
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()


_lowerCamelCase :       Union[str, Any]     					=    logging.getLogger(__name__)




@dataclass
class      SCREAMING_SNAKE_CASE__  :





 '''simple docstring'''



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




@dataclass
class      SCREAMING_SNAKE_CASE__  :





 '''simple docstring'''



 _UpperCAmelCase :	str					     =      field(
     metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}      )
 _UpperCAmelCase :	Optional[str]					     =      field(
     metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}      )
 _UpperCAmelCase :	int					     =      field(
     default=1_2_8							,metadata={
         "help": (
             "The maximum total input sequence length after tokenization. Sequences longer "
             "than this will be truncated, sequences shorter will be padded."
         )
     }							,)
 _UpperCAmelCase :	bool					     =      field(
     default=lowerCAmelCase_							,metadata={"help": "Overwrite the cached training and evaluation sets"}      )






def  a_	( )				->						int:
   # 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.
   _snake_case						=    HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
   _snake_case			,      _snake_case			,      _snake_case						=    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.'				)

   _snake_case						=    import_module('tasks'				)

   try:
      _snake_case						=    getattr(__lowercase ,      model_args.task_type				)
      _snake_case						=    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' ,      __lowercase				)

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

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

   _snake_case						=    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 ,      )
   _snake_case						=    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():
      _snake_case						=    TFAutoModelForTokenClassification.from_pretrained(
          model_args.model_name_or_path ,      from_pt=bool('.bin' in model_args.model_name_or_path				) ,      config=__lowercase ,      cache_dir=model_args.cache_dir ,      )

   # Get datasets
   _snake_case						=    (
       TFTokenClassificationDataset(
           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
   )
   _snake_case						=    (
       TFTokenClassificationDataset(
           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     :			Dict ,      __lowercase     :			Union[str, Any]				) -> Tuple[List[int], List[int]]:
      _snake_case						=    np.argmax(__lowercase ,      axis=2				)
      _snake_case			,      _snake_case						=    preds.shape
      _snake_case						=    [[] for _ in range(__lowercase				)]
      _snake_case						=    [[] for _ in range(__lowercase				)]

      for i in range(__lowercase				):
         for j in range(__lowercase				):
            if label_ids[i, j] != -100:
               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     :			Any				) -> Dict:
      _snake_case			,      _snake_case						=    align_predictions(p.predictions ,      p.label_ids				)

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

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

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

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

      _snake_case						=    trainer.evaluate()
      _snake_case						=    os.path.join(training_args.output_dir ,      'eval_results.txt'				)

      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:
      _snake_case						=    TFTokenClassificationDataset(
          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 ,      )

      _snake_case			,      _snake_case			,      _snake_case						=    trainer.predict(test_dataset.get_dataset()				)
      _snake_case			,      _snake_case						=    align_predictions(__lowercase ,      __lowercase				)
      _snake_case						=    classification_report(__lowercase ,      __lowercase				)

      logger.info('\n%s' ,      __lowercase				)

      _snake_case						=    os.path.join(training_args.output_dir ,      'test_results.txt'				)

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

      # Save predictions
      _snake_case						=    os.path.join(training_args.output_dir ,      'test_predictions.txt'				)

      with open(__lowercase ,      'w'				) as writer:
         with open(os.path.join(data_args.data_dir ,      'test.txt'				) ,      'r'				) as f:
            _snake_case						=    0

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

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

                  writer.write(__lowercase				)
               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 unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
			import tensorflow as tf

			from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel




@require_tf
class      SCREAMING_SNAKE_CASE__  :





	'''simple docstring'''



	_UpperCAmelCase :	Optional[int]					     =      MarianConfig
	_UpperCAmelCase :	str					     =      {}
	_UpperCAmelCase :	Union[str, Any]					     =      "gelu"



	def __init__(						self			:					List[Any]    ,						lowercase			:					List[str]    ,						lowercase			:					str=13    ,						lowercase			:					Union[str, Any]=7    ,						lowercase			:					List[Any]=True    ,						lowercase			:					Tuple=False    ,						lowercase			:					Dict=99    ,						lowercase			:					Optional[Any]=32    ,						lowercase			:					Optional[int]=2    ,						lowercase			:					int=4    ,						lowercase			:					List[str]=37    ,						lowercase			:					Tuple=0.1    ,						lowercase			:					Union[str, Any]=0.1    ,						lowercase			:					Optional[int]=20    ,						lowercase			:					List[Any]=2    ,						lowercase			:					Dict=1    ,						lowercase			:					str=0    ,						):


				'''simple docstring'''


				_snake_case						=    parent
				_snake_case						=    batch_size
				_snake_case						=    seq_length
				_snake_case						=    is_training
				_snake_case						=    use_labels
				_snake_case						=    vocab_size
				_snake_case						=    hidden_size
				_snake_case						=    num_hidden_layers
				_snake_case						=    num_attention_heads
				_snake_case						=    intermediate_size

				_snake_case						=    hidden_dropout_prob
				_snake_case						=    attention_probs_dropout_prob
				_snake_case						=    max_position_embeddings
				_snake_case						=    eos_token_id
				_snake_case						=    pad_token_id
				_snake_case						=    bos_token_id



	def 				A							(						self			:					List[Any]			):


				'''simple docstring'''


				_snake_case						=    ids_tensor([self.batch_size, self.seq_length - 1]    ,						self.vocab_size			)
				_snake_case						=    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size			)    ,						1			)
				_snake_case						=    tf.concat([input_ids, eos_tensor]    ,						axis=1			)

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

				_snake_case						=    self.config_cls(
				    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_ids=[2]    ,						bos_token_id=self.bos_token_id    ,						pad_token_id=self.pad_token_id    ,						decoder_start_token_id=self.pad_token_id    ,						**self.config_updates    ,						)
				_snake_case						=    prepare_marian_inputs_dict(lowercase    ,						lowercase    ,						lowercase			)
				return config, inputs_dict





	def 				A							(						self			:					List[Any]    ,						lowercase			:					List[str]    ,						lowercase			:					Optional[Any]			):


				'''simple docstring'''


				_snake_case						=    TFMarianModel(config=lowercase			).get_decoder()
				_snake_case						=    inputs_dict['input_ids']

				_snake_case						=    input_ids[:1, :]
				_snake_case						=    inputs_dict['attention_mask'][:1, :]
				_snake_case						=    inputs_dict['head_mask']
				_snake_case						=    1

				# first forward pass
				_snake_case						=    model(lowercase    ,						attention_mask=lowercase    ,						head_mask=lowercase    ,						use_cache=lowercase			)

				_snake_case			,      _snake_case						=    outputs.to_tuple()

				# create hypothetical next token and extent to next_input_ids
				_snake_case						=    ids_tensor((self.batch_size, 3)    ,						config.vocab_size			)
				_snake_case						=    tf.cast(ids_tensor((self.batch_size, 3)    ,						2			)    ,						tf.inta			)

				# append to next input_ids and
				_snake_case						=    tf.concat([input_ids, next_tokens]    ,						axis=-1			)
				_snake_case						=    tf.concat([attention_mask, next_attn_mask]    ,						axis=-1			)

				_snake_case						=    model(lowercase    ,						attention_mask=lowercase			)[0]
				_snake_case						=    model(lowercase    ,						attention_mask=lowercase    ,						past_key_values=lowercase			)[0]

				self.parent.assertEqual(next_tokens.shape[1]    ,						output_from_past.shape[1]			)

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

				# test that outputs are equal for slice
				tf.debugging.assert_near(lowercase    ,						lowercase    ,						rtol=1E-3			)






def  a_	( __lowercase     :			List[str] ,      __lowercase     :			List[str] ,      __lowercase     :			Any ,      __lowercase     :			int=None ,      __lowercase     :			Tuple=None ,      __lowercase     :			str=None ,      __lowercase     :			Tuple=None ,      __lowercase     :			str=None ,      )				->						List[str]:
			if attention_mask is None:
						_snake_case						=    tf.cast(tf.math.not_equal(__lowercase ,      config.pad_token_id				) ,      tf.inta				)
			if decoder_attention_mask is None:
						_snake_case						=    tf.concat(
						    [
						        tf.ones(decoder_input_ids[:, :1].shape ,      dtype=tf.inta				),
						        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,      config.pad_token_id				) ,      tf.inta				),
						    ] ,      axis=-1 ,      )
			if head_mask is None:
						_snake_case						=    tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
			if decoder_head_mask is None:
						_snake_case						=    tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
			if cross_attn_head_mask is None:
						_snake_case						=    tf.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": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}




@require_tf
class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase							,UpperCAmelCase							,unittest.TestCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	int					     =      (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
	_UpperCAmelCase :	Tuple					     =      (TFMarianMTModel,) if is_tf_available() else ()
	_UpperCAmelCase :	Optional[Any]					     =      (
	    {
	        "conversational": TFMarianMTModel,
	        "feature-extraction": TFMarianModel,
	        "summarization": TFMarianMTModel,
	        "text2text-generation": TFMarianMTModel,
	        "translation": TFMarianMTModel,
	    }
	    if is_tf_available()
	    else {}
	)
	_UpperCAmelCase :	Union[str, Any]					     =      True
	_UpperCAmelCase :	Union[str, Any]					     =      False
	_UpperCAmelCase :	Tuple					     =      False



	def 				A							(						self			:					int			):


				'''simple docstring'''


				_snake_case						=    TFMarianModelTester(self			)
				_snake_case						=    ConfigTester(self    ,						config_class=lowercase			)



	def 				A							(						self			:					Any			):


				'''simple docstring'''


				self.config_tester.run_common_tests()





	def 				A							(						self			:					Tuple			):


				'''simple docstring'''


				_snake_case						=    self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*lowercase			)




@require_tf
class      SCREAMING_SNAKE_CASE__  (			unittest.TestCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	List[Any]					     =      1_0_0_0  # show more chars for failing integration tests



	@classmethod
	def 				A							(						cls			:					Tuple			):


				'''simple docstring'''


				_snake_case						=    f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
				return cls



	@cached_property
	def 				A							(						self			:					List[str]			):


				'''simple docstring'''


				return AutoTokenizer.from_pretrained(self.model_name			)



	@property
	def 				A							(						self			:					Tuple			):


				'''simple docstring'''


				return self.tokenizer.eos_token_id



	@cached_property
	def 				A							(						self			:					List[Any]			):


				'''simple docstring'''


				warnings.simplefilter('error'			)
				_snake_case						=    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name			)
				assert isinstance(lowercase    ,						lowercase			)
				_snake_case						=    model.config
				self.assertListEqual(c.bad_words_ids    ,						[[c.pad_token_id]]			)
				self.assertEqual(c.max_length    ,						512			)
				self.assertEqual(c.decoder_start_token_id    ,						c.pad_token_id			)
				return model



	def 				A							(						self			:					Optional[Any]    ,						**lowercase			:					int			):


				'''simple docstring'''


				_snake_case						=    self.translate_src_text(**lowercase			)
				self.assertListEqual(self.expected_text    ,						lowercase			)





	def 				A							(						self			:					Optional[int]    ,						**lowercase			:					List[Any]			):


				'''simple docstring'''


				_snake_case						=    self.tokenizer(self.src_text    ,						**lowercase    ,						padding=lowercase    ,						return_tensors='tf'			)
				_snake_case						=    self.model.generate(
				    model_inputs.input_ids    ,						attention_mask=model_inputs.attention_mask    ,						num_beams=2    ,						max_length=128			)
				_snake_case						=    self.tokenizer.batch_decode(generated_ids.numpy()    ,						skip_special_tokens=lowercase			)
				return generated_words




@require_sentencepiece
@require_tokenizers
@require_tf
class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	Union[str, Any]					     =      "mt"
	_UpperCAmelCase :	List[str]					     =      "en"
	_UpperCAmelCase :	str					     =      ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
	_UpperCAmelCase :	Any					     =      ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]



	@unittest.skip('Skipping until #12647 is resolved.'			)
	@slow
	def 				A							(						self			:					Union[str, Any]			):


				'''simple docstring'''


				self._assert_generated_batch_equal_expected()




@require_sentencepiece
@require_tokenizers
@require_tf
class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	Tuple					     =      "en"
	_UpperCAmelCase :	Any					     =      "zh"
	_UpperCAmelCase :	int					     =      ["My name is Wolfgang and I live in Berlin"]
	_UpperCAmelCase :	List[str]					     =      ["我叫沃尔夫冈 我住在柏林"]



	@unittest.skip('Skipping until #12647 is resolved.'			)
	@slow
	def 				A							(						self			:					Optional[Any]			):


				'''simple docstring'''


				self._assert_generated_batch_equal_expected()




@require_sentencepiece
@require_tokenizers
@require_tf
class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	Any					     =      "en"
	_UpperCAmelCase :	Any					     =      "ROMANCE"
	_UpperCAmelCase :	int					     =      [
	    ">>fr<< Don't spend so much time watching TV.",
	    ">>pt<< Your message has been sent.",
	    ">>es<< He's two years older than me.",
	]
	_UpperCAmelCase :	Tuple					     =      [
	    "Ne passez pas autant de temps à regarder la télé.",
	    "A sua mensagem foi enviada.",
	    "Es dos años más viejo que yo.",
	]



	@unittest.skip('Skipping until #12647 is resolved.'			)
	@slow
	def 				A							(						self			:					List[str]			):


				'''simple docstring'''


				self._assert_generated_batch_equal_expected()





	@unittest.skip('Skipping until #12647 is resolved.'			)
	@slow
	def 				A							(						self			:					List[Any]			):


				'''simple docstring'''


				_snake_case						=    TranslationPipeline(self.model    ,						self.tokenizer    ,						framework='tf'			)
				_snake_case						=    pipeline(self.src_text			)
				self.assertEqual(self.expected_text    ,						[x['translation_text'] for x in output]			)