
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 		a(						SCREAMING_SNAKE_CASE__=None      ,			SCREAMING_SNAKE_CASE__=None       )				->					int:
       return field(default_factory=lambda: default      ,			metadata=lowerCAmelCase__       )

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







def 		a(						SCREAMING_SNAKE_CASE__       )				->					Any:
       try:
              int(lowerCAmelCase__       )
              return True
       except ValueError:
              return False







def 		a(						SCREAMING_SNAKE_CASE__       )				->					Union[str, Any]:
       try:
              float(lowerCAmelCase__       )
              return True
       except ValueError:
              return False

class __snake_case	:
    def __init__(						self	,snake_case   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   args
           lowercase							:  List[Any] 			=   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}   )

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



           '''simple docstring'''







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

                  (lowercase)							:  Dict 			=   (
                      (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
                  )

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

                         (lowercase)							:  Optional[Any] 			=   (
                             ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
                         )

                         lowercase							:  List[Any] 			=   np.asarray(snake_case	,snake_case   )[: len(snake_case   )]
                         plt.scatter(
                             snake_case	,snake_case	,label=f"{label_model_name} - {inner_loop_label}: {inner_loop_value}"   )
                         plt.plot(snake_case	,snake_case	,"""--"""   )

                  title_str += f" {label_model_name} vs."

           lowercase							:  Optional[int] 			=   title_str[:-4]
           lowercase							:  List[str] 			=   'Time in s' if self.args.is_time else 'Memory in MB'

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

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







def 		a(						)				->					str:
       lowercase							:  str 			=   HfArgumentParser(lowerCAmelCase__       )
       lowercase							:  List[str] 			=   parser.parse_args_into_dataclasses()[0]
       lowercase							:  List[Any] 			=   Plot(args=lowerCAmelCase__       )
       plot.plot()


if __name__ == "__main__":
  main()


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import VideoMAEImageProcessor

class __snake_case	(   unittest.TestCase  ):
    def __init__(						self	,snake_case	,snake_case=7	,snake_case=3	,snake_case=10	,snake_case=18	,snake_case=30	,snake_case=400	,snake_case=True	,snake_case=None	,snake_case=True	,snake_case=[0.5, 0.5, 0.5]	,snake_case=[0.5, 0.5, 0.5]	,snake_case=None	,):



           '''simple docstring'''







           lowercase							:  Dict 			=   size if size is not None else {"""shortest_edge""": 18}
           lowercase							:  Tuple 			=   crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

           lowercase							:  Optional[int] 			=   parent
           lowercase							:  Any 			=   batch_size
           lowercase							:  int 			=   num_channels
           lowercase							:  Dict 			=   num_frames
           lowercase							:  Any 			=   image_size
           lowercase							:  str 			=   min_resolution
           lowercase							:  Any 			=   max_resolution
           lowercase							:  Tuple 			=   do_resize
           lowercase							:  Optional[Any] 			=   size
           lowercase							:  str 			=   do_normalize
           lowercase							:  Optional[Any] 			=   image_mean
           lowercase							:  Optional[Any] 			=   image_std
           lowercase							:  Union[str, Any] 			=   crop_size
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
               "crop_size": self.crop_size,
           }






@require_torch
@require_vision
class __snake_case	(   lowerCAmelCase      ,			unittest.TestCase  ):
    _a	:		Optional[Any]=       VideoMAEImageProcessor if is_vision_available() else None
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   VideoMAEImageProcessingTester(self   )
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           return self.image_processor_tester.prepare_image_processor_dict()
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   self.image_processing_class(**self.image_processor_dict   )
           self.assertTrue(hasattr(snake_case	,"""image_mean"""   )   )
           self.assertTrue(hasattr(snake_case	,"""image_std"""   )   )
           self.assertTrue(hasattr(snake_case	,"""do_normalize"""   )   )
           self.assertTrue(hasattr(snake_case	,"""do_resize"""   )   )
           self.assertTrue(hasattr(snake_case	,"""do_center_crop"""   )   )
           self.assertTrue(hasattr(snake_case	,"""size"""   )   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   self.image_processing_class.from_dict(self.image_processor_dict   )
           self.assertEqual(image_processor.size	,{"""shortest_edge""": 18}   )
           self.assertEqual(image_processor.crop_size	,{"""height""": 18, """width""": 18}   )

           lowercase							:  Any 			=   self.image_processing_class.from_dict(self.image_processor_dict	,size=42	,crop_size=84   )
           self.assertEqual(image_processor.size	,{"""shortest_edge""": 42}   )
           self.assertEqual(image_processor.crop_size	,{"""height""": 84, """width""": 84}   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           pass
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Tuple 			=   self.image_processing_class(**self.image_processor_dict   )
           # create random PIL videos
           lowercase							:  Optional[int] 			=   prepare_video_inputs(self.image_processor_tester	,equal_resolution=snake_case   )
           for video in video_inputs:
                  self.assertIsInstance(snake_case	,snake_case   )
                  self.assertIsInstance(video[0]	,Image.Image   )

           # Test not batched input
           lowercase							:  Optional[Any] 			=   image_processing(video_inputs[0]	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)

           # Test batched
           lowercase							:  Union[str, Any] 			=   image_processing(snake_case	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   self.image_processing_class(**self.image_processor_dict   )
           # create random numpy tensors
           lowercase							:  Optional[Any] 			=   prepare_video_inputs(self.image_processor_tester	,equal_resolution=snake_case	,numpify=snake_case   )
           for video in video_inputs:
                  self.assertIsInstance(snake_case	,snake_case   )
                  self.assertIsInstance(video[0]	,np.ndarray   )

           # Test not batched input
           lowercase							:  int 			=   image_processing(video_inputs[0]	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)

           # Test batched
           lowercase							:  Optional[int] 			=   image_processing(snake_case	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   self.image_processing_class(**self.image_processor_dict   )
           # create random PyTorch tensors
           lowercase							:  List[str] 			=   prepare_video_inputs(self.image_processor_tester	,equal_resolution=snake_case	,torchify=snake_case   )
           for video in video_inputs:
                  self.assertIsInstance(snake_case	,snake_case   )
                  self.assertIsInstance(video[0]	,torch.Tensor   )

           # Test not batched input
           lowercase							:  Optional[Any] 			=   image_processing(video_inputs[0]	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)

           # Test batched
           lowercase							:  Union[str, Any] 			=   image_processing(snake_case	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)
