
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
SCREAMING_SNAKE_CASE							:Tuple										=    'src/diffusers'

# Matches is_xxx_available()
SCREAMING_SNAKE_CASE							:Optional[Any]										=    re.compile(R'is\_([a-z_]*)_available\(\)')
# Matches from xxx import bla
SCREAMING_SNAKE_CASE							:List[Any]										=    re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')


SCREAMING_SNAKE_CASE							:int										=    '\n{0} = None\n'

SCREAMING_SNAKE_CASE							:str										=    '\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n'


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


def 						UpperCAmelCase      (     a_						)		->     str:







				"""simple docstring"""



				__A             =			_re_backend.findall(a_						)
				if len(a_						) == 0:
								return None

				return "_and_".join(a_						)


def 						UpperCAmelCase      (     )		->     int:







				"""simple docstring"""



				with open(os.path.join(a_		,       "__init__.py"						)		,       "r"		,       encoding="utf-8"		,       newline="\n"						) as f:
								__A             =			f.readlines()

				# Get to the point we do the actual imports for type checking
				__A             =			0
				__A             =			{}
				# Go through the end of the file
				while line_index < len(a_						):
								# If the line contains is_backend_available, we grab all objects associated with the `else` block
								__A             =			find_backend(lines[line_index]						)
								if backend is not None:
												while not lines[line_index].startswith("else:"						):
																line_index += 1
												line_index += 1
												__A             =			[]
												# Until we unindent, add backend objects to the list
												while line_index < len(a_						) and len(lines[line_index]						) > 1:
																__A             =			lines[line_index]
																__A             =			_re_single_line_import.search(a_						)
																if single_line_import_search is not None:
																				objects.extend(single_line_import_search.groups()[0].split(", "						)						)
																elif line.startswith(" " * 8						):
																				objects.append(line[8:-2]						)
																line_index += 1

												if len(a_						) > 0:
																__A             =			objects
								else:
												line_index += 1

				return backend_specific_objects


def 						UpperCAmelCase      (     a_		,       a_						)		->     int:







				"""simple docstring"""



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


def 						UpperCAmelCase      (     a_=None						)		->     Dict:







				"""simple docstring"""



				if backend_specific_objects is None:
								__A             =			read_init()
				# For special correspondence backend to module name as used in the function requires_modulename
				__A             =			{}

				for backend, objects in backend_specific_objects.items():
								__A             =			"[" + ", ".join(F'''"{b}"''' for b in backend.split("_and_"						)						) + "]"
								__A             =			"# 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(a_		,       a_						) for o in objects]						)
								__A             =			dummy_file

				return dummy_files


def 						UpperCAmelCase      (     a_=False						)		->     List[str]:







				"""simple docstring"""



				__A             =			create_dummy_files()
				# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
				__A             =			{"torch": "pt"}

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

				__A             =			{}
				for backend, file_path in dummy_file_paths.items():
								if os.path.isfile(a_						):
												with open(a_		,       "r"		,       encoding="utf-8"		,       newline="\n"						) as f:
																__A             =			f.read()
								else:
												__A             =			""

				for backend in dummy_files.keys():
								if dummy_files[backend] != actual_dummies[backend]:
												if overwrite:
																print(
																    F'''Updating diffusers.utils.dummy_{short_names.get(a_		,       a_						)}_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'''diffusers.utils.dummy_{short_names.get(a_		,       a_						)}_objects.py. Run `make fix-copies` '''
																    "to fix this."						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:int										=    argparse.ArgumentParser()
					parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
					SCREAMING_SNAKE_CASE							:Dict										=    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_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import PoolFormerImageProcessor





class 							UpperCAmelCase				(  unittest.TestCase  ):




	'''simple docstring'''





	def __init__(	self :			Optional[int]							,A :			List[str]							,A :			List[Any]=7							,A :			Any=3							,A :			int=30							,A :			List[Any]=4_00							,A :			str=True							,A :			int=None							,A :			List[str]=0.9							,A :			Dict=None							,A :			int=True							,A :			Any=[0.5, 0.5, 0.5]							,A :			Optional[int]=[0.5, 0.5, 0.5]							,):
					__A             =			size if size is not None else {"shortest_edge": 30}
					__A             =			crop_size if crop_size is not None else {"height": 30, "width": 30}
					__A             =			parent
					__A             =			batch_size
					__A             =			num_channels
					__A             =			min_resolution
					__A             =			max_resolution
					__A             =			do_resize_and_center_crop
					__A             =			size
					__A             =			crop_pct
					__A             =			crop_size
					__A             =			do_normalize
					__A             =			image_mean
					__A             =			image_std







	def     UpperCamelCase_  (	self :			int			):
					return {
					    "size": self.size,
					    "do_resize_and_center_crop": self.do_resize_and_center_crop,
					    "crop_pct": self.crop_pct,
					    "crop_size": self.crop_size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					}





@require_torch
@require_vision
class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE      ,      unittest.TestCase  ):




	'''simple docstring'''





	snake_case_    =					PoolFormerImageProcessor if is_vision_available() else None






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			PoolFormerImageProcessingTester(self			)






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






	def     UpperCamelCase_  (	self :			Tuple			):
					__A             =			self.image_processing_class(**self.image_processor_dict			)
					self.assertTrue(hasattr(A							,"do_resize_and_center_crop"			)			)
					self.assertTrue(hasattr(A							,"size"			)			)
					self.assertTrue(hasattr(A							,"crop_pct"			)			)
					self.assertTrue(hasattr(A							,"do_normalize"			)			)
					self.assertTrue(hasattr(A							,"image_mean"			)			)
					self.assertTrue(hasattr(A							,"image_std"			)			)






	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					__A             =			self.image_processing_class.from_dict(self.image_processor_dict			)
					self.assertEqual(image_processor.size							,{"shortest_edge": 30}			)
					self.assertEqual(image_processor.crop_size							,{"height": 30, "width": 30}			)

					__A             =			self.image_processing_class.from_dict(self.image_processor_dict							,size=42							,crop_size=84			)
					self.assertEqual(image_processor.size							,{"shortest_edge": 42}			)
					self.assertEqual(image_processor.crop_size							,{"height": 84, "width": 84}			)






	def     UpperCamelCase_  (	self :			List[str]			):
					pass






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

					# Test not batched input
					__A             =			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
					__A             =			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,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )							,)






	def     UpperCamelCase_  (	self :			List[Any]			):
					# Initialize image_processing
					__A             =			self.image_processing_class(**self.image_processor_dict			)
					# create random numpy tensors
					__A             =			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
					__A             =			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
					__A             =			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,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )							,)







	def     UpperCamelCase_  (	self :			List[Any]			):
					# Initialize image_processing
					__A             =			self.image_processing_class(**self.image_processor_dict			)
					# create random PyTorch tensors
					__A             =			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
					__A             =			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
					__A             =			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,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )							,)
