





"""simple docstring"""
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


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

if is_tf_available():
		import tensorflow as tf






@dataclass
class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							'''tf'''
					UpperCamelCase								=							field(
					    default=lowercase_					,							metadata={'''help''': '''Name of TPU'''}					,							)

					UpperCamelCase								=							field(
					    default=lowercase_					,							metadata={'''help''': '''Zone of TPU'''}					,							)

					UpperCamelCase								=							field(
					    default=lowercase_					,							metadata={'''help''': '''Name of Cloud TPU-enabled project'''}					,							)

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

					UpperCamelCase								=							field(default=lowercase_					,							metadata={'''help''': '''Whether to activate the XLA compilation or not'''}			)


					@cached_property
					def      lowerCamelCase	( self      :Tuple		):
												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(__UpperCamelCase		)
																										tf.tpu.experimental.initialize_tpu_system(__UpperCamelCase		)

																										A		=  tf.distribute.TPUStrategy(__UpperCamelCase		)

																			elif len(__UpperCamelCase		) == 0:
																										A		=  tf.distribute.OneDeviceStrategy(device="/cpu:0"		)
																			elif len(__UpperCamelCase		) == 1:
																										A		=  tf.distribute.OneDeviceStrategy(device="/gpu:0"		)
																			elif len(__UpperCamelCase		) > 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      lowerCamelCase	( self      :Tuple		):
												requires_backends(self					,	["tf"]		)
												return self._setup_strategy


					@property
					def      lowerCamelCase	( self      :Optional[Any]		):
												requires_backends(self					,	["tf"]		)
												return self._setup_strategy.num_replicas_in_sync


					@property
					def      lowerCamelCase	( self      :Union[str, Any]		):
												return False  # TF Logging is handled by Keras not the Trainer


					@property
					def      lowerCamelCase	( self      :Any		):
												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      lowerCamelCase	( self      :List[Any]		):
												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      lowerCamelCase	( self      :List[str]		):
												requires_backends(self					,	["tf"]		)
												warnings.warn(
												    "The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead."					,	__UpperCamelCase					,	)
												return self._setup_strategy.num_replicas_in_sync









"""simple docstring"""
import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_reformer import ReformerTokenizer
else:
		_snake_case					:		Dict							=			None


_snake_case					:		int							=			logging.get_logger(__name__)


_snake_case					:		List[Any]							=			'▁'

_snake_case					:		Tuple							=			{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_snake_case					:		Tuple							=			{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

_snake_case					:		List[Any]							=			{
    'google/reformer-crime-and-punishment': 52_4288,
}






class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							VOCAB_FILES_NAMES
					UpperCamelCase								=							PRETRAINED_VOCAB_FILES_MAP
					UpperCamelCase								=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCamelCase								=							['''input_ids''', '''attention_mask''']
					UpperCamelCase								=							ReformerTokenizer


					def __init__( self      :Dict					,	__UpperCamelCase      :Dict=None					,	__UpperCamelCase      :Optional[Any]=None					,	__UpperCamelCase      :Dict="</s>"					,	__UpperCamelCase      :Optional[Any]="<unk>"					,	__UpperCamelCase      :Union[str, Any]=[]					,	**__UpperCamelCase      :str					,	):
												super().__init__(
												    __UpperCamelCase					,	tokenizer_file=__UpperCamelCase					,	eos_token=__UpperCamelCase					,	unk_token=__UpperCamelCase					,	additional_special_tokens=__UpperCamelCase					,	**__UpperCamelCase					,	)

												A		=  vocab_file
												A		=  False if not self.vocab_file else True


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :str					,	__UpperCamelCase      :Optional[str] = None		):
												if not self.can_save_slow_tokenizer:
																			raise ValueError(
																			    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
																			    "tokenizer."		)

												if not os.path.isdir(__UpperCamelCase		):
																			logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
																			return
												A		=  os.path.join(
												    __UpperCamelCase					,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

												if os.path.abspath(self.vocab_file		) != os.path.abspath(__UpperCamelCase		):
																			copyfile(self.vocab_file					,	__UpperCamelCase		)

												return (out_vocab_file,)


