






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

__a      :       Dict				  =   logging.get_logger(__name__)



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			bool			,				__lowercase   :			bool    )						->      str:





    """simple docstring"""

    def run_func(__lowercase   :			int    ):
        @wraps(__lowercase    )
        def run_in_eager_mode(*__lowercase   :			Dict			,				**__lowercase   :			Any    ):
            return func(*__lowercase			,				**__lowercase    )

        @wraps(__lowercase    )
        @tf.function(experimental_compile=__lowercase    )
        def run_in_graph_mode(*__lowercase   :			List[Any]			,				**__lowercase   :			Any    ):
            return func(*__lowercase			,				**__lowercase    )

        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 			_SCREAMING_SNAKE_CASE						( __lowercase   :			int			,				__lowercase   :			int			,				__lowercase   :			int    )						->      ["tf.Tensor"]:





    """simple docstring"""

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






class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				42
     SCREAMING_SNAKE_CASE   						=				42
     SCREAMING_SNAKE_CASE   						=				"TensorFlow"




     @property
     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



         return tf.__version__




     def 		lowerCAmelCase_     (				self       :			int ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			int				):


         """simple docstring"""



         __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       :			Optional[Any] ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			int				):


         """simple docstring"""



         __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       :			int ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			int				):


         """simple docstring"""



         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       :			Optional[int] ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			int				):


         """simple docstring"""



         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 ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			int				):


         """simple docstring"""



         __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       :			int ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			int				):


         """simple docstring"""



         __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       :			List[str] ,	UpperCamelCase_       :			Tuple				):


         """simple docstring"""



         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       :			Tuple ,	UpperCamelCase_       :			Callable[[], None]				):


         """simple docstring"""



         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












def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[Any]			,				__lowercase   :			Dict			,				__lowercase   :			str    )						->      Dict:





    """simple docstring"""

    if n == 0:
        return 1

    elif n % 2 == 1:
        return (binary_exponentiation(__lowercase			,				n - 1			,				__lowercase    ) * a) % mod

    else:
        __A         =	binary_exponentiation(__lowercase			,				n / 2			,				__lowercase    )
        return (b * b) % mod


# a prime number
__a      :       Union[str, Any]				  =   701

__a      :       Dict				  =   1000000000
__a      :       Union[str, Any]				  =   10

# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)






print((a / b) % p == (a * b ** (p - 2)) % p)




