
import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 			UpperCamelCase_				(  A					,       unittest.TestCase     ):




      '''simple docstring'''







      a		:str						 =      HerbertTokenizer
      a		:Dict						 =      HerbertTokenizerFast
      a		:int						 =      True







      def lowercase__		( self):
            super().setUp()

            # Use a simpler test file without japanese/chinese characters
            with open(f'{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'					,					encoding='''utf-8''') as f_data:
                  lowerCAmelCase_						=      f_data.read().replace('''\n\n'''					,					'''\n''').strip()

            lowerCAmelCase_						=      [
                '''<s>''',
                '''</s>''',
                '''l''',
                '''o''',
                '''w''',
                '''e''',
                '''r''',
                '''s''',
                '''t''',
                '''i''',
                '''d''',
                '''n''',
                '''w</w>''',
                '''r</w>''',
                '''t</w>''',
                '''lo''',
                '''low''',
                '''er</w>''',
                '''low</w>''',
                '''lowest</w>''',
                '''newer</w>''',
                '''wider</w>''',
                ''',</w>''',
                '''<unk>''',
            ]
            lowerCAmelCase_						=      dict(zip(SCREAMING_SNAKE_CASE_					,					range(len(SCREAMING_SNAKE_CASE_))))
            lowerCAmelCase_						=      ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

            lowerCAmelCase_						=      os.path.join(self.tmpdirname					,					VOCAB_FILES_NAMES['''vocab_file'''])
            lowerCAmelCase_						=      os.path.join(self.tmpdirname					,					VOCAB_FILES_NAMES['''merges_file'''])
            with open(self.vocab_file					,					'''w''') as fp:
                  fp.write(json.dumps(SCREAMING_SNAKE_CASE_))
            with open(self.merges_file					,					'''w''') as fp:
                  fp.write('''\n'''.join(SCREAMING_SNAKE_CASE_))







      def lowercase__		( self					,					_UpperCAmelCase):
            lowerCAmelCase_						=      '''lower newer'''
            lowerCAmelCase_						=      '''lower newer'''
            return input_text, output_text







      def lowercase__		( self):
            lowerCAmelCase_						=      self.tokenizer_class(vocab_file=self.vocab_file					,					merges_file=self.merges_file)

            lowerCAmelCase_						=      '''lower'''
            lowerCAmelCase_						=      ['''low''', '''er</w>''']
            lowerCAmelCase_						=      tokenizer.tokenize(SCREAMING_SNAKE_CASE_)
            self.assertListEqual(SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_)

            lowerCAmelCase_						=      tokens + ['''<unk>''']
            lowerCAmelCase_						=      [16, 17, 23]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_)					,					SCREAMING_SNAKE_CASE_)







      def lowercase__		( self):
            if not self.test_rust_tokenizer:
                  return

            lowerCAmelCase_						=      self.get_tokenizer()
            lowerCAmelCase_						=      self.get_rust_tokenizer()

            lowerCAmelCase_						=      '''lower,newer'''

            lowerCAmelCase_						=      tokenizer.tokenize(SCREAMING_SNAKE_CASE_)
            lowerCAmelCase_						=      rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_)
            self.assertListEqual(SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_)

            lowerCAmelCase_						=      tokenizer.encode(SCREAMING_SNAKE_CASE_					,					add_special_tokens=SCREAMING_SNAKE_CASE_)
            lowerCAmelCase_						=      rust_tokenizer.encode(SCREAMING_SNAKE_CASE_					,					add_special_tokens=SCREAMING_SNAKE_CASE_)
            self.assertListEqual(SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_)

            lowerCAmelCase_						=      self.get_rust_tokenizer()
            lowerCAmelCase_						=      tokenizer.encode(SCREAMING_SNAKE_CASE_)
            lowerCAmelCase_						=      rust_tokenizer.encode(SCREAMING_SNAKE_CASE_)
            self.assertListEqual(SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_)







      @slow
      def lowercase__		( self):
            lowerCAmelCase_						=      self.tokenizer_class.from_pretrained('''allegro/herbert-base-cased''')

            lowerCAmelCase_						=      tokenizer.encode('''konstruowanie sekwencji'''					,					add_special_tokens=SCREAMING_SNAKE_CASE_)
            lowerCAmelCase_						=      tokenizer.encode('''konstruowanie wielu sekwencji'''					,					add_special_tokens=SCREAMING_SNAKE_CASE_)

            lowerCAmelCase_						=      tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_)
            lowerCAmelCase_						=      tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_)

            assert encoded_sentence == [0] + text + [2]
            assert encoded_pair == [0] + text + [2] + text_a + [2]





import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


lowerCamelCase_				:							Optional[Any]       =    logging.get_logger(__name__)

if is_tf_available():
 import tensorflow as tf







@dataclass
class  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			"""tf"""
     lowercase__				  =			field(
         default=lowerCamelCase							,					metadata={"""help""": """Name of TPU"""}							,					)

     lowercase__				  =			field(
         default=lowerCamelCase							,					metadata={"""help""": """Zone of TPU"""}							,					)

     lowercase__				  =			field(
         default=lowerCamelCase							,					metadata={"""help""": """Name of Cloud TPU-enabled project"""}							,					)

     lowercase__				  =			field(
         default=1.0							,					metadata={"""help""": """Power for the Polynomial decay LR scheduler."""}							,					)

     lowercase__				  =			field(default=lowerCamelCase							,					metadata={"""help""": """Whether to activate the XLA compilation or not"""}					)

     @cached_property
     def 						__lowerCamelCase				(  self    ):
            requires_backends(self							,       ['tf']    )
            logger.info('Tensorflow: setting up strategy'    )

            __snake_case						       =   tf.config.list_physical_devices('GPU'    )

            # Set to float16 at first
            if self.fpaa:
                   tf.keras.mixed_precision.set_global_policy('mixed_float16'    )

            if self.no_cuda:
                   __snake_case						       =   tf.distribute.OneDeviceStrategy(device='/cpu:0'    )
            else:
                   try:
                          if self.tpu_name:
                                 __snake_case						       =   tf.distribute.cluster_resolver.TPUClusterResolver(
                                     self.tpu_name							,       zone=self.tpu_zone							,       project=self.gcp_project    )
                          else:
                                 __snake_case						       =   tf.distribute.cluster_resolver.TPUClusterResolver()
                   except ValueError:
                          if self.tpu_name:
                                 raise RuntimeError(f'''Couldn\'t connect to TPU {self.tpu_name}!'''    )
                          else:
                                 __snake_case						       =   None

                   if tpu:
                          # Set to bfloat16 in case of TPU
                          if self.fpaa:
                                 tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'    )

                          tf.config.experimental_connect_to_cluster(SCREAMING_SNAKE_CASE_    )
                          tf.tpu.experimental.initialize_tpu_system(SCREAMING_SNAKE_CASE_    )

                          __snake_case						       =   tf.distribute.TPUStrategy(SCREAMING_SNAKE_CASE_    )

                   elif len(SCREAMING_SNAKE_CASE_    ) == 0:
                          __snake_case						       =   tf.distribute.OneDeviceStrategy(device='/cpu:0'    )
                   elif len(SCREAMING_SNAKE_CASE_    ) == 1:
                          __snake_case						       =   tf.distribute.OneDeviceStrategy(device='/gpu:0'    )
                   elif len(SCREAMING_SNAKE_CASE_    ) > 1:
                          # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
                          __snake_case						       =   tf.distribute.MirroredStrategy()
                   else:
                          raise ValueError('Cannot find the proper strategy, please check your environment properties.'    )

            return strategy

     @property
     def 						__lowerCamelCase				(  self    ):
            requires_backends(self							,       ['tf']    )
            return self._setup_strategy

     @property
     def 						__lowerCamelCase				(  self    ):
            requires_backends(self							,       ['tf']    )
            return self._setup_strategy.num_replicas_in_sync

     @property
     def 						__lowerCamelCase				(  self    ):
            return False  # TF Logging is handled by Keras not the Trainer

     @property
     def 						__lowerCamelCase				(  self    ):
            if self.per_gpu_train_batch_size:
                   logger.warning(
                       'Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future '
                       'version. Using `--per_device_train_batch_size` is preferred.'    )
            __snake_case						       =   self.per_gpu_train_batch_size or self.per_device_train_batch_size
            return per_device_batch_size * self.n_replicas

     @property
     def 						__lowerCamelCase				(  self    ):
            if self.per_gpu_eval_batch_size:
                   logger.warning(
                       'Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future '
                       'version. Using `--per_device_eval_batch_size` is preferred.'    )
            __snake_case						       =   self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
            return per_device_batch_size * self.n_replicas

     @property
     def 						__lowerCamelCase				(  self    ):
            requires_backends(self							,       ['tf']    )
            warnings.warn(
                'The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.'							,       SCREAMING_SNAKE_CASE_							,       )
            return self._setup_strategy.num_replicas_in_sync
