'''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
_UpperCAmelCase    :     List[Any]         =       '''src/diffusers'''

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


_UpperCAmelCase    :     Optional[Any]         =       '''\n{0} = None\n'''

_UpperCAmelCase    :     int         =       '''\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'''


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





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int				):
     _A    		=  _re_backend.findall(SCREAMING_SNAKE_CASE__				)
     if len(SCREAMING_SNAKE_CASE__				) == 0:
          return None

     return "_and_".join(SCREAMING_SNAKE_CASE__				)





def  _SCREAMING_SNAKE_CASE		(      ):





     with open(os.path.join(SCREAMING_SNAKE_CASE__   ,		'__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(SCREAMING_SNAKE_CASE__				):
          # 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(SCREAMING_SNAKE_CASE__				) and len(lines[line_index]				) > 1:
                    _A    		=  lines[line_index]
                    _A    		=  _re_single_line_import.search(SCREAMING_SNAKE_CASE__				)
                    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(SCREAMING_SNAKE_CASE__				) > 0:
                    _A    		=  objects
          else:
               line_index += 1

     return backend_specific_objects





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			str   ,		__snake_case							:			Optional[Any]				):





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





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int=None				):





     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(SCREAMING_SNAKE_CASE__   ,		SCREAMING_SNAKE_CASE__				) for o in objects]				)
          _A    		=  dummy_file

     return dummy_files





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			List[Any]=False				):
     _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(SCREAMING_SNAKE_CASE__   ,		'utils'				)
     _A    		=  {
         backend: os.path.join(SCREAMING_SNAKE_CASE__   ,		F'dummy_{short_names.get(SCREAMING_SNAKE_CASE__   ,		SCREAMING_SNAKE_CASE__				)}_objects.py'				)
         for backend in dummy_files.keys()
     }

     _A    		=  {}
     for backend, file_path in dummy_file_paths.items():
          if os.path.isfile(SCREAMING_SNAKE_CASE__				):
               with open(SCREAMING_SNAKE_CASE__   ,		'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(SCREAMING_SNAKE_CASE__   ,		SCREAMING_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'diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE__   ,		SCREAMING_SNAKE_CASE__				)}_objects.py. Run `make fix-copies` '
                        'to fix this.'				)


if __name__ == "__main__":
   _UpperCAmelCase    :     Dict         =       argparse.ArgumentParser()
   parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
   _UpperCAmelCase    :     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 A    (  unittest.TestCase	):






      def __init__(   self		:	Any     ,  lowercase_		:	List[str]     ,  lowercase_		:	Union[str, Any]=7     ,  lowercase_		:	Dict=3     ,  lowercase_		:	List[Any]=30     ,  lowercase_		:	int=400     ,  lowercase_		:	List[Any]=True     ,  lowercase_		:	str=None     ,  lowercase_		:	str=0.9     ,  lowercase_		:	Union[str, Any]=None     ,  lowercase_		:	List[str]=True     ,  lowercase_		:	List[str]=[0.5, 0.5, 0.5]     ,  lowercase_		:	int=[0.5, 0.5, 0.5]     ,  )   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						int            =size if size is not None else {'shortest_edge': 30}
             _lowerCamelCase			:						Optional[Any]            =crop_size if crop_size is not None else {'height': 30, 'width': 30}
             _lowerCamelCase			:						List[Any]            =parent
             _lowerCamelCase			:						Union[str, Any]            =batch_size
             _lowerCamelCase			:						Dict            =num_channels
             _lowerCamelCase			:						Any            =min_resolution
             _lowerCamelCase			:						Union[str, Any]            =max_resolution
             _lowerCamelCase			:						Any            =do_resize_and_center_crop
             _lowerCamelCase			:						Any            =size
             _lowerCamelCase			:						Optional[Any]            =crop_pct
             _lowerCamelCase			:						Optional[Any]            =crop_size
             _lowerCamelCase			:						List[Any]            =do_normalize
             _lowerCamelCase			:						int            =image_mean
             _lowerCamelCase			:						Optional[int]            =image_std






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Tuple:
             """simple docstring"""




             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 A    (  UpperCamelCase_       ,				unittest.TestCase	):
      UpperCamelCase__				:    Dict =PoolFormerImageProcessor if is_vision_available() else None






      def 			lowerCamelCase			(   self		:	int					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						Any            =PoolFormerImageProcessingTester(self					)






      @property
      def 			lowerCamelCase			(   self		:	Any					)   ->		Any:
             """simple docstring"""




             return self.image_processor_tester.prepare_image_processor_dict()






      def 			lowerCamelCase			(   self		:	Union[str, Any]					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =self.image_processing_class(**self.image_processor_dict					)
             self.assertTrue(hasattr(lowercase_     ,  'do_resize_and_center_crop'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'size'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'crop_pct'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'do_normalize'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'image_mean'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'image_std'					)					)






      def 			lowerCamelCase			(   self		:	int					)   ->		Dict:
             """simple docstring"""




             _lowerCamelCase			:						Any            =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}					)

             _lowerCamelCase			:						List[Any]            =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 			lowerCamelCase			(   self		:	Any					)   ->		Any:
             """simple docstring"""




             pass






      def 			lowerCamelCase			(   self		:	str					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            =self.image_processing_class(**self.image_processor_dict					)
             # create random PIL images
             _lowerCamelCase			:						Dict            =prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  Image.Image					)

             # Test not batched input
             _lowerCamelCase			:						int            =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
             _lowerCamelCase			:						Optional[int]            =image_processing(lowercase_     ,  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 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						Dict            =self.image_processing_class(**self.image_processor_dict					)
             # create random numpy tensors
             _lowerCamelCase			:						Optional[Any]            =prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_     ,  numpify=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  np.ndarray					)

             # Test not batched input
             _lowerCamelCase			:						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.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )     ,  )

             # Test batched
             _lowerCamelCase			:						Optional[int]            =image_processing(lowercase_     ,  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 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						int            =self.image_processing_class(**self.image_processor_dict					)
             # create random PyTorch tensors
             _lowerCamelCase			:						Dict            =prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_     ,  torchify=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  torch.Tensor					)

             # Test not batched input
             _lowerCamelCase			:						str            =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
             _lowerCamelCase			:						Tuple            =image_processing(lowercase_     ,  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'],
                 )     ,  )


