

import argparse
import os
import re


A_	:			Dict  	=      'src/transformers'

# Pattern that looks at the indentation in a line.
A_	:			str  	=      re.compile(r'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
A_	:			int  	=      re.compile(r'^\s*\"([^\"]+)\":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
A_	:			str  	=      re.compile(r'^\s*_import_structure\[\"([^\"]+)\"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
A_	:			Tuple  	=      re.compile(r'^\s*\"([^\"]+)\",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
A_	:			Tuple  	=      re.compile(r'\[([^\]]+)\]')

def 						UpperCamelCase (lowercase_:			int )					->				Optional[Any]:
	A__							:   Union[str, Any]					       =		_re_indent.search(_lowerCAmelCase )
	return "" if search is None else search.groups()[0]

def 						UpperCamelCase (lowercase_:			Tuple   ,				lowercase_:			Optional[int]=""   ,				lowercase_:			Union[str, Any]=None   ,				lowercase_:			Optional[int]=None )					->				int:
	A__							:   List[str]					       =		0
	A__							:   str					       =		code.split("""\n""" )
	if start_prompt is not None:
		while not lines[index].startswith(_lowerCAmelCase ):
			index += 1
		A__							:   int					       =		["""\n""".join(lines[:index] )]
	else:
		A__							:   Optional[Any]					       =		[]

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

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

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

	return blocks

def 						UpperCamelCase (lowercase_:			str )					->				int:



	def _inner(lowercase_:			str ):
		return key(_lowerCAmelCase ).lower().replace("""_"""   ,				"""""" )

	return _inner

def 						UpperCamelCase (lowercase_:			List[Any]   ,				lowercase_:			str=None )					->				Optional[int]:



	def noop(lowercase_:			Optional[Any] ):
		return x

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

	A__							:   Any					       =		ignore_underscore(_lowerCAmelCase )
	return sorted(_lowerCAmelCase   ,				key=_lowerCAmelCase ) + sorted(_lowerCAmelCase   ,				key=_lowerCAmelCase ) + sorted(_lowerCAmelCase   ,				key=_lowerCAmelCase )

def 						UpperCamelCase (lowercase_:			Any )					->				List[str]:



	def _replace(lowercase_:			Tuple ):
		A__							:   List[Any]					       =		match.groups()[0]
		if "," not in imports:
			return f"""[{imports}]"""
		A__							:   List[str]					       =		[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:
			A__							:   List[str]					       =		keys[:-1]
		return "[" + ", ".join([f"""\"{k}\"""" for k in sort_objects(_lowerCAmelCase )] ) + "]"

	A__							:   int					       =		import_statement.split("""\n""" )
	if len(_lowerCAmelCase ) > 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.
		A__							:   Dict					       =		2 if lines[1].strip() == """[""" else 1
		A__							:   Optional[Any]					       =		[(i, _re_strip_line.search(_lowerCAmelCase ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
		A__							:   Optional[int]					       =		sort_objects(_lowerCAmelCase   ,				key=lambda lowercase_ : x[1] )
		A__							:   Optional[Any]					       =		[lines[x[0] + idx] for x in sorted_indices]
		return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
	elif len(_lowerCAmelCase ) == 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:
			A__							:   Union[str, Any]					       =		_re_bracket_content.sub(_replace   ,				lines[1] )
		else:
			A__							:   Dict					       =		[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:
				A__							:   Any					       =		keys[:-1]
			A__							:   List[str]					       =		get_indent(lines[1] ) + """, """.join([f"""\"{k}\"""" for k in sort_objects(_lowerCAmelCase )] )
		return "\n".join(_lowerCAmelCase )
	else:
		# Finally we have to deal with imports fitting on one line
		A__							:   List[str]					       =		_re_bracket_content.sub(_replace   ,				_lowerCAmelCase )
		return import_statement

def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			Union[str, Any]=True )					->				Tuple:



	with open(_lowerCAmelCase   ,				encoding="""utf-8""" ) as f:
		A__							:   Tuple					       =		f.read()

	if "_import_structure" not in code:
		return

	# Blocks of indent level 0
	A__							:   List[Any]					       =		split_code_in_indented_blocks(
	    _lowerCAmelCase   ,				start_prompt="""_import_structure = {"""   ,				end_prompt="""if TYPE_CHECKING:""" )

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

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

		# Ignore beginning and last line: they don't contain anything.
		A__							:   int					       =		"""\n""".join(block_lines[line_idx:-1] )
		A__							:   str					       =		get_indent(block_lines[1] )
		# Slit the internal block into blocks of indent level 1.
		A__							:   Dict					       =		split_code_in_indented_blocks(_lowerCAmelCase   ,				indent_level=_lowerCAmelCase )
		# We have two categories of import key: list or _import_structure[key].append/extend
		A__							:   Any					       =		_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.
		A__							:   Union[str, Any]					       =		[(pattern.search(_lowerCAmelCase ).groups()[0] if pattern.search(_lowerCAmelCase ) is not None else None) for b in internal_blocks]
		# We only sort the lines with a key.
		A__							:   Tuple					       =		[(i, key) for i, key in enumerate(_lowerCAmelCase ) if key is not None]
		A__							:   int					       =		[x[0] for x in sorted(_lowerCAmelCase   ,				key=lambda lowercase_ : x[1] )]

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

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

	if code != "\n".join(_lowerCAmelCase ):
		if check_only:
			return True
		else:
			print(f"""Overwriting {file}.""" )
			with open(_lowerCAmelCase   ,				"""w"""   ,				encoding="""utf-8""" ) as f:
				f.write("""\n""".join(_lowerCAmelCase ) )

def 						UpperCamelCase (lowercase_:			Optional[int]=True )					->				int:
	A__							:   Tuple					       =		[]
	for root, _, files in os.walk(_lowerCAmelCase ):
		if "__init__.py" in files:
			A__							:   List[Any]					       =		sort_imports(os.path.join(_lowerCAmelCase   ,				"""__init__.py""" )   ,				check_only=_lowerCAmelCase )
			if result:
				A__							:   int					       =		[os.path.join(_lowerCAmelCase   ,				"""__init__.py""" )]
	if len(_lowerCAmelCase ) > 0:
		raise ValueError(f"""Would overwrite {len(_lowerCAmelCase )} files, run `make style`.""" )


if __name__ == "__main__":
	A_	:			Optional[int]  	=      argparse.ArgumentParser()
	parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
	A_	:			Optional[Any]  	=      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,
    KandinskyVaaImgaImgPipeline,
    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    _a						(__magic_name__		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       Any  							=	KandinskyVaaImgaImgPipeline
					UpperCAmelCase__:       Optional[Any]  							=	['''image_embeds''', '''negative_image_embeds''', '''image''']
					UpperCAmelCase__:       str  							=	[
					    '''image_embeds''',
					    '''negative_image_embeds''',
					    '''image''',
					]
					UpperCAmelCase__:       int  							=	[
					    '''generator''',
					    '''height''',
					    '''width''',
					    '''strength''',
					    '''guidance_scale''',
					    '''num_inference_steps''',
					    '''return_dict''',
					    '''guidance_scale''',
					    '''num_images_per_prompt''',
					    '''output_type''',
					    '''return_dict''',
					]
					UpperCAmelCase__:       Union[str, Any]  							=	False






					@property
					def   __A      (			self      ):
						return 32






					@property
					def   __A      (			self      ):
						return 32






					@property
					def   __A      (			self      ):
						return self.time_input_dim






					@property
					def   __A      (			self      ):
						return self.time_input_dim * 4






					@property
					def   __A      (			self      ):
						return 100






					@property
					def   __A      (			self      ):
						torch.manual_seed(0      )

						A__							:   Dict					       =		{
						    """in_channels""": 4,
						    # 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__							:   List[str]					       =		UNetaDConditionModel(**A__      )
						return model






					@property
					def   __A      (			self      ):
						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      ):
						torch.manual_seed(0      )
						A__							:   Tuple					       =		VQModel(**self.dummy_movq_kwargs      )
						return model






					def   __A      (			self      ):
						A__							:   Optional[int]					       =		self.dummy_unet
						A__							:   Dict					       =		self.dummy_movq

						A__							:   List[Any]					       =		{
						    """num_train_timesteps""": 1000,
						    """beta_schedule""": """linear""",
						    """beta_start""": 0.0_0_0_8_5,
						    """beta_end""": 0.0_1_2,
						    """clip_sample""": False,
						    """set_alpha_to_one""": False,
						    """steps_offset""": 0,
						    """prediction_type""": """epsilon""",
						    """thresholding""": False,
						}

						A__							:   List[str]					       =		DDIMScheduler(**A__      )

						A__							:   List[str]					       =		{
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """movq""": movq,
						}

						return components






					def   __A      (			self   ,	A__   ,	A__=0      ):
						A__							:   List[str]					       =		floats_tensor((1, self.text_embedder_hidden_size)   ,	rng=random.Random(A__      )      ).to(A__      )
						A__							:   Any					       =		floats_tensor((1, self.text_embedder_hidden_size)   ,	rng=random.Random(seed + 1      )      ).to(
						    A__      )
						# create init_image
						A__							:   Dict					       =		floats_tensor((1, 3, 64, 64)   ,	rng=random.Random(A__      )      ).to(A__      )
						A__							:   Optional[int]					       =		image.cpu().permute(0   ,	2   ,	3   ,	1      )[0]
						A__							:   Dict					       =		Image.fromarray(np.uinta(A__      )      ).convert("""RGB"""      ).resize((256, 256)      )

						if str(A__      ).startswith("""mps"""      ):
							A__							:   Any					       =		torch.manual_seed(A__      )
						else:
							A__							:   List[Any]					       =		torch.Generator(device=A__      ).manual_seed(A__      )
						A__							:   Optional[int]					       =		{
						    """image""": init_image,
						    """image_embeds""": image_embeds,
						    """negative_image_embeds""": negative_image_embeds,
						    """generator""": generator,
						    """height""": 64,
						    """width""": 64,
						    """num_inference_steps""": 10,
						    """guidance_scale""": 7.0,
						    """strength""": 0.2,
						    """output_type""": """np""",
						}
						return inputs






					def   __A      (			self      ):
						A__							:   str					       =		"""cpu"""

						A__							:   Any					       =		self.get_dummy_components()

						A__							:   Union[str, Any]					       =		self.pipeline_class(**A__      )
						A__							:   List[str]					       =		pipe.to(A__      )

						pipe.set_progress_bar_config(disable=A__      )

						A__							:   Dict					       =		pipe(**self.get_dummy_inputs(A__      )      )
						A__							:   Any					       =		output.images

						A__							:   List[str]					       =		pipe(
						    **self.get_dummy_inputs(A__      )   ,	return_dict=A__   ,	)[0]

						A__							:   Optional[int]					       =		image[0, -3:, -3:, -1]
						A__							:   str					       =		image_from_tuple[0, -3:, -3:, -1]

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

						A__							:   str					       =		np.array(
						    [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3]      )
						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()}"""



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


					'''simple docstring'''






					def   __A      (			self      ):
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






					def   __A      (			self      ):
						A__							:   Optional[int]					       =		load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/kandinskyv22/kandinskyv22_img2img_frog.npy"""      )

						A__							:   List[Any]					       =		load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""      )
						A__							:   str					       =		"""A red cartoon frog, 4k"""

						A__							:   int					       =		KandinskyVaaPriorPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-2-prior"""   ,	torch_dtype=torch.floataa      )
						pipe_prior.to(A__      )

						A__							:   List[Any]					       =		KandinskyVaaImgaImgPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-2-decoder"""   ,	torch_dtype=torch.floataa      )
						A__							:   List[str]					       =		pipeline.to(A__      )

						pipeline.set_progress_bar_config(disable=A__      )

						A__							:   Tuple					       =		torch.Generator(device="""cpu"""      ).manual_seed(0      )
						A__       ,       A__							:   Optional[Any]					       =		pipe_prior(
						    A__   ,	generator=A__   ,	num_inference_steps=5   ,	negative_prompt=""""""   ,	).to_tuple()

						A__							:   str					       =		pipeline(
						    image=A__   ,	image_embeds=A__   ,	negative_image_embeds=A__   ,	generator=A__   ,	num_inference_steps=100   ,	height=768   ,	width=768   ,	strength=0.2   ,	output_type="""np"""   ,	)

						A__							:   Optional[int]					       =		output.images[0]

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

						assert_mean_pixel_difference(A__   ,	A__      )



