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 DetaImageProcessor
class  A_			(							unittest.TestCase			):

  def __init__(		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=7  ,				__SCREAMING_SNAKE_CASE			:   Any=3  ,				__SCREAMING_SNAKE_CASE			:   int=30  ,				__SCREAMING_SNAKE_CASE			:   int=4_00  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=True  ,				__SCREAMING_SNAKE_CASE			:   int=None  ,				__SCREAMING_SNAKE_CASE			:   str=True  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=[0.5, 0.5, 0.5]  ,				__SCREAMING_SNAKE_CASE			:   Any=[0.5, 0.5, 0.5]  ,				__SCREAMING_SNAKE_CASE			:   Dict=True  ,				__SCREAMING_SNAKE_CASE			:   Any=1 / 2_55  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=True  ,				):
   __a							=	size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
   __a							=	parent
   __a							=	batch_size
   __a							=	num_channels
   __a							=	min_resolution
   __a							=	max_resolution
   __a							=	do_resize
   __a							=	size
   __a							=	do_normalize
   __a							=	image_mean
   __a							=	image_std
   __a							=	do_rescale
   __a							=	rescale_factor
   __a							=	do_pad

  def 		_UpperCAmelCase (		self			:   Tuple							):
   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 		_UpperCAmelCase (		self			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   str=False							):




   if not batched:
    __a							=	image_inputs[0]
    if isinstance(__SCREAMING_SNAKE_CASE  ,				Image.Image							):
     __a							=	image.size
    else:
     __a							=	image.shape[1], image.shape[2]
    if w < h:
     __a							=	int(self.size["shortest_edge"] * h / w							)
     __a							=	self.size["shortest_edge"]
    elif w > h:
     __a							=	self.size["shortest_edge"]
     __a							=	int(self.size["shortest_edge"] * w / h							)
    else:
     __a							=	self.size["shortest_edge"]
     __a							=	self.size["shortest_edge"]

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

   return expected_height, expected_width
@require_torch
@require_vision
class  A_			(							lowerCamelCase__      ,							unittest.TestCase			):
  _SCREAMING_SNAKE_CASE     					=      DetaImageProcessor if is_vision_available() else None

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   __a							=	DetaImageProcessingTester(self							)

  @property
  def 		_UpperCAmelCase (		self			:   str							):
   return self.image_processor_tester.prepare_image_processor_dict()

  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	self.image_processing_class(**self.image_processor_dict							)
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,				"image_mean"							)							)
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,				"image_std"							)							)
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,				"do_normalize"							)							)
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,				"do_resize"							)							)
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,				"do_rescale"							)							)
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,				"do_pad"							)							)
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,				"size"							)							)

  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   __a							=	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  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Tuple							):
   pass

  def 		_UpperCAmelCase (		self			:   Dict							):
   __a							=	self.image_processing_class(**self.image_processor_dict							)
   # create random PIL images
   __a							=	prepare_image_inputs(self.image_processor_tester  ,				equal_resolution=__SCREAMING_SNAKE_CASE							)
   for image in image_inputs:
    self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,				Image.Image							)

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

   __a							=	self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE							)

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

   # Test batched
   __a							=	self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE  ,				batched=__SCREAMING_SNAKE_CASE							)

   __a							=	image_processing(__SCREAMING_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 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	self.image_processing_class(**self.image_processor_dict							)
   # create random numpy tensors
   __a							=	prepare_image_inputs(self.image_processor_tester  ,				equal_resolution=__SCREAMING_SNAKE_CASE  ,				numpify=__SCREAMING_SNAKE_CASE							)
   for image in image_inputs:
    self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,				np.ndarray							)

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

   __a							=	self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE							)

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

   # Test batched
   __a							=	image_processing(__SCREAMING_SNAKE_CASE  ,				return_tensors="pt"							).pixel_values

   __a							=	self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE  ,				batched=__SCREAMING_SNAKE_CASE							)

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

  def 		_UpperCAmelCase (		self			:   List[str]							):
   __a							=	self.image_processing_class(**self.image_processor_dict							)
   # create random PyTorch tensors
   __a							=	prepare_image_inputs(self.image_processor_tester  ,				equal_resolution=__SCREAMING_SNAKE_CASE  ,				torchify=__SCREAMING_SNAKE_CASE							)
   for image in image_inputs:
    self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,				torch.Tensor							)

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

   __a							=	self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE							)

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

   # Test batched
   __a							=	image_processing(__SCREAMING_SNAKE_CASE  ,				return_tensors="pt"							).pixel_values

   __a							=	self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE  ,				batched=__SCREAMING_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 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
   with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"  ,				"r"							) as f:
    __a							=	json.loads(f.read()							)

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

   # encode them
   __a							=	DetaImageProcessor()
   __a							=	image_processing(images=__SCREAMING_SNAKE_CASE  ,				annotations=__SCREAMING_SNAKE_CASE  ,				return_tensors="pt"							)

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

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

   # verify area
   __a							=	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"]  ,				__SCREAMING_SNAKE_CASE							)							)
   # verify boxes
   __a							=	torch.Size([6, 4]							)
   self.assertEqual(encoding["labels"][0]["boxes"].shape  ,				__SCREAMING_SNAKE_CASE							)
   __a							=	torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]							)
   self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]  ,				__SCREAMING_SNAKE_CASE  ,				atol=1E-3							)							)
   # verify image_id
   __a							=	torch.tensor([3_97_69]							)
   self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]  ,				__SCREAMING_SNAKE_CASE							)							)
   # verify is_crowd
   __a							=	torch.tensor([0, 0, 0, 0, 0, 0]							)
   self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]  ,				__SCREAMING_SNAKE_CASE							)							)
   # verify class_labels
   __a							=	torch.tensor([75, 75, 63, 65, 17, 17]							)
   self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]  ,				__SCREAMING_SNAKE_CASE							)							)
   # verify orig_size
   __a							=	torch.tensor([4_80, 6_40]							)
   self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]  ,				__SCREAMING_SNAKE_CASE							)							)
   # verify size
   __a							=	torch.tensor([8_00, 10_66]							)
   self.assertTrue(torch.allclose(encoding["labels"][0]["size"]  ,				__SCREAMING_SNAKE_CASE							)							)

  @slow
  def 		_UpperCAmelCase (		self			:   int							):
   __a							=	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
   with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"  ,				"r"							) as f:
    __a							=	json.loads(f.read()							)

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

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

   # encode them
   __a							=	DetaImageProcessor(format="coco_panoptic"							)
   __a							=	image_processing(images=__SCREAMING_SNAKE_CASE  ,				annotations=__SCREAMING_SNAKE_CASE  ,				masks_path=__SCREAMING_SNAKE_CASE  ,				return_tensors="pt"							)

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

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

   # verify area
   __a							=	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"]  ,				__SCREAMING_SNAKE_CASE							)							)
   # verify boxes
   __a							=	torch.Size([6, 4]							)
   self.assertEqual(encoding["labels"][0]["boxes"].shape  ,				__SCREAMING_SNAKE_CASE							)
   __a							=	torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]							)
   self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]  ,				__SCREAMING_SNAKE_CASE  ,				atol=1E-3							)							)
   # verify image_id
   __a							=	torch.tensor([3_97_69]							)
   self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]  ,				__SCREAMING_SNAKE_CASE							)							)
   # verify is_crowd
   __a							=	torch.tensor([0, 0, 0, 0, 0, 0]							)
   self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]  ,				__SCREAMING_SNAKE_CASE							)							)
   # verify class_labels
   __a							=	torch.tensor([17, 17, 63, 75, 75, 93]							)
   self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]  ,				__SCREAMING_SNAKE_CASE							)							)
   # verify masks
   __a							=	82_28_73
   self.assertEqual(encoding["labels"][0]["masks"].sum().item()  ,				__SCREAMING_SNAKE_CASE							)
   # verify orig_size
   __a							=	torch.tensor([4_80, 6_40]							)
   self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]  ,				__SCREAMING_SNAKE_CASE							)							)
   # verify size
   __a							=	torch.tensor([8_00, 10_66]							)
   self.assertTrue(torch.allclose(encoding["labels"][0]["size"]  ,				__SCREAMING_SNAKE_CASE							)							)









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    (       _lowerCAmelCase			:			Optional[int]=None					,					_lowerCAmelCase			:			List[Any]=None						)			->    Optional[Any]:
   return field(default_factory=lambda: default					,					metadata=_lowerCAmelCase						)


@dataclass
class        __magic_name__	:





     """simple docstring"""



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




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

def   __snake_case    (       _lowerCAmelCase			:			str						)			->    Union[str, Any]:
   try:
      float(_lowerCAmelCase						)
      return True
   except ValueError:
      return False


class        __magic_name__	:





     """simple docstring"""



     def __init__(				self							:Any      ,   snake_case							:List[Any]	):





        '''simple docstring'''




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

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





     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




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

           ((A_)   ,    (A_))       :					Optional[Any]        =      (
               (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
           )

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

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

              A_       :					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."

        A_       :					Optional[int]        =      title_str[:-4]
        A_       :					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   __snake_case    (       )			->    Any:
   A_       :					Any        =      HfArgumentParser(_lowerCAmelCase						)
   A_       :					str        =      parser.parse_args_into_dataclasses()[0]
   A_       :					Optional[Any]        =      Plot(args=_lowerCAmelCase						)
   plot.plot()


if __name__ == "__main__":
 main()
