



import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser



def __lowerCAmelCase       (   SCREAMING_SNAKE_CASE_=None  ,     SCREAMING_SNAKE_CASE_=None     ):
			return field(default_factory=lambda: default  ,     metadata=a__     )
@dataclass
class  _snake_case	:
				UpperCamelCase__ :    str   						=field(
				    metadata={"""help""": """The csv file to plot."""}			,		)
				UpperCamelCase__ :    Optional[Any]   						=field(
				    default=UpperCamelCase__			,		metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}			,		)
				UpperCamelCase__ :    Union[str, Any]   						=field(
				    default=UpperCamelCase__			,		metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}			,		)
				UpperCamelCase__ :    int   						=field(
				    default=UpperCamelCase__			,		metadata={"""help""": """Disable logarithmic scale when plotting"""}			,		)
				UpperCamelCase__ :    Optional[int]   						=field(
				    default=UpperCamelCase__			,		metadata={
				        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
				    }			,		)
				UpperCamelCase__ :    Union[str, Any]   						=field(
				    default=UpperCamelCase__			,		metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}			,		)
				UpperCamelCase__ :    Any   						=list_field(
				    default=UpperCamelCase__			,		metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""})



def __lowerCAmelCase       (   SCREAMING_SNAKE_CASE_     ):
			try:
						int(a__     )
						return True
			except ValueError:
						return False



def __lowerCAmelCase       (   SCREAMING_SNAKE_CASE_     ):
			try:
						float(a__     )
						return True
			except ValueError:
						return False
class  _snake_case	:




				def __init__(  self    :  Optional[int],    __lowercase    :  List[str]  ):
							lowercase__         = args
							lowercase__         = defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}  )

							with open(self.args.csv_file,    newline=""  ) as csv_file:
										lowercase__         = csv.DictReader(_a  )
										for row in reader:
													lowercase__         = row["model"]
													self.result_dict[model_name]["bsz"].append(int(row["batch_size"]  )  )
													self.result_dict[model_name]["seq_len"].append(int(row["sequence_length"]  )  )
													if can_convert_to_int(row["result"]  ):
																# value is not None
																lowercase__         = int(row["result"]  )
													elif can_convert_to_float(row["result"]  ):
																# value is not None
																lowercase__         = float(row["result"]  )




				def 							A__					(  self    :  Optional[int]  ):
							lowercase__     ,				lowercase__         = plt.subplots()
							lowercase__         = "Time usage" if self.args.is_time else "Memory usage"
							lowercase__         = title_str + " for training" if self.args.is_train else title_str + " for inference"

							if not self.args.no_log_scale:
										# set logarithm scales
										ax.set_xscale("log"  )
										ax.set_yscale("log"  )

							for axis in [ax.xaxis, ax.yaxis]:
										axis.set_major_formatter(ScalarFormatter()  )

							for model_name_idx, model_name in enumerate(self.result_dict.keys()  ):
										lowercase__         = sorted(set(self.result_dict[model_name]["bsz"]  )  )
										lowercase__         = sorted(set(self.result_dict[model_name]["seq_len"]  )  )
										lowercase__         = self.result_dict[model_name]["result"]

										((lowercase__)     ,				(lowercase__))         = (
										    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
										)

										lowercase__         = (
										    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
										)

										for inner_loop_value in inner_loop_array:
													if self.args.plot_along_batch:
																lowercase__         = np.asarray(
																    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results],    dtype=_a,    )
													else:
																lowercase__         = np.asarray(
																    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results],    dtype=np.floataa,    )

													((lowercase__)     ,				(lowercase__))         = (
													    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
													)

													lowercase__         = np.asarray(_a,    _a  )[: len(_a  )]
													plt.scatter(
													    _a,    _a,    label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''  )
													plt.plot(_a,    _a,    "--"  )

										title_str += F''' {label_model_name} vs.'''

							lowercase__         = title_str[:-4]
							lowercase__         = "Time in s" if self.args.is_time else "Memory in MB"

							# plot
							plt.title(_a  )
							plt.xlabel(_a  )
							plt.ylabel(_a  )
							plt.legend()

							if self.args.figure_png_file is not None:
										plt.savefig(self.args.figure_png_file  )
							else:
										plt.show()



def __lowerCAmelCase       (   ):
			lowercase__         = HfArgumentParser(a__     )
			lowercase__         = parser.parse_args_into_dataclasses()[0]
			lowercase__         = Plot(args=a__     )
			plot.plot()


if __name__ == "__main__":
							main()








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


snake_case_  :	Any							  =							logging.get_logger(__name__)

snake_case_  :	Any							  =							{
    '''apple/mobilevit-small''': '''https://huggingface.co/apple/mobilevit-small/resolve/main/config.json''',
    '''apple/mobilevit-x-small''': '''https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json''',
    '''apple/mobilevit-xx-small''': '''https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json''',
    '''apple/deeplabv3-mobilevit-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-x-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-xx-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'''
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}

class   A__							(						UpperCamelCase__     ):
						UpperCAmelCase									=  "mobilevit"




						def __init__(				self    :					Tuple ,			_a    :					Any=3 ,			_a    :					Any=256 ,			_a    :					Any=2 ,			_a    :					List[str]=[144, 192, 240] ,			_a    :					Optional[int]=[16, 32, 64, 96, 128, 160, 640] ,			_a    :					Union[str, Any]=4 ,			_a    :					Any=2.0 ,			_a    :					List[str]=4.0 ,			_a    :					Tuple="silu" ,			_a    :					Optional[Any]=3 ,			_a    :					List[str]=32 ,			_a    :					Union[str, Any]=0.1 ,			_a    :					Tuple=0.0 ,			_a    :					Union[str, Any]=0.1 ,			_a    :					Union[str, Any]=0.02 ,			_a    :					Tuple=1E-5 ,			_a    :					Any=True ,			_a    :					str=256 ,			_a    :					Tuple=[6, 12, 18] ,			_a    :					int=0.1 ,			_a    :					str=255 ,			**_a    :					Any ,			)							->   Optional[int]:


										"""simple docstring"""





										super().__init__(**_a     )

										_SCREAMING_SNAKE_CASE	     =num_channels
										_SCREAMING_SNAKE_CASE	     =image_size
										_SCREAMING_SNAKE_CASE	     =patch_size
										_SCREAMING_SNAKE_CASE	     =hidden_sizes
										_SCREAMING_SNAKE_CASE	     =neck_hidden_sizes
										_SCREAMING_SNAKE_CASE	     =num_attention_heads
										_SCREAMING_SNAKE_CASE	     =mlp_ratio
										_SCREAMING_SNAKE_CASE	     =expand_ratio
										_SCREAMING_SNAKE_CASE	     =hidden_act
										_SCREAMING_SNAKE_CASE	     =conv_kernel_size
										_SCREAMING_SNAKE_CASE	     =output_stride
										_SCREAMING_SNAKE_CASE	     =hidden_dropout_prob
										_SCREAMING_SNAKE_CASE	     =attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE	     =classifier_dropout_prob
										_SCREAMING_SNAKE_CASE	     =initializer_range
										_SCREAMING_SNAKE_CASE	     =layer_norm_eps
										_SCREAMING_SNAKE_CASE	     =qkv_bias

										# decode head attributes for semantic segmentation
										_SCREAMING_SNAKE_CASE	     =aspp_out_channels
										_SCREAMING_SNAKE_CASE	     =atrous_rates
										_SCREAMING_SNAKE_CASE	     =aspp_dropout_prob
										_SCREAMING_SNAKE_CASE	     =semantic_loss_ignore_index

class   A__							(						UpperCamelCase__     ):
						UpperCAmelCase									=  version.parse("1.11"     )




						@property
						def 							__UpperCamelCase (				self    :					Union[str, Any]     )							->   Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





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




						@property
						def 							__UpperCamelCase (				self    :					Dict     )							->   Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





										if self.task == "image-classification":
														return OrderedDict([('''logits''', {0: '''batch'''})]     )
										else:
														return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]     )




						@property
						def 							__UpperCamelCase (				self    :					List[Any]     )							->   float:


										"""simple docstring"""





										return 1E-4