



"""simple docstring"""





import argparse
import os
import re


__lowerCAmelCase							:       Any          =       '''src/diffusers'''

# Pattern that looks at the indentation in a line.
__lowerCAmelCase							:       Any          =       re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__lowerCAmelCase							:       Union[str, Any]          =       re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__lowerCAmelCase							:       Tuple          =       re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__lowerCAmelCase							:       Any          =       re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__lowerCAmelCase							:       List[Any]          =       re.compile(R'''\[([^\]]+)\]''')
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Dict     ):


						'''simple docstring'''






						snake_case_ :			Any            =      _re_indent.search(__UpperCamelCase     )
						return "" if search is None else search.groups()[0]
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Union[str, Any]       ,			__UpperCamelCase   :      List[Any]=""       ,			__UpperCamelCase   :      Union[str, Any]=None       ,			__UpperCamelCase   :      List[Any]=None     ):


						'''simple docstring'''






						snake_case_ :			int            =      0
						snake_case_ :			Dict            =      code.split("""\n"""     )
						if start_prompt is not None:
												while not lines[index].startswith(__UpperCamelCase     ):
																		index += 1
												snake_case_ :			Optional[int]            =      ["""\n""".join(lines[:index]     )]
						else:
												snake_case_ :			List[str]            =      []

						# We split into blocks until we get to the `end_prompt` (or the end of the block).
						snake_case_ :			Any            =      [lines[index]]
						index += 1
						while index < len(__UpperCamelCase     ) and (end_prompt is None or not lines[index].startswith(__UpperCamelCase     )):
												if len(lines[index]     ) > 0 and get_indent(lines[index]     ) == indent_level:
																		if len(__UpperCamelCase     ) > 0 and get_indent(current_block[-1]     ).startswith(indent_level + """ """     ):
																								current_block.append(lines[index]     )
																								blocks.append("""\n""".join(__UpperCamelCase     )     )
																								if index < len(__UpperCamelCase     ) - 1:
																														snake_case_ :			Union[str, Any]            =      [lines[index + 1]]
																														index += 1
																								else:
																														snake_case_ :			Optional[int]            =      []
																		else:
																								blocks.append("""\n""".join(__UpperCamelCase     )     )
																								snake_case_ :			Union[str, Any]            =      [lines[index]]
												else:
																		current_block.append(lines[index]     )
												index += 1

						# Adds current block if it's nonempty.
						if len(__UpperCamelCase     ) > 0:
												blocks.append("""\n""".join(__UpperCamelCase     )     )

						# Add final block after end_prompt if provided.
						if end_prompt is not None and index < len(__UpperCamelCase     ):
												blocks.append("""\n""".join(lines[index:]     )     )

						return blocks
def 						__lowerCAmelCase    (		__UpperCamelCase   :      List[str]     ):


						'''simple docstring'''






						def _inner(__UpperCamelCase   :      Optional[int]     ):
												return key(__UpperCamelCase     ).lower().replace("""_"""       ,			""""""     )

						return _inner
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Union[str, Any]       ,			__UpperCamelCase   :      List[Any]=None     ):


						'''simple docstring'''






						def noop(__UpperCamelCase   :      Any     ):
												return x

						if key is None:
												snake_case_ :			Tuple            =      noop
						# Constants are all uppercase, they go first.
						snake_case_ :			Dict            =      [obj for obj in objects if key(__UpperCamelCase     ).isupper()]
						# Classes are not all uppercase but start with a capital, they go second.
						snake_case_ :			str            =      [obj for obj in objects if key(__UpperCamelCase     )[0].isupper() and not key(__UpperCamelCase     ).isupper()]
						# Functions begin with a lowercase, they go last.
						snake_case_ :			int            =      [obj for obj in objects if not key(__UpperCamelCase     )[0].isupper()]

						snake_case_ :			Optional[int]            =      ignore_underscore(__UpperCamelCase     )
						return sorted(__UpperCamelCase       ,			key=__UpperCamelCase     ) + sorted(__UpperCamelCase       ,			key=__UpperCamelCase     ) + sorted(__UpperCamelCase       ,			key=__UpperCamelCase     )
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Union[str, Any]     ):


						'''simple docstring'''






						def _replace(__UpperCamelCase   :      int     ):
												snake_case_ :			List[Any]            =      match.groups()[0]
												if "," not in imports:
																		return F'[{imports}]'
												snake_case_ :			Tuple            =      [part.strip().replace("""\""""       ,			""""""     ) for part in imports.split(""","""     )]
												# We will have a final empty element if the line finished with a comma.
												if len(keys[-1]     ) == 0:
																		snake_case_ :			Optional[Any]            =      keys[:-1]
												return "[" + ", ".join([F'"{k}"' for k in sort_objects(__UpperCamelCase     )]     ) + "]"

						snake_case_ :			Optional[Any]            =      import_statement.split("""\n"""     )
						if len(__UpperCamelCase     ) > 3:
												# Here we have to sort internal imports that are on several lines (one per name):
												# key: [
												#     "object1",
												#     "object2",
												#     ...
												# ]

												# We may have to ignore one or two lines on each side.
												snake_case_ :			Any            =      2 if lines[1].strip() == """[""" else 1
												snake_case_ :			Any            =      [(i, _re_strip_line.search(__UpperCamelCase     ).groups()[0]) for i, line in enumerate(lines[idx:-idx]     )]
												snake_case_ :			List[str]            =      sort_objects(__UpperCamelCase       ,			key=lambda __UpperCamelCase     : x[1]     )
												snake_case_ :			Union[str, Any]            =      [lines[x[0] + idx] for x in sorted_indices]
												return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]     )
						elif len(__UpperCamelCase     ) == 3:
												# Here we have to sort internal imports that are on one separate line:
												# key: [
												#     "object1", "object2", ...
												# ]
												if _re_bracket_content.search(lines[1]     ) is not None:
																		snake_case_ :			Any            =      _re_bracket_content.sub(_replace       ,			lines[1]     )
												else:
																		snake_case_ :			List[str]            =      [part.strip().replace("""\""""       ,			""""""     ) for part in lines[1].split(""","""     )]
																		# We will have a final empty element if the line finished with a comma.
																		if len(keys[-1]     ) == 0:
																								snake_case_ :			Tuple            =      keys[:-1]
																		snake_case_ :			List[str]            =      get_indent(lines[1]     ) + """, """.join([F'"{k}"' for k in sort_objects(__UpperCamelCase     )]     )
												return "\n".join(__UpperCamelCase     )
						else:
												# Finally we have to deal with imports fitting on one line
												snake_case_ :			Any            =      _re_bracket_content.sub(_replace       ,			__UpperCamelCase     )
												return import_statement
def 						__lowerCAmelCase    (		__UpperCamelCase   :      str       ,			__UpperCamelCase   :      str=True     ):


						'''simple docstring'''






						with open(__UpperCamelCase       ,			"""r"""     ) as f:
												snake_case_ :			str            =      f.read()

						if "_import_structure" not in code:
												return

						# Blocks of indent level 0
						snake_case_ :			int            =      split_code_in_indented_blocks(
						    __UpperCamelCase       ,			start_prompt="""_import_structure = {"""       ,			end_prompt="""if TYPE_CHECKING:"""     )

						# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
						for block_idx in range(1       ,			len(__UpperCamelCase     ) - 1     ):
												# Check if the block contains some `_import_structure`s thingy to sort.
												snake_case_ :			List[str]            =      main_blocks[block_idx]
												snake_case_ :			List[str]            =      block.split("""\n"""     )

												# Get to the start of the imports.
												snake_case_ :			int            =      0
												while line_idx < len(__UpperCamelCase     ) and "_import_structure" not in block_lines[line_idx]:
																		# Skip dummy import blocks
																		if "import dummy" in block_lines[line_idx]:
																								snake_case_ :			int            =      len(__UpperCamelCase     )
																		else:
																								line_idx += 1
												if line_idx >= len(__UpperCamelCase     ):
																		continue

												# Ignore beginning and last line: they don't contain anything.
												snake_case_ :			Dict            =      """\n""".join(block_lines[line_idx:-1]     )
												snake_case_ :			Tuple            =      get_indent(block_lines[1]     )
												# Slit the internal block into blocks of indent level 1.
												snake_case_ :			List[str]            =      split_code_in_indented_blocks(__UpperCamelCase       ,			indent_level=__UpperCamelCase     )
												# We have two categories of import key: list or _import_structure[key].append/extend
												snake_case_ :			Dict            =      _re_direct_key if """_import_structure""" in block_lines[0] else _re_indirect_key
												# Grab the keys, but there is a trap: some lines are empty or just comments.
												snake_case_ :			str            =      [(pattern.search(__UpperCamelCase     ).groups()[0] if pattern.search(__UpperCamelCase     ) is not None else None) for b in internal_blocks]
												# We only sort the lines with a key.
												snake_case_ :			str            =      [(i, key) for i, key in enumerate(__UpperCamelCase     ) if key is not None]
												snake_case_ :			int            =      [x[0] for x in sorted(__UpperCamelCase       ,			key=lambda __UpperCamelCase     : x[1]     )]

												# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
												snake_case_ :			int            =      0
												snake_case_ :			int            =      []
												for i in range(len(__UpperCamelCase     )     ):
																		if keys[i] is None:
																								reordered_blocks.append(internal_blocks[i]     )
																		else:
																								snake_case_ :			str            =      sort_objects_in_import(internal_blocks[sorted_indices[count]]     )
																								reordered_blocks.append(__UpperCamelCase     )
																								count += 1

        # And we put our main block back together with its first and last line.
												snake_case_ :			Any            =      """\n""".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]     )

						if code != "\n".join(__UpperCamelCase     ):
												if check_only:
																		return True
												else:
																		print(F'Overwriting {file}.'     )
																		with open(__UpperCamelCase       ,			"""w"""     ) as f:
																								f.write("""\n""".join(__UpperCamelCase     )     )
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Union[str, Any]=True     ):


						'''simple docstring'''






						snake_case_ :			Any            =      []
						for root, _, files in os.walk(__UpperCamelCase     ):
												if "__init__.py" in files:
																		snake_case_ :			Tuple            =      sort_imports(os.path.join(__UpperCamelCase       ,			"""__init__.py"""     )       ,			check_only=__UpperCamelCase     )
																		if result:
																								snake_case_ :			List[str]            =      [os.path.join(__UpperCamelCase       ,			"""__init__.py"""     )]
						if len(__UpperCamelCase     ) > 0:
												raise ValueError(F'Would overwrite {len(__UpperCamelCase     )} files, run `make style`.'     )


if __name__ == "__main__":
					__lowerCAmelCase							:       Tuple          =       argparse.ArgumentParser()
					parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
					__lowerCAmelCase							:       Dict          =       parser.parse_args()

					sort_imports_in_all_inits(check_only=args.check_only)









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 						lowercase_   (      lowercase					,    unittest.TestCase  ):






      '''simple docstring'''




      __snake_case	    =    KandinskyVaaInpaintPipeline
      __snake_case	    =    ['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
      __snake_case	    =    [
          '''image_embeds''',
          '''negative_image_embeds''',
          '''image''',
          '''mask_image''',
      ]
      __snake_case	    =    [
          '''generator''',
          '''height''',
          '''width''',
          '''latents''',
          '''guidance_scale''',
          '''num_inference_steps''',
          '''return_dict''',
          '''guidance_scale''',
          '''num_images_per_prompt''',
          '''output_type''',
          '''return_dict''',
      ]
      __snake_case	    =    False







      @property
      def  __lowerCAmelCase   ( self :		Optional[int]    )       ->Tuple:




       """simple docstring"""


       return 32







      @property
      def  __lowerCAmelCase   ( self :		Optional[Any]    )       ->Dict:




       """simple docstring"""


       return 32







      @property
      def  __lowerCAmelCase   ( self :		List[Any]    )       ->Union[str, Any]:




       """simple docstring"""


       return self.time_input_dim







      @property
      def  __lowerCAmelCase   ( self :		Tuple    )       ->Optional[Any]:




       """simple docstring"""


       return self.time_input_dim * 4







      @property
      def  __lowerCAmelCase   ( self :		Any    )       ->Optional[int]:




       """simple docstring"""


       return 100







      @property
      def  __lowerCAmelCase   ( self :		Tuple    )       ->Optional[int]:




       """simple docstring"""


       torch.manual_seed(0    )

       a						    = {
           '''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,
       }

       a						    = UNetaDConditionModel(**__UpperCAmelCase    )
       return model







      @property
      def  __lowerCAmelCase   ( self :		List[Any]    )       ->List[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  __lowerCAmelCase   ( self :		str    )       ->List[Any]:




       """simple docstring"""


       torch.manual_seed(0    )
       a						    = VQModel(**self.dummy_movq_kwargs    )
       return model







      def  __lowerCAmelCase   ( self :		Optional[Any]    )       ->Any:




       """simple docstring"""


       a						    = self.dummy_unet
       a						    = self.dummy_movq

       a						    = DDIMScheduler(
           num_train_timesteps=1_000					,   beta_schedule='''linear'''					,   beta_start=0.00085					,   beta_end=0.012					,   clip_sample=__UpperCAmelCase					,   set_alpha_to_one=__UpperCAmelCase					,   steps_offset=1					,   prediction_type='''epsilon'''					,   thresholding=__UpperCAmelCase					,   )

       a						    = {
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''movq''': movq,
       }

       return components







      def  __lowerCAmelCase   ( self :		Dict					,   __UpperCAmelCase :		List[str]					,   __UpperCAmelCase :		int=0    )       ->Optional[int]:




       """simple docstring"""


       a						    = floats_tensor((1, self.text_embedder_hidden_size)					,   rng=random.Random(__UpperCAmelCase    )    ).to(__UpperCAmelCase    )
       a						    = floats_tensor((1, self.text_embedder_hidden_size)					,   rng=random.Random(seed + 1    )    ).to(
           __UpperCAmelCase    )
       # create init_image
       a						    = floats_tensor((1, 3, 64, 64)					,   rng=random.Random(__UpperCAmelCase    )    ).to(__UpperCAmelCase    )
       a						    = image.cpu().permute(0					,   2					,   3					,   1    )[0]
       a						    = Image.fromarray(np.uinta(__UpperCAmelCase    )    ).convert('''RGB'''    ).resize((256, 256)    )
       # create mask
       a						    = np.ones((64, 64)					,   dtype=np.floataa    )
       a						    = 0

       if str(__UpperCAmelCase    ).startswith('''mps'''    ):
        a						    = torch.manual_seed(__UpperCAmelCase    )
       else:
        a						    = torch.Generator(device=__UpperCAmelCase    ).manual_seed(__UpperCAmelCase    )
       a						    = {
           '''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  __lowerCAmelCase   ( self :		Dict    )       ->int:




       """simple docstring"""


       a						    = '''cpu'''

       a						    = self.get_dummy_components()

       a						    = self.pipeline_class(**__UpperCAmelCase    )
       a						    = pipe.to(__UpperCAmelCase    )

       pipe.set_progress_bar_config(disable=__UpperCAmelCase    )

       a						    = pipe(**self.get_dummy_inputs(__UpperCAmelCase    )    )
       a						    = output.images

       a						    = pipe(
           **self.get_dummy_inputs(__UpperCAmelCase    )					,   return_dict=__UpperCAmelCase					,   )[0]

       a						    = image[0, -3:, -3:, -1]
       a						    = image_from_tuple[0, -3:, -3:, -1]

       print(F"""image.shape {image.shape}"""    )

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

       a						    = np.array(
           [0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848]    )

       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  __lowerCAmelCase   ( self :		Union[str, Any]    )       ->Optional[int]:




       """simple docstring"""


       super().test_inference_batch_single_identical(expected_max_diff=3e-3    )






@slow
@require_torch_gpu
class 						lowercase_   (      unittest.TestCase  ):






      '''simple docstring'''




      def  __lowerCAmelCase   ( self :		Any    )       ->List[Any]:




       """simple docstring"""


       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







      def  __lowerCAmelCase   ( self :		List[str]    )       ->str:




       """simple docstring"""


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

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

       a						    = '''a hat'''

       a						    = KandinskyVaaPriorPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-prior'''					,   torch_dtype=torch.floataa    )
       pipe_prior.to(__UpperCAmelCase    )

       a						    = KandinskyVaaInpaintPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''					,   torch_dtype=torch.floataa    )
       a						    = pipeline.to(__UpperCAmelCase    )
       pipeline.set_progress_bar_config(disable=__UpperCAmelCase    )

       a						    = torch.Generator(device='''cpu'''    ).manual_seed(0    )
       a    ,	a						    = pipe_prior(
           __UpperCAmelCase					,   generator=__UpperCAmelCase					,   num_inference_steps=5					,   negative_prompt=''''''					,   ).to_tuple()

       a						    = pipeline(
           image=__UpperCAmelCase					,   mask_image=__UpperCAmelCase					,   image_embeds=__UpperCAmelCase					,   negative_image_embeds=__UpperCAmelCase					,   generator=__UpperCAmelCase					,   num_inference_steps=100					,   height=768					,   width=768					,   output_type='''np'''					,   )

       a						    = output.images[0]

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

       assert_mean_pixel_difference(__UpperCAmelCase					,   __UpperCAmelCase    )


