str, Any],     _lowercase:		List[str]):
																	if isinstance(_lowercase,     (List, Tuple)):
																					for tuple_iterable_value, dict_iterable_value in zip(_lowercase,     _lowercase):
																									recursive_check(_lowercase,     _lowercase)
																	elif tuple_object is None:
																					return
																	else:
																					self.assertTrue(
																					    all(tf.equal(_lowercase,     _lowercase)),     msg=(
																					        """Tuple and dict output are not equal. Difference:"""
																					        f''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}'''
																					    ),     )

																	recursive_check(_lowercase,     _lowercase)

									for model_class in self.all_model_classes:
													__lowerCAmelCase					=  model_class(_lowercase)

													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase)
													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase)
													check_equivalence(_lowercase,     _lowercase,     _lowercase)

													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase,     return_labels=_lowercase)
													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase,     return_labels=_lowercase)
													check_equivalence(_lowercase,     _lowercase,     _lowercase)

													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase)
													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase)
													check_equivalence(_lowercase,     _lowercase,     _lowercase,     {"""output_hidden_states""": True})

													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase,     return_labels=_lowercase)
													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase,     return_labels=_lowercase)
													check_equivalence(_lowercase,     _lowercase,     _lowercase,     {"""output_hidden_states""": True})




					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_lowercase)







					@slow
					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase					=  TFConvNextModel.from_pretrained("""facebook/convnext-tiny-224""")
									self.assertIsNotNone(_lowercase)





def      UpperCAmelCase  (						)     ->     Any:







				'''simple docstring'''


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

@require_tf
@require_vision
class 		lowercase_       (	unittest.TestCase				):




					@cached_property
					def     _lowercase						(   self:		int):


									'''simple docstring'''


									return ConvNextImageProcessor.from_pretrained("""facebook/convnext-tiny-224""") if is_vision_available() else None







					@slow
					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  TFConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224""")

									__lowerCAmelCase					=  self.default_image_processor
									__lowerCAmelCase					=  prepare_img()
									__lowerCAmelCase					=  image_processor(images=_lowercase,     return_tensors="""tf""")

									# forward pass
									__lowerCAmelCase					=  model(**_lowercase)

									# verify the logits
									__lowerCAmelCase					=  tf.TensorShape((1, 1000))
									self.assertEqual(outputs.logits.shape,     _lowercase)

									__lowerCAmelCase					=  tf.constant([-0.0_260, -0.4_739, 0.1_911])

									tf.debugging.assert_near(outputs.logits[0, :3],     _lowercase,     atol=1e-4)





