



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A       =						logging.get_logger(__name__)

__A       =						{
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}






class 		lowercase						(			SCREAMING_SNAKE_CASE__):






	"""simple docstring"""


	a__				: List[str]		       =							"beit"




	def __init__(     self     :						List[Any]						,  __UpperCAmelCase     :						Any=8_192						,  __UpperCAmelCase     :						str=768						,  __UpperCAmelCase     :						str=12						,  __UpperCAmelCase     :						Optional[int]=12						,  __UpperCAmelCase     :						Optional[int]=3_072						,  __UpperCAmelCase     :						Optional[int]="gelu"						,  __UpperCAmelCase     :						Optional[int]=0.0						,  __UpperCAmelCase     :						int=0.0						,  __UpperCAmelCase     :						Dict=0.02						,  __UpperCAmelCase     :						Any=1E-12						,  __UpperCAmelCase     :						Tuple=224						,  __UpperCAmelCase     :						Optional[int]=16						,  __UpperCAmelCase     :						Optional[int]=3						,  __UpperCAmelCase     :						List[str]=False						,  __UpperCAmelCase     :						Optional[int]=False						,  __UpperCAmelCase     :						int=False						,  __UpperCAmelCase     :						str=False						,  __UpperCAmelCase     :						int=0.1						,  __UpperCAmelCase     :						str=0.1						,  __UpperCAmelCase     :						Tuple=True						,  __UpperCAmelCase     :						Optional[int]=[3, 5, 7, 11]						,  __UpperCAmelCase     :						int=[1, 2, 3, 6]						,  __UpperCAmelCase     :						Any=True						,  __UpperCAmelCase     :						Union[str, Any]=0.4						,  __UpperCAmelCase     :						int=256						,  __UpperCAmelCase     :						Optional[Any]=1						,  __UpperCAmelCase     :						List[Any]=False						,  __UpperCAmelCase     :						List[Any]=255						,  **__UpperCAmelCase     :						str						,  )      ->			Optional[Any]:
							super().__init__(**__UpperCAmelCase					)

							UpperCAmelCase_=       vocab_size
							UpperCAmelCase_=       hidden_size
							UpperCAmelCase_=       num_hidden_layers
							UpperCAmelCase_=       num_attention_heads
							UpperCAmelCase_=       intermediate_size
							UpperCAmelCase_=       hidden_act
							UpperCAmelCase_=       hidden_dropout_prob
							UpperCAmelCase_=       attention_probs_dropout_prob
							UpperCAmelCase_=       initializer_range
							UpperCAmelCase_=       layer_norm_eps

							UpperCAmelCase_=       image_size
							UpperCAmelCase_=       patch_size
							UpperCAmelCase_=       num_channels
							UpperCAmelCase_=       use_mask_token
							UpperCAmelCase_=       use_absolute_position_embeddings
							UpperCAmelCase_=       use_relative_position_bias
							UpperCAmelCase_=       use_shared_relative_position_bias
							UpperCAmelCase_=       layer_scale_init_value
							UpperCAmelCase_=       drop_path_rate
							UpperCAmelCase_=       use_mean_pooling
							# decode head attributes (semantic segmentation)
							UpperCAmelCase_=       out_indices
							UpperCAmelCase_=       pool_scales
							# auxiliary head attributes (semantic segmentation)
							UpperCAmelCase_=       use_auxiliary_head
							UpperCAmelCase_=       auxiliary_loss_weight
							UpperCAmelCase_=       auxiliary_channels
							UpperCAmelCase_=       auxiliary_num_convs
							UpperCAmelCase_=       auxiliary_concat_input
							UpperCAmelCase_=       semantic_loss_ignore_index






class 		lowercase						(			SCREAMING_SNAKE_CASE__):






	"""simple docstring"""


	a__				: List[Any]		       =							version.parse("1.11")




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
							    ]					)




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			float:
							return 1E-4









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 	__a					(   lowerCAmelCase_       : Tuple=None      ,lowerCAmelCase_       : Optional[Any]=None    )				->			Tuple:






						'''simple docstring'''



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






@dataclass
class 		lowercase						:






	"""simple docstring"""


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

	a__				: List[int]		       =							list_field(
	    default=[8, 32, 128, 512]			,   metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}			,   )

	a__				: bool		       =							field(
	    default=snake_case__			,   metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}			,   )
	a__				: bool		       =							field(
	    default=snake_case__			,   metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}			,   )
	a__				: bool		       =							field(
	    default=snake_case__			,   metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."})
	a__				: bool		       =							field(default=snake_case__			,   metadata={"help": "Use FP16 to accelerate inference."})
	a__				: bool		       =							field(default=snake_case__			,   metadata={"help": "Benchmark training of model"})
	a__				: bool		       =							field(default=snake_case__			,   metadata={"help": "Verbose memory tracing"})
	a__				: bool		       =							field(
	    default=snake_case__			,   metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}			,   )
	a__				: bool		       =							field(
	    default=snake_case__			,   metadata={
	        "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
	    }			,   )
	a__				: bool		       =							field(default=snake_case__			,   metadata={"help": "Trace memory line by line"})
	a__				: bool		       =							field(default=snake_case__			,   metadata={"help": "Save result to a CSV file"})
	a__				: bool		       =							field(default=snake_case__			,   metadata={"help": "Save all print statements in a log file"})
	a__				: bool		       =							field(default=snake_case__			,   metadata={"help": "Whether to print environment information"})
	a__				: bool		       =							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."
	        )
	    }			,   )
	a__				: str		       =							field(
	    default=F'inference_time_{round(time())}.csv'			,   metadata={"help": "CSV filename used if saving time results to csv."}			,   )
	a__				: str		       =							field(
	    default=F'inference_memory_{round(time())}.csv'			,   metadata={"help": "CSV filename used if saving memory results to csv."}			,   )
	a__				: str		       =							field(
	    default=F'train_time_{round(time())}.csv'			,   metadata={"help": "CSV filename used if saving time results to csv for training."}			,   )
	a__				: str		       =							field(
	    default=F'train_memory_{round(time())}.csv'			,   metadata={"help": "CSV filename used if saving memory results to csv for training."}			,   )
	a__				: str		       =							field(
	    default=F'env_info_{round(time())}.csv'			,   metadata={"help": "CSV filename used if saving environment information."}			,   )
	a__				: str		       =							field(
	    default=F'log_{round(time())}.csv'			,   metadata={"help": "Log filename used if print statements are saved in log."}			,   )
	a__				: int		       =							field(default=3			,   metadata={"help": "Times an experiment will be run."})
	a__				: bool		       =							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   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			str:
							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."""						,  __UpperCAmelCase						,  )




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			List[str]:
							return json.dumps(dataclasses.asdict(self					)						,  indent=2					)




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			List[str]:
							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   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			Tuple:
							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




