





"""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       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						List[Any]=None							,						SCREAMING_SNAKE_CASE					:						Any=None      ):







      '''simple docstring'''




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

@dataclass
class 		lowercase :






      """simple docstring"""




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







def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int      ):







      '''simple docstring'''




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


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Optional[int]      ):







      '''simple docstring'''




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

class 		lowercase :






      """simple docstring"""




      def __init__(	self				,						lowercase			)		->					Dict:
            lowerCAmelCase  						=	args
            lowerCAmelCase  						=	defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}			)

            with open(self.args.csv_file				,						newline=""""""			) as csv_file:
                  lowerCAmelCase  						=	csv.DictReader(lowercase			)
                  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    _snake_case      (	self			)		->					List[str]:
            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)  						=	(
                      (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=lowercase				,						)
                        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)  						=	(
                            ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
                        )

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

                  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(lowercase			)
            plt.xlabel(lowercase			)
            plt.ylabel(lowercase			)
            plt.legend()

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







def       UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=	HfArgumentParser(_a      )
      lowerCAmelCase  						=	parser.parse_args_into_dataclasses()[0]
      lowerCAmelCase  						=	Plot(args=_a      )
      plot.plot()


if __name__ == "__main__":
       main()













"""simple docstring"""





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_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import ConvNextImageProcessor

class 		lowercase ( unittest.TestCase ):





      def __init__(	self				,						lowercase				,						lowercase=7				,						lowercase=3				,						lowercase=18				,						lowercase=30				,						lowercase=400				,						lowercase=True				,						lowercase=None				,						lowercase=0.875				,						lowercase=True				,						lowercase=[0.5, 0.5, 0.5]				,						lowercase=[0.5, 0.5, 0.5]				,						)		->					List[Any]:
            lowerCAmelCase  						=	size if size is not None else {"""shortest_edge""": 20}
            lowerCAmelCase  						=	parent
            lowerCAmelCase  						=	batch_size
            lowerCAmelCase  						=	num_channels
            lowerCAmelCase  						=	image_size
            lowerCAmelCase  						=	min_resolution
            lowerCAmelCase  						=	max_resolution
            lowerCAmelCase  						=	do_resize
            lowerCAmelCase  						=	size
            lowerCAmelCase  						=	crop_pct
            lowerCAmelCase  						=	do_normalize
            lowerCAmelCase  						=	image_mean
            lowerCAmelCase  						=	image_std





      def    _snake_case      (	self			)		->					Optional[Any]:
            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_pct": self.crop_pct,
            }







@require_torch
@require_vision
class 		lowercase ( _UpperCAmelCase					,  unittest.TestCase ):
      _SCREAMING_SNAKE_CASE         =				ConvNextImageProcessor if is_vision_available() else None





      def    _snake_case      (	self			)		->					List[str]:
            lowerCAmelCase  						=	ConvNextImageProcessingTester(self			)





      @property
      def    _snake_case      (	self			)		->					List[str]:
            return self.image_processor_tester.prepare_image_processor_dict()





      def    _snake_case      (	self			)		->					int:
            lowerCAmelCase  						=	self.image_processing_class(**self.image_processor_dict			)
            self.assertTrue(hasattr(lowercase				,						"""do_resize"""			)			)
            self.assertTrue(hasattr(lowercase				,						"""size"""			)			)
            self.assertTrue(hasattr(lowercase				,						"""crop_pct"""			)			)
            self.assertTrue(hasattr(lowercase				,						"""do_normalize"""			)			)
            self.assertTrue(hasattr(lowercase				,						"""image_mean"""			)			)
            self.assertTrue(hasattr(lowercase				,						"""image_std"""			)			)





      def    _snake_case      (	self			)		->					int:
            lowerCAmelCase  						=	self.image_processing_class.from_dict(self.image_processor_dict			)
            self.assertEqual(image_processor.size				,						{"""shortest_edge""": 20}			)

            lowerCAmelCase  						=	self.image_processing_class.from_dict(self.image_processor_dict				,						size=42			)
            self.assertEqual(image_processor.size				,						{"""shortest_edge""": 42}			)





      def    _snake_case      (	self			)		->					Optional[int]:
            pass





      def    _snake_case      (	self			)		->					Optional[int]:
            # Initialize image_processing
            lowerCAmelCase  						=	self.image_processing_class(**self.image_processor_dict			)
            # create random PIL images
            lowerCAmelCase  						=	prepare_image_inputs(self.image_processor_tester				,						equal_resolution=lowercase			)
            for image in image_inputs:
                  self.assertIsInstance(lowercase				,						Image.Image			)

            # Test not batched input
            lowerCAmelCase  						=	image_processing(image_inputs[0]				,						return_tensors="""pt"""			).pixel_values
            self.assertEqual(
                encoded_images.shape				,						(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )				,						)

            # Test batched
            lowerCAmelCase  						=	image_processing(lowercase				,						return_tensors="""pt"""			).pixel_values
            self.assertEqual(
                encoded_images.shape				,						(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )				,						)





      def    _snake_case      (	self			)		->					str:
            # Initialize image_processing
            lowerCAmelCase  						=	self.image_processing_class(**self.image_processor_dict			)
            # create random numpy tensors
            lowerCAmelCase  						=	prepare_image_inputs(self.image_processor_tester				,						equal_resolution=lowercase				,						numpify=lowercase			)
            for image in image_inputs:
                  self.assertIsInstance(lowercase				,						np.ndarray			)

            # Test not batched input
            lowerCAmelCase  						=	image_processing(image_inputs[0]				,						return_tensors="""pt"""			).pixel_values
            self.assertEqual(
                encoded_images.shape				,						(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )				,						)

            # Test batched
            lowerCAmelCase  						=	image_processing(lowercase				,						return_tensors="""pt"""			).pixel_values
            self.assertEqual(
                encoded_images.shape				,						(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )				,						)





      def    _snake_case      (	self			)		->					Optional[int]:
            # Initialize image_processing
            lowerCAmelCase  						=	self.image_processing_class(**self.image_processor_dict			)
            # create random PyTorch tensors
            lowerCAmelCase  						=	prepare_image_inputs(self.image_processor_tester				,						equal_resolution=lowercase				,						torchify=lowercase			)
            for image in image_inputs:
                  self.assertIsInstance(lowercase				,						torch.Tensor			)

            # Test not batched input
            lowerCAmelCase  						=	image_processing(image_inputs[0]				,						return_tensors="""pt"""			).pixel_values
            self.assertEqual(
                encoded_images.shape				,						(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )				,						)

            # Test batched
            lowerCAmelCase  						=	image_processing(lowercase				,						return_tensors="""pt"""			).pixel_values
            self.assertEqual(
                encoded_images.shape				,						(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )				,						)






