






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 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Tuple=None					,  snake_case_ :							str=None       ):
	return field(default_factory=lambda: default					,  metadata=lowercase_       )



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







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Union[str, Any]       ):
	try:
		int(lowercase_       )
		return True
	except ValueError:
		return False







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Union[str, Any]       ):
	try:
		float(lowercase_       )
		return True
	except ValueError:
		return False



class 							SCREAMING_SNAKE_CASE__						:



					def __init__( self					:  Union[str, Any]							,		__A					:  int	):
						snake_case__			:				str       =  args
						snake_case__			:				Any       =  defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

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






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

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

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

								((snake_case__),						(snake_case__))			:				str       =  (
								    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
								)

								snake_case__			:				str       =  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.'''

						snake_case__			:				Any       =  title_str[:-4]
						snake_case__			:				List[str]       =  "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 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				Any       =  HfArgumentParser(lowercase_       )
	snake_case__			:				List[Any]       =  parser.parse_args_into_dataclasses()[0]
	snake_case__			:				Dict       =  Plot(args=lowercase_       )
	plot.plot()


if __name__ == "__main__":
	main()











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 DeformableDetrImageProcessor



class 							SCREAMING_SNAKE_CASE__						(    unittest.TestCase						):





					"""simple docstring"""






					def __init__( self					:  List[str]							,		__A					:  int							,		__A					:  str=7							,		__A					:  Union[str, Any]=3							,		__A					:  Union[str, Any]=3_0							,		__A					:  Optional[int]=4_0_0							,		__A					:  Optional[Any]=True							,		__A					:  Optional[int]=None							,		__A					:  Union[str, Any]=True							,		__A					:  Optional[int]=[0.5, 0.5, 0.5]							,		__A					:  Any=[0.5, 0.5, 0.5]							,		__A					:  Optional[int]=True							,		__A					:  Optional[Any]=1 / 2_5_5							,		__A					:  Union[str, Any]=True							,		):
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						snake_case__			:				Optional[Any]       =  size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
						snake_case__			:				List[Any]       =  parent
						snake_case__			:				Union[str, Any]       =  batch_size
						snake_case__			:				Tuple       =  num_channels
						snake_case__			:				List[Any]       =  min_resolution
						snake_case__			:				Optional[Any]       =  max_resolution
						snake_case__			:				str       =  do_resize
						snake_case__			:				List[str]       =  size
						snake_case__			:				List[Any]       =  do_normalize
						snake_case__			:				Dict       =  image_mean
						snake_case__			:				List[Any]       =  image_std
						snake_case__			:				int       =  do_rescale
						snake_case__			:				Tuple       =  rescale_factor
						snake_case__			:				str       =  do_pad



					def 	_lowercase					( self					:  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					:  Optional[Any]							,		__A					:  Dict							,		__A					:  Union[str, Any]=False	):
						if not batched:
							snake_case__			:				List[str]       =  image_inputs[0]
							if isinstance(__A							,		Image.Image	):
								snake_case__,						snake_case__			:				Any       =  image.size
							else:
								snake_case__,						snake_case__			:				List[str]       =  image.shape[1], image.shape[2]
							if w < h:
								snake_case__			:				List[str]       =  int(self.size["shortest_edge"] * h / w	)
								snake_case__			:				Tuple       =  self.size["shortest_edge"]
							elif w > h:
								snake_case__			:				Optional[int]       =  self.size["shortest_edge"]
								snake_case__			:				Union[str, Any]       =  int(self.size["shortest_edge"] * w / h	)
							else:
								snake_case__			:				Optional[Any]       =  self.size["shortest_edge"]
								snake_case__			:				List[Any]       =  self.size["shortest_edge"]

						else:
							snake_case__			:				Union[str, Any]       =  []
							for image in image_inputs:
								snake_case__,						snake_case__			:				int       =  self.get_expected_values([image]	)
								expected_values.append((expected_height, expected_width)	)
							snake_case__			:				Any       =  max(__A							,		key=lambda __A	: item[0]	)[0]
							snake_case__			:				Optional[int]       =  max(__A							,		key=lambda __A	: item[1]	)[1]

						return expected_height, expected_width



@require_torch
@require_vision
class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_			,       unittest.TestCase						):





					"""simple docstring"""






					a_     =		DeformableDetrImageProcessor if is_vision_available() else None



					def 	_lowercase					( self					:  Optional[int]	):
						snake_case__			:				str       =  DeformableDetrImageProcessingTester(self	)



					@property
					def 	_lowercase					( self					:  List[Any]	):
						return self.image_processor_tester.prepare_image_processor_dict()



					def 	_lowercase					( self					:  Union[str, Any]	):
						snake_case__			:				int       =  self.image_processing_class(**self.image_processor_dict	)
						self.assertTrue(hasattr(__A							,		"image_mean"	)	)
						self.assertTrue(hasattr(__A							,		"image_std"	)	)
						self.assertTrue(hasattr(__A							,		"do_normalize"	)	)
						self.assertTrue(hasattr(__A							,		"do_resize"	)	)
						self.assertTrue(hasattr(__A							,		"do_rescale"	)	)
						self.assertTrue(hasattr(__A							,		"do_pad"	)	)
						self.assertTrue(hasattr(__A							,		"size"	)	)



					def 	_lowercase					( self					:  Tuple	):
						snake_case__			:				int       =  self.image_processing_class.from_dict(self.image_processor_dict	)
						self.assertEqual(image_processor.size							,		{"shortest_edge": 1_8, "longest_edge": 1_3_3_3}	)
						self.assertEqual(image_processor.do_pad							,		__A	)

						snake_case__			:				List[Any]       =  self.image_processing_class.from_dict(
						    self.image_processor_dict							,		size=4_2							,		max_size=8_4							,		pad_and_return_pixel_mask=__A	)
						self.assertEqual(image_processor.size							,		{"shortest_edge": 4_2, "longest_edge": 8_4}	)
						self.assertEqual(image_processor.do_pad							,		__A	)



					def 	_lowercase					( self					:  Any	):
						pass



					def 	_lowercase					( self					:  Optional[int]	):
						# Initialize image_processing
						snake_case__			:				List[str]       =  self.image_processing_class(**self.image_processor_dict	)
						# create random PIL images
						snake_case__			:				Tuple       =  prepare_image_inputs(self.image_processor_tester							,		equal_resolution=__A	)
						for image in image_inputs:
							self.assertIsInstance(__A							,		Image.Image	)

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

						snake_case__,						snake_case__			:				Dict       =  self.image_processor_tester.get_expected_values(__A	)

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

						# Test batched
						snake_case__,						snake_case__			:				Optional[Any]       =  self.image_processor_tester.get_expected_values(__A							,		batched=__A	)

						snake_case__			:				Union[str, Any]       =  image_processing(__A							,		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					:  Any	):
						# Initialize image_processing
						snake_case__			:				Optional[Any]       =  self.image_processing_class(**self.image_processor_dict	)
						# create random numpy tensors
						snake_case__			:				List[Any]       =  prepare_image_inputs(self.image_processor_tester							,		equal_resolution=__A							,		numpify=__A	)
						for image in image_inputs:
							self.assertIsInstance(__A							,		np.ndarray	)

						# Test not batched input
						snake_case__			:				Union[str, Any]       =  image_processing(image_inputs[0]							,		return_tensors="pt"	).pixel_values

						snake_case__,						snake_case__			:				Union[str, Any]       =  self.image_processor_tester.get_expected_values(__A	)

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

						# Test batched
						snake_case__			:				Union[str, Any]       =  image_processing(__A							,		return_tensors="pt"	).pixel_values

						snake_case__,						snake_case__			:				Any       =  self.image_processor_tester.get_expected_values(__A							,		batched=__A	)

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



					def 	_lowercase					( self					:  Union[str, Any]	):
						# Initialize image_processing
						snake_case__			:				str       =  self.image_processing_class(**self.image_processor_dict	)
						# create random PyTorch tensors
						snake_case__			:				List[Any]       =  prepare_image_inputs(self.image_processor_tester							,		equal_resolution=__A							,		torchify=__A	)
						for image in image_inputs:
							self.assertIsInstance(__A							,		torch.Tensor	)

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

						snake_case__,						snake_case__			:				List[Any]       =  self.image_processor_tester.get_expected_values(__A	)

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

						# Test batched
						snake_case__			:				Optional[Any]       =  image_processing(__A							,		return_tensors="pt"	).pixel_values

						snake_case__,						snake_case__			:				int       =  self.image_processor_tester.get_expected_values(__A							,		batched=__A	)

						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]	):
						# prepare image and target
						snake_case__			:				List[Any]       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
						with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"							,		"r"	) as f:
							snake_case__			:				Any       =  json.loads(f.read()	)

						snake_case__			:				List[str]       =  {"image_id": 3_9_7_6_9, "annotations": target}

						# encode them
						snake_case__			:				Optional[Any]       =  DeformableDetrImageProcessor()
						snake_case__			:				Tuple       =  image_processing(images=__A							,		annotations=__A							,		return_tensors="pt"	)

						# verify pixel values
						snake_case__			:				str       =  torch.Size([1, 3, 8_0_0, 1_0_6_6]	)
						self.assertEqual(encoding["pixel_values"].shape							,		__A	)

						snake_case__			:				int       =  torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]	)
						self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,		__A							,		atol=1e-4	)	)

						# verify area
						snake_case__			:				int       =  torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,		__A	)	)
						# verify boxes
						snake_case__			:				str       =  torch.Size([6, 4]	)
						self.assertEqual(encoding["labels"][0]["boxes"].shape							,		__A	)
						snake_case__			:				List[Any]       =  torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,		__A							,		atol=1e-3	)	)
						# verify image_id
						snake_case__			:				int       =  torch.tensor([3_9_7_6_9]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,		__A	)	)
						# verify is_crowd
						snake_case__			:				Dict       =  torch.tensor([0, 0, 0, 0, 0, 0]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,		__A	)	)
						# verify class_labels
						snake_case__			:				Optional[int]       =  torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,		__A	)	)
						# verify orig_size
						snake_case__			:				Union[str, Any]       =  torch.tensor([4_8_0, 6_4_0]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,		__A	)	)
						# verify size
						snake_case__			:				List[str]       =  torch.tensor([8_0_0, 1_0_6_6]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,		__A	)	)






					@slow
					def 	_lowercase					( self					:  Union[str, Any]	):
						# prepare image, target and masks_path
						snake_case__			:				str       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
						with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"							,		"r"	) as f:
							snake_case__			:				Optional[int]       =  json.loads(f.read()	)

						snake_case__			:				Any       =  {"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}

						snake_case__			:				List[Any]       =  pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"	)

						# encode them
						snake_case__			:				Dict       =  DeformableDetrImageProcessor(format="coco_panoptic"	)
						snake_case__			:				List[Any]       =  image_processing(images=__A							,		annotations=__A							,		masks_path=__A							,		return_tensors="pt"	)

						# verify pixel values
						snake_case__			:				Optional[int]       =  torch.Size([1, 3, 8_0_0, 1_0_6_6]	)
						self.assertEqual(encoding["pixel_values"].shape							,		__A	)

						snake_case__			:				Tuple       =  torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]	)
						self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,		__A							,		atol=1e-4	)	)

						# verify area
						snake_case__			:				Any       =  torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,		__A	)	)
						# verify boxes
						snake_case__			:				Union[str, Any]       =  torch.Size([6, 4]	)
						self.assertEqual(encoding["labels"][0]["boxes"].shape							,		__A	)
						snake_case__			:				Tuple       =  torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,		__A							,		atol=1e-3	)	)
						# verify image_id
						snake_case__			:				Union[str, Any]       =  torch.tensor([3_9_7_6_9]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,		__A	)	)
						# verify is_crowd
						snake_case__			:				List[Any]       =  torch.tensor([0, 0, 0, 0, 0, 0]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,		__A	)	)
						# verify class_labels
						snake_case__			:				List[str]       =  torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,		__A	)	)
						# verify masks
						snake_case__			:				Optional[Any]       =  8_2_2_8_7_3
						self.assertEqual(encoding["labels"][0]["masks"].sum().item()							,		__A	)
						# verify orig_size
						snake_case__			:				Dict       =  torch.tensor([4_8_0, 6_4_0]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,		__A	)	)
						# verify size
						snake_case__			:				Dict       =  torch.tensor([8_0_0, 1_0_6_6]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,		__A	)	)



