
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		(				snake_case__:   bool		,  snake_case__:   bool   ):


				'''simple docstring'''
				def run_func(snake_case__:   List[Any]   ):
								@wraps(snake_case__   )
								def run_in_eager_mode(*snake_case__:   List[Any]		,  **snake_case__:   str   ):
												return func(*snake_case__		,  **snake_case__   )

								@wraps(snake_case__   )
								@tf.function(experimental_compile=snake_case__   )
								def run_in_graph_mode(*snake_case__:   str		,  **snake_case__:   Dict   ):
												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		(				snake_case__:   int		,  snake_case__:   int		,  snake_case__:   int   ):


				'''simple docstring'''
				lowercase_	    =   random.Random()
				lowercase_	    =   [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__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :TensorFlowBenchmarkArguments
	a     :PretrainedConfig
	a     :str       	=      "TensorFlow"
	@property
	def 		_lowercase    (			self    :     Tuple			)			->	Dict:
					return tf.__version__
	def 		_lowercase    (			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     int			)			->	float:
					# initialize GPU on separate process
					lowercase_	    =   self.args.strategy
					if strategy is None:
									raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''			)
					lowercase_	    =   self._prepare_inference_func(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
					return self._measure_speed(_inference			)
	def 		_lowercase    (			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     int			)			->	float:
					lowercase_	    =   self.args.strategy
					if strategy is None:
									raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''			)
					lowercase_	    =   self._prepare_train_func(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
					return self._measure_speed(_train			)
	def 		_lowercase    (			self    :     Any			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     int			)			->	[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]			,				SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   self.args.strategy
					if strategy is None:
									raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''			)
					lowercase_	    =   self._prepare_inference_func(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
					return self._measure_memory(_inference			)
	def 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     int			)			->	[Memory, Optional[MemorySummary]]:
					if self.args.is_gpu:
									tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]			,				SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   self.args.strategy
					if strategy is None:
									raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''			)

					lowercase_	    =   self._prepare_train_func(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
					return self._measure_memory(_train			)
	def 		_lowercase    (			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     int			)			->	Callable[[], None]:
					lowercase_	    =   self.config_dict[model_name]

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

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

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

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

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

					lowercase_	    =   encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

					return _inference
	def 		_lowercase    (			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     int			)			->	Callable[[], None]:
					lowercase_	    =   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.'''			)

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

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

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

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

					lowercase_	    =   encoder_decoder_train if config.is_encoder_decoder else encoder_train

					return _train
	def 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     Tuple			)			->	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(SCREAMING_SNAKE_CASE_			,				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
													lowercase_	    =   timeit.repeat(
													    SCREAMING_SNAKE_CASE_			,				repeat=self.args.repeat			,				number=1_0			,				)

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




	def 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Callable[[], None]			)			->	[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.'''			)
																	lowercase_	    =   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.'''			)
																					lowercase_	    =   '''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()
																					lowercase_	    =   nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx			)
																					lowercase_	    =   nvml.nvmlDeviceGetMemoryInfo(SCREAMING_SNAKE_CASE_			)
																					lowercase_	    =   meminfo.used
																					lowercase_	    =   Memory(SCREAMING_SNAKE_CASE_			)
																					# 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.'''			)
																					lowercase_	    =   None
																	else:
																					lowercase_	    =   measure_peak_memory_cpu(SCREAMING_SNAKE_CASE_			)
																					lowercase_	    =   Memory(SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			) else memory_bytes
													if self.args.trace_memory_line_by_line:
																	lowercase_	    =   stop_memory_tracing(SCREAMING_SNAKE_CASE_			)
																	if memory is None:
																					lowercase_	    =   summary.total
													else:
																	lowercase_	    =   None

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








"""simple docstring"""


from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							float = 1 / sqrt(2  )  ):
 lowercase__       :       str				=						tau * frequency / samplerate
 lowercase__       :       Union[str, Any]				=						sin(lowerCamelCase__  )
 lowercase__       :       int				=						cos(lowerCamelCase__  )
 lowercase__       :       Union[str, Any]				=						_sin / (2 * q_factor)

 lowercase__       :       str				=						(1 - _cos) / 2
 lowercase__       :       Optional[Any]				=						1 - _cos

 lowercase__       :       List[Any]				=						1 + alpha
 lowercase__       :       Dict				=						-2 * _cos
 lowercase__       :       List[str]				=						1 - alpha

 lowercase__       :       int				=						IIRFilter(2  )
 filt.set_coefficients([aa, aa, aa]       ,		[ba, ba, ba]  )
 return filt


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							float = 1 / sqrt(2  )  ):
 lowercase__       :       Optional[int]				=						tau * frequency / samplerate
 lowercase__       :       Any				=						sin(lowerCamelCase__  )
 lowercase__       :       Any				=						cos(lowerCamelCase__  )
 lowercase__       :       Tuple				=						_sin / (2 * q_factor)

 lowercase__       :       Tuple				=						(1 + _cos) / 2
 lowercase__       :       str				=						-1 - _cos

 lowercase__       :       Dict				=						1 + alpha
 lowercase__       :       Dict				=						-2 * _cos
 lowercase__       :       int				=						1 - alpha

 lowercase__       :       str				=						IIRFilter(2  )
 filt.set_coefficients([aa, aa, aa]       ,		[ba, ba, ba]  )
 return filt


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							float = 1 / sqrt(2  )  ):
 lowercase__       :       Optional[int]				=						tau * frequency / samplerate
 lowercase__       :       str				=						sin(lowerCamelCase__  )
 lowercase__       :       int				=						cos(lowerCamelCase__  )
 lowercase__       :       Dict				=						_sin / (2 * q_factor)

 lowercase__       :       Dict				=						_sin / 2
 lowercase__       :       Union[str, Any]				=						0
 lowercase__       :       int				=						-ba

 lowercase__       :       List[Any]				=						1 + alpha
 lowercase__       :       Tuple				=						-2 * _cos
 lowercase__       :       List[str]				=						1 - alpha

 lowercase__       :       Dict				=						IIRFilter(2  )
 filt.set_coefficients([aa, aa, aa]       ,		[ba, ba, ba]  )
 return filt


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							float = 1 / sqrt(2  )  ):
 lowercase__       :       str				=						tau * frequency / samplerate
 lowercase__       :       Dict				=						sin(lowerCamelCase__  )
 lowercase__       :       Union[str, Any]				=						cos(lowerCamelCase__  )
 lowercase__       :       Tuple				=						_sin / (2 * q_factor)

 lowercase__       :       List[Any]				=						1 - alpha
 lowercase__       :       Any				=						-2 * _cos
 lowercase__       :       Optional[Any]				=						1 + alpha

 lowercase__       :       List[Any]				=						IIRFilter(2  )
 filt.set_coefficients([ba, ba, ba]       ,		[ba, ba, ba]  )
 return filt


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							float       ,		lowerCamelCase__	:							float = 1 / sqrt(2  )       ,		):
 lowercase__       :       Tuple				=						tau * frequency / samplerate
 lowercase__       :       str				=						sin(lowerCamelCase__  )
 lowercase__       :       List[str]				=						cos(lowerCamelCase__  )
 lowercase__       :       Optional[int]				=						_sin / (2 * q_factor)
 lowercase__       :       Optional[Any]				=						10 ** (gain_db / 40)

 lowercase__       :       Optional[Any]				=						1 + alpha * big_a
 lowercase__       :       Dict				=						-2 * _cos
 lowercase__       :       Tuple				=						1 - alpha * big_a
 lowercase__       :       List[str]				=						1 + alpha / big_a
 lowercase__       :       List[Any]				=						-2 * _cos
 lowercase__       :       Optional[int]				=						1 - alpha / big_a

 lowercase__       :       Union[str, Any]				=						IIRFilter(2  )
 filt.set_coefficients([aa, aa, aa]       ,		[ba, ba, ba]  )
 return filt


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							float       ,		lowerCamelCase__	:							float = 1 / sqrt(2  )       ,		):
 lowercase__       :       Optional[int]				=						tau * frequency / samplerate
 lowercase__       :       Dict				=						sin(lowerCamelCase__  )
 lowercase__       :       Union[str, Any]				=						cos(lowerCamelCase__  )
 lowercase__       :       int				=						_sin / (2 * q_factor)
 lowercase__       :       Union[str, Any]				=						10 ** (gain_db / 40)
 lowercase__       :       Dict				=						(big_a + 1) - (big_a - 1) * _cos
 lowercase__       :       Union[str, Any]				=						(big_a + 1) + (big_a - 1) * _cos
 lowercase__       :       List[Any]				=						(big_a - 1) - (big_a + 1) * _cos
 lowercase__       :       Dict				=						(big_a - 1) + (big_a + 1) * _cos
 lowercase__       :       Optional[int]				=						2 * sqrt(lowerCamelCase__  ) * alpha

 lowercase__       :       str				=						big_a * (pmc + aaa)
 lowercase__       :       Optional[Any]				=						2 * big_a * mpc
 lowercase__       :       str				=						big_a * (pmc - aaa)
 lowercase__       :       Dict				=						ppmc + aaa
 lowercase__       :       List[Any]				=						-2 * pmpc
 lowercase__       :       List[Any]				=						ppmc - aaa

 lowercase__       :       Optional[int]				=						IIRFilter(2  )
 filt.set_coefficients([aa, aa, aa]       ,		[ba, ba, ba]  )
 return filt






def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							float       ,		lowerCamelCase__	:							float = 1 / sqrt(2  )       ,		):
 lowercase__       :       int				=						tau * frequency / samplerate
 lowercase__       :       List[Any]				=						sin(lowerCamelCase__  )
 lowercase__       :       List[Any]				=						cos(lowerCamelCase__  )
 lowercase__       :       List[str]				=						_sin / (2 * q_factor)
 lowercase__       :       int				=						10 ** (gain_db / 40)
 lowercase__       :       str				=						(big_a + 1) - (big_a - 1) * _cos
 lowercase__       :       Optional[Any]				=						(big_a + 1) + (big_a - 1) * _cos
 lowercase__       :       Optional[int]				=						(big_a - 1) - (big_a + 1) * _cos
 lowercase__       :       int				=						(big_a - 1) + (big_a + 1) * _cos
 lowercase__       :       Optional[Any]				=						2 * sqrt(lowerCamelCase__  ) * alpha

 lowercase__       :       str				=						big_a * (ppmc + aaa)
 lowercase__       :       Union[str, Any]				=						-2 * big_a * pmpc
 lowercase__       :       List[Any]				=						big_a * (ppmc - aaa)
 lowercase__       :       str				=						pmc + aaa
 lowercase__       :       int				=						2 * mpc
 lowercase__       :       Any				=						pmc - aaa

 lowercase__       :       int				=						IIRFilter(2  )
 filt.set_coefficients([aa, aa, aa]       ,		[ba, ba, ba]  )
 return filt