





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


__UpperCAmelCase						=      logging.get_logger(__name__)

if is_tf_available():
  import tensorflow as tf

@dataclass
class 		__a							(	__UpperCamelCase      ):
       __snake_case						:   Tuple      =		"""tf"""
       __snake_case						:   Optional[str]      =		field(
           default=__UpperCamelCase			,metadata={"""help""": """Name of TPU"""}			,)

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

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

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

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



       @cached_property
       def  A							(     self	:   List[str]			):
           requires_backends(self					,			["""tf"""]			)
           logger.info("""Tensorflow: setting up strategy"""			)

           lowerCAmelCase_					:	Tuple						=   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:
               lowerCAmelCase_					:	List[Any]						=   tf.distribute.OneDeviceStrategy(device="""/cpu:0"""			)
           else:
               try:
                   if self.tpu_name:
                       lowerCAmelCase_					:	Dict						=   tf.distribute.cluster_resolver.TPUClusterResolver(
                           self.tpu_name					,			zone=self.tpu_zone					,			project=self.gcp_project			)
                   else:
                       lowerCAmelCase_					:	str						=   tf.distribute.cluster_resolver.TPUClusterResolver()
               except ValueError:
                   if self.tpu_name:
                       raise RuntimeError(F'Couldn\'t connect to TPU {self.tpu_name}!'			)
                   else:
                       lowerCAmelCase_					:	int						=   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			)

                   lowerCAmelCase_					:	Union[str, Any]						=   tf.distribute.TPUStrategy(UpperCAmelCase			)

               elif len(UpperCAmelCase			) == 0:
                   lowerCAmelCase_					:	Tuple						=   tf.distribute.OneDeviceStrategy(device="""/cpu:0"""			)
               elif len(UpperCAmelCase			) == 1:
                   lowerCAmelCase_					:	Dict						=   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`
                   lowerCAmelCase_					:	List[str]						=   tf.distribute.MirroredStrategy()
               else:
                   raise ValueError("""Cannot find the proper strategy, please check your environment properties."""			)

           return strategy



       @property
       def  A							(     self	:   Tuple			):
           requires_backends(self					,			["""tf"""]			)
           return self._setup_strategy



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



       @property
       def  A							(     self	:   Optional[Any]			):
           return False  # TF Logging is handled by Keras not the Trainer



       @property
       def  A							(     self	:   List[str]			):
           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."""			)
           lowerCAmelCase_					:	Optional[Any]						=   self.per_gpu_train_batch_size or self.per_device_train_batch_size
           return per_device_batch_size * self.n_replicas



       @property
       def  A							(     self	:   Optional[int]			):
           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."""			)
           lowerCAmelCase_					:	Optional[int]						=   self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
           return per_device_batch_size * self.n_replicas



       @property
       def  A							(     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









import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property

class 		__a							(	unittest.TestCase      ):



       @cached_property
       def  A							(     self	:   Optional[int]			):
           return BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""			)



       @cached_property
       def  A							(     self	:   Optional[int]			):
           return BlenderbotTokenizerFast.from_pretrained("""facebook/blenderbot-3B"""			)



       def  A							(     self	:   Optional[Any]			):
           lowerCAmelCase_					:	List[str]						=   self.tokenizer_ab
           lowerCAmelCase_					:	Any						=   """ I am a small frog."""
           lowerCAmelCase_					:	Union[str, Any]						=   tok([src_text]					,			padding=UpperCAmelCase					,			truncation=UpperCAmelCase			)["""input_ids"""]
           lowerCAmelCase_					:	int						=   tok.batch_decode(UpperCAmelCase					,			skip_special_tokens=UpperCAmelCase					,			clean_up_tokenization_spaces=UpperCAmelCase			)[0]
           assert src_text == decoded



       def  A							(     self	:   List[Any]			):
           lowerCAmelCase_					:	Dict						=   self.rust_tokenizer_ab
           lowerCAmelCase_					:	str						=   """ I am a small frog."""
           lowerCAmelCase_					:	Tuple						=   tok([src_text]					,			padding=UpperCAmelCase					,			truncation=UpperCAmelCase			)["""input_ids"""]
           lowerCAmelCase_					:	int						=   tok.batch_decode(UpperCAmelCase					,			skip_special_tokens=UpperCAmelCase					,			clean_up_tokenization_spaces=UpperCAmelCase			)[0]
           assert src_text == decoded



       def  A							(     self	:   str			):
           assert self.tokenizer_ab.add_prefix_space
           assert self.tokenizer_ab([""" Sam""", """Sam"""]			).input_ids == [[55_02, 2], [55_02, 2]]



       def  A							(     self	:   List[str]			):
           assert self.rust_tokenizer_ab.add_prefix_space
           assert self.rust_tokenizer_ab([""" Sam""", """Sam"""]			).input_ids == [[55_02, 2], [55_02, 2]]


