





'''simple docstring'''






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 ConditionalDetrImageProcessor




class 			UpperCAmelCase					(      unittest.TestCase       ):




						def __init__(      self      :			Optional[int]      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			str=7      ,      __snake_case      :			str=3      ,      __snake_case      :			Union[str, Any]=30      ,      __snake_case      :			Tuple=4_00      ,      __snake_case      :			str=True      ,      __snake_case      :			List[Any]=None      ,      __snake_case      :			str=True      ,      __snake_case      :			Tuple=[0.5, 0.5, 0.5]      ,      __snake_case      :			Optional[Any]=[0.5, 0.5, 0.5]      ,      __snake_case      :			Dict=True      ,      __snake_case      :			int=1 / 2_55      ,      __snake_case      :			Dict=True      ,      )    ->				Tuple:
												# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
												_lowerCAmelCase												=							size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
												_lowerCAmelCase												=							parent
												_lowerCAmelCase												=							batch_size
												_lowerCAmelCase												=							num_channels
												_lowerCAmelCase												=							min_resolution
												_lowerCAmelCase												=							max_resolution
												_lowerCAmelCase												=							do_resize
												_lowerCAmelCase												=							size
												_lowerCAmelCase												=							do_normalize
												_lowerCAmelCase												=							image_mean
												_lowerCAmelCase												=							image_std
												_lowerCAmelCase												=							do_rescale
												_lowerCAmelCase												=							rescale_factor
												_lowerCAmelCase												=							do_pad




						def     lowercase__   (      self      :			Dict  )    ->				List[str]:
												return {
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_normalize": self.do_normalize,
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_rescale": self.do_rescale,
												    "rescale_factor": self.rescale_factor,
												    "do_pad": self.do_pad,
												}




						def     lowercase__   (      self      :			str      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			str=False  )    ->				Tuple:
												if not batched:
																		_lowerCAmelCase												=							image_inputs[0]
																		if isinstance(snake_case_      ,      Image.Image  ):
																								_lowerCAmelCase												=							image.size
																		else:
																								_lowerCAmelCase												=							image.shape[1], image.shape[2]
																		if w < h:
																								_lowerCAmelCase												=							int(self.size["""shortest_edge"""] * h / w  )
																								_lowerCAmelCase												=							self.size["""shortest_edge"""]
																		elif w > h:
																								_lowerCAmelCase												=							self.size["""shortest_edge"""]
																								_lowerCAmelCase												=							int(self.size["""shortest_edge"""] * w / h  )
																		else:
																								_lowerCAmelCase												=							self.size["""shortest_edge"""]
																								_lowerCAmelCase												=							self.size["""shortest_edge"""]

												else:
																		_lowerCAmelCase												=							[]
																		for image in image_inputs:
																								_lowerCAmelCase												=							self.get_expected_values([image]  )
																								expected_values.append((expected_height, expected_width)  )
																		_lowerCAmelCase												=							max(snake_case_      ,      key=lambda __snake_case  : item[0]  )[0]
																		_lowerCAmelCase												=							max(snake_case_      ,      key=lambda __snake_case  : item[1]  )[1]

												return expected_height, expected_width




@require_torch
@require_vision
class 			UpperCAmelCase					(      _a							,		unittest.TestCase       ):
						_lowercase:      Optional[Any]											=       ConditionalDetrImageProcessor if is_vision_available() else None




						def     lowercase__   (      self      :			Any  )    ->				Optional[Any]:
												_lowerCAmelCase												=							ConditionalDetrImageProcessingTester(self  )




						@property
						def     lowercase__   (      self      :			str  )    ->				Dict:
												return self.image_processor_tester.prepare_image_processor_dict()




						def     lowercase__   (      self      :			List[Any]  )    ->				int:
												_lowerCAmelCase												=							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_      ,      """size"""  )  )




						def     lowercase__   (      self      :			int  )    ->				List[Any]:
												_lowerCAmelCase												=							self.image_processing_class.from_dict(self.image_processor_dict  )
												self.assertEqual(image_processor.size      ,      {"""shortest_edge""": 18, """longest_edge""": 13_33}  )
												self.assertEqual(image_processor.do_pad      ,      snake_case_  )

												_lowerCAmelCase												=							self.image_processing_class.from_dict(
												    self.image_processor_dict      ,      size=42      ,      max_size=84      ,      pad_and_return_pixel_mask=snake_case_  )
												self.assertEqual(image_processor.size      ,      {"""shortest_edge""": 42, """longest_edge""": 84}  )
												self.assertEqual(image_processor.do_pad      ,      snake_case_  )




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




						def     lowercase__   (      self      :			Any  )    ->				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=snake_case_  )
												for image in image_inputs:
																		self.assertIsInstance(snake_case_      ,      Image.Image  )

												# Test not batched input
												_lowerCAmelCase												=							image_processing(image_inputs[0]      ,      return_tensors="""pt"""  ).pixel_values

												_lowerCAmelCase												=							self.image_processor_tester.get_expected_values(snake_case_  )

												self.assertEqual(
												    encoded_images.shape      ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,      )

												# Test batched
												_lowerCAmelCase												=							self.image_processor_tester.get_expected_values(snake_case_      ,      batched=snake_case_  )

												_lowerCAmelCase												=							image_processing(snake_case_      ,      return_tensors="""pt"""  ).pixel_values
												self.assertEqual(
												    encoded_images.shape      ,      (
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )      ,      )




						def     lowercase__   (      self      :			Dict  )    ->				Optional[int]:
												# 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=snake_case_      ,      numpify=snake_case_  )
												for image in image_inputs:
																		self.assertIsInstance(snake_case_      ,      np.ndarray  )

												# Test not batched input
												_lowerCAmelCase												=							image_processing(image_inputs[0]      ,      return_tensors="""pt"""  ).pixel_values

												_lowerCAmelCase												=							self.image_processor_tester.get_expected_values(snake_case_  )

												self.assertEqual(
												    encoded_images.shape      ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,      )

												# Test batched
												_lowerCAmelCase												=							image_processing(snake_case_      ,      return_tensors="""pt"""  ).pixel_values

												_lowerCAmelCase												=							self.image_processor_tester.get_expected_values(snake_case_      ,      batched=snake_case_  )

												self.assertEqual(
												    encoded_images.shape      ,      (
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )      ,      )




						def     lowercase__   (      self      :			str  )    ->				List[str]:
												# 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=snake_case_      ,      torchify=snake_case_  )
												for image in image_inputs:
																		self.assertIsInstance(snake_case_      ,      torch.Tensor  )

												# Test not batched input
												_lowerCAmelCase												=							image_processing(image_inputs[0]      ,      return_tensors="""pt"""  ).pixel_values

												_lowerCAmelCase												=							self.image_processor_tester.get_expected_values(snake_case_  )

												self.assertEqual(
												    encoded_images.shape      ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,      )

												# Test batched
												_lowerCAmelCase												=							image_processing(snake_case_      ,      return_tensors="""pt"""  ).pixel_values

												_lowerCAmelCase												=							self.image_processor_tester.get_expected_values(snake_case_      ,      batched=snake_case_  )

												self.assertEqual(
												    encoded_images.shape      ,      (
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )      ,      )




						@slow
						def     lowercase__   (      self      :			Optional[int]  )    ->				Tuple:
												# prepare image and target
												_lowerCAmelCase												=							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
												with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""      ,      """r"""  ) as f:
																		_lowerCAmelCase												=							json.loads(f.read()  )

												_lowerCAmelCase												=							{"""image_id""": 3_97_69, """annotations""": target}

												# encode them
												_lowerCAmelCase												=							ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50"""  )
												_lowerCAmelCase												=							image_processing(images=snake_case_      ,      annotations=snake_case_      ,      return_tensors="""pt"""  )

												# verify pixel values
												_lowerCAmelCase												=							torch.Size([1, 3, 8_00, 10_66]  )
												self.assertEqual(encoding["""pixel_values"""].shape      ,      snake_case_  )

												_lowerCAmelCase												=							torch.tensor([0.27_96, 0.31_38, 0.34_81]  )
												self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,      snake_case_      ,      atol=1E-4  )  )

												# verify area
												_lowerCAmelCase												=							torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]  )
												self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,      snake_case_  )  )
												# verify boxes
												_lowerCAmelCase												=							torch.Size([6, 4]  )
												self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,      snake_case_  )
												_lowerCAmelCase												=							torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]  )
												self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,      snake_case_      ,      atol=1E-3  )  )
												# verify image_id
												_lowerCAmelCase												=							torch.tensor([3_97_69]  )
												self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,      snake_case_  )  )
												# verify is_crowd
												_lowerCAmelCase												=							torch.tensor([0, 0, 0, 0, 0, 0]  )
												self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,      snake_case_  )  )
												# verify class_labels
												_lowerCAmelCase												=							torch.tensor([75, 75, 63, 65, 17, 17]  )
												self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,      snake_case_  )  )
												# verify orig_size
												_lowerCAmelCase												=							torch.tensor([4_80, 6_40]  )
												self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,      snake_case_  )  )
												# verify size
												_lowerCAmelCase												=							torch.tensor([8_00, 10_66]  )
												self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,      snake_case_  )  )




						@slow
						def     lowercase__   (      self      :			Tuple  )    ->				int:
												# prepare image, target and masks_path
												_lowerCAmelCase												=							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
												with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""      ,      """r"""  ) as f:
																		_lowerCAmelCase												=							json.loads(f.read()  )

												_lowerCAmelCase												=							{"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

												_lowerCAmelCase												=							pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""  )

												# encode them
												_lowerCAmelCase												=							ConditionalDetrImageProcessor(format="""coco_panoptic"""  )
												_lowerCAmelCase												=							image_processing(images=snake_case_      ,      annotations=snake_case_      ,      masks_path=snake_case_      ,      return_tensors="""pt"""  )

												# verify pixel values
												_lowerCAmelCase												=							torch.Size([1, 3, 8_00, 10_66]  )
												self.assertEqual(encoding["""pixel_values"""].shape      ,      snake_case_  )

												_lowerCAmelCase												=							torch.tensor([0.27_96, 0.31_38, 0.34_81]  )
												self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,      snake_case_      ,      atol=1E-4  )  )

												# verify area
												_lowerCAmelCase												=							torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]  )
												self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,      snake_case_  )  )
												# verify boxes
												_lowerCAmelCase												=							torch.Size([6, 4]  )
												self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,      snake_case_  )
												_lowerCAmelCase												=							torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]  )
												self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,      snake_case_      ,      atol=1E-3  )  )
												# verify image_id
												_lowerCAmelCase												=							torch.tensor([3_97_69]  )
												self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,      snake_case_  )  )
												# verify is_crowd
												_lowerCAmelCase												=							torch.tensor([0, 0, 0, 0, 0, 0]  )
												self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,      snake_case_  )  )
												# verify class_labels
												_lowerCAmelCase												=							torch.tensor([17, 17, 63, 75, 75, 93]  )
												self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,      snake_case_  )  )
												# verify masks
												_lowerCAmelCase												=							82_28_73
												self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()      ,      snake_case_  )
												# verify orig_size
												_lowerCAmelCase												=							torch.tensor([4_80, 6_40]  )
												self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,      snake_case_  )  )
												# verify size
												_lowerCAmelCase												=							torch.tensor([8_00, 10_66]  )
												self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,      snake_case_  )  )













'''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__				(		lowerCAmelCase=None							,     lowerCAmelCase=None  ):





      """simple docstring"""


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




@dataclass
class 			UpperCAmelCase					:
      _lowercase:      str											=       field(
          metadata={'''help''': '''The csv file to plot.'''}							,		)
      _lowercase:      bool											=       field(
          default=snake_case_							,		metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}							,		)
      _lowercase:      bool											=       field(
          default=snake_case_							,		metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}							,		)
      _lowercase:      bool											=       field(
          default=snake_case_							,		metadata={'''help''': '''Disable logarithmic scale when plotting'''}							,		)
      _lowercase:      bool											=       field(
          default=snake_case_							,		metadata={
              '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
          }							,		)
      _lowercase:      Optional[str]											=       field(
          default=snake_case_							,		metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}							,		)
      _lowercase:      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        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


      try:
            int(lowerCAmelCase  )
            return True
      except ValueError:
            return False
def        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


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




class 			UpperCAmelCase					:




      def __init__(      self      :			List[str]      ,      __snake_case      :			Union[str, Any]  )    ->				int:
            _lowerCAmelCase												=							args
            _lowerCAmelCase												=							defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}  )

            with open(self.args.csv_file      ,      newline=""""""  ) as csv_file:
                  _lowerCAmelCase												=							csv.DictReader(__snake_case  )
                  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     lowercase__   (      self      :			Dict  )    ->				str:
            _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=__snake_case      ,      )
                        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(__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."

            _lowerCAmelCase												=							title_str[:-4]
            _lowerCAmelCase												=							"""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        UpperCamelCase__				(		):





      """simple docstring"""


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


if __name__ == "__main__":
      main()






