






import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 	a_(		lowerCAmelCase__			,							unittest.TestCase	):



     """simple docstring"""



     __snake_case		:				List[Any]  					=PreTrainedTokenizerFast
     __snake_case		:				List[Any]  					=False
     __snake_case		:				Union[str, Any]  					=True
     __snake_case		:				int  					="tokenizer_file"





     def     __UpperCamelCase						(		self					:	str)							->	int:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		False  # because we don't have pretrained_vocab_files_map
      super().setUp()
      SCREAMING_SNAKE_CASE				    =		True

      SCREAMING_SNAKE_CASE				    =		['robot-test/dummy-tokenizer-fast', 'robot-test/dummy-tokenizer-wordlevel']
      SCREAMING_SNAKE_CASE				    =		'SaulLu/dummy-tokenizer-bytelevel-bpe'

      # Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
      SCREAMING_SNAKE_CASE				    =		[(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

      SCREAMING_SNAKE_CASE				    =		PreTrainedTokenizerFast.from_pretrained(model_paths[0])
      tokenizer.save_pretrained(self.tmpdirname)





     def     __UpperCamelCase						(		self					:	str)							->	Union[str, Any]:

      """simple docstring"""
      pass





     def     __UpperCamelCase						(		self					:	str)							->	Optional[Any]:

      """simple docstring"""
      pass





     def     __UpperCamelCase						(		self					:	Optional[Any])							->	str:

      """simple docstring"""
      pass





     def     __UpperCamelCase						(		self					:	Dict)							->	Any:

      """simple docstring"""
      pass





     def     __UpperCamelCase						(		self					:	Tuple)							->	Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.tmpdirname
      # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''):
        try:
         SCREAMING_SNAKE_CASE				    =		tempfile.mkdtemp()
         SCREAMING_SNAKE_CASE				    =		self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase  ,  **_UpperCAmelCase)

         tokenizer.save_pretrained(self.tmpdirname)
         super().test_training_new_tokenizer()
        finally:
         # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
         # is restored
         shutil.rmtree(self.tmpdirname)
         SCREAMING_SNAKE_CASE				    =		tmpdirname_orig





     def     __UpperCamelCase						(		self					:	Any)							->	Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.tmpdirname
      # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''):
        try:
         SCREAMING_SNAKE_CASE				    =		tempfile.mkdtemp()
         SCREAMING_SNAKE_CASE				    =		self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase  ,  **_UpperCAmelCase)

         tokenizer.save_pretrained(self.tmpdirname)
         super().test_training_new_tokenizer_with_special_tokens_change()
        finally:
         # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
         # is restored
         shutil.rmtree(self.tmpdirname)
         SCREAMING_SNAKE_CASE				    =		tmpdirname_orig





     def     __UpperCamelCase						(		self					:	Dict)							->	str:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name)

      SCREAMING_SNAKE_CASE				    =		('a' for _ in range(1_0_0_0))
      SCREAMING_SNAKE_CASE				    =		tokenizer.train_new_from_iterator(text_iterator=_UpperCAmelCase  ,  length=1_0_0_0  ,  vocab_size=5_0)

      SCREAMING_SNAKE_CASE				    =		new_tokenizer.encode('a🤗')
      self.assertEqual(_UpperCAmelCase  ,  [6_4, 1_7_2, 2_5_3, 9_7, 2_4_5])

@require_tokenizers
class 	a_(		unittest.TestCase	):



     """simple docstring"""



     def     __UpperCamelCase						(		self					:	str)							->	str:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		AutoTokenizer.from_pretrained('bert-base-cased')
      SCREAMING_SNAKE_CASE				    =		json.loads(tokenizer._tokenizer.to_str())
      SCREAMING_SNAKE_CASE				    =		len(_UpperCAmelCase)

      with tempfile.TemporaryDirectory() as tmp_dir:
       # Hack to save this in the tokenizer_config.json
       SCREAMING_SNAKE_CASE				    =		['tokenizer.4.0.0.json']
       tokenizer.save_pretrained(_UpperCAmelCase)
       json.dump(_UpperCAmelCase  ,  open(os.path.join(_UpperCAmelCase  ,  'tokenizer.4.0.0.json')  ,  'w'))

       # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
       SCREAMING_SNAKE_CASE				    =		AutoTokenizer.from_pretrained(_UpperCAmelCase)
       self.assertEqual(len(_UpperCAmelCase)  ,  len(_UpperCAmelCase) + 1)
       SCREAMING_SNAKE_CASE				    =		json.loads(new_tokenizer._tokenizer.to_str())
       self.assertIn('huggingface'  ,  json_tokenizer['model']['vocab'])

       # Will need to be adjusted if we reach v42 and this test is still here.
       # Should pick the old tokenizer file as the version of Transformers is < 4.0.0
       shutil.move(os.path.join(_UpperCAmelCase  ,  'tokenizer.4.0.0.json')  ,  os.path.join(_UpperCAmelCase  ,  'tokenizer.42.0.0.json'))
       SCREAMING_SNAKE_CASE				    =		['tokenizer.42.0.0.json']
       tokenizer.save_pretrained(_UpperCAmelCase)
       SCREAMING_SNAKE_CASE				    =		AutoTokenizer.from_pretrained(_UpperCAmelCase)
       self.assertEqual(len(_UpperCAmelCase)  ,  len(_UpperCAmelCase))
       SCREAMING_SNAKE_CASE				    =		json.loads(new_tokenizer._tokenizer.to_str())
       self.assertNotIn('huggingface'  ,  json_tokenizer['model']['vocab'])





     def     __UpperCamelCase						(		self					:	Union[str, Any])							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		'hf-internal-testing/test-two-tokenizers'

      # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
      SCREAMING_SNAKE_CASE				    =		AutoTokenizer.from_pretrained(_UpperCAmelCase)
      self.assertEqual(len(_UpperCAmelCase)  ,  2_8_9_9_7)
      SCREAMING_SNAKE_CASE				    =		json.loads(tokenizer._tokenizer.to_str())
      self.assertIn('huggingface'  ,  json_tokenizer['model']['vocab'])

      # Testing an older version by monkey-patching the version in the module it's used.
      import transformers as old_transformers

      SCREAMING_SNAKE_CASE				    =		'3.0.0'
      SCREAMING_SNAKE_CASE				    =		old_transformers.models.auto.AutoTokenizer.from_pretrained(_UpperCAmelCase)
      self.assertEqual(len(_UpperCAmelCase)  ,  2_8_9_9_6)
      SCREAMING_SNAKE_CASE				    =		json.loads(old_tokenizer._tokenizer.to_str())
      self.assertNotIn('huggingface'  ,  json_tokenizer['model']['vocab'])

@require_tokenizers
class 	a_(		unittest.TestCase	):



     """simple docstring"""



     def     __UpperCamelCase						(		self					:	Any)							->	str:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		PreTrainedTokenizerFast.from_pretrained('robot-test/dummy-tokenizer-wordlevel')
      SCREAMING_SNAKE_CASE				    =		'The Matrix is a 1999 science fiction action film.'

      with concurrent.futures.ThreadPoolExecutor() as executor:
       SCREAMING_SNAKE_CASE				    =		[executor.submit(self.fetch  ,  _UpperCAmelCase  ,  _UpperCAmelCase) for i in range(1_0)]
       SCREAMING_SNAKE_CASE				    =		[future.result() for future in futures]
       self.assertEqual(_UpperCAmelCase  ,  [[1, 1_0, 0, 8, 0, 1_8, 0, 0, 0, 2] for i in range(1_0)])





     def     __UpperCamelCase						(		self					:	Optional[int]  ,  lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	List[str])							->	str:

      """simple docstring"""
      return tokenizer.encode(_UpperCAmelCase  ,  truncation='longest_first'  ,  padding='longest')






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()


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






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






def 				__SCREAMING_SNAKE_CASE  (						)	->		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.
				__lowercase								=   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)     )
				__lowercase						,   __lowercase						,   __lowercase								=   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.'     )

				__lowercase								=   import_module('tasks'     )

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

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

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

				__lowercase								=   AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,				num_labels=SCREAMING_SNAKE_CASE       ,				idalabel=SCREAMING_SNAKE_CASE       ,				labelaid={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE     )}       ,				cache_dir=model_args.cache_dir       ,				)
				__lowercase								=   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():
								__lowercase								=   TFAutoModelForTokenClassification.from_pretrained(
								    model_args.model_name_or_path       ,				from_pt=bool('.bin' in model_args.model_name_or_path     )       ,				config=SCREAMING_SNAKE_CASE       ,				cache_dir=model_args.cache_dir       ,				)

				# Get datasets
				__lowercase								=   (
				    TFTokenClassificationDataset(
				        token_classification_task=SCREAMING_SNAKE_CASE       ,				data_dir=data_args.data_dir       ,				tokenizer=SCREAMING_SNAKE_CASE       ,				labels=SCREAMING_SNAKE_CASE       ,				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
				)
				__lowercase								=   (
				    TFTokenClassificationDataset(
				        token_classification_task=SCREAMING_SNAKE_CASE       ,				data_dir=data_args.data_dir       ,				tokenizer=SCREAMING_SNAKE_CASE       ,				labels=SCREAMING_SNAKE_CASE       ,				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(SCREAMING_SNAKE_CASE			: np.ndarray       ,				SCREAMING_SNAKE_CASE			: np.ndarray     ) -> Tuple[List[int], List[int]]:
								__lowercase								=   np.argmax(SCREAMING_SNAKE_CASE       ,				axis=2     )
								__lowercase						,   __lowercase								=   preds.shape
								__lowercase								=   [[] for _ in range(SCREAMING_SNAKE_CASE     )]
								__lowercase								=   [[] for _ in range(SCREAMING_SNAKE_CASE     )]

								for i in range(SCREAMING_SNAKE_CASE     ):
												for j in range(SCREAMING_SNAKE_CASE     ):
																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(SCREAMING_SNAKE_CASE			: EvalPrediction     ) -> Dict:
								__lowercase						,   __lowercase								=   align_predictions(p.predictions       ,				p.label_ids     )

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

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

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

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

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

								with open(SCREAMING_SNAKE_CASE       ,				'w'     ) as writer:
												logger.info('***** Eval results *****'     )

												for key, value in result.items():
																logger.info('  %s = %s'       ,				SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
																writer.write('%s = %s\n' % (key, value)     )

												results.update(SCREAMING_SNAKE_CASE     )

    # Predict
				if training_args.do_predict:
								__lowercase								=   TFTokenClassificationDataset(
								    token_classification_task=SCREAMING_SNAKE_CASE       ,				data_dir=data_args.data_dir       ,				tokenizer=SCREAMING_SNAKE_CASE       ,				labels=SCREAMING_SNAKE_CASE       ,				model_type=config.model_type       ,				max_seq_length=data_args.max_seq_length       ,				overwrite_cache=data_args.overwrite_cache       ,				mode=Split.test       ,				)

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

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

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

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

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

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

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

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

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

				return results


if __name__ == "__main__":
							main()





