


'''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  UpperCamelCase__(     unittest.TestCase ):




				def __init__(	self    :				Tuple   ,					lowerCAmelCase    :				Optional[Any]   ,					lowerCAmelCase    :				int=7   ,					lowerCAmelCase    :				List[str]=3   ,					lowerCAmelCase    :				Optional[int]=18   ,					lowerCAmelCase    :				Union[str, Any]=30   ,					lowerCAmelCase    :				List[Any]=400   ,					lowerCAmelCase    :				Any=True   ,					lowerCAmelCase    :				int=None   ,					lowerCAmelCase    :				Union[str, Any]=True   ,					lowerCAmelCase    :				Dict=None   ,					lowerCAmelCase    :				Any=True   ,					lowerCAmelCase    :				Optional[int]=[0.5, 0.5, 0.5]   ,					lowerCAmelCase    :				int=[0.5, 0.5, 0.5]   ,					lowerCAmelCase    :				Optional[Any]=False   ,					)->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			size if size is not None else {'''height''': 20, '''width''': 20}
										UpperCAmelCase         =			crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
										UpperCAmelCase         =			parent
										UpperCAmelCase         =			batch_size
										UpperCAmelCase         =			num_channels
										UpperCAmelCase         =			image_size
										UpperCAmelCase         =			min_resolution
										UpperCAmelCase         =			max_resolution
										UpperCAmelCase         =			do_resize
										UpperCAmelCase         =			size
										UpperCAmelCase         =			do_center_crop
										UpperCAmelCase         =			crop_size
										UpperCAmelCase         =			do_normalize
										UpperCAmelCase         =			image_mean
										UpperCAmelCase         =			image_std
										UpperCAmelCase         =			do_reduce_labels





				def   a__(	self    :				Any   )->  Tuple:

										"""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    lowerCamelCase__   ( ):




						'''simple docstring'''






						UpperCAmelCase         =			load_dataset('''hf-internal-testing/fixtures_ade20k'''   ,     split='''test'''  )

						UpperCAmelCase         =			Image.open(dataset[0]['''file''']  )
						UpperCAmelCase         =			Image.open(dataset[1]['''file''']  )

						return image, map


def    lowerCamelCase__   ( ):




						'''simple docstring'''






						UpperCAmelCase         =			load_dataset('''hf-internal-testing/fixtures_ade20k'''   ,     split='''test'''  )

						UpperCAmelCase         =			Image.open(ds[0]['''file''']  )
						UpperCAmelCase         =			Image.open(ds[1]['''file''']  )
						UpperCAmelCase         =			Image.open(ds[2]['''file''']  )
						UpperCAmelCase         =			Image.open(ds[3]['''file''']  )

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






@require_torch
@require_vision
class  UpperCamelCase__(     lowerCAmelCase		,       unittest.TestCase ):
				__magic_name__       :   Union[str, Any]							= BeitImageProcessor if is_vision_available() else None




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

										"""simple docstring"""




										UpperCAmelCase         =			BeitImageProcessingTester(self   )




				@property
				def   a__(	self    :				Union[str, Any]   )->  List[str]:

										"""simple docstring"""




										return self.image_processor_tester.prepare_image_processor_dict()




				def   a__(	self    :				Dict   )->  int:

										"""simple docstring"""




										UpperCAmelCase         =			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   a__(	self    :				str   )->  int:

										"""simple docstring"""




										UpperCAmelCase         =			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         =			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   a__(	self    :				Tuple   )->  str:

										"""simple docstring"""




										pass




				def   a__(	self    :				Optional[int]   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			self.image_processing_class(**self.image_processor_dict   )
										# create random PIL images
										UpperCAmelCase         =			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         =			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         =			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   a__(	self    :				Optional[int]   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			self.image_processing_class(**self.image_processor_dict   )
										# create random numpy tensors
										UpperCAmelCase         =			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         =			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         =			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   a__(	self    :				Any   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			self.image_processing_class(**self.image_processor_dict   )
										# create random PyTorch tensors
										UpperCAmelCase         =			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         =			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         =			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   a__(	self    :				Union[str, Any]   )->  List[str]:

										"""simple docstring"""




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

										# Test not batched input
										UpperCAmelCase         =			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         =			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       ,       UpperCAmelCase         =			prepare_semantic_single_inputs()

										UpperCAmelCase         =			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       ,       UpperCAmelCase         =			prepare_semantic_batch_inputs()

										UpperCAmelCase         =			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   a__(	self    :				Dict   )->  int:

										"""simple docstring"""




										UpperCAmelCase         =			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       ,       UpperCAmelCase         =			prepare_semantic_single_inputs()
										UpperCAmelCase         =			image_processing(lowerCAmelCase   ,					lowerCAmelCase   ,					return_tensors='''pt'''   )
										self.assertTrue(encoding['''labels'''].min().item() >= 0   )
										self.assertTrue(encoding['''labels'''].max().item() <= 150   )

										UpperCAmelCase         =			True
										UpperCAmelCase         =			image_processing(lowerCAmelCase   ,					lowerCAmelCase   ,					return_tensors='''pt'''   )
										self.assertTrue(encoding['''labels'''].min().item() >= 0   )
										self.assertTrue(encoding['''labels'''].max().item() <= 255   )









'''simple docstring'''



import argparse
import os
import re

import packaging.version


_lowercase    :    Optional[int]			     =  """examples/"""
_lowercase    :    str			     =  {
    """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"""),
}
_lowercase    :    Dict			     =  {
    """init""": """src/transformers/__init__.py""",
    """setup""": """setup.py""",
}
_lowercase    :    List[Any]			     =  """README.md"""


def    lowerCamelCase__   ( A     :    int   ,     A     :    str   ,     A     :    Optional[Any]  ):




						'''simple docstring'''






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


def    lowerCamelCase__   ( A     :    Optional[int]  ):




						'''simple docstring'''






						for folder, directories, fnames in os.walk(A  ):
												# 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(A   ,     A  )   ,     A   ,     pattern='''examples'''  )


def    lowerCamelCase__   ( A     :    str   ,     A     :    Dict=False  ):




						'''simple docstring'''






						for pattern, fname in REPLACE_FILES.items():
												update_version_in_file(A   ,     A   ,     A  )
						if not patch:
												update_version_in_examples(A  )


def    lowerCamelCase__   ( ):




						'''simple docstring'''






						UpperCAmelCase         =			'''🤗 Transformers currently provides the following architectures'''
						UpperCAmelCase         =			'''1. Want to contribute a new model?'''
						with open(A   ,     '''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(A   ,     '''w'''   ,     encoding='''utf-8'''   ,     newline='''\n'''  ) as f:
												f.writelines(A  )


def    lowerCamelCase__   ( ):




						'''simple docstring'''






						with open(REPLACE_FILES['''init''']   ,     '''r'''  ) as f:
												UpperCAmelCase         =			f.read()
						UpperCAmelCase         =			REPLACE_PATTERNS['''init'''][0].search(A  ).groups()[0]
						return packaging.version.parse(A  )


def    lowerCamelCase__   ( A     :    Tuple=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(A  ) == 0:
												UpperCAmelCase         =			default_version

						print(f"""Updating version to {version}."""  )
						global_version_update(A   ,     patch=A  )
						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(A  ) == 0:
												UpperCAmelCase         =			dev_version

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


if __name__ == "__main__":
	_lowercase    :    Optional[Any]			     =  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.""")
	_lowercase    :    Union[str, Any]			     =  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()





