


import collections
import importlib.util
import os
import re
from pathlib import Path


SCREAMING_SNAKE_CASE							    =							'src/transformers'


# Matches is_xxx_available()
SCREAMING_SNAKE_CASE							    =							re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
SCREAMING_SNAKE_CASE							    =							re.compile(r'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
SCREAMING_SNAKE_CASE							    =							re.compile(r'\s+"\S*":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
SCREAMING_SNAKE_CASE							    =							re.compile(r'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
SCREAMING_SNAKE_CASE							    =							re.compile(r'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
SCREAMING_SNAKE_CASE							    =							re.compile(r'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma:     "MyModel",
SCREAMING_SNAKE_CASE							    =							re.compile('^\s+"([^"]+)",')
# Catches a line with objects between brackets only:    ["foo", "bar"],
SCREAMING_SNAKE_CASE							    =							re.compile('^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
SCREAMING_SNAKE_CASE							    =							re.compile(r'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
SCREAMING_SNAKE_CASE							    =							re.compile(r'^\s*try:')
# Catches a line with else:
SCREAMING_SNAKE_CASE							    =							re.compile(r'^\s*else:')




def  a   (lowerCAmelCase__   ):
   if _re_test_backend.search(lowerCAmelCase__   ) is None:
      return None
   __a					=  [b[0] for b in _re_backend.findall(lowerCAmelCase__   )]
   backends.sort()
   return "_and_".join(lowerCAmelCase__   )




def  a   (lowerCAmelCase__   ):
   with open(lowerCAmelCase__		,				"""r"""		,				encoding="""utf-8"""		,				newline="""\n"""   ) as f:
      __a					=  f.readlines()

   __a					=  0
   while line_index < len(lowerCAmelCase__   ) and not lines[line_index].startswith("""_import_structure = {"""   ):
      line_index += 1

   # If this is a traditional init, just return.
   if line_index >= len(lowerCAmelCase__   ):
      return None

   # First grab the objects without a specific backend in _import_structure
   __a					=  []
   while not lines[line_index].startswith("""if TYPE_CHECKING"""   ) and find_backend(lines[line_index]   ) is None:
      __a					=  lines[line_index]
      # If we have everything on a single line, let's deal with it.
      if _re_one_line_import_struct.search(lowerCAmelCase__   ):
         __a					=  _re_one_line_import_struct.search(lowerCAmelCase__   ).groups()[0]
         __a					=  re.findall("""\[([^\]]+)\]"""		,				lowerCAmelCase__   )
         for imp in imports:
            objects.extend([obj[1:-1] for obj in imp.split(""", """   )]   )
         line_index += 1
         continue
      __a					=  _re_import_struct_key_value.search(lowerCAmelCase__   )
      if single_line_import_search is not None:
         __a					=  [obj[1:-1] for obj in single_line_import_search.groups()[0].split(""", """   ) if len(lowerCAmelCase__   ) > 0]
         objects.extend(lowerCAmelCase__   )
      elif line.startswith(""" """ * 8 + """\""""   ):
         objects.append(line[9:-3]   )
      line_index += 1

   __a					=  {"""none""": objects}
   # Let's continue with backend-specific objects in _import_structure
   while not lines[line_index].startswith("""if TYPE_CHECKING"""   ):
      # If the line is an if not is_backend_available, we grab all objects associated.
      __a					=  find_backend(lines[line_index]   )
      # Check if the backend declaration is inside a try block:
      if _re_try.search(lines[line_index - 1]   ) is None:
         __a					=  None

      if backend is not None:
         line_index += 1

         # Scroll until we hit the else block of try-except-else
         while _re_else.search(lines[line_index]   ) is None:
            line_index += 1

         line_index += 1

         __a					=  []
         # Until we unindent, add backend objects to the list
         while len(lines[line_index]   ) <= 1 or lines[line_index].startswith(""" """ * 4   ):
            __a					=  lines[line_index]
            if _re_import_struct_add_one.search(lowerCAmelCase__   ) is not None:
               objects.append(_re_import_struct_add_one.search(lowerCAmelCase__   ).groups()[0]   )
            elif _re_import_struct_add_many.search(lowerCAmelCase__   ) is not None:
               __a					=  _re_import_struct_add_many.search(lowerCAmelCase__   ).groups()[0].split(""", """   )
               __a					=  [obj[1:-1] for obj in imports if len(lowerCAmelCase__   ) > 0]
               objects.extend(lowerCAmelCase__   )
            elif _re_between_brackets.search(lowerCAmelCase__   ) is not None:
               __a					=  _re_between_brackets.search(lowerCAmelCase__   ).groups()[0].split(""", """   )
               __a					=  [obj[1:-1] for obj in imports if len(lowerCAmelCase__   ) > 0]
               objects.extend(lowerCAmelCase__   )
            elif _re_quote_object.search(lowerCAmelCase__   ) is not None:
               objects.append(_re_quote_object.search(lowerCAmelCase__   ).groups()[0]   )
            elif line.startswith(""" """ * 8 + """\""""   ):
               objects.append(line[9:-3]   )
            elif line.startswith(""" """ * 12 + """\""""   ):
               objects.append(line[13:-3]   )
            line_index += 1

         __a					=  objects
      else:
         line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
   __a					=  []
   while (
       line_index < len(lowerCAmelCase__   )
       and find_backend(lines[line_index]   ) is None
       and not lines[line_index].startswith("""else"""   )
   ):
      __a					=  lines[line_index]
      __a					=  _re_import.search(lowerCAmelCase__   )
      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

   __a					=  {"""none""": objects}
   # Let's continue with backend-specific objects
   while line_index < len(lowerCAmelCase__   ):
      # If the line is an if is_backend_available, we grab all objects associated.
      __a					=  find_backend(lines[line_index]   )
      # Check if the backend declaration is inside a try block:
      if _re_try.search(lines[line_index - 1]   ) is None:
         __a					=  None

      if backend is not None:
         line_index += 1

         # Scroll until we hit the else block of try-except-else
         while _re_else.search(lines[line_index]   ) is None:
            line_index += 1

         line_index += 1

         __a					=  []
         # Until we unindent, add backend objects to the list
         while len(lines[line_index]   ) <= 1 or lines[line_index].startswith(""" """ * 8   ):
            __a					=  lines[line_index]
            __a					=  _re_import.search(lowerCAmelCase__   )
            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

         __a					=  objects
      else:
         line_index += 1

   return import_dict_objects, type_hint_objects




def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):

   def find_duplicates(lowerCAmelCase__   ):
      return [k for k, v in collections.Counter(lowerCAmelCase__   ).items() if v > 1]

   if list(import_dict_objects.keys()   ) != list(type_hint_objects.keys()   ):
      return ["Both sides of the init do not have the same backends!"]

   __a					=  []
   for key in import_dict_objects.keys():
      __a					=  find_duplicates(import_dict_objects[key]   )
      if duplicate_imports:
         errors.append(f'''Duplicate _import_structure definitions for: {duplicate_imports}'''   )
      __a					=  find_duplicates(type_hint_objects[key]   )
      if duplicate_type_hints:
         errors.append(f'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}'''   )

      if sorted(set(import_dict_objects[key]   )   ) != sorted(set(type_hint_objects[key]   )   ):
         __a					=  """base imports""" if key == """none""" else f'''{key} backend'''
         errors.append(f'''Differences for {name}:'''   )
         for a in type_hint_objects[key]:
            if a not in import_dict_objects[key]:
               errors.append(f'''  {a} in TYPE_HINT but not in _import_structure.'''   )
         for a in import_dict_objects[key]:
            if a not in type_hint_objects[key]:
               errors.append(f'''  {a} in _import_structure but not in TYPE_HINT.'''   )
   return errors




def  a   ():
   __a					=  []
   for root, _, files in os.walk(lowerCAmelCase__   ):
      if "__init__.py" in files:
         __a					=  os.path.join(lowerCAmelCase__		,				"""__init__.py"""   )
         __a					=  parse_init(lowerCAmelCase__   )
         if objects is not None:
            __a					=  analyze_results(*lowerCAmelCase__   )
            if len(lowerCAmelCase__   ) > 0:
               __a					=  f'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
               failures.append("""\n""".join(lowerCAmelCase__   )   )
   if len(lowerCAmelCase__   ) > 0:
      raise ValueError("""\n\n""".join(lowerCAmelCase__   )   )




def  a   ():
   __a					=  []
   for path, directories, files in os.walk(lowerCAmelCase__   ):
      for folder in directories:
         # Ignore private modules
         if folder.startswith("""_"""   ):
            directories.remove(lowerCAmelCase__   )
            continue
         # Ignore leftovers from branches (empty folders apart from pycache)
         if len(list((Path(lowerCAmelCase__   ) / folder).glob("""*.py"""   )   )   ) == 0:
            continue
         __a					=  str((Path(lowerCAmelCase__   ) / folder).relative_to(lowerCAmelCase__   )   )
         __a					=  short_path.replace(os.path.sep		,				"""."""   )
         submodules.append(lowerCAmelCase__   )
      for fname in files:
         if fname == "__init__.py":
            continue
         __a					=  str((Path(lowerCAmelCase__   ) / fname).relative_to(lowerCAmelCase__   )   )
         __a					=  short_path.replace(""".py"""		,				""""""   ).replace(os.path.sep		,				"""."""   )
         if len(submodule.split("""."""   )   ) == 1:
            submodules.append(lowerCAmelCase__   )
   return submodules


SCREAMING_SNAKE_CASE							    =							[
    'convert_pytorch_checkpoint_to_tf2',
    'modeling_flax_pytorch_utils',
]




def  a   ():
   # This is to make sure the transformers module imported is the one in the repo.
   __a					=  importlib.util.spec_from_file_location(
       """transformers"""		,				os.path.join(lowerCAmelCase__		,				"""__init__.py"""   )		,				submodule_search_locations=[PATH_TO_TRANSFORMERS]		,				)
   __a					=  spec.loader.load_module()

   __a					=  [
       module
       for module in get_transformers_submodules()
       if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
   ]
   if len(lowerCAmelCase__   ) > 0:
      __a					=  """\n""".join(f'''- {module}''' for module in module_not_registered   )
      raise ValueError(
          """The following submodules are not properly registered in the main init of Transformers:\n"""
          f'''{list_of_modules}\n'''
          """Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value."""   )


if __name__ == "__main__":
  check_all_inits()
  check_submodules()








'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class  snake_case__						(    SCREAMING_SNAKE_CASE_  ,     unittest.TestCase				):
 A__				= KandinskyVaaInpaintPipeline
 A__				= ['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
 A__				= [
     '''image_embeds''',
     '''negative_image_embeds''',
     '''image''',
     '''mask_image''',
 ]
 A__				= [
     '''generator''',
     '''height''',
     '''width''',
     '''latents''',
     '''guidance_scale''',
     '''num_inference_steps''',
     '''return_dict''',
     '''guidance_scale''',
     '''num_images_per_prompt''',
     '''output_type''',
     '''return_dict''',
 ]
 A__				= False
 @property
 def A_   (   self						:							int  )      ->   int:







       '''simple docstring'''
       return 32
 @property
 def A_   (   self						:							Union[str, Any]  )      ->   List[str]:







       '''simple docstring'''
       return 32
 @property
 def A_   (   self						:							List[Any]  )      ->   Union[str, Any]:







       '''simple docstring'''
       return self.time_input_dim
 @property
 def A_   (   self						:							List[Any]  )      ->   Union[str, Any]:







       '''simple docstring'''
       return self.time_input_dim * 4
 @property
 def A_   (   self						:							Optional[int]  )      ->   Tuple:







       '''simple docstring'''
       return 100
 @property
 def A_   (   self						:							Union[str, Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       torch.manual_seed(0  )

       __snake_case :   Optional[int]													=    {
           'in_channels': 9,
           # Out channels is double in channels because predicts mean and variance
           'out_channels': 8,
           'addition_embed_type': 'image',
           'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
           'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
           'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
           'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
           'layers_per_block': 1,
           'encoder_hid_dim': self.text_embedder_hidden_size,
           'encoder_hid_dim_type': 'image_proj',
           'cross_attention_dim': self.cross_attention_dim,
           'attention_head_dim': 4,
           'resnet_time_scale_shift': 'scale_shift',
           'class_embed_type': None,
       }

       __snake_case :   Optional[Any]													=    UNetaDConditionModel(**__a  )
       return model
 @property
 def A_   (   self						:							Any  )      ->   Any:







       '''simple docstring'''
       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }
 @property
 def A_   (   self						:							str  )      ->   Union[str, Any]:







       '''simple docstring'''
       torch.manual_seed(0  )
       __snake_case :   str													=    VQModel(**self.dummy_movq_kwargs  )
       return model
 def A_   (   self						:							List[str]  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   int													=    self.dummy_unet
       __snake_case :   Dict													=    self.dummy_movq

       __snake_case :   int													=    DDIMScheduler(
           num_train_timesteps=1000      ,     beta_schedule='linear'      ,     beta_start=0.0_0_0_8_5      ,     beta_end=0.0_1_2      ,     clip_sample=__a      ,     set_alpha_to_one=__a      ,     steps_offset=1      ,     prediction_type='epsilon'      ,     thresholding=__a      ,     )

       __snake_case :   Optional[Any]													=    {
           'unet': unet,
           'scheduler': scheduler,
           'movq': movq,
       }

       return components
 def A_   (   self						:							str      ,     __a						:							Union[str, Any]      ,     __a						:							List[str]=0  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   Any													=    floats_tensor((1, self.text_embedder_hidden_size)      ,     rng=random.Random(__a  )  ).to(__a  )
       __snake_case :   int													=    floats_tensor((1, self.text_embedder_hidden_size)      ,     rng=random.Random(seed + 1  )  ).to(
           __a  )
       # create init_image
       __snake_case :   Union[str, Any]													=    floats_tensor((1, 3, 64, 64)      ,     rng=random.Random(__a  )  ).to(__a  )
       __snake_case :   List[Any]													=    image.cpu().permute(0      ,     2      ,     3      ,     1  )[0]
       __snake_case :   Optional[Any]													=    Image.fromarray(np.uinta(__a  )  ).convert('RGB'  ).resize((256, 256)  )
       # create mask
       __snake_case :   Optional[int]													=    np.ones((64, 64)      ,     dtype=np.floataa  )
       __snake_case :   List[str]													=    0

       if str(__a  ).startswith('mps'  ):
             __snake_case :   Optional[int]													=    torch.manual_seed(__a  )
       else:
             __snake_case :   List[Any]													=    torch.Generator(device=__a  ).manual_seed(__a  )
       __snake_case :   Optional[Any]													=    {
           'image': init_image,
           'mask_image': mask,
           'image_embeds': image_embeds,
           'negative_image_embeds': negative_image_embeds,
           'generator': generator,
           'height': 64,
           'width': 64,
           'num_inference_steps': 2,
           'guidance_scale': 4.0,
           'output_type': 'np',
       }
       return inputs
 def A_   (   self						:							List[str]  )      ->   int:







       '''simple docstring'''
       __snake_case :   Tuple													=    'cpu'

       __snake_case :   Dict													=    self.get_dummy_components()

       __snake_case :   List[Any]													=    self.pipeline_class(**__a  )
       __snake_case :   List[Any]													=    pipe.to(__a  )

       pipe.set_progress_bar_config(disable=__a  )

       __snake_case :   Optional[Any]													=    pipe(**self.get_dummy_inputs(__a  )  )
       __snake_case :   Union[str, Any]													=    output.images

       __snake_case :   Optional[int]													=    pipe(
           **self.get_dummy_inputs(__a  )      ,     return_dict=__a      ,     )[0]

       __snake_case :   Optional[int]													=    image[0, -3:, -3:, -1]
       __snake_case :   Tuple													=    image_from_tuple[0, -3:, -3:, -1]

       print(f'''image.shape {image.shape}'''  )

       assert image.shape == (1, 64, 64, 3)

       __snake_case :   Union[str, Any]													=    np.array(
           [0.5_0_7_7_5_9_0_3, 0.4_9_5_2_7_1_9_5, 0.4_8_8_2_4_5_4_3, 0.5_0_1_9_2_2_3_7, 0.4_8_6_4_4_9_0_6, 0.4_9_3_7_3_8_1_4, 0.4_7_8_0_5_9_8, 0.4_7_2_3_4_8_2_7, 0.4_8_3_2_7_8_4_8]  )

       assert (
           np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
       ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1e-2
       ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''





 def A_   (   self						:							Tuple  )      ->   Optional[int]:







       '''simple docstring'''
       super().test_inference_batch_single_identical(expected_max_diff=3e-3  )



@slow
@require_torch_gpu
class  snake_case__						(    unittest.TestCase				):
 def A_   (   self						:							Optional[int]  )      ->   int:







       '''simple docstring'''
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





 def A_   (   self						:							Union[str, Any]  )      ->   int:







       '''simple docstring'''
       __snake_case :   Dict													=    load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'  )

       __snake_case :   Optional[int]													=    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'  )
       __snake_case :   int													=    np.ones((768, 768)      ,     dtype=np.floataa  )
       __snake_case :   Optional[Any]													=    0

       __snake_case :   Union[str, Any]													=    'a hat'

       __snake_case :   Optional[int]													=    KandinskyVaaPriorPipeline.from_pretrained(
           'kandinsky-community/kandinsky-2-2-prior'      ,     torch_dtype=torch.floataa  )
       pipe_prior.to(__a  )

       __snake_case :   Optional[Any]													=    KandinskyVaaInpaintPipeline.from_pretrained(
           'kandinsky-community/kandinsky-2-2-decoder-inpaint'      ,     torch_dtype=torch.floataa  )
       __snake_case :   Optional[Any]													=    pipeline.to(__a  )
       pipeline.set_progress_bar_config(disable=__a  )

       __snake_case :   Dict													=    torch.Generator(device='cpu'  ).manual_seed(0  )
       __snake_case							,     __snake_case :   Optional[int]													=    pipe_prior(
           __a      ,     generator=__a      ,     num_inference_steps=5      ,     negative_prompt=''      ,     ).to_tuple()

       __snake_case :   List[str]													=    pipeline(
           image=__a      ,     mask_image=__a      ,     image_embeds=__a      ,     negative_image_embeds=__a      ,     generator=__a      ,     num_inference_steps=100      ,     height=768      ,     width=768      ,     output_type='np'      ,     )

       __snake_case :   List[str]													=    output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(__a      ,     __a  )



