
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							:List[Any]											=       logging.get_logger(__name__)

if is_tf_available():
	import tensorflow as tf





@dataclass
class 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE						):




	'''simple docstring'''





	snake_case_      					=  "tf"
	snake_case_      					=  field(
	    default=__SCREAMING_SNAKE_CASE    ,    metadata={"help": "Name of TPU"}    ,    )

	snake_case_      					=  field(
	    default=__SCREAMING_SNAKE_CASE    ,    metadata={"help": "Zone of TPU"}    ,    )

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

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

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






	@cached_property
	def        UpperCamelCase_						( self      :				List[str]				):
					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        UpperCamelCase_						( self      :				Optional[Any]				):
					requires_backends(self							,["tf"]				)
					return self._setup_strategy






	@property
	def        UpperCamelCase_						( self      :				Union[str, Any]				):
					requires_backends(self							,["tf"]				)
					return self._setup_strategy.num_replicas_in_sync






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






	@property
	def        UpperCamelCase_						( self      :				Dict				):
					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        UpperCamelCase_						( self      :				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."				)
					__A         =				self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
					return per_device_batch_size * self.n_replicas







	@property
	def        UpperCamelCase_						( self      :				Optional[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."							,A							,)
					return self._setup_strategy.num_replicas_in_sync


from datetime import date

import requests

SCREAMING_SNAKE_CASE							:str											=       'https://api.carbonintensity.org.uk/intensity'


def 		UpperCAmelCase    (			)	->			str:







				"""simple docstring"""



				__A         =				requests.get(a_		).json()["data"][0]
				return last_half_hour["intensity"]["actual"]


def 		UpperCAmelCase    (			a_	,     a_		)	->			list:







				"""simple docstring"""



				return requests.get(F'''{BASE_URL}/{start}/{end}'''		).json()["data"]


if __name__ == "__main__":
					for entry in fetch_from_to(start=date(2020, 10, 1), end=date(2020, 10, 3)):
										print('from {from} to {to}: {intensity[actual]}'.format(**entry))
					print(f'''{fetch_last_half_hour() = }''')
