


'''simple docstring'''
import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor
class      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_=7					,     UpperCamelCase_=3					,     UpperCamelCase_=18					,     UpperCamelCase_=30					,     UpperCamelCase_=400					,     UpperCamelCase_=True					,     UpperCamelCase_=None					,     UpperCamelCase_=True					,     UpperCamelCase_=None					,     UpperCamelCase_=True					,     UpperCamelCase_=[0.5, 0.5, 0.5]					,     UpperCamelCase_=[0.5, 0.5, 0.5]					,     UpperCamelCase_=False					,     ):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				size if size is not None else {'''height''': 20, '''width''': 20}
							UpperCamelCase__     :Any							      =				crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
							UpperCamelCase__     :str							      =				parent
							UpperCamelCase__     :Any							      =				batch_size
							UpperCamelCase__     :Optional[Any]							      =				num_channels
							UpperCamelCase__     :List[str]							      =				image_size
							UpperCamelCase__     :Optional[int]							      =				min_resolution
							UpperCamelCase__     :str							      =				max_resolution
							UpperCamelCase__     :Dict							      =				do_resize
							UpperCamelCase__     :Optional[int]							      =				size
							UpperCamelCase__     :Any							      =				do_center_crop
							UpperCamelCase__     :Union[str, Any]							      =				crop_size
							UpperCamelCase__     :Dict							      =				do_normalize
							UpperCamelCase__     :Union[str, Any]							      =				image_mean
							UpperCamelCase__     :int							      =				image_std
							UpperCamelCase__     :Optional[int]							      =				do_reduce_labels




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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



def     a   (    )						->			Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__     :Any							      =				load_dataset('''hf-internal-testing/fixtures_ade20k'''				,      split='''test'''       )

					UpperCamelCase__     :Union[str, Any]							      =				Image.open(dataset[0]['''file''']       )
					UpperCamelCase__     :Dict							      =				Image.open(dataset[1]['''file''']       )

					return image, map


def     a   (    )						->			Dict:



					'''simple docstring'''






					UpperCamelCase__     :List[str]							      =				load_dataset('''hf-internal-testing/fixtures_ade20k'''				,      split='''test'''       )

					UpperCamelCase__     :Tuple							      =				Image.open(ds[0]['''file''']       )
					UpperCamelCase__     :int							      =				Image.open(ds[1]['''file''']       )
					UpperCamelCase__     :Any							      =				Image.open(ds[2]['''file''']       )
					UpperCamelCase__     :List[str]							      =				Image.open(ds[3]['''file''']       )

					return [imagea, imagea], [mapa, mapa]







@require_torch
@require_vision
class      lowercase			(							_UpperCAmelCase			,	unittest.TestCase      ):

		"""simple docstring"""

		_a									=       BeitImageProcessor if is_vision_available() else None




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[Any]							      =				BeitImageProcessingTester(self							)




		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							return self.image_processor_tester.prepare_image_processor_dict()




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				self.image_processing_class(**self.image_processor_dict							)
							self.assertTrue(hasattr(lowerCamelCase_					,     '''do_resize'''							)							)
							self.assertTrue(hasattr(lowerCamelCase_					,     '''size'''							)							)
							self.assertTrue(hasattr(lowerCamelCase_					,     '''do_center_crop'''							)							)
							self.assertTrue(hasattr(lowerCamelCase_					,     '''center_crop'''							)							)
							self.assertTrue(hasattr(lowerCamelCase_					,     '''do_normalize'''							)							)
							self.assertTrue(hasattr(lowerCamelCase_					,     '''image_mean'''							)							)
							self.assertTrue(hasattr(lowerCamelCase_					,     '''image_std'''							)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				self.image_processing_class.from_dict(self.image_processor_dict							)
							self.assertEqual(image_processor.size					,     {'''height''': 20, '''width''': 20}							)
							self.assertEqual(image_processor.crop_size					,     {'''height''': 18, '''width''': 18}							)
							self.assertEqual(image_processor.do_reduce_labels					,     lowerCamelCase_							)

							UpperCamelCase__     :int							      =				self.image_processing_class.from_dict(
							    self.image_processor_dict					,     size=42					,     crop_size=84					,     reduce_labels=lowerCamelCase_							)
							self.assertEqual(image_processor.size					,     {'''height''': 42, '''width''': 42}							)
							self.assertEqual(image_processor.crop_size					,     {'''height''': 84, '''width''': 84}							)
							self.assertEqual(image_processor.do_reduce_labels					,     lowerCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							pass




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				self.image_processing_class(**self.image_processor_dict							)
							# create random PIL images
							UpperCamelCase__     :Union[str, Any]							      =				prepare_image_inputs(self.image_processor_tester					,     equal_resolution=lowerCamelCase_							)
							for image in image_inputs:
												self.assertIsInstance(lowerCamelCase_					,     Image.Image							)

							# Test not batched input
							UpperCamelCase__     :Union[str, Any]							      =				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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )					,     )

							# Test batched
							UpperCamelCase__     :Tuple							      =				image_processing(lowerCamelCase_					,     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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )					,     )




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				self.image_processing_class(**self.image_processor_dict							)
							# create random numpy tensors
							UpperCamelCase__     :List[Any]							      =				prepare_image_inputs(self.image_processor_tester					,     equal_resolution=lowerCamelCase_					,     numpify=lowerCamelCase_							)
							for image in image_inputs:
												self.assertIsInstance(lowerCamelCase_					,     np.ndarray							)

							# Test not batched input
							UpperCamelCase__     :Optional[int]							      =				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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )					,     )

							# Test batched
							UpperCamelCase__     :Union[str, Any]							      =				image_processing(lowerCamelCase_					,     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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )					,     )




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				self.image_processing_class(**self.image_processor_dict							)
							# create random PyTorch tensors
							UpperCamelCase__     :Optional[Any]							      =				prepare_image_inputs(self.image_processor_tester					,     equal_resolution=lowerCamelCase_					,     torchify=lowerCamelCase_							)
							for image in image_inputs:
												self.assertIsInstance(lowerCamelCase_					,     torch.Tensor							)

							# Test not batched input
							UpperCamelCase__     :int							      =				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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )					,     )

							# Test batched
							UpperCamelCase__     :Tuple							      =				image_processing(lowerCamelCase_					,     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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )					,     )




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				self.image_processing_class(**self.image_processor_dict							)
							# create random PyTorch tensors
							UpperCamelCase__     :str							      =				prepare_image_inputs(self.image_processor_tester					,     equal_resolution=lowerCamelCase_					,     torchify=lowerCamelCase_							)
							UpperCamelCase__     :Optional[int]							      =				[]
							for image in image_inputs:
												self.assertIsInstance(lowerCamelCase_					,     torch.Tensor							)
												maps.append(torch.zeros(image.shape[-2:]							).long()							)

							# Test not batched input
							UpperCamelCase__     :List[str]							      =				image_processing(image_inputs[0]					,     maps[0]					,     return_tensors='''pt'''							)
							self.assertEqual(
							    encoding['''pixel_values'''].shape					,     (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )					,     )
							self.assertEqual(
							    encoding['''labels'''].shape					,     (
							        1,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )					,     )
							self.assertEqual(encoding['''labels'''].dtype					,     torch.long							)
							self.assertTrue(encoding['''labels'''].min().item() >= 0							)
							self.assertTrue(encoding['''labels'''].max().item() <= 255							)

							# Test batched
							UpperCamelCase__     :str							      =				image_processing(lowerCamelCase_					,     lowerCamelCase_					,     return_tensors='''pt'''							)
							self.assertEqual(
							    encoding['''pixel_values'''].shape					,     (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )					,     )
							self.assertEqual(
							    encoding['''labels'''].shape					,     (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )					,     )
							self.assertEqual(encoding['''labels'''].dtype					,     torch.long							)
							self.assertTrue(encoding['''labels'''].min().item() >= 0							)
							self.assertTrue(encoding['''labels'''].max().item() <= 255							)

							# Test not batched input (PIL images)
							UpperCamelCase__     :Dict							      =				prepare_semantic_single_inputs()

							UpperCamelCase__     :Union[str, Any]							      =				image_processing(lowerCamelCase_					,     lowerCamelCase_					,     return_tensors='''pt'''							)
							self.assertEqual(
							    encoding['''pixel_values'''].shape					,     (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )					,     )
							self.assertEqual(
							    encoding['''labels'''].shape					,     (
							        1,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )					,     )
							self.assertEqual(encoding['''labels'''].dtype					,     torch.long							)
							self.assertTrue(encoding['''labels'''].min().item() >= 0							)
							self.assertTrue(encoding['''labels'''].max().item() <= 255							)

							# Test batched input (PIL images)
							UpperCamelCase__     :int							      =				prepare_semantic_batch_inputs()

							UpperCamelCase__     :List[str]							      =				image_processing(lowerCamelCase_					,     lowerCamelCase_					,     return_tensors='''pt'''							)
							self.assertEqual(
							    encoding['''pixel_values'''].shape					,     (
							        2,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )					,     )
							self.assertEqual(
							    encoding['''labels'''].shape					,     (
							        2,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )					,     )
							self.assertEqual(encoding['''labels'''].dtype					,     torch.long							)
							self.assertTrue(encoding['''labels'''].min().item() >= 0							)
							self.assertTrue(encoding['''labels'''].max().item() <= 255							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				self.image_processing_class(**self.image_processor_dict							)

							# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
							UpperCamelCase__     :str							      =				prepare_semantic_single_inputs()
							UpperCamelCase__     :Any							      =				image_processing(lowerCamelCase_					,     lowerCamelCase_					,     return_tensors='''pt'''							)
							self.assertTrue(encoding['''labels'''].min().item() >= 0							)
							self.assertTrue(encoding['''labels'''].max().item() <= 150							)

							UpperCamelCase__     :int							      =				True
							UpperCamelCase__     :List[Any]							      =				image_processing(lowerCamelCase_					,     lowerCamelCase_					,     return_tensors='''pt'''							)
							self.assertTrue(encoding['''labels'''].min().item() >= 0							)
							self.assertTrue(encoding['''labels'''].max().item() <= 255							)



'''simple docstring'''
import json
import sys


def     a   (    __a				,      __a     )						->			str:



					'''simple docstring'''






					with open(__a				,      encoding='''utf-8'''     ) as f:
										UpperCamelCase__     :List[str]							      =				json.load(__a     )

					UpperCamelCase__     :int							      =				['''<details>''', '''<summary>Show updated benchmarks!</summary>''', ''' ''']

					for benchmark_name in sorted(__a     ):
										UpperCamelCase__     :Optional[Any]							      =				results[benchmark_name]

										UpperCamelCase__     :int							      =				benchmark_name.split('''/'''     )[-1]
										output_md.append(f'''### Benchmark: {benchmark_file_name}'''     )

										UpperCamelCase__     :List[str]							      =				'''| metric |'''
										UpperCamelCase__     :str							      =				'''|--------|'''
										UpperCamelCase__     :Union[str, Any]							      =				'''| new / old (diff) |'''
										for metric_name in sorted(__a     ):
															UpperCamelCase__     :List[Any]							      =				benchmark_res[metric_name]
															UpperCamelCase__     :Optional[int]							      =				metric_vals['''new''']
															UpperCamelCase__     :Any							      =				metric_vals.get('''old'''				,      __a     )
															UpperCamelCase__     :Optional[int]							      =				metric_vals.get('''diff'''				,      __a     )

															UpperCamelCase__     :List[str]							      =				f''' {new_val:f}''' if isinstance(__a				,      (int, float)     ) else '''None'''

															if old_val is not None:
																				val_str += f''' / {old_val:f}''' if isinstance(__a				,      (int, float)     ) else "None"
															if dif_val is not None:
																				val_str += f''' ({dif_val:f})''' if isinstance(__a				,      (int, float)     ) else "None"

															title += " " + metric_name + " |"
															lines += "---|"
															value += val_str + " |"

										output_md += [title, lines, value, " "]

					output_md.append('''</details>'''     )

					with open(__a				,      '''w'''				,      encoding='''utf-8'''     ) as f:
										f.writelines('''\n'''.join(__a     )     )


if __name__ == "__main__":
		__snake_case							   =  sys.argv[1]
		__snake_case							   =  sys.argv[2]

		format_json_to_md(input_json_file, output_md_file)