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       =						logging.get_logger(__name__)



def 	__a					(   lowerCAmelCase_       : bool      ,lowerCAmelCase_       : bool    )				->			Union[str, Any]:






						'''simple docstring'''



						def run_func(lowerCAmelCase_       : Any    ):
												@wraps(snake_case__    )
												def run_in_eager_mode(*lowerCAmelCase_       : Dict      ,**lowerCAmelCase_       : Tuple    ):
																		return func(*snake_case__      ,**snake_case__    )

												@wraps(snake_case__    )
												@tf.function(experimental_compile=snake_case__    )
												def run_in_graph_mode(*lowerCAmelCase_       : int      ,**lowerCAmelCase_       : Any    ):
																		return func(*snake_case__      ,**snake_case__    )

												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 	__a					(   lowerCAmelCase_       : int      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : int    )				->			["tf.Tensor"]:






						'''simple docstring'''



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






class 		lowercase						(			__lowerCAmelCase):






	"""simple docstring"""


	a__				: TensorFlowBenchmarkArguments
	a__				: PretrainedConfig
	a__				: str		       =							"TensorFlow"




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			Dict:
							return tf.__version__




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						Optional[int]						,  __UpperCAmelCase     :						Optional[int]						,  __UpperCAmelCase     :						Optional[int]					)      ->			float:
							# initialize GPU on separate process
							UpperCAmelCase_=       self.args.strategy
							if strategy is None:
													raise ValueError("""A device strategy has to be initialized before using TensorFlow."""					)
							UpperCAmelCase_=       self._prepare_inference_func(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_					)
							return self._measure_speed(_inference					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]						,  __UpperCAmelCase     :						List[Any]						,  __UpperCAmelCase     :						List[Any]						,  __UpperCAmelCase     :						List[Any]					)      ->			float:
							UpperCAmelCase_=       self.args.strategy
							if strategy is None:
													raise ValueError("""A device strategy has to be initialized before using TensorFlow."""					)
							UpperCAmelCase_=       self._prepare_train_func(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_					)
							return self._measure_speed(_train					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]						,  __UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						Union[str, Any]						,  __UpperCAmelCase     :						List[str]					)      ->			[Memory, Optional[MemorySummary]]:
							# initialize GPU on separate process
							if self.args.is_gpu:
													tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]						,  lowerCAmelCase_					)
							UpperCAmelCase_=       self.args.strategy
							if strategy is None:
													raise ValueError("""A device strategy has to be initialized before using TensorFlow."""					)
							UpperCAmelCase_=       self._prepare_inference_func(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_					)
							return self._measure_memory(_inference					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  __UpperCAmelCase     :						Union[str, Any]						,  __UpperCAmelCase     :						List[str]						,  __UpperCAmelCase     :						Any					)      ->			[Memory, Optional[MemorySummary]]:
							if self.args.is_gpu:
													tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]						,  lowerCAmelCase_					)
							UpperCAmelCase_=       self.args.strategy
							if strategy is None:
													raise ValueError("""A device strategy has to be initialized before using TensorFlow."""					)

							UpperCAmelCase_=       self._prepare_train_func(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_					)
							return self._measure_memory(_train					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  __UpperCAmelCase     :						str						,  __UpperCAmelCase     :						List[Any]						,  __UpperCAmelCase     :						str					)      ->			Callable[[], None]:
							UpperCAmelCase_=       self.config_dict[model_name]

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

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

							# encoder-decoder has vocab size saved differently
							UpperCAmelCase_=       config.vocab_size if hasattr(lowerCAmelCase_						,  """vocab_size"""					) else config.encoder.vocab_size
							UpperCAmelCase_=       random_input_ids(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_					)

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

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

							UpperCAmelCase_=       encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

							return _inference




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]						,  __UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						Dict					)      ->			Callable[[], None]:
							UpperCAmelCase_=       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."""					)

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

							# encoder-decoder has vocab size saved differently
							UpperCAmelCase_=       config.vocab_size if hasattr(lowerCAmelCase_						,  """vocab_size"""					) else config.encoder.vocab_size
							UpperCAmelCase_=       random_input_ids(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_					)

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

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

							UpperCAmelCase_=       encoder_decoder_train if config.is_encoder_decoder else encoder_train

							return _train




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict						,  __UpperCAmelCase     :						str					)      ->			float:
							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(lowerCAmelCase_						,  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
																			UpperCAmelCase_=       timeit.repeat(
																			    lowerCAmelCase_						,  repeat=self.args.repeat						,  number=10						,  )

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						str						,  __UpperCAmelCase     :						List[Any]					)      ->			[Memory, MemorySummary]:
							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."""					)
																									UpperCAmelCase_=       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."""					)
																															UpperCAmelCase_=       """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()
																															UpperCAmelCase_=       nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx					)
																															UpperCAmelCase_=       nvml.nvmlDeviceGetMemoryInfo(lowerCAmelCase_					)
																															UpperCAmelCase_=       meminfo.used
																															UpperCAmelCase_=       Memory(lowerCAmelCase_					)
																															# 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."""					)
																															UpperCAmelCase_=       None
																									else:
																															UpperCAmelCase_=       measure_peak_memory_cpu(lowerCAmelCase_					)
																															UpperCAmelCase_=       Memory(lowerCAmelCase_					) if isinstance(lowerCAmelCase_						,  lowerCAmelCase_					) else memory_bytes
																			if self.args.trace_memory_line_by_line:
																									UpperCAmelCase_=       stop_memory_tracing(lowerCAmelCase_					)
																									if memory is None:
																															UpperCAmelCase_=       summary.total
																			else:
																									UpperCAmelCase_=       None

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









__A       =						6_5521



def 	__a					(   lowerCAmelCase_       : str    )				->			int:






						'''simple docstring'''



						UpperCAmelCase_=       1
						UpperCAmelCase_=       0
						for plain_chr in plain_text:
												UpperCAmelCase_=       (a + ord(lowerCAmelCase_    )) % MOD_ADLER
												UpperCAmelCase_=       (b + a) % MOD_ADLER
						return (b << 16) | a




