'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


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


class     __UpperCAmelCase       ( __a		):
  __A :				int											= ['pixel_values']





  def __init__(	self    ,     _lowerCamelCase = True    ,     _lowerCamelCase = None    ,     _lowerCamelCase = PIL.Image.BICUBIC    ,     _lowerCamelCase = True    ,     _lowerCamelCase = None    ,     _lowerCamelCase = 1 / 255    ,     _lowerCamelCase = True    ,     _lowerCamelCase = True    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
     super().__init__(**_lowerCamelCase )
     lowerCAmelCase_			=      size if size is not None else {'''height''': 256, '''width''': 256}
     lowerCAmelCase_			=      get_size_dict(_lowerCamelCase )
     lowerCAmelCase_			=      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
     lowerCAmelCase_			=      get_size_dict(_lowerCamelCase    ,     param_name='''crop_size''' )

     lowerCAmelCase_			=      do_resize
     lowerCAmelCase_			=      size
     lowerCAmelCase_			=      resample
     lowerCAmelCase_			=      do_center_crop
     lowerCAmelCase_			=      crop_size
     lowerCAmelCase_			=      do_rescale
     lowerCAmelCase_			=      rescale_factor
     lowerCAmelCase_			=      do_normalize
     lowerCAmelCase_			=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     lowerCAmelCase_			=      image_std if image_std is not None else IMAGENET_STANDARD_STD





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = PIL.Image.BICUBIC    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
     lowerCAmelCase_			=      get_size_dict(_lowerCamelCase )
     if "height" not in size or "width" not in size:
        raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}''' )
     return resize(
         _lowerCamelCase    ,     size=(size['''height'''], size['''width'''])    ,     resample=_lowerCamelCase    ,     data_format=_lowerCamelCase    ,     **_lowerCamelCase )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
     lowerCAmelCase_			=      get_size_dict(_lowerCamelCase )
     if "height" not in size or "width" not in size:
        raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}''' )
     return center_crop(_lowerCamelCase    ,     size=(size['''height'''], size['''width'''])    ,     data_format=_lowerCamelCase    ,     **_lowerCamelCase )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
     return rescale(_lowerCamelCase    ,     scale=_lowerCamelCase    ,     data_format=_lowerCamelCase    ,     **_lowerCamelCase )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
     return normalize(_lowerCamelCase    ,     mean=_lowerCamelCase    ,     std=_lowerCamelCase    ,     data_format=_lowerCamelCase    ,     **_lowerCamelCase )







  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase=None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = ChannelDimension.FIRST    ,     **_lowerCamelCase    ,     ):
     lowerCAmelCase_			=      do_resize if do_resize is not None else self.do_resize
     lowerCAmelCase_			=      resample if resample is not None else self.resample
     lowerCAmelCase_			=      do_center_crop if do_center_crop is not None else self.do_center_crop
     lowerCAmelCase_			=      do_rescale if do_rescale is not None else self.do_rescale
     lowerCAmelCase_			=      rescale_factor if rescale_factor is not None else self.rescale_factor
     lowerCAmelCase_			=      do_normalize if do_normalize is not None else self.do_normalize
     lowerCAmelCase_			=      image_mean if image_mean is not None else self.image_mean
     lowerCAmelCase_			=      image_std if image_std is not None else self.image_std

     lowerCAmelCase_			=      size if size is not None else self.size
     lowerCAmelCase_			=      get_size_dict(_lowerCamelCase )
     lowerCAmelCase_			=      crop_size if crop_size is not None else self.crop_size
     lowerCAmelCase_			=      get_size_dict(_lowerCamelCase    ,     param_name='''crop_size''' )

     lowerCAmelCase_			=      make_list_of_images(_lowerCamelCase )

     if not valid_images(_lowerCamelCase ):
        raise ValueError(
            '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
            '''torch.Tensor, tf.Tensor or jax.ndarray.''' )

     if do_resize and size is None or resample is None:
        raise ValueError('''Size and resample must be specified if do_resize is True.''' )

     if do_center_crop and crop_size is None:
        raise ValueError('''Crop size must be specified if do_center_crop is True.''' )

     if do_rescale and rescale_factor is None:
        raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )

     if do_normalize and (image_mean is None or image_std is None):
        raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )

     # All transformations expect numpy arrays.
     lowerCAmelCase_			=      [to_numpy_array(_lowerCamelCase ) for image in images]

     if do_resize:
        lowerCAmelCase_			=      [self.resize(image=_lowerCamelCase    ,     size=_lowerCamelCase    ,     resample=_lowerCamelCase ) for image in images]

     if do_center_crop:
        lowerCAmelCase_			=      [self.center_crop(image=_lowerCamelCase    ,     size=_lowerCamelCase ) for image in images]

     if do_rescale:
        lowerCAmelCase_			=      [self.rescale(image=_lowerCamelCase    ,     scale=_lowerCamelCase ) for image in images]

     if do_normalize:
        lowerCAmelCase_			=      [self.normalize(image=_lowerCamelCase    ,     mean=_lowerCamelCase    ,     std=_lowerCamelCase ) for image in images]

     lowerCAmelCase_			=      [to_channel_dimension_format(_lowerCamelCase    ,     _lowerCamelCase ) for image in images]

     lowerCAmelCase_			=      {'''pixel_values''': images}
     return BatchFeature(data=_lowerCamelCase    ,     tensor_type=_lowerCamelCase )






'''simple docstring'''

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 snake_case_      (					__snake_case  :							Tuple=None	,   __snake_case  :							int=None)			->					int:
   return field(default_factory=lambda: default	,   metadata=__snake_case)


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






def snake_case_      (					__snake_case  :							Optional[Any])			->					Dict:
   try:
      int(__snake_case)
      return True
   except ValueError:
      return False



def snake_case_      (					__snake_case  :							Dict)			->					int:
   try:
      float(__snake_case)
      return True
   except ValueError:
      return False


class     __UpperCAmelCase       :





  def __init__(	self    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      args
     lowerCAmelCase_			=      defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}} )

     with open(self.args.csv_file    ,     newline='''''' ) as csv_file:
        lowerCAmelCase_			=      csv.DictReader(_lowerCamelCase )
        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 						UpperCAmelCase_	(	self ):
     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=_lowerCamelCase    ,     )
           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(_lowerCamelCase    ,     _lowerCamelCase )[: len(_lowerCamelCase )]
           plt.scatter(
               _lowerCamelCase    ,     _lowerCamelCase    ,     label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}''' )
           plt.plot(_lowerCamelCase    ,     _lowerCamelCase    ,     '''--''' )

        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(_lowerCamelCase )
     plt.xlabel(_lowerCamelCase )
     plt.ylabel(_lowerCamelCase )
     plt.legend()

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






def snake_case_      (					)			->					Tuple:
   lowerCAmelCase_			=      HfArgumentParser(__snake_case)
   lowerCAmelCase_			=      parser.parse_args_into_dataclasses()[0]
   lowerCAmelCase_			=      Plot(args=__snake_case)
   plot.plot()


if __name__ == "__main__":
 main()





