


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    _UpperCAmelCase	(   __lowerCamelCase=None ,     __lowerCamelCase=None       )		->Any:


 '''simple docstring'''
 return field(default_factory=lambda: default ,     metadata=__lowerCamelCase       )

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







def    _UpperCAmelCase	(   __lowerCamelCase       )		->Any:


 '''simple docstring'''
 try:
  int(__lowerCamelCase       )
  return True
 except ValueError:
  return False






def    _UpperCAmelCase	(   __lowerCamelCase       )		->Union[str, Any]:


 '''simple docstring'''
 try:
  float(__lowerCamelCase       )
  return True
 except ValueError:
  return False

class      lowerCamelCase	:

       def __init__(			self	,						a__					):






        '''simple docstring'''






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

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



       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case  ,					snake_case   : List[str]	  =      plt.subplots()
        snake_case   : Union[str, Any]	  =      '''Time usage''' if self.args.is_time else '''Memory usage'''
        snake_case   : List[str]	  =      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()					):
         snake_case   : List[str]	  =      sorted(set(self.result_dict[model_name]['''bsz''']					)					)
         snake_case   : Optional[int]	  =      sorted(set(self.result_dict[model_name]['''seq_len''']					)					)
         snake_case   : List[str]	  =      self.result_dict[model_name]['''result''']

         ((snake_case)  ,					(snake_case))   : Tuple	  =      (
             (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
         )

         snake_case   : Any	  =      (
             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:
           snake_case   : Optional[Any]	  =      np.asarray(
               [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]	,						dtype=a__	,						)
          else:
           snake_case   : List[str]	  =      np.asarray(
               [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]	,						dtype=np.floataa	,						)

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

          snake_case   : Optional[int]	  =      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.'''

        snake_case   : Union[str, Any]	  =      title_str[:-4]
        snake_case   : Any	  =      '''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    _UpperCAmelCase	(   )		->int:


 '''simple docstring'''
 snake_case   : List[Any]	  =      HfArgumentParser(__lowerCamelCase       )
 snake_case   : str	  =      parser.parse_args_into_dataclasses()[0]
 snake_case   : Any	  =      Plot(args=__lowerCamelCase       )
 plot.plot()


if __name__ == "__main__":
  main()










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a				:							List[str]					=			{
    'configuration_donut_swin': ['DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DonutSwinConfig'],
    'processing_donut': ['DonutProcessor'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a				:							Dict					=			[
      'DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DonutSwinModel',
      'DonutSwinPreTrainedModel',
  ]

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a				:							Any					=			['DonutFeatureExtractor']
  a				:							Dict					=			['DonutImageProcessor']


if TYPE_CHECKING:
  from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
  from .processing_donut import DonutProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_donut_swin import (
        DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
        DonutSwinModel,
        DonutSwinPreTrainedModel,
    )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_donut import DonutFeatureExtractor
    from .image_processing_donut import DonutImageProcessor

else:
  import sys

  a				:							Union[str, Any]					=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






