
'''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


lowerCAmelCase					:    List[Any]           = logging.get_logger(__name__)







def 		A_(     A						:		str=None		,     A						:		Union[str, Any]=None):
							return field(default_factory=lambda: default		,     metadata=A)





@dataclass
class 			SCREAMING_SNAKE_CASE__      :
				lowerCAmelCase_     			=							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"""
				        )
				    }							,       )

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

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

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







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											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.'						,				A_						,				)







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											return json.dumps(dataclasses.asdict(self     )						,				indent=2     )







				@property
				def 						UpperCAmelCase_    (       self     )->		List[str]:







											'''simple docstring'''






											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 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											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_torch_available, is_vision_available


_UpperCAmelCase    :     Any         =       {
    '''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
    '''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     str         =       ['''MaskFormerFeatureExtractor''']
   _UpperCAmelCase    :     str         =       ['''MaskFormerImageProcessor''']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     Tuple         =       [
       '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''MaskFormerForInstanceSegmentation''',
       '''MaskFormerModel''',
       '''MaskFormerPreTrainedModel''',
   ]
   _UpperCAmelCase    :     int         =       [
       '''MaskFormerSwinBackbone''',
       '''MaskFormerSwinModel''',
       '''MaskFormerSwinPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
   from .configuration_maskformer_swin import MaskFormerSwinConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_maskformer import MaskFormerFeatureExtractor
      from .image_processing_maskformer import MaskFormerImageProcessor
   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_maskformer import (
          MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaskFormerForInstanceSegmentation,
          MaskFormerModel,
          MaskFormerPreTrainedModel,
      )
      from .modeling_maskformer_swin import (
          MaskFormerSwinBackbone,
          MaskFormerSwinModel,
          MaskFormerSwinPreTrainedModel,
      )


else:
   import sys

   _UpperCAmelCase    :     str         =       _LazyModule(__name__, globals()['''__file__'''], _import_structure)



