




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(		lowercase__       ,	unittest.TestCase ):





     """simple docstring"""



     __UpperCAmelCase	: int						=    HerbertTokenizer
     __UpperCAmelCase	: Optional[int]						=    HerbertTokenizerFast
     __UpperCAmelCase	: str						=    True

     def 				__SCREAMING_SNAKE_CASE		(      self :       Any    )			->			List[Any]:






         """simple docstring"""
         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:
             a__      =       f_data.read().replace("""\n\n"""						,				"""\n"""    ).strip()

         a__      =       [
             """<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>""",
         ]
         a__      =       dict(zip(A						,				range(len(A    )    )    )    )
         a__      =       ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

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

     def 				__SCREAMING_SNAKE_CASE		(      self :       Any						,				A :       Dict    )			->			int:






         """simple docstring"""
         a__      =       """lower newer"""
         a__      =       """lower newer"""
         return input_text, output_text

     def 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			List[Any]:






         """simple docstring"""
         a__      =       self.tokenizer_class(vocab_file=self.vocab_file						,				merges_file=self.merges_file    )

         a__      =       """lower"""
         a__      =       ["""low""", """er</w>"""]
         a__      =       tokenizer.tokenize(A    )
         self.assertListEqual(A						,				A    )

         a__      =       tokens + ["""<unk>"""]
         a__      =       [16, 17, 23]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(A    )						,				A    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			Tuple:






         """simple docstring"""
         if not self.test_rust_tokenizer:
             return

         a__      =       self.get_tokenizer()
         a__      =       self.get_rust_tokenizer()

         a__      =       """lower,newer"""

         a__      =       tokenizer.tokenize(A    )
         a__      =       rust_tokenizer.tokenize(A    )
         self.assertListEqual(A						,				A    )

         a__      =       tokenizer.encode(A						,				add_special_tokens=A    )
         a__      =       rust_tokenizer.encode(A						,				add_special_tokens=A    )
         self.assertListEqual(A						,				A    )

         a__      =       self.get_rust_tokenizer()
         a__      =       tokenizer.encode(A    )
         a__      =       rust_tokenizer.encode(A    )
         self.assertListEqual(A						,				A    )






     @slow
     def 				__SCREAMING_SNAKE_CASE		(      self :       int    )			->			List[Any]:






         """simple docstring"""
         a__      =       self.tokenizer_class.from_pretrained("""allegro/herbert-base-cased"""    )

         a__      =       tokenizer.encode("""konstruowanie sekwencji"""						,				add_special_tokens=A    )
         a__      =       tokenizer.encode("""konstruowanie wielu sekwencji"""						,				add_special_tokens=A    )

         a__      =       tokenizer.build_inputs_with_special_tokens(A    )
         a__      =       tokenizer.build_inputs_with_special_tokens(A						,				A    )

         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


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

if is_tf_available():
 import tensorflow as tf







@dataclass
class 		__UpperCAmelCase(		lowercase__ ):





     """simple docstring"""



     __UpperCAmelCase	: int						=    """tf"""
     __UpperCAmelCase	: Optional[str]						=    field(
         default=lowercase__       ,	metadata={"""help""": """Name of TPU"""}       ,	)

     __UpperCAmelCase	: Optional[str]						=    field(
         default=lowercase__       ,	metadata={"""help""": """Zone of TPU"""}       ,	)

     __UpperCAmelCase	: Optional[str]						=    field(
         default=lowercase__       ,	metadata={"""help""": """Name of Cloud TPU-enabled project"""}       ,	)

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

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

     @cached_property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			Tuple["tf.distribute.Strategy", int]:






         """simple docstring"""
         requires_backends(self						,				["""tf"""]    )
         logger.info("""Tensorflow: setting up strategy"""    )

         a__      =       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:
             a__      =       tf.distribute.OneDeviceStrategy(device="""/cpu:0"""    )
         else:
             try:
                 if self.tpu_name:
                     a__      =       tf.distribute.cluster_resolver.TPUClusterResolver(
                         self.tpu_name						,				zone=self.tpu_zone						,				project=self.gcp_project    )
                 else:
                     a__      =       tf.distribute.cluster_resolver.TPUClusterResolver()
             except ValueError:
                 if self.tpu_name:
                     raise RuntimeError(f"""Couldn't connect to TPU {self.tpu_name}!"""    )
                 else:
                     a__      =       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(A    )
                 tf.tpu.experimental.initialize_tpu_system(A    )

                 a__      =       tf.distribute.TPUStrategy(A    )

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

         return strategy

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			"tf.distribute.Strategy":






         """simple docstring"""
         requires_backends(self						,				["""tf"""]    )
         return self._setup_strategy

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			int:






         """simple docstring"""
         requires_backends(self						,				["""tf"""]    )
         return self._setup_strategy.num_replicas_in_sync

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			List[Any]:






         """simple docstring"""
         return False  # TF Logging is handled by Keras not the Trainer

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]    )			->			int:






         """simple docstring"""
         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."""    )
         a__      =       self.per_gpu_train_batch_size or self.per_device_train_batch_size
         return per_device_batch_size * self.n_replicas

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]    )			->			int:






         """simple docstring"""
         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."""    )
         a__      =       self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
         return per_device_batch_size * self.n_replicas






     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			int:






         """simple docstring"""
         requires_backends(self						,				["""tf"""]    )
         warnings.warn(
             """The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead."""						,				A						,				)
         return self._setup_strategy.num_replicas_in_sync






