





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 lowerCAmelCase__(__snake_case ,__snake_case							)       ->   Optional[Any]:







	'''simple docstring'''







	def run_func(__snake_case							):
		@wraps(__UpperCamelCase							)
		def run_in_eager_mode(*__snake_case ,**__snake_case							):
			return func(*__UpperCamelCase ,**__UpperCamelCase							)

		@wraps(__UpperCamelCase							)
		@tf.function(experimental_compile=__UpperCamelCase							)
		def run_in_graph_mode(*__snake_case ,**__snake_case							):
			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 lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case							)       ->   ["tf.Tensor"]:







	'''simple docstring'''







	lowerCamelCase__              =	random.Random()
	lowerCamelCase__              =	[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       __A					(					SCREAMING_SNAKE_CASE_				):

			'''simple docstring'''







			lowerCAmelCase_							  =		42
			lowerCAmelCase_							  =		42
			lowerCAmelCase_							  =		"""TensorFlow"""





			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				return tf.__version__





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	self.args.strategy
				if strategy is None:
					raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''							)
				lowerCamelCase__              =	self._prepare_inference_func(UpperCamelCase__ ,						UpperCamelCase__ ,						UpperCamelCase__							)
				return self._measure_speed(_inference							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	self.args.strategy
				if strategy is None:
					raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''							)
				lowerCamelCase__              =	self._prepare_train_func(UpperCamelCase__ ,						UpperCamelCase__ ,						UpperCamelCase__							)
				return self._measure_speed(_train							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				if self.args.is_gpu:
					tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] ,						UpperCamelCase__							)
				lowerCamelCase__              =	self.args.strategy
				if strategy is None:
					raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''							)
				lowerCamelCase__              =	self._prepare_inference_func(UpperCamelCase__ ,						UpperCamelCase__ ,						UpperCamelCase__							)
				return self._measure_memory(_inference							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				if self.args.is_gpu:
					tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] ,						UpperCamelCase__							)
				lowerCamelCase__              =	self.args.strategy
				if strategy is None:
					raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''							)

				lowerCamelCase__              =	self._prepare_train_func(UpperCamelCase__ ,						UpperCamelCase__ ,						UpperCamelCase__							)
				return self._measure_memory(_train							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	self.config_dict[model_name]

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

				lowerCamelCase__              =	(
				    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:
						lowerCamelCase__              =	'''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
						lowerCamelCase__              =	__import__('''transformers''' ,						fromlist=[model_class]							)
						lowerCamelCase__              =	getattr(UpperCamelCase__ ,						UpperCamelCase__							)
						lowerCamelCase__              =	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:
					lowerCamelCase__              =	TF_MODEL_MAPPING[config.__class__](UpperCamelCase__							)

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

				lowerCamelCase__              =	encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

				return _inference





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	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.'''							)

				lowerCamelCase__              =	(
				    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:
						lowerCamelCase__              =	'''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
						lowerCamelCase__              =	__import__('''transformers''' ,						fromlist=[model_class]							)
						lowerCamelCase__              =	getattr(UpperCamelCase__ ,						UpperCamelCase__							)
						lowerCamelCase__              =	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:
					lowerCamelCase__              =	TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](UpperCamelCase__							)

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

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

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

				lowerCamelCase__              =	encoder_decoder_train if config.is_encoder_decoder else encoder_train

				return _train





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''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
						lowerCamelCase__              =	timeit.repeat(
						    UpperCamelCase__ ,						repeat=self.args.repeat ,						number=1_0 ,						)

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





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''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.'''							)
							lowerCamelCase__              =	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.'''							)
								lowerCamelCase__              =	'''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()
								lowerCamelCase__              =	nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx							)
								lowerCamelCase__              =	nvml.nvmlDeviceGetMemoryInfo(UpperCamelCase__							)
								lowerCamelCase__              =	meminfo.used
								lowerCamelCase__              =	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.'''							)
								lowerCamelCase__              =	None
							else:
								lowerCamelCase__              =	measure_peak_memory_cpu(UpperCamelCase__							)
								lowerCamelCase__              =	Memory(UpperCamelCase__							) if isinstance(UpperCamelCase__ ,						UpperCamelCase__							) else memory_bytes
						if self.args.trace_memory_line_by_line:
							lowerCamelCase__              =	stop_memory_tracing(UpperCamelCase__							)
							if memory is None:
								lowerCamelCase__              =	summary.total
						else:
							lowerCamelCase__              =	None

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










def lowerCAmelCase__(__snake_case							)       ->   int:







	'''simple docstring'''







	if not grid or not grid[0]:
		raise TypeError('''The grid does not contain the appropriate information'''							)

	for cell_n in range(1 ,len(grid[0]							)							):
		grid[0][cell_n] += grid[0][cell_n - 1]
	lowerCamelCase__              =	grid[0]

	for row_n in range(1 ,len(__snake_case							)							):
		lowerCamelCase__              =	grid[row_n]
		lowerCamelCase__              =	fill_row(__snake_case ,__snake_case							)
		lowerCamelCase__              =	grid[row_n]

	return grid[-1][-1]





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   list:







	'''simple docstring'''







	current_row[0] += row_above[0]
	for cell_n in range(1 ,len(__snake_case							)							):
		current_row[cell_n] += min(current_row[cell_n - 1] ,row_above[cell_n]							)

	return current_row


if __name__ == "__main__":
			import doctest

			doctest.testmod()



