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

from ..utils import logging


UpperCAmelCase_								=							logging.get_logger(__name__)

def 							UpperCamelCase				(    lowerCAmelCase_=None       ,					lowerCAmelCase_=None   )		->						str:




       '''simple docstring'''






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





@dataclass
class      lowerCAmelCase						:
     _SCREAMING_SNAKE_CASE     :     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"""
             )
         }			,		)

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

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

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



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



     @property
     def 			a__  (					self       ):
            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 			a__  (					self       ):
            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
import warnings
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


UpperCAmelCase_								=							logging.get_logger(__name__)

UpperCAmelCase_								=							{
    '''nvidia/segformer-b0-finetuned-ade-512-512''': (
        '''https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'''
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Tuple		="""segformer"""



     def __init__(					self				,      lowerCAmelCase__=3				,      lowerCAmelCase__=4				,      lowerCAmelCase__=[2, 2, 2, 2]				,      lowerCAmelCase__=[8, 4, 2, 1]				,      lowerCAmelCase__=[32, 64, 160, 256]				,      lowerCAmelCase__=[7, 3, 3, 3]				,      lowerCAmelCase__=[4, 2, 2, 2]				,      lowerCAmelCase__=[1, 2, 5, 8]				,      lowerCAmelCase__=[4, 4, 4, 4]				,      lowerCAmelCase__="gelu"				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=0.02				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=1E-6				,      lowerCAmelCase__=256				,      lowerCAmelCase__=255				,      **lowerCAmelCase__				,      ):
            super().__init__(**lowerCAmelCase__       )

            if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
                   warnings.warn(
                       'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
                       ' removed, as the behaviour will default to that of reshape_last_stage = True.'				,      lowerCAmelCase__				,      )

            _A=	num_channels
            _A=	num_encoder_blocks
            _A=	depths
            _A=	sr_ratios
            _A=	hidden_sizes
            _A=	patch_sizes
            _A=	strides
            _A=	mlp_ratios
            _A=	num_attention_heads
            _A=	hidden_act
            _A=	hidden_dropout_prob
            _A=	attention_probs_dropout_prob
            _A=	classifier_dropout_prob
            _A=	initializer_range
            _A=	drop_path_rate
            _A=	layer_norm_eps
            _A=	decoder_hidden_size
            _A=	kwargs.get('reshape_last_stage'				,      lowerCAmelCase__       )
            _A=	semantic_loss_ignore_index





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     List[str]		=version.parse("""1.11"""			)



     @property
     def 			a__  (					self       ):
            return OrderedDict(
                [
                    ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                ]       )



     @property
     def 			a__  (					self       ):
            return 1E-4





     @property
     def 			a__  (					self       ):
            return 12