



import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
A__  :  Union[str, Any]       = '''src/transformers'''

# Matches is_xxx_available()
A__  :  Optional[Any]       = re.compile(R'''is\_([a-z_]*)_available()''')
# Matches from xxx import bla
A__  :  Tuple       = re.compile(R'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
A__  :  Optional[Any]       = re.compile(R'''^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)''')


A__  :  Optional[int]       = '''\n{0} = None\n'''

A__  :  Union[str, Any]       = '''\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n'''


A__  :  Tuple       = '''\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'''


def       UpperCamelCase(  __UpperCamelCase							:		str			):






		if _re_test_backend.search(snake_case__			) is None:
				return None
		lowerCAmelCase_      :					Optional[Any]     =			[b[0] for b in _re_backend.findall(snake_case__			)]
		backends.sort()
		return "_and_".join(snake_case__			)


def       UpperCamelCase(  ):






		with open(os.path.join(snake_case__					,'''__init__.py'''			)					,'''r'''					,encoding='''utf-8'''					,newline='''\n'''			) as f:
				lowerCAmelCase_      :					Any     =			f.readlines()

		# Get to the point we do the actual imports for type checking
		lowerCAmelCase_      :					Optional[Any]     =			0
		while not lines[line_index].startswith('''if TYPE_CHECKING'''			):
				line_index += 1

		lowerCAmelCase_      :					Any     =			{}
		# Go through the end of the file
		while line_index < len(snake_case__			):
				# If the line is an if is_backend_available, we grab all objects associated.
				lowerCAmelCase_      :					int     =			find_backend(lines[line_index]			)
				if backend is not None:
						while not lines[line_index].startswith('''    else:'''			):
								line_index += 1
						line_index += 1

						lowerCAmelCase_      :					Optional[int]     =			[]
						# Until we unindent, add backend objects to the list
						while len(lines[line_index]			) <= 1 or lines[line_index].startswith(''' ''' * 8			):
								lowerCAmelCase_      :					Union[str, Any]     =			lines[line_index]
								lowerCAmelCase_      :					int     =			_re_single_line_import.search(snake_case__			)
								if single_line_import_search is not None:
										objects.extend(single_line_import_search.groups()[0].split(''', '''			)			)
								elif line.startswith(''' ''' * 12			):
										objects.append(line[12:-2]			)
								line_index += 1

						lowerCAmelCase_      :					Union[str, Any]     =			objects
				else:
						line_index += 1

		return backend_specific_objects


def       UpperCamelCase(  __UpperCamelCase							:		int					,__UpperCamelCase							:		Optional[Any]			):






		if name.isupper():
				return DUMMY_CONSTANT.format(snake_case__			)
		elif name.islower():
				return DUMMY_FUNCTION.format(snake_case__					,snake_case__			)
		else:
				return DUMMY_CLASS.format(snake_case__					,snake_case__			)


def       UpperCamelCase(  __UpperCamelCase							:		Union[str, Any]=None			):






		if backend_specific_objects is None:
				lowerCAmelCase_      :					List[str]     =			read_init()
		# For special correspondence backend to module name as used in the function requires_modulename
		lowerCAmelCase_      :					Tuple     =			{}

		for backend, objects in backend_specific_objects.items():
				lowerCAmelCase_      :					int     =			'''[''' + ''', '''.join(f"""\"{b}\"""" for b in backend.split('''_and_'''			)			) + ''']'''
				lowerCAmelCase_      :					Optional[int]     =			'''# This file is autogenerated by the command `make fix-copies`, do not edit.\n'''
				dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
				dummy_file += "\n".join([create_dummy_object(snake_case__					,snake_case__			) for o in objects]			)
				lowerCAmelCase_      :					List[Any]     =			dummy_file

		return dummy_files


def       UpperCamelCase(  __UpperCamelCase							:		Union[str, Any]=False			):
		lowerCAmelCase_      :					Any     =			create_dummy_files()
		# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
		lowerCAmelCase_      :					Any     =			{'''torch''': '''pt'''}

		# Locate actual dummy modules and read their content.
		lowerCAmelCase_      :					int     =			os.path.join(snake_case__					,'''utils'''			)
		lowerCAmelCase_      :					Dict     =			{
		    backend: os.path.join(snake_case__					,f"""dummy_{short_names.get(snake_case__					,snake_case__			)}_objects.py"""			)
		    for backend in dummy_files.keys()
		}

		lowerCAmelCase_      :					Optional[int]     =			{}
		for backend, file_path in dummy_file_paths.items():
				if os.path.isfile(snake_case__			):
						with open(snake_case__					,'''r'''					,encoding='''utf-8'''					,newline='''\n'''			) as f:
								lowerCAmelCase_      :					Any     =			f.read()
				else:
						lowerCAmelCase_      :					Any     =			''''''

		for backend in dummy_files.keys():
				if dummy_files[backend] != actual_dummies[backend]:
						if overwrite:
								print(
								    f"""Updating transformers.utils.dummy_{short_names.get(snake_case__					,snake_case__			)}_objects.py as the main """
								    '''__init__ has new objects.'''			)
								with open(dummy_file_paths[backend]					,'''w'''					,encoding='''utf-8'''					,newline='''\n'''			) as f:
										f.write(dummy_files[backend]			)
						else:
								raise ValueError(
								    '''The main __init__ has objects that are not present in '''
								    f"""transformers.utils.dummy_{short_names.get(snake_case__					,snake_case__			)}_objects.py. Run `make fix-copies` """
								    '''to fix this.'''			)


if __name__ == "__main__":
						A__  :  int       = argparse.ArgumentParser()
						parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
						A__  :  Optional[Any]       = parser.parse_args()

						check_dummies(args.fix_and_overwrite)









import unittest

import numpy as np

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_video_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import VideoMAEImageProcessor



class     __snake_case						(					unittest.TestCase						):




							def __init__(	self					:	str    ,    A_					:	str    ,    A_					:	int=7    ,    A_					:	List[Any]=3    ,    A_					:	Dict=1_0    ,    A_					:	List[str]=1_8    ,    A_					:	List[Any]=3_0    ,    A_					:	str=4_0_0    ,    A_					:	str=True    ,    A_					:	Tuple=None    ,    A_					:	Optional[Any]=True    ,    A_					:	Tuple=[0.5, 0.5, 0.5]    ,    A_					:	Any=[0.5, 0.5, 0.5]    ,    A_					:	List[str]=None    ,    ):
									lowerCAmelCase_      :					Optional[Any]     =			size if size is not None else {'''shortest_edge''': 1_8}
									lowerCAmelCase_      :					Optional[int]     =			crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}

									lowerCAmelCase_      :					Any     =			parent
									lowerCAmelCase_      :					Optional[Any]     =			batch_size
									lowerCAmelCase_      :					List[Any]     =			num_channels
									lowerCAmelCase_      :					Optional[int]     =			num_frames
									lowerCAmelCase_      :					Optional[int]     =			image_size
									lowerCAmelCase_      :					List[Any]     =			min_resolution
									lowerCAmelCase_      :					str     =			max_resolution
									lowerCAmelCase_      :					List[str]     =			do_resize
									lowerCAmelCase_      :					int     =			size
									lowerCAmelCase_      :					Union[str, Any]     =			do_normalize
									lowerCAmelCase_      :					Union[str, Any]     =			image_mean
									lowerCAmelCase_      :					List[str]     =			image_std
									lowerCAmelCase_      :					Any     =			crop_size







							def        UpperCAmelCase__      (	self					:	Any):
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "crop_size": self.crop_size,
									}





@require_torch
@require_vision
class     __snake_case						(					UpperCamelCase_						,unittest.TestCase						):
							_a      				=			VideoMAEImageProcessor if is_vision_available() else None




							def        UpperCAmelCase__      (	self					:	Tuple):
									lowerCAmelCase_      :					int     =			VideoMAEImageProcessingTester(self)




							@property
							def        UpperCAmelCase__      (	self					:	Tuple):
									return self.image_processor_tester.prepare_image_processor_dict()




							def        UpperCAmelCase__      (	self					:	Optional[int]):
									lowerCAmelCase_      :					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_center_crop'''))
									self.assertTrue(hasattr(A_    ,    '''size'''))




							def        UpperCAmelCase__      (	self					:	Dict):
									lowerCAmelCase_      :					int     =			self.image_processing_class.from_dict(self.image_processor_dict)
									self.assertEqual(image_processor.size    ,    {'''shortest_edge''': 1_8})
									self.assertEqual(image_processor.crop_size    ,    {'''height''': 1_8, '''width''': 1_8})

									lowerCAmelCase_      :					str     =			self.image_processing_class.from_dict(self.image_processor_dict    ,    size=4_2    ,    crop_size=8_4)
									self.assertEqual(image_processor.size    ,    {'''shortest_edge''': 4_2})
									self.assertEqual(image_processor.crop_size    ,    {'''height''': 8_4, '''width''': 8_4})




							def        UpperCAmelCase__      (	self					:	Dict):
									pass




							def        UpperCAmelCase__      (	self					:	str):
									# Initialize image_processing
									lowerCAmelCase_      :					int     =			self.image_processing_class(**self.image_processor_dict)
									# create random PIL videos
									lowerCAmelCase_      :					Optional[int]     =			prepare_video_inputs(self.image_processor_tester    ,    equal_resolution=A_)
									for video in video_inputs:
											self.assertIsInstance(A_    ,    A_)
											self.assertIsInstance(video[0]    ,    Image.Image)

									# Test not batched input
									lowerCAmelCase_      :					int     =			image_processing(video_inputs[0]    ,    return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,    (
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,    )

									# Test batched
									lowerCAmelCase_      :					Tuple     =			image_processing(A_    ,    return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,    (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,    )




							def        UpperCAmelCase__      (	self					:	int):
									# Initialize image_processing
									lowerCAmelCase_      :					Dict     =			self.image_processing_class(**self.image_processor_dict)
									# create random numpy tensors
									lowerCAmelCase_      :					str     =			prepare_video_inputs(self.image_processor_tester    ,    equal_resolution=A_    ,    numpify=A_)
									for video in video_inputs:
											self.assertIsInstance(A_    ,    A_)
											self.assertIsInstance(video[0]    ,    np.ndarray)

									# Test not batched input
									lowerCAmelCase_      :					Tuple     =			image_processing(video_inputs[0]    ,    return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,    (
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,    )

									# Test batched
									lowerCAmelCase_      :					Union[str, Any]     =			image_processing(A_    ,    return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,    (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,    )







							def        UpperCAmelCase__      (	self					:	List[str]):
									# Initialize image_processing
									lowerCAmelCase_      :					Tuple     =			self.image_processing_class(**self.image_processor_dict)
									# create random PyTorch tensors
									lowerCAmelCase_      :					Tuple     =			prepare_video_inputs(self.image_processor_tester    ,    equal_resolution=A_    ,    torchify=A_)
									for video in video_inputs:
											self.assertIsInstance(A_    ,    A_)
											self.assertIsInstance(video[0]    ,    torch.Tensor)

									# Test not batched input
									lowerCAmelCase_      :					List[str]     =			image_processing(video_inputs[0]    ,    return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,    (
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,    )

									# Test batched
									lowerCAmelCase_      :					List[Any]     =			image_processing(A_    ,    return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,    (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,    )





