





import gc
import threading
import time

import psutil
import torch

class 	A_       :
	def __init__(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				psutil.Process()
				UpperCAmelCase   		=				False
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				-1

				while True:
							UpperCAmelCase   		=				max(self.process.memory_info().rss							,					self.cpu_memory_peak		)

							# can't sleep or will not catch the peak right (this comment is here on purpose)
							if not self.peak_monitoring:
										break
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				True
				UpperCAmelCase   		=				threading.Thread(target=self.peak_monitor		)
				UpperCAmelCase   		=				True
				self.thread.start()







	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				False
				self.thread.join()
				return self.cpu_memory_peak
__A				:    str       =   PeakCPUMemory()





def 						__SCREAMING_SNAKE_CASE  (				)  ->				Any:







			'''simple docstring'''

			UpperCAmelCase   		=				{'''time''': time.time()}

			gc.collect()
			torch.cuda.empty_cache()

			# CPU mem
			UpperCAmelCase   		=				psutil.Process().memory_info().rss
			cpu_peak_tracker.start()

			# GPU mem
			for i in range(torch.cuda.device_count() ):
						UpperCAmelCase   		=				torch.cuda.memory_allocated(UpperCamelCase__ )
			torch.cuda.reset_peak_memory_stats()

			return measures





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				List[Any]:







			'''simple docstring'''

			UpperCAmelCase   		=				{'''time''': time.time() - start_measures['''time''']}

			gc.collect()
			torch.cuda.empty_cache()

			# CPU mem
			UpperCAmelCase   		=				(psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
			UpperCAmelCase   		=				(cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20

			# GPU mem
			for i in range(torch.cuda.device_count() ):
						UpperCAmelCase   		=				(torch.cuda.memory_allocated(UpperCamelCase__ ) - start_measures[str(UpperCamelCase__ )]) / 2**20
						UpperCAmelCase   		=				(torch.cuda.max_memory_allocated(UpperCamelCase__ ) - start_measures[str(UpperCamelCase__ )]) / 2**20

			return measures






def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				List[Any]:







			'''simple docstring'''

			print(F"""{description}:""" )
			print(F"""- Time: {measures["time"]:.2f}s""" )
			for i in range(torch.cuda.device_count() ):
						print(F"""- GPU {i} allocated: {measures[str(UpperCamelCase__ )]:.2f}MiB""" )
						UpperCAmelCase   		=				measures[F"""{i}-peak"""]
						print(F"""- GPU {i} peak: {peak:.2f}MiB""" )
			print(F"""- CPU RAM allocated: {measures["cpu"]:.2f}MiB""" )
			print(F"""- CPU RAM peak: {measures["cpu-peak"]:.2f}MiB""" )










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


__A				:    Union[str, Any]       =   {
    "configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig"],
    "processing_speech_to_text": ["Speech2TextProcessor"],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    List[Any]       =   ["Speech2TextTokenizer"]

try:
				if not is_speech_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    List[Any]       =   ["Speech2TextFeatureExtractor"]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    int       =   [
				    "TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFSpeech2TextForConditionalGeneration",
				    "TFSpeech2TextModel",
				    "TFSpeech2TextPreTrainedModel",
				]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    Tuple       =   [
				    "SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "Speech2TextForConditionalGeneration",
				    "Speech2TextModel",
				    "Speech2TextPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
				from .processing_speech_to_text import SpeechaTextProcessor

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_speech_to_text import SpeechaTextTokenizer

				try:
								if not is_speech_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_speech_to_text import (
								    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFSpeechaTextForConditionalGeneration,
								    TFSpeechaTextModel,
								    TFSpeechaTextPreTrainedModel,
								)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_speech_to_text import (
								    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SpeechaTextForConditionalGeneration,
								    SpeechaTextModel,
								    SpeechaTextPreTrainedModel,
								)

else:
				import sys

				__A				:    Dict       =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



