




"""simple docstring"""







import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


a      =							logging.get_logger(__name__)





def 							lowercase					(snake_case__   : str=None      ,       snake_case__   : List[Any]=None )     ->  Optional[Any]:


				'''simple docstring'''






				return field(default_factory=lambda: default      ,       metadata=snake_case__ )






@dataclass
class       SCREAMING_SNAKE_CASE__					:
						_a    						=						list_field(
						    default=[]						,				metadata={
						        'help': (
						            'Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'
						            ' of all available models'
						        )
						    }						,				)

						_a    						=						list_field(
						    default=[8]						,				metadata={'help': 'List of batch sizes for which memory and time performance will be evaluated'}					)

						_a    						=						list_field(
						    default=[8, 32, 1_28, 5_12]						,				metadata={'help': 'List of sequence lengths for which memory and time performance will be evaluated'}						,				)

						_a    						=						field(
						    default=_a						,				metadata={'help': 'Whether to benchmark inference of model. Inference can be disabled via --no-inference.'}						,				)
						_a    						=						field(
						    default=_a						,				metadata={'help': 'Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'}						,				)
						_a    						=						field(
						    default=_a						,				metadata={'help': 'Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'}					)
						_a    						=						field(default=_a						,				metadata={'help': 'Use FP16 to accelerate inference.'}					)
						_a    						=						field(default=_a						,				metadata={'help': 'Benchmark training of model'}					)
						_a    						=						field(default=_a						,				metadata={'help': 'Verbose memory tracing'}					)
						_a    						=						field(
						    default=_a						,				metadata={'help': 'Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'}						,				)
						_a    						=						field(
						    default=_a						,				metadata={
						        'help': 'Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'
						    }						,				)
						_a    						=						field(default=_a						,				metadata={'help': 'Trace memory line by line'}					)
						_a    						=						field(default=_a						,				metadata={'help': 'Save result to a CSV file'}					)
						_a    						=						field(default=_a						,				metadata={'help': 'Save all print statements in a log file'}					)
						_a    						=						field(default=_a						,				metadata={'help': 'Whether to print environment information'}					)
						_a    						=						field(
						    default=_a						,				metadata={
						        'help': (
						            'Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'
						            ' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'
						            ' for debugging / testing and on TPU.'
						        )
						    }						,				)
						_a    						=						field(
						    default=f"""inference_time_{round(time()					)}.csv"""						,				metadata={'help': 'CSV filename used if saving time results to csv.'}						,				)
						_a    						=						field(
						    default=f"""inference_memory_{round(time()					)}.csv"""						,				metadata={'help': 'CSV filename used if saving memory results to csv.'}						,				)
						_a    						=						field(
						    default=f"""train_time_{round(time()					)}.csv"""						,				metadata={'help': 'CSV filename used if saving time results to csv for training.'}						,				)
						_a    						=						field(
						    default=f"""train_memory_{round(time()					)}.csv"""						,				metadata={'help': 'CSV filename used if saving memory results to csv for training.'}						,				)
						_a    						=						field(
						    default=f"""env_info_{round(time()					)}.csv"""						,				metadata={'help': 'CSV filename used if saving environment information.'}						,				)
						_a    						=						field(
						    default=f"""log_{round(time()					)}.csv"""						,				metadata={'help': 'Log filename used if print statements are saved in log.'}						,				)
						_a    						=						field(default=3						,				metadata={'help': 'Times an experiment will be run.'}					)
						_a    						=						field(
						    default=_a						,				metadata={
						        'help': (
						            'Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'
						            ' model weights.'
						        )
						    }						,				)


						def   __lowercase (							self				:			Any					):
										warnings.warn(
										    f'''The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'''
										    """ are deprecated in general and it is advised to use external Benchmarking libraries """
										    """ to benchmark Transformer models."""						,						lowerCAmelCase						,						)


						def   __lowercase (							self				:			List[str]					):
										return json.dumps(dataclasses.asdict(self					)						,						indent=2					)


						@property
						def   __lowercase (							self				:			int					):
										if len(self.models					) <= 0:
														raise ValueError(
														    """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
														    """ bert-base-cased` or `args.models = ['bert-base-cased']."""					)
										return self.models




						@property
						def   __lowercase (							self				:			Optional[Any]					):
										if not self.multi_process:
														return False
										elif self.is_tpu:
														logger.info("""Multiprocessing is currently not possible on TPU."""					)
														return False
										else:
														return True











"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a      =							{
    'configuration_owlvit': [
        'OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'OwlViTConfig',
        'OwlViTOnnxConfig',
        'OwlViTTextConfig',
        'OwlViTVisionConfig',
    ],
    'processing_owlvit': ['OwlViTProcessor'],
}


try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							['OwlViTFeatureExtractor']
		a      =							['OwlViTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							[
		    'OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'OwlViTModel',
		    'OwlViTPreTrainedModel',
		    'OwlViTTextModel',
		    'OwlViTVisionModel',
		    'OwlViTForObjectDetection',
		]

if TYPE_CHECKING:
		from .configuration_owlvit import (
		    OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    OwlViTConfig,
		    OwlViTOnnxConfig,
		    OwlViTTextConfig,
		    OwlViTVisionConfig,
		)
		from .processing_owlvit import OwlViTProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_owlvit import OwlViTFeatureExtractor
				from .image_processing_owlvit import OwlViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_owlvit import (
				    OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OwlViTForObjectDetection,
				    OwlViTModel,
				    OwlViTPreTrainedModel,
				    OwlViTTextModel,
				    OwlViTVisionModel,
				)

else:
		import sys

		a      =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





