




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      _SCREAMING_SNAKE_CASE     (				lowercase      :			Union[str, Any]=None      ,				lowercase      :			Dict=None	):


      '''simple docstring'''



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







@dataclass
class     A:



 '''simple docstring'''


 UpperCamelCase		  =		field(
     metadata={'''help''': '''The csv file to plot.'''}				, )
 UpperCamelCase		  =		field(
     default=UpperCamelCase				, metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}				, )
 UpperCamelCase		  =		field(
     default=UpperCamelCase				, metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}				, )
 UpperCamelCase		  =		field(
     default=UpperCamelCase				, metadata={'''help''': '''Disable logarithmic scale when plotting'''}				, )
 UpperCamelCase		  =		field(
     default=UpperCamelCase				, metadata={
         '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
     }				, )
 UpperCamelCase		  =		field(
     default=UpperCamelCase				, metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}				, )
 UpperCamelCase		  =		list_field(
     default=UpperCamelCase				, metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''}  )

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			List[Any]	):


      '''simple docstring'''



      try:
            int(lowercase	)
            return True
      except ValueError:
            return False

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			List[Any]	):


      '''simple docstring'''



      try:
            float(lowercase	)
            return True
      except ValueError:
            return False







class     A:



 '''simple docstring'''


 def __init__(							self   :    Optional[int]		,				A_   :    List[Any]    )  ->      Dict:

       """simple docstring"""






       lowerCamelCase_							=       args
       lowerCamelCase_							=       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}    )

       with open(self.args.csv_file		,				newline=''    ) as csv_file:
             lowerCamelCase_							=       csv.DictReader(A_    )
             for row in reader:
                   lowerCamelCase_							=       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
                         lowerCamelCase_							=       int(row['result']    )
                   elif can_convert_to_float(row['result']    ):
                         # value is not None
                         lowerCamelCase_							=       float(row['result']    )





 def 							a__					(							self   :    Union[str, Any]    )  ->      Dict:

       """simple docstring"""






       lowerCamelCase_	,							lowerCamelCase_							=       plt.subplots()
       lowerCamelCase_							=       'Time usage' if self.args.is_time else 'Memory usage'
       lowerCamelCase_							=       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()    ):
             lowerCamelCase_							=       sorted(set(self.result_dict[model_name]['bsz']    )    )
             lowerCamelCase_							=       sorted(set(self.result_dict[model_name]['seq_len']    )    )
             lowerCamelCase_							=       self.result_dict[model_name]['result']

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

             lowerCamelCase_							=       (
                 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:
                         lowerCamelCase_							=       np.asarray(
                             [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]		,				dtype=A_		,				)
                   else:
                         lowerCamelCase_							=       np.asarray(
                             [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]		,				dtype=np.floataa		,				)

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

                   lowerCamelCase_							=       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."""

       lowerCamelCase_							=       title_str[:-4]
       lowerCamelCase_							=       '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      _SCREAMING_SNAKE_CASE     (				):


      '''simple docstring'''



      lowerCamelCase_							=       HfArgumentParser(lowercase	)
      lowerCamelCase_							=       parser.parse_args_into_dataclasses()[0]
      lowerCamelCase_							=       Plot(args=lowercase	)
      plot.plot()


if __name__ == "__main__":
 main()








'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__    :   Optional[Any]           =					logging.get_logger(__name__)

A__    :   List[str]           =					{
    '''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= '''cvt'''
 def __init__(   self						:							Optional[Any]      ,     __a						:							Union[str, Any]=3      ,     __a						:							List[Any]=[7, 3, 3]      ,     __a						:							Optional[int]=[4, 2, 2]      ,     __a						:							Dict=[2, 1, 1]      ,     __a						:							Union[str, Any]=[64, 192, 384]      ,     __a						:							int=[1, 3, 6]      ,     __a						:							List[str]=[1, 2, 10]      ,     __a						:							Optional[Any]=[4.0, 4.0, 4.0]      ,     __a						:							Any=[0.0, 0.0, 0.0]      ,     __a						:							List[str]=[0.0, 0.0, 0.0]      ,     __a						:							List[Any]=[0.0, 0.0, 0.1]      ,     __a						:							List[str]=[True, True, True]      ,     __a						:							int=[False, False, True]      ,     __a						:							Dict=["dw_bn", "dw_bn", "dw_bn"]      ,     __a						:							List[str]=[3, 3, 3]      ,     __a						:							Union[str, Any]=[1, 1, 1]      ,     __a						:							Optional[int]=[2, 2, 2]      ,     __a						:							Optional[Any]=[1, 1, 1]      ,     __a						:							List[str]=[1, 1, 1]      ,     __a						:							List[str]=0.0_2      ,     __a						:							List[str]=1e-12      ,     **__a						:							List[str]      ,     )      ->   Dict:







       '''simple docstring'''
       super().__init__(**__a  )
       __snake_case :   int													=    num_channels
       __snake_case :   Union[str, Any]													=    patch_sizes
       __snake_case :   Any													=    patch_stride
       __snake_case :   List[str]													=    patch_padding
       __snake_case :   Optional[Any]													=    embed_dim
       __snake_case :   Union[str, Any]													=    num_heads
       __snake_case :   Dict													=    depth
       __snake_case :   Optional[Any]													=    mlp_ratio
       __snake_case :   List[str]													=    attention_drop_rate
       __snake_case :   Optional[int]													=    drop_rate
       __snake_case :   Optional[int]													=    drop_path_rate
       __snake_case :   Any													=    qkv_bias
       __snake_case :   int													=    cls_token
       __snake_case :   Optional[int]													=    qkv_projection_method
       __snake_case :   List[Any]													=    kernel_qkv
       __snake_case :   List[Any]													=    padding_kv
       __snake_case :   int													=    stride_kv
       __snake_case :   List[str]													=    padding_q
       __snake_case :   Dict													=    stride_q
       __snake_case :   Tuple													=    initializer_range
       __snake_case :   List[Any]													=    layer_norm_eps



