






import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
		import tensorflow as tf
		from tensorflow.python.framework.errors_impl import ResourceExhaustedError

		from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
		import pyanvml.pyanvml as nvml

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

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						def run_func(_A			):
												@wraps(UpperCAmelCase__			)
												def run_in_eager_mode(*_A,   **_A			):
																		return func(*UpperCAmelCase__,   **UpperCAmelCase__			)

												@wraps(UpperCAmelCase__			)
												@tf.function(experimental_compile=UpperCAmelCase__			)
												def run_in_graph_mode(*_A,   **_A			):
																		return func(*UpperCAmelCase__,   **UpperCAmelCase__			)

												if do_eager_mode is True:
																		if use_xla is not False:
																								raise ValueError(
																								    """Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`."""			)
																		return run_in_eager_mode
												else:
																		return run_in_graph_mode

						return run_func

def    UpperCamelCase						(	_A,   _A,   _A			):






						"""simple docstring"""







						__magic_name__      :       str       						= random.Random()
						__magic_name__      :       int       						= [rng.randint(0,   vocab_size - 1			) for i in range(batch_size * sequence_length			)]
						return tf.constant(UpperCAmelCase__,   shape=(batch_size, sequence_length),   dtype=tf.intaa			)





class 	snake_case__ (							_UpperCAmelCase				):
	lowercase__						:					Optional[int]											=   42
	lowercase__						:					List[str]											=   42
	lowercase__						:					Optional[Any]											=   '''TensorFlow'''
	@property
	def       __magic_name__    (    self			)							->				Optional[Any]:
							return tf.__version__
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Any:
							# initialize GPU on separate process
							__magic_name__      :       Union[str, Any]       						= self.args.strategy
							if strategy is None:
													raise ValueError("""A device strategy has to be initialized before using TensorFlow."""			)
							__magic_name__      :       Optional[int]       						= self._prepare_inference_func(lowercase_      ,       lowercase_      ,       lowercase_			)
							return self._measure_speed(_inference			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Any:
							__magic_name__      :       int       						= self.args.strategy
							if strategy is None:
													raise ValueError("""A device strategy has to be initialized before using TensorFlow."""			)
							__magic_name__      :       List[Any]       						= self._prepare_train_func(lowercase_      ,       lowercase_      ,       lowercase_			)
							return self._measure_speed(_train			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				List[str]:
							# initialize GPU on separate process
							if self.args.is_gpu:
													tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]      ,       lowercase_			)
							__magic_name__      :       str       						= self.args.strategy
							if strategy is None:
													raise ValueError("""A device strategy has to be initialized before using TensorFlow."""			)
							__magic_name__      :       str       						= self._prepare_inference_func(lowercase_      ,       lowercase_      ,       lowercase_			)
							return self._measure_memory(_inference			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Dict:
							if self.args.is_gpu:
													tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]      ,       lowercase_			)
							__magic_name__      :       Tuple       						= self.args.strategy
							if strategy is None:
													raise ValueError("""A device strategy has to be initialized before using TensorFlow."""			)

							__magic_name__      :       List[Any]       						= self._prepare_train_func(lowercase_      ,       lowercase_      ,       lowercase_			)
							return self._measure_memory(_train			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Union[str, Any]:
							__magic_name__      :       Any       						= self.config_dict[model_name]

							if self.args.fpaa:
													raise NotImplementedError("""Mixed precision is currently not supported."""			)

							__magic_name__      :       Optional[int]       						= (
							    hasattr(lowercase_      ,       """architectures"""			)
							    and isinstance(config.architectures      ,       lowercase_			)
							    and len(config.architectures			) > 0
							)
							if not self.args.only_pretrain_model and has_model_class_in_config:
													try:
																			__magic_name__      :       int       						= """TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
																			__magic_name__      :       int       						= __import__("""transformers"""      ,       fromlist=[model_class]			)
																			__magic_name__      :       Optional[Any]       						= getattr(lowercase_      ,       lowercase_			)
																			__magic_name__      :       Any       						= model_cls(lowercase_			)
													except ImportError:
																			raise ImportError(
																			    F'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
																			    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""			)
							else:
													__magic_name__      :       Union[str, Any]       						= TF_MODEL_MAPPING[config.__class__](lowercase_			)

							# encoder-decoder has vocab size saved differently
							__magic_name__      :       Tuple       						= config.vocab_size if hasattr(lowercase_      ,       """vocab_size"""			) else config.encoder.vocab_size
							__magic_name__      :       str       						= random_input_ids(lowercase_      ,       lowercase_      ,       lowercase_			)

							@run_with_tf_optimizations(self.args.eager_mode      ,       self.args.use_xla			)
							def encoder_decoder_forward():
													return model(lowercase_      ,       decoder_input_ids=lowercase_      ,       training=lowercase_			)

							@run_with_tf_optimizations(self.args.eager_mode      ,       self.args.use_xla			)
							def encoder_forward():
													return model(lowercase_      ,       training=lowercase_			)

							__magic_name__      :       List[Any]       						= encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

							return _inference
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Union[str, Any]:
							__magic_name__      :       int       						= self.config_dict[model_name]

							if self.args.eager_mode is not False:
													raise ValueError("""Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`."""			)

							if self.args.fpaa:
													raise NotImplementedError("""Mixed precision is currently not supported."""			)

							__magic_name__      :       str       						= (
							    hasattr(lowercase_      ,       """architectures"""			)
							    and isinstance(config.architectures      ,       lowercase_			)
							    and len(config.architectures			) > 0
							)
							if not self.args.only_pretrain_model and has_model_class_in_config:
													try:
																			__magic_name__      :       str       						= """TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
																			__magic_name__      :       Dict       						= __import__("""transformers"""      ,       fromlist=[model_class]			)
																			__magic_name__      :       Optional[Any]       						= getattr(lowercase_      ,       lowercase_			)
																			__magic_name__      :       int       						= model_cls(lowercase_			)
													except ImportError:
																			raise ImportError(
																			    F'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
																			    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""			)
							else:
													__magic_name__      :       Tuple       						= TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowercase_			)

							# encoder-decoder has vocab size saved differently
							__magic_name__      :       Optional[Any]       						= config.vocab_size if hasattr(lowercase_      ,       """vocab_size"""			) else config.encoder.vocab_size
							__magic_name__      :       str       						= random_input_ids(lowercase_      ,       lowercase_      ,       lowercase_			)

							@run_with_tf_optimizations(self.args.eager_mode      ,       self.args.use_xla			)
							def encoder_decoder_train():
													__magic_name__      :       Dict       						= model(lowercase_      ,       decoder_input_ids=lowercase_      ,       labels=lowercase_      ,       training=lowercase_			)[0]
													__magic_name__      :       Dict       						= tf.gradients(lowercase_      ,       model.trainable_variables			)
													return gradients

							@run_with_tf_optimizations(self.args.eager_mode      ,       self.args.use_xla			)
							def encoder_train():
													__magic_name__      :       Optional[Any]       						= model(lowercase_      ,       labels=lowercase_      ,       training=lowercase_			)[0]
													__magic_name__      :       Any       						= tf.gradients(lowercase_      ,       model.trainable_variables			)
													return gradients

							__magic_name__      :       Union[str, Any]       						= encoder_decoder_train if config.is_encoder_decoder else encoder_train

							return _train
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				Any:
							with self.args.strategy.scope():
													try:
																			if self.args.is_tpu or self.args.use_xla:
																									# run additional 10 times to stabilize compilation for tpu
																									logger.info("""Do inference on TPU. Running model 5 times to stabilize compilation"""			)
																									timeit.repeat(lowercase_      ,       repeat=1      ,       number=5			)

																			# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
																			__magic_name__      :       Tuple       						= timeit.repeat(
																			    lowercase_      ,       repeat=self.args.repeat      ,       number=10      ,       )

																			return min(lowercase_			) / 1_0.0
													except ResourceExhaustedError as e:
																			self.print_fn(F'Doesn\'t fit on GPU. {e}'			)







	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				List[Any]:
							logger.info(
							    """Note that TensorFlow allocates more memory than """
							    """it might need to speed up computation. """
							    """The memory reported here corresponds to the memory """
							    """reported by `nvidia-smi`, which can vary depending """
							    """on total available memory on the GPU that is used."""			)
							with self.args.strategy.scope():
													try:
																			if self.args.trace_memory_line_by_line:
																									if not self.args.eager_mode:
																															raise ValueError(
																															    """`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"""
																															    """ consumption line by line."""			)
																									__magic_name__      :       int       						= start_memory_tracing("""transformers"""			)

																			if self.args.is_tpu:
																									# tpu
																									raise NotImplementedError(
																									    """Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"""
																									    """ with `args.memory=False`"""			)
																			elif self.args.is_gpu:
																									# gpu
																									if not is_pyanvml_available():
																															logger.warning(
																															    """py3nvml not installed, we won't log GPU memory usage. """
																															    """Install py3nvml (pip install py3nvml) to log information about GPU."""			)
																															__magic_name__      :       Dict       						= """N/A"""
																									else:
																															logger.info(
																															    """Measuring total GPU usage on GPU device. Make sure to not have additional processes"""
																															    """ running on the same GPU."""			)
																															# init nvml
																															nvml.nvmlInit()
																															func()
																															__magic_name__      :       Union[str, Any]       						= nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx			)
																															__magic_name__      :       Optional[Any]       						= nvml.nvmlDeviceGetMemoryInfo(lowercase_			)
																															__magic_name__      :       Optional[Any]       						= meminfo.used
																															__magic_name__      :       Optional[Any]       						= Memory(lowercase_			)
																															# shutdown nvml
																															nvml.nvmlShutdown()
																			else:
																									# cpu
																									if self.args.trace_memory_line_by_line:
																															logger.info(
																															    """When enabling line by line tracing, the max peak memory for CPU is inaccurate in"""
																															    """ TensorFlow."""			)
																															__magic_name__      :       Any       						= None
																									else:
																															__magic_name__      :       Union[str, Any]       						= measure_peak_memory_cpu(lowercase_			)
																															__magic_name__      :       List[str]       						= Memory(lowercase_			) if isinstance(lowercase_      ,       lowercase_			) else memory_bytes
																			if self.args.trace_memory_line_by_line:
																									__magic_name__      :       List[str]       						= stop_memory_tracing(lowercase_			)
																									if memory is None:
																															__magic_name__      :       List[Any]       						= summary.total
																			else:
																									__magic_name__      :       Optional[Any]       						= None

																			return memory, summary
													except ResourceExhaustedError as e:
																			self.print_fn(F'Doesn\'t fit on GPU. {e}'			)
																			return "N/A", None








from decimal import Decimal, getcontext
from math import ceil, factorial

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						if not isinstance(_A,   _A			):
												raise TypeError("""Undefined for non-integers"""			)
						elif precision < 1:
												raise ValueError("""Undefined for non-natural numbers"""			)

						__magic_name__      :       Dict       						= precision
						__magic_name__      :       str       						= ceil(precision / 14			)
						__magic_name__      :       List[str]       						= 426880 * Decimal(10005			).sqrt()
						__magic_name__      :       List[Any]       						= 1
						__magic_name__      :       Dict       						= 13591409
						__magic_name__      :       Tuple       						= Decimal(_A			)
						for k in range(1,   _A			):
												__magic_name__      :       List[Any]       						= factorial(6 * k			) // (factorial(3 * k			) * factorial(_A			) ** 3)
												linear_term += 545140134
												exponential_term *= -262537412640768000
												partial_sum += Decimal(multinomial_term * linear_term			) / exponential_term
						return str(constant_term / partial_sum			)[:-1]


if __name__ == "__main__":
		__magic_name__:					Tuple							=     50
		print(F"""The first {n} digits of pi is: {pi(n)}""")
