






"""simple docstring"""







import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor


class    __UpperCAmelCase(       unittest.TestCase							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__=7	,  snake_case__=3	,  snake_case__=18	,  snake_case__=30	,  snake_case__=400	,  snake_case__=True	,  snake_case__=None	,  snake_case__=True	,  snake_case__=[0.5, 0.5, 0.5]	,  snake_case__=[0.5, 0.5, 0.5]	,  ):
												'''simple docstring'''
												lowercase__    :				int=	size if size is not None else {"height": 18, "width": 18}
												lowercase__    :				Union[str, Any]=	parent
												lowercase__    :				Dict=	batch_size
												lowercase__    :				Tuple=	num_channels
												lowercase__    :				List[Any]=	image_size
												lowercase__    :				Union[str, Any]=	min_resolution
												lowercase__    :				Dict=	max_resolution
												lowercase__    :				Tuple=	do_resize
												lowercase__    :				Dict=	size
												lowercase__    :				List[str]=	do_normalize
												lowercase__    :				str=	image_mean
												lowercase__    :				Any=	image_std




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return {
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_normalize": self.do_normalize,
												    "do_resize": self.do_resize,
												    "size": self.size,
												}


@require_torch
@require_vision
class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__  ,  unittest.TestCase							):




						"""simple docstring"""




						__lowerCamelCase    	= DPTImageProcessor if is_vision_available() else None




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Any=	DPTImageProcessingTester(self      )




						@property
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return self.image_processor_tester.prepare_image_processor_dict()




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Optional[Any]=	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 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				List[str]=	self.image_processing_class.from_dict(self.image_processor_dict      )
												self.assertEqual(image_processor.size	,  {"height": 18, "width": 18}      )

												lowercase__    :				Any=	self.image_processing_class.from_dict(self.image_processor_dict	,  size=42      )
												self.assertEqual(image_processor.size	,  {"height": 42, "width": 42}      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__    :				Optional[int]=	self.image_processing_class(**self.image_processor_dict      )
												# create random PIL images
												lowercase__    :				Optional[Any]=	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
												lowercase__    :				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.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,  )

												# Test batched
												lowercase__    :				Optional[int]=	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,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,  )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__    :				Dict=	self.image_processing_class(**self.image_processor_dict      )
												# create random numpy tensors
												lowercase__    :				Dict=	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
												lowercase__    :				Tuple=	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.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,  )

												# Test batched
												lowercase__    :				str=	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,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,  )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__    :				str=	self.image_processing_class(**self.image_processor_dict      )
												# create random PyTorch tensors
												lowercase__    :				Optional[int]=	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
												lowercase__    :				Dict=	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.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,  )

												# Test batched
												lowercase__    :				Tuple=	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,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,  )














"""simple docstring"""







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			:    Any				 =		"""src/diffusers"""

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


a			:    Dict				 =		"""
{0} = None
"""

a			:    List[Any]				 =		"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
"""


a			:    Any				 =		"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""





def 						lowercase__(A						)    ->List[str]:




						"""simple docstring"""



						lowercase__    :				Optional[int]=	_re_backend.findall(A						)
						if len(A						) == 0:
												return None

						return "_and_".join(A						)





def 						lowercase__()    ->int:




						"""simple docstring"""



						with open(os.path.join(A ,		"__init__.py"						) ,		"r" ,		encoding="utf-8" ,		newline="\n"						) as f:
												lowercase__    :				Union[str, Any]=	f.readlines()

						# Get to the point we do the actual imports for type checking
						lowercase__    :				List[Any]=	0
						lowercase__    :				List[Any]=	{}
						# 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
												lowercase__    :				Optional[Any]=	find_backend(lines[line_index]						)
												if backend is not None:
																		while not lines[line_index].startswith("else:"						):
																								line_index += 1
																		line_index += 1
																		lowercase__    :				str=	[]
																		# Until we unindent, add backend objects to the list
																		while line_index < len(A						) and len(lines[line_index]						) > 1:
																								lowercase__    :				List[str]=	lines[line_index]
																								lowercase__    :				Any=	_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:
																								lowercase__    :				str=	objects
												else:
																		line_index += 1

						return backend_specific_objects





def 						lowercase__(A ,		A						)    ->List[Any]:




						"""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 						lowercase__(A=None						)    ->Optional[Any]:




						"""simple docstring"""



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

						for backend, objects in backend_specific_objects.items():
												lowercase__    :				str=	"[" + ", ".join(f'''"{b}"''' for b in backend.split("_and_"						)						) + "]"
												lowercase__    :				List[str]=	"# 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]						)
												lowercase__    :				Optional[Any]=	dummy_file

						return dummy_files





def 						lowercase__(A=False						)    ->List[Any]:




						"""simple docstring"""



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

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

						lowercase__    :				Union[str, Any]=	{}
						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:
																								lowercase__    :				List[Any]=	f.read()
												else:
																		lowercase__    :				Tuple=	""

						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__":
			a			:    Optional[Any]				 =		argparse.ArgumentParser()
			parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
			a			:    Tuple				 =		parser.parse_args()

			check_dummies(args.fix_and_overwrite)






