






"""simple docstring"""





import unittest

import numpy as np
import requests

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

  from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
  A__					: Union[str, Any]=	False

if is_vision_available():
  from PIL import Image

  from transformers import PixaStructImageProcessor
class    __lowerCamelCase    (  unittest.TestCase  ):

   def __init__(					self ,				snake_case_ ,				snake_case_=7 ,				snake_case_=3 ,				snake_case_=18 ,				snake_case_=30 ,				snake_case_=400 ,				snake_case_=None ,				snake_case_=True ,				snake_case_=True ,				snake_case_=None ,				)		->       List[str]:
       UpperCamelCase__         =    size if size is not None else {'height': 20, 'width': 20}
       UpperCamelCase__         =    parent
       UpperCamelCase__         =    batch_size
       UpperCamelCase__         =    num_channels
       UpperCamelCase__         =    image_size
       UpperCamelCase__         =    min_resolution
       UpperCamelCase__         =    max_resolution
       UpperCamelCase__         =    size
       UpperCamelCase__         =    do_normalize
       UpperCamelCase__         =    do_convert_rgb
       UpperCamelCase__         =    [512, 1024, 2048, 4096]
       UpperCamelCase__         =    patch_size if patch_size is not None else {'height': 16, 'width': 16}

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       UpperCamelCase__         =    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
       UpperCamelCase__         =    Image.open(requests.get(snake_case_ ,				stream=snake_case_      ).raw      ).convert('RGB'      )
       return raw_image







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11							,	reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""							,	)
@require_torch
@require_vision
class    __lowerCamelCase    (  _a							,	unittest.TestCase  ):
   a      :				Dict 						=PixaStructImageProcessor if is_vision_available() else None

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       UpperCamelCase__         =    PixaStructImageProcessingTester(self      )

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       return self.image_processor_tester.prepare_image_processor_dict()

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )
       self.assertTrue(hasattr(snake_case_ ,				'do_normalize'      )      )
       self.assertTrue(hasattr(snake_case_ ,				'do_convert_rgb'      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    self.image_processor_tester.prepare_dummy_image()

       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )
       UpperCamelCase__         =    2048

       UpperCamelCase__         =    image_processor(snake_case_ ,				return_tensors='pt' ,				max_patches=snake_case_      )
       self.assertTrue(torch.allclose(inputs.flattened_patches.mean() ,				torch.tensor(0.0_606      ) ,				atol=1E-3 ,				rtol=1E-3      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       # Initialize image_processor
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )
       # create random PIL images
       UpperCamelCase__         =    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
       UpperCamelCase__         =    (
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
           # Test not batched input
           UpperCamelCase__         =    image_processor(
               image_inputs[0] ,				return_tensors='pt' ,				max_patches=snake_case_      ).flattened_patches
           self.assertEqual(
               encoded_images.shape ,				(1, max_patch, expected_hidden_dim) ,				)

           # Test batched
           UpperCamelCase__         =    image_processor(
               snake_case_ ,				return_tensors='pt' ,				max_patches=snake_case_      ).flattened_patches
           self.assertEqual(
               encoded_images.shape ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,				)

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       # Initialize image_processor
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )
       # create random PIL images
       UpperCamelCase__         =    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
       UpperCamelCase__         =    (
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       UpperCamelCase__         =    True

       for max_patch in self.image_processor_tester.max_patches:
           # Test not batched input
           with self.assertRaises(snake_case_      ):
               UpperCamelCase__         =    image_processor(
                   image_inputs[0] ,				return_tensors='pt' ,				max_patches=snake_case_      ).flattened_patches

           UpperCamelCase__         =    'Hello'

           UpperCamelCase__         =    image_processor(
               image_inputs[0] ,				return_tensors='pt' ,				max_patches=snake_case_ ,				header_text=snake_case_      ).flattened_patches
           self.assertEqual(
               encoded_images.shape ,				(1, max_patch, expected_hidden_dim) ,				)

           # Test batched
           UpperCamelCase__         =    image_processor(
               snake_case_ ,				return_tensors='pt' ,				max_patches=snake_case_ ,				header_text=snake_case_      ).flattened_patches
           self.assertEqual(
               encoded_images.shape ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,				)

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       # Initialize image_processor
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )
       # create random numpy tensors
       UpperCamelCase__         =    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      )

       UpperCamelCase__         =    (
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
           # Test not batched input
           UpperCamelCase__         =    image_processor(
               image_inputs[0] ,				return_tensors='pt' ,				max_patches=snake_case_      ).flattened_patches
           self.assertEqual(
               encoded_images.shape ,				(1, max_patch, expected_hidden_dim) ,				)

           # Test batched
           UpperCamelCase__         =    image_processor(
               snake_case_ ,				return_tensors='pt' ,				max_patches=snake_case_      ).flattened_patches
           self.assertEqual(
               encoded_images.shape ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,				)







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       # Initialize image_processor
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )
       # create random PyTorch tensors
       UpperCamelCase__         =    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
       UpperCamelCase__         =    (
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
           # Test not batched input
           UpperCamelCase__         =    image_processor(
               image_inputs[0] ,				return_tensors='pt' ,				max_patches=snake_case_      ).flattened_patches
           self.assertEqual(
               encoded_images.shape ,				(1, max_patch, expected_hidden_dim) ,				)

           # Test batched
           UpperCamelCase__         =    image_processor(
               snake_case_ ,				return_tensors='pt' ,				max_patches=snake_case_      ).flattened_patches
           self.assertEqual(
               encoded_images.shape ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,				)







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11							,	reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""							,	)
@require_torch
@require_vision
class    __lowerCamelCase    (  _a							,	unittest.TestCase  ):
   a      :				Optional[Any] 						=PixaStructImageProcessor if is_vision_available() else None

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    PixaStructImageProcessingTester(self ,				num_channels=4      )
       UpperCamelCase__         =    3

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       return self.image_processor_tester.prepare_image_processor_dict()

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )
       self.assertTrue(hasattr(snake_case_ ,				'do_normalize'      )      )
       self.assertTrue(hasattr(snake_case_ ,				'do_convert_rgb'      )      )







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       # Initialize image_processor
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )
       # create random PIL images
       UpperCamelCase__         =    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
       UpperCamelCase__         =    (
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * (self.image_processor_tester.num_channels - 1)
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
           # Test not batched input
           UpperCamelCase__         =    image_processor(
               image_inputs[0] ,				return_tensors='pt' ,				max_patches=snake_case_      ).flattened_patches
           self.assertEqual(
               encoded_images.shape ,				(1, max_patch, expected_hidden_dim) ,				)

           # Test batched
           UpperCamelCase__         =    image_processor(
               snake_case_ ,				return_tensors='pt' ,				max_patches=snake_case_      ).flattened_patches
           self.assertEqual(
               encoded_images.shape ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,				)












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


A__					: Optional[Any]=	"""
{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__					: Dict=	"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Union[str, Any]:
    """simple docstring"""

    UpperCamelCase__         =    _re_backend.findall(SCREAMING_SNAKE_CASE					)
    if len(SCREAMING_SNAKE_CASE					) == 0:
        return None

    return "_and_".join(SCREAMING_SNAKE_CASE					)







def   lowerCAmelCase_( )					->	str:
    """simple docstring"""

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

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

    return backend_specific_objects







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	List[Any]:
    """simple docstring"""

    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   lowerCAmelCase_( SCREAMING_SNAKE_CASE=None					)					->	int:
    """simple docstring"""

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

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

    return dummy_files







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE=False					)					->	int:
    """simple docstring"""

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

    # Locate actual dummy modules and read their content.
    UpperCamelCase__         =    os.path.join(SCREAMING_SNAKE_CASE ,	'utils'					)
    UpperCamelCase__         =    {
        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()
    }

    UpperCamelCase__         =    {}
    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:
                UpperCamelCase__         =    f.read()
        else:
            UpperCamelCase__         =    ''

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

  check_dummies(args.fix_and_overwrite)




