


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

snake_case							: Any      =   logging.get_logger(__name__)






def 							__lowercase     (				__lowerCAmelCase							:       bool      ,					__lowerCAmelCase							:       bool     ):







							def run_func(__lowerCAmelCase							:       Optional[int]     ):
														@wraps(__UpperCamelCase     )
														def run_in_eager_mode(*__lowerCAmelCase							:       Tuple      ,					**__lowerCAmelCase							:       Tuple     ):
																					return func(*__UpperCamelCase      ,					**__UpperCamelCase     )

														@wraps(__UpperCamelCase     )
														@tf.function(experimental_compile=__UpperCamelCase     )
														def run_in_graph_mode(*__lowerCAmelCase							:       Optional[Any]      ,					**__lowerCAmelCase							:       Optional[int]     ):
																					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 							__lowercase     (				__lowerCAmelCase							:       int      ,					__lowerCAmelCase							:       int      ,					__lowerCAmelCase							:       int     ):
							a__    =  random.Random()
							a__    =  [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_     (SCREAMING_SNAKE_CASE_   ):
					UpperCAmelCase__      :					int					   =    4_2
					UpperCAmelCase__      :					Optional[int]					   =    4_2
					UpperCAmelCase__      :					Optional[Any]					   =    '''TensorFlow'''
					@property
					def 				lowerCamelCase__(   self     :Any   )      ->					List[str]:
												return tf.__version__
					def 				lowerCamelCase__(   self     :List[str]    ,__snake_case     :List[str]    ,__snake_case     :Optional[int]    ,__snake_case     :Optional[Any]   )      ->					List[str]:
												# initialize GPU on separate process
												a__    =  self.args.strategy
												if strategy is None:
																			raise ValueError('A device strategy has to be initialized before using TensorFlow.'   )
												a__    =  self._prepare_inference_func(UpperCamelCase__    ,UpperCamelCase__    ,UpperCamelCase__   )
												return self._measure_speed(_inference   )
					def 				lowerCamelCase__(   self     :List[str]    ,__snake_case     :Dict    ,__snake_case     :str    ,__snake_case     :Tuple   )      ->					Any:
												a__    =  self.args.strategy
												if strategy is None:
																			raise ValueError('A device strategy has to be initialized before using TensorFlow.'   )
												a__    =  self._prepare_train_func(UpperCamelCase__    ,UpperCamelCase__    ,UpperCamelCase__   )
												return self._measure_speed(_train   )
					def 				lowerCamelCase__(   self     :List[Any]    ,__snake_case     :Tuple    ,__snake_case     :int    ,__snake_case     :Tuple   )      ->					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]    ,UpperCamelCase__   )
												a__    =  self.args.strategy
												if strategy is None:
																			raise ValueError('A device strategy has to be initialized before using TensorFlow.'   )
												a__    =  self._prepare_inference_func(UpperCamelCase__    ,UpperCamelCase__    ,UpperCamelCase__   )
												return self._measure_memory(_inference   )
					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :Tuple    ,__snake_case     :Union[str, Any]    ,__snake_case     :Dict   )      ->					List[str]:
												if self.args.is_gpu:
																			tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]    ,UpperCamelCase__   )
												a__    =  self.args.strategy
												if strategy is None:
																			raise ValueError('A device strategy has to be initialized before using TensorFlow.'   )

												a__    =  self._prepare_train_func(UpperCamelCase__    ,UpperCamelCase__    ,UpperCamelCase__   )
												return self._measure_memory(_train   )
					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :str    ,__snake_case     :Optional[Any]    ,__snake_case     :int   )      ->					Union[str, Any]:
												a__    =  self.config_dict[model_name]

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

												a__    =  (
												    hasattr(UpperCamelCase__    ,'architectures'   )
												    and isinstance(config.architectures    ,UpperCamelCase__   )
												    and len(config.architectures   ) > 0
												)
												if not self.args.only_pretrain_model and has_model_class_in_config:
																			try:
																										a__    =  '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
																										a__    =  __import__('transformers'    ,fromlist=[model_class]   )
																										a__    =  getattr(UpperCamelCase__    ,UpperCamelCase__   )
																										a__    =  model_cls(UpperCamelCase__   )
																			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:
																			a__    =  TF_MODEL_MAPPING[config.__class__](UpperCamelCase__   )

												# encoder-decoder has vocab size saved differently
												a__    =  config.vocab_size if hasattr(UpperCamelCase__    ,'vocab_size'   ) else config.encoder.vocab_size
												a__    =  random_input_ids(UpperCamelCase__    ,UpperCamelCase__    ,UpperCamelCase__   )

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

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

												a__    =  encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

												return _inference
					def 				lowerCamelCase__(   self     :List[Any]    ,__snake_case     :Dict    ,__snake_case     :Optional[int]    ,__snake_case     :Tuple   )      ->					List[str]:
												a__    =  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.'   )

												a__    =  (
												    hasattr(UpperCamelCase__    ,'architectures'   )
												    and isinstance(config.architectures    ,UpperCamelCase__   )
												    and len(config.architectures   ) > 0
												)
												if not self.args.only_pretrain_model and has_model_class_in_config:
																			try:
																										a__    =  '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
																										a__    =  __import__('transformers'    ,fromlist=[model_class]   )
																										a__    =  getattr(UpperCamelCase__    ,UpperCamelCase__   )
																										a__    =  model_cls(UpperCamelCase__   )
																			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:
																			a__    =  TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](UpperCamelCase__   )

												# encoder-decoder has vocab size saved differently
												a__    =  config.vocab_size if hasattr(UpperCamelCase__    ,'vocab_size'   ) else config.encoder.vocab_size
												a__    =  random_input_ids(UpperCamelCase__    ,UpperCamelCase__    ,UpperCamelCase__   )

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

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

												a__    =  encoder_decoder_train if config.is_encoder_decoder else encoder_train

												return _train
					def 				lowerCamelCase__(   self     :Any    ,__snake_case     :Tuple   )      ->					List[str]:
												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(UpperCamelCase__    ,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
																										a__    =  timeit.repeat(
																										    UpperCamelCase__    ,repeat=self.args.repeat    ,number=10    ,)

																										return min(UpperCamelCase__   ) / 10.0
																			except ResourceExhaustedError as e:
																										self.print_fn(F'Doesn\'t fit on GPU. {e}'   )



					def 				lowerCamelCase__(   self     :str    ,__snake_case     :Optional[Any]   )      ->					Tuple:
												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.'   )
																																	a__    =  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.'   )
																																								a__    =  '''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()
																																								a__    =  nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx   )
																																								a__    =  nvml.nvmlDeviceGetMemoryInfo(UpperCamelCase__   )
																																								a__    =  meminfo.used
																																								a__    =  Memory(UpperCamelCase__   )
																																								# 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.'   )
																																								a__    =  None
																																	else:
																																								a__    =  measure_peak_memory_cpu(UpperCamelCase__   )
																																								a__    =  Memory(UpperCamelCase__   ) if isinstance(UpperCamelCase__    ,UpperCamelCase__   ) else memory_bytes
																										if self.args.trace_memory_line_by_line:
																																	a__    =  stop_memory_tracing(UpperCamelCase__   )
																																	if memory is None:
																																								a__    =  summary.total
																										else:
																																	a__    =  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 							__lowercase     (				__lowerCAmelCase							:       int     ):

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

							a__    =  precision
							a__    =  ceil(precision / 1_4     )
							a__    =  4_2_6_8_8_0 * Decimal(1_0_0_0_5     ).sqrt()
							a__    =  1
							a__    =  1_3_5_9_1_4_0_9
							a__    =  Decimal(__lowerCAmelCase     )
							for k in range(1      ,					__lowerCAmelCase     ):
														a__    =  factorial(6 * k     ) // (factorial(3 * k     ) * factorial(__lowerCAmelCase     ) ** 3)
														linear_term += 5_4_5_1_4_0_1_3_4
														exponential_term *= -2_6_2_5_3_7_4_1_2_6_4_0_7_6_8_0_0_0
														partial_sum += Decimal(multinomial_term * linear_term     ) / exponential_term
							return str(constant_term / partial_sum     )[:-1]


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




