
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      lowerCAmelCase__  (			__lowercase    ):
		a__ :		List[str]   						=	"""tf"""
		a__ :		Optional[str]   						=	field(
		    default=__lowercase						,			metadata={"""help""": """Name of TPU"""}						,			)

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

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

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

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






		@cached_property
		def 				__A (	self	:							int						)  ->							Tuple["tf.distribute.Strategy", int]:
						requires_backends(self							,    ['''tf''']						)
						logger.info('''Tensorflow: setting up strategy'''						)

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

														__lowerCamelCase					      =			tf.distribute.TPUStrategy(SCREAMING_SNAKE_CASE__						)

										elif len(SCREAMING_SNAKE_CASE__						) == 0:
														__lowerCamelCase					      =			tf.distribute.OneDeviceStrategy(device='''/cpu:0'''						)
										elif len(SCREAMING_SNAKE_CASE__						) == 1:
														__lowerCamelCase					      =			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`
														__lowerCamelCase					      =			tf.distribute.MirroredStrategy()
										else:
														raise ValueError('''Cannot find the proper strategy, please check your environment properties.'''						)

						return strategy






		@property
		def 				__A (	self	:							List[str]						)  ->							"tf.distribute.Strategy":
						requires_backends(self							,    ['''tf''']						)
						return self._setup_strategy






		@property
		def 				__A (	self	:							List[str]						)  ->							int:
						requires_backends(self							,    ['''tf''']						)
						return self._setup_strategy.num_replicas_in_sync






		@property
		def 				__A (	self	:							Optional[int]						)  ->							List[str]:
						return False  # TF Logging is handled by Keras not the Trainer






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





from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest

@require_tokenizers
class      lowerCAmelCase__  (			__lowercase    ):
		a__ :		Any   						=	SqueezeBertTokenizer
		a__ :		Union[str, Any]   						=	SqueezeBertTokenizerFast
		a__ :		int   						=	True






		def 				__A (	self	:							Optional[int]							,    **SCREAMING_SNAKE_CASE__	:							Union[str, Any]						)  ->							int:
						return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname							,    **SCREAMING_SNAKE_CASE__						)







		@slow
		def 				__A (	self	:							int						)  ->							Optional[int]:
						__lowerCamelCase					      =			SqueezeBertTokenizer.from_pretrained('''squeezebert/squeezebert-mnli-headless'''						)

						__lowerCamelCase					      =			tokenizer.encode('''sequence builders'''							,    add_special_tokens=SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			tokenizer.encode('''multi-sequence build'''							,    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 == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
						assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
						    tokenizer.sep_token_id
						]



