



import argparse
import os
import re


__snake_case						:Tuple						=					'''src/transformers'''

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





def 					__snake_case     (      _UpperCAmelCase    ):
    __a							=    _re_indent.search(_UpperCAmelCase    )
    return "" if search is None else search.groups()[0]





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase=""       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=None    ):
    __a							=    0
    __a							=    code.split('''\n'''    )
    if start_prompt is not None:
        while not lines[index].startswith(_UpperCAmelCase    ):
            index += 1
        __a							=    ['''\n'''.join(lines[:index]    )]
    else:
        __a							=    []

    # We split into blocks until we get to the `end_prompt` (or the end of the block).
    __a							=    [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:
                    __a							=    [lines[index + 1]]
                    index += 1
                else:
                    __a							=    []
            else:
                blocks.append('''\n'''.join(_UpperCAmelCase    )    )
                __a							=    [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 					__snake_case     (      _UpperCAmelCase    ):

    def _inner(_UpperCAmelCase    ):
        return key(_UpperCAmelCase    ).lower().replace('''_'''       ,			''''''    )

    return _inner





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase=None    ):

    # If no key is provided, we use a noop.
    def noop(_UpperCAmelCase    ):
        return x

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

    __a							=    ignore_underscore(_UpperCAmelCase    )
    return sorted(_UpperCAmelCase       ,			key=_UpperCAmelCase    ) + sorted(_UpperCAmelCase       ,			key=_UpperCAmelCase    ) + sorted(_UpperCAmelCase       ,			key=_UpperCAmelCase    )





def 					__snake_case     (      _UpperCAmelCase    ):

    # This inner function sort imports between [ ].
    def _replace(_UpperCAmelCase    ):
        __a							=    match.groups()[0]
        if "," not in imports:
            return f'[{imports}]'
        __a							=    [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							=    keys[:-1]
        return "[" + ", ".join([f'"{k}"' for k in sort_objects(_UpperCAmelCase    )]    ) + "]"

    __a							=    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.
        __a							=    2 if lines[1].strip() == '''[''' else 1
        __a							=    [(i, _re_strip_line.search(_UpperCAmelCase    ).groups()[0]) for i, line in enumerate(lines[idx:-idx]    )]
        __a							=    sort_objects(_UpperCAmelCase       ,			key=lambda _UpperCAmelCase    : x[1]    )
        __a							=    [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:
            __a							=    _re_bracket_content.sub(_replace       ,			lines[1]    )
        else:
            __a							=    [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							=    keys[:-1]
            __a							=    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
        __a							=    _re_bracket_content.sub(_replace       ,			_UpperCAmelCase    )
        return import_statement





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase=True    ):
    with open(_UpperCAmelCase       ,			encoding='''utf-8'''    ) as f:
        __a							=    f.read()

    if "_import_structure" not in code:
        return

    # Blocks of indent level 0
    __a							=    split_code_in_indented_blocks(
        _UpperCAmelCase       ,			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(_UpperCAmelCase    ) - 1    ):
        # Check if the block contains some `_import_structure`s thingy to sort.
        __a							=    main_blocks[block_idx]
        __a							=    block.split('''\n'''    )

        # Get to the start of the imports.
        __a							=    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]:
                __a							=    len(_UpperCAmelCase    )
            else:
                line_idx += 1
        if line_idx >= len(_UpperCAmelCase    ):
            continue

        # Ignore beginning and last line: they don't contain anything.
        __a							=    '''\n'''.join(block_lines[line_idx:-1]    )
        __a							=    get_indent(block_lines[1]    )
        # Slit the internal block into blocks of indent level 1.
        __a							=    split_code_in_indented_blocks(_UpperCAmelCase       ,			indent_level=_UpperCAmelCase    )
        # We have two categories of import key: list or _import_structure[key].append/extend
        __a							=    _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							=    [(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.
        __a							=    [(i, key) for i, key in enumerate(_UpperCAmelCase    ) if key is not None]
        __a							=    [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.
        __a							=    0
        __a							=    []
        for i in range(len(_UpperCAmelCase    )    ):
            if keys[i] is None:
                reorderded_blocks.append(internal_blocks[i]    )
            else:
                __a							=    sort_objects_in_import(internal_blocks[sorted_indices[count]]    )
                reorderded_blocks.append(_UpperCAmelCase    )
                count += 1

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

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





def 					__snake_case     (      _UpperCAmelCase=True    ):
    __a							=    []
    for root, _, files in os.walk(_UpperCAmelCase    ):
        if "__init__.py" in files:
            __a							=    sort_imports(os.path.join(_UpperCAmelCase       ,			'''__init__.py'''    )       ,			check_only=_UpperCAmelCase    )
            if result:
                __a							=    [os.path.join(_UpperCAmelCase       ,			'''__init__.py'''    )]
    if len(_UpperCAmelCase    ) > 0:
        raise ValueError(f'Would overwrite {len(_UpperCAmelCase    )} files, run `make style`.'    )


if __name__ == "__main__":
 __snake_case						:List[str]						=					argparse.ArgumentParser()
 parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
 __snake_case						:Tuple						=					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	(     __UpperCAmelCase				,unittest.TestCase   ):
       UpperCamelCase__  :   str						 =						KandinskyVaaImgaImgPipeline
       UpperCamelCase__  :   Optional[Any]						 =						['''image_embeds''', '''negative_image_embeds''', '''image''']
       UpperCamelCase__  :   Dict						 =						[
           '''image_embeds''',
           '''negative_image_embeds''',
           '''image''',
       ]
       UpperCamelCase__  :   Any						 =						[
           '''generator''',
           '''height''',
           '''width''',
           '''strength''',
           '''guidance_scale''',
           '''num_inference_steps''',
           '''return_dict''',
           '''guidance_scale''',
           '''num_images_per_prompt''',
           '''output_type''',
           '''return_dict''',
       ]
       UpperCamelCase__  :   List[Any]						 =						False



       @property
       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           return 32



       @property
       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           return 32



       @property
       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           return self.time_input_dim



       @property
       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           return self.time_input_dim * 4



       @property
       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           return 100



       @property
       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           torch.manual_seed(0)

           __a							=    {
               '''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							=    UNetaDConditionModel(**__SCREAMING_SNAKE_CASE)
           return model



       @property
       def 		_lowerCamelCase      (		self							:					Tuple):


           '''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							:					Optional[int]):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    self.dummy_unet
           __a							=    self.dummy_movq

           __a							=    {
               '''num_train_timesteps''': 1_000,
               '''beta_schedule''': '''linear''',
               '''beta_start''': 0.0_00_85,
               '''beta_end''': 0.0_12,
               '''clip_sample''': False,
               '''set_alpha_to_one''': False,
               '''steps_offset''': 0,
               '''prediction_type''': '''epsilon''',
               '''thresholding''': False,
           }

           __a							=    DDIMScheduler(**__SCREAMING_SNAKE_CASE)

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

           return components



       def 		_lowerCamelCase      (		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=0):


           '''simple docstring'''

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

           if str(__SCREAMING_SNAKE_CASE).startswith('''mps'''):
               __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           else:
               __a							=    torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(__SCREAMING_SNAKE_CASE)
           __a							=    {
               '''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 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    '''cpu'''

           __a							=    self.get_dummy_components()

           __a							=    self.pipeline_class(**__SCREAMING_SNAKE_CASE)
           __a							=    pipe.to(__SCREAMING_SNAKE_CASE)

           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a							=    pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE))
           __a							=    output.images

           __a							=    pipe(
               **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE)					,		return_dict=__SCREAMING_SNAKE_CASE					,		)[0]

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

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

           __a							=    np.array(
               [0.6_19_97_78, 0.63_98_44_06, 0.46_14_57_85, 0.62_94_49_84, 0.5_62_22_15, 0.47_30_61_32, 0.47_44_14_56, 0.4_60_76_06, 0.48_71_92_63])
           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   ):



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

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





       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/kandinskyv22/kandinskyv22_img2img_frog.npy''')

           __a							=    load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''')
           __a							=    '''A red cartoon frog, 4k'''

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

           __a							=    KandinskyVaaImgaImgPipeline.from_pretrained(
               '''kandinsky-community/kandinsky-2-2-decoder'''					,		torch_dtype=torch.floataa)
           __a							=    pipeline.to(__SCREAMING_SNAKE_CASE)

           pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

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

           __a							=    pipeline(
               image=__SCREAMING_SNAKE_CASE					,		image_embeds=__SCREAMING_SNAKE_CASE					,		negative_image_embeds=__SCREAMING_SNAKE_CASE					,		generator=__SCREAMING_SNAKE_CASE					,		num_inference_steps=100					,		height=768					,		width=768					,		strength=0.2					,		output_type='''np'''					,		)

           __a							=    output.images[0]

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

           assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)





