'''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 YolosImageProcessor






class 						A   (   unittest.TestCase   ):





							def __init__(      self				:							Union[str, Any]     ,		__a				:							List[str]     ,		__a				:							List[str]=7     ,		__a				:							Dict=3     ,		__a				:							List[str]=3_0     ,		__a				:							Dict=4_0_0     ,		__a				:							List[str]=True     ,		__a				:							int=None     ,		__a				:							Optional[Any]=True     ,		__a				:							Tuple=[0.5, 0.5, 0.5]     ,		__a				:							List[Any]=[0.5, 0.5, 0.5]     ,		__a				:							Any=True     ,		__a				:							Optional[int]=1 / 2_5_5     ,		__a				:							Any=True     ,		)							->	Dict:
								__UpperCAmelCase							=			size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
								__UpperCAmelCase							=			parent
								__UpperCAmelCase							=			batch_size
								__UpperCAmelCase							=			num_channels
								__UpperCAmelCase							=			min_resolution
								__UpperCAmelCase							=			max_resolution
								__UpperCAmelCase							=			do_resize
								__UpperCAmelCase							=			size
								__UpperCAmelCase							=			do_normalize
								__UpperCAmelCase							=			image_mean
								__UpperCAmelCase							=			image_std
								__UpperCAmelCase							=			do_rescale
								__UpperCAmelCase							=			rescale_factor
								__UpperCAmelCase							=			do_pad





							def       snake_case__						(      self				:							str	)							->	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       snake_case__						(      self				:							List[str]     ,		__a				:							Tuple     ,		__a				:							str=False	)							->	List[str]:


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

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

								return expected_height, expected_width






@require_torch
@require_vision
class 						A   (   UpperCAmelCase_       ,	unittest.TestCase   ):
							a_        =      YolosImageProcessor if is_vision_available() else None





							def       snake_case__						(      self				:							List[Any]	)							->	Optional[int]:
								__UpperCAmelCase							=			YolosImageProcessingTester(self	)





							@property
							def       snake_case__						(      self				:							int	)							->	Union[str, Any]:
								return self.image_processor_tester.prepare_image_processor_dict()





							def       snake_case__						(      self				:							str	)							->	str:
								__UpperCAmelCase							=			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       snake_case__						(      self				:							str	)							->	Optional[Any]:
								__UpperCAmelCase							=			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     ,		_snake_case	)

								__UpperCAmelCase							=			self.image_processing_class.from_dict(
								    self.image_processor_dict     ,		size=4_2     ,		max_size=8_4     ,		pad_and_return_pixel_mask=_snake_case	)
								self.assertEqual(image_processor.size     ,		{'''shortest_edge''': 4_2, '''longest_edge''': 8_4}	)
								self.assertEqual(image_processor.do_pad     ,		_snake_case	)





							def       snake_case__						(      self				:							List[str]	)							->	int:
								pass





							def       snake_case__						(      self				:							Union[str, Any]	)							->	Any:
								__UpperCAmelCase							=			self.image_processing_class(**self.image_processor_dict	)
								# create random PIL images
								__UpperCAmelCase							=			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
								__UpperCAmelCase							=			image_processing(image_inputs[0]     ,		return_tensors='''pt'''	).pixel_values

								__UpperCAmelCase				,			__UpperCAmelCase							=			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
								__UpperCAmelCase				,			__UpperCAmelCase							=			self.image_processor_tester.get_expected_values(_snake_case     ,		batched=_snake_case	)

								__UpperCAmelCase							=			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       snake_case__						(      self				:							Tuple	)							->	List[Any]:
								__UpperCAmelCase							=			self.image_processing_class(**self.image_processor_dict	)
								# create random numpy tensors
								__UpperCAmelCase							=			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
								__UpperCAmelCase							=			image_processing(image_inputs[0]     ,		return_tensors='''pt'''	).pixel_values

								__UpperCAmelCase				,			__UpperCAmelCase							=			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
								__UpperCAmelCase							=			image_processing(_snake_case     ,		return_tensors='''pt'''	).pixel_values

								__UpperCAmelCase				,			__UpperCAmelCase							=			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       snake_case__						(      self				:							Optional[int]	)							->	List[Any]:
								__UpperCAmelCase							=			self.image_processing_class(**self.image_processor_dict	)
								# create random PyTorch tensors
								__UpperCAmelCase							=			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
								__UpperCAmelCase							=			image_processing(image_inputs[0]     ,		return_tensors='''pt'''	).pixel_values

								__UpperCAmelCase				,			__UpperCAmelCase							=			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
								__UpperCAmelCase							=			image_processing(_snake_case     ,		return_tensors='''pt'''	).pixel_values

								__UpperCAmelCase				,			__UpperCAmelCase							=			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       snake_case__						(      self				:							Any	)							->	Tuple:
								__UpperCAmelCase							=			self.image_processing_class(**self.image_processor_dict	)
								__UpperCAmelCase							=			self.image_processing_class(do_resize=_snake_case     ,		do_normalize=_snake_case     ,		do_rescale=_snake_case	)
								# create random PyTorch tensors
								__UpperCAmelCase							=			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 whether the method "pad" and calling the image processor return the same tensors
								__UpperCAmelCase							=			image_processing_a.pad(_snake_case     ,		return_tensors='''pt'''	)
								__UpperCAmelCase							=			image_processing_a(_snake_case     ,		return_tensors='''pt'''	)

								self.assertTrue(
								    torch.allclose(encoded_images_with_method['''pixel_values''']     ,		encoded_images['''pixel_values''']     ,		atol=1e-4	)	)





							@slow
							def       snake_case__						(      self				:							List[str]	)							->	List[Any]:
								__UpperCAmelCase							=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
								with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''     ,		'''r'''	) as f:
									__UpperCAmelCase							=			json.loads(f.read()	)

								__UpperCAmelCase							=			{'''image_id''': 3_9_7_6_9, '''annotations''': target}

								# encode them
								__UpperCAmelCase							=			YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''	)
								__UpperCAmelCase							=			image_processing(images=_snake_case     ,		annotations=_snake_case     ,		return_tensors='''pt'''	)

								# verify pixel values
								__UpperCAmelCase							=			torch.Size([1, 3, 8_0_0, 1_0_6_6]	)
								self.assertEqual(encoding['''pixel_values'''].shape     ,		_snake_case	)

								__UpperCAmelCase							=			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]     ,		_snake_case     ,		atol=1e-4	)	)

								# verify area
								__UpperCAmelCase							=			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''']     ,		_snake_case	)	)
								# verify boxes
								__UpperCAmelCase							=			torch.Size([6, 4]	)
								self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape     ,		_snake_case	)
								__UpperCAmelCase							=			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]     ,		_snake_case     ,		atol=1e-3	)	)
								# verify image_id
								__UpperCAmelCase							=			torch.tensor([3_9_7_6_9]	)
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']     ,		_snake_case	)	)
								# verify is_crowd
								__UpperCAmelCase							=			torch.tensor([0, 0, 0, 0, 0, 0]	)
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']     ,		_snake_case	)	)
								# verify class_labels
								__UpperCAmelCase							=			torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]	)
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']     ,		_snake_case	)	)
								# verify orig_size
								__UpperCAmelCase							=			torch.tensor([4_8_0, 6_4_0]	)
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']     ,		_snake_case	)	)
								# verify size
								__UpperCAmelCase							=			torch.tensor([8_0_0, 1_0_6_6]	)
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']     ,		_snake_case	)	)






							@slow
							def       snake_case__						(      self				:							List[str]	)							->	Any:
								__UpperCAmelCase							=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
								with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''     ,		'''r'''	) as f:
									__UpperCAmelCase							=			json.loads(f.read()	)

								__UpperCAmelCase							=			{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

								__UpperCAmelCase							=			pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''	)

								# encode them
								__UpperCAmelCase							=			YolosImageProcessor(format='''coco_panoptic'''	)
								__UpperCAmelCase							=			image_processing(images=_snake_case     ,		annotations=_snake_case     ,		masks_path=_snake_case     ,		return_tensors='''pt'''	)

								# verify pixel values
								__UpperCAmelCase							=			torch.Size([1, 3, 8_0_0, 1_0_6_6]	)
								self.assertEqual(encoding['''pixel_values'''].shape     ,		_snake_case	)

								__UpperCAmelCase							=			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]     ,		_snake_case     ,		atol=1e-4	)	)

								# verify area
								__UpperCAmelCase							=			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''']     ,		_snake_case	)	)
								# verify boxes
								__UpperCAmelCase							=			torch.Size([6, 4]	)
								self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape     ,		_snake_case	)
								__UpperCAmelCase							=			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]     ,		_snake_case     ,		atol=1e-3	)	)
								# verify image_id
								__UpperCAmelCase							=			torch.tensor([3_9_7_6_9]	)
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']     ,		_snake_case	)	)
								# verify is_crowd
								__UpperCAmelCase							=			torch.tensor([0, 0, 0, 0, 0, 0]	)
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']     ,		_snake_case	)	)
								# verify class_labels
								__UpperCAmelCase							=			torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]	)
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']     ,		_snake_case	)	)
								# verify masks
								__UpperCAmelCase							=			8_2_2_8_7_3
								self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()     ,		_snake_case	)
								# verify orig_size
								__UpperCAmelCase							=			torch.tensor([4_8_0, 6_4_0]	)
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']     ,		_snake_case	)	)
								# verify size
								__UpperCAmelCase							=			torch.tensor([8_0_0, 1_0_6_6]	)
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']     ,		_snake_case	)	)

'''simple docstring'''


import argparse
import os
import re

import packaging.version


__lowerCAmelCase	:       Optional[int]						       =	"examples/"
__lowerCAmelCase	:       Dict						       =	{
    "examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
    "init": (re.compile(r"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
    "setup": (re.compile(r"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), r"\1version=\"VERSION\","),
    "doc": (re.compile(r"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
__lowerCAmelCase	:       List[str]						       =	{
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
__lowerCAmelCase	:       int						       =	"README.md"





def       lowerCAmelCase					(		UpperCamelCase__				:   List[str]    ,					UpperCamelCase__				:   int    ,					UpperCamelCase__				:   Tuple       ):


	"""simple docstring"""
	with open(UpperCamelCase__    ,					'''r'''    ,					encoding='''utf-8'''    ,					newline='''\n'''       ) as f:
		__UpperCAmelCase							=			f.read()
	__UpperCAmelCase				,			__UpperCAmelCase							=			REPLACE_PATTERNS[pattern]
	__UpperCAmelCase							=			replace.replace('''VERSION'''    ,					UpperCamelCase__       )
	__UpperCAmelCase							=			re_pattern.sub(UpperCamelCase__    ,					UpperCamelCase__       )
	with open(UpperCamelCase__    ,					'''w'''    ,					encoding='''utf-8'''    ,					newline='''\n'''       ) as f:
		f.write(UpperCamelCase__       )





def       lowerCAmelCase					(		UpperCamelCase__				:   Optional[int]       ):


	"""simple docstring"""
	for folder, directories, fnames in os.walk(UpperCamelCase__       ):
		# Removing some of the folders with non-actively maintained examples from the walk
		if "research_projects" in directories:
			directories.remove('''research_projects'''       )
		if "legacy" in directories:
			directories.remove('''legacy'''       )
		for fname in fnames:
			if fname.endswith('''.py'''       ):
				update_version_in_file(os.path.join(UpperCamelCase__    ,					UpperCamelCase__       )    ,					UpperCamelCase__    ,					pattern='''examples'''       )





def       lowerCAmelCase					(		UpperCamelCase__				:   Any    ,					UpperCamelCase__				:   Any=False       ):


	"""simple docstring"""
	for pattern, fname in REPLACE_FILES.items():
		update_version_in_file(UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__       )
	if not patch:
		update_version_in_examples(UpperCamelCase__       )





def       lowerCAmelCase					(		):


	"""simple docstring"""
	__UpperCAmelCase							=			'''🤗 Transformers currently provides the following architectures'''
	__UpperCAmelCase							=			'''1. Want to contribute a new model?'''
	with open(UpperCamelCase__    ,					'''r'''    ,					encoding='''utf-8'''    ,					newline='''\n'''       ) as f:
		__UpperCAmelCase							=			f.readlines()

	# Find the start of the list.
	__UpperCAmelCase							=			0
	while not lines[start_index].startswith(_start_prompt       ):
		start_index += 1
	start_index += 1

	__UpperCAmelCase							=			start_index
	# Update the lines in the model list.
	while not lines[index].startswith(_end_prompt       ):
		if lines[index].startswith('''1.'''       ):
			__UpperCAmelCase							=			lines[index].replace(
			    '''https://huggingface.co/docs/transformers/main/model_doc'''    ,					'''https://huggingface.co/docs/transformers/model_doc'''    ,					)
		index += 1

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





def       lowerCAmelCase					(		):


	"""simple docstring"""
	with open(REPLACE_FILES['''init''']    ,					'''r'''       ) as f:
		__UpperCAmelCase							=			f.read()
	__UpperCAmelCase							=			REPLACE_PATTERNS['''init'''][0].search(UpperCamelCase__       ).groups()[0]
	return packaging.version.parse(UpperCamelCase__       )





def       lowerCAmelCase					(		UpperCamelCase__				:   Any=False       ):


	"""simple docstring"""
	__UpperCAmelCase							=			get_version()
	if patch and default_version.is_devrelease:
		raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!'''       )
	if default_version.is_devrelease:
		__UpperCAmelCase							=			default_version.base_version
	elif patch:
		__UpperCAmelCase							=			f"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
	else:
		__UpperCAmelCase							=			f"""{default_version.major}.{default_version.minor + 1}.0"""

	# Now let's ask nicely if that's the right one.
	__UpperCAmelCase							=			input(f"""Which version are you releasing? [{default_version}]"""       )
	if len(UpperCamelCase__       ) == 0:
		__UpperCAmelCase							=			default_version

	print(f"""Updating version to {version}."""       )
	global_version_update(UpperCamelCase__    ,					patch=UpperCamelCase__       )
	if not patch:
		print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''       )
		clean_main_ref_in_model_list()





def       lowerCAmelCase					(		):


	"""simple docstring"""
	__UpperCAmelCase							=			get_version()
	__UpperCAmelCase							=			f"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
	__UpperCAmelCase							=			current_version.base_version

	# Check with the user we got that right.
	__UpperCAmelCase							=			input(f"""Which version are we developing now? [{dev_version}]"""       )
	if len(UpperCamelCase__       ) == 0:
		__UpperCAmelCase							=			dev_version

	print(f"""Updating version to {version}."""       )
	global_version_update(UpperCamelCase__       )
	print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''       )
	clean_main_ref_in_model_list()


if __name__ == "__main__":
					__lowerCAmelCase	:       Tuple						       =	argparse.ArgumentParser()
					parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
					parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
					__lowerCAmelCase	:       Tuple						       =	parser.parse_args()
					if not args.post_release:
										pre_release_work(patch=args.patch)
					elif args.patch:
										print("Nothing to do after a patch :-)")
					else:
										post_release_work()
