


# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


snake_case							:       List[str]										=	get_logger(__name__)







class 						_snake_case    :
 SCREAMING_SNAKE_CASE__ =			'dummy_data'
 SCREAMING_SNAKE_CASE__ =			'datasets'
 SCREAMING_SNAKE_CASE__ =			False




 def __init__(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase = None   ,    _lowerCamelCase = False   ,    _lowerCamelCase = True   ,    _lowerCamelCase = None   ,    ):
       a		:List[str]									=			0
       a		:Union[str, Any]									=			dataset_name
       a		:Dict									=			cache_dir
       a		:Union[str, Any]									=			use_local_dummy_data
       a		:int									=			config
       # download_callbacks take a single url as input
       a		:List[Callable]									=			download_callbacks or []
       # if False, it doesn't load existing files and it returns the paths of the dummy files relative
       # to the dummy_data zip file root
       a		:Dict									=			load_existing_dummy_data

       # TODO(PVP, QL) might need to make this more general
       a		:str									=			str(_lowerCamelCase       )
       # to be downloaded
       a		:Optional[int]									=			None
       a		:Optional[int]									=			None




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       if self._dummy_file is None:
             a		:Union[str, Any]									=			self.download_dummy_data()
       return self._dummy_file




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       if self.config is not None:
             # structure is dummy / config_name / version_name
             return os.path.join('''dummy'''   ,    self.config.name   ,    self.version_name       )
       # structure is dummy / version_name
       return os.path.join('''dummy'''   ,    self.version_name       )




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return os.path.join(self.dummy_data_folder   ,    '''dummy_data.zip'''       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:str									=			(
           self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
       )

       a		:Dict									=			cached_path(
           _lowerCamelCase   ,    cache_dir=self.cache_dir   ,    extract_compressed_file=_lowerCamelCase   ,    force_extract=_lowerCamelCase       )

       return os.path.join(_lowerCamelCase   ,    self.dummy_file_name       )




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return os.path.join(self.datasets_scripts_dir   ,    self.dataset_name   ,    self.dummy_zip_file       )




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       if self._bucket_url is None:
             a		:Dict									=			hf_github_url(self.dataset_name   ,    self.dummy_zip_file.replace(os.sep   ,    '''/'''       )       )
       return self._bucket_url




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       # return full path if its a dir
       if os.path.isdir(self.dummy_file       ):
             return self.dummy_file
       # else cut off path to file -> example `xsum`.
       return "/".join(self.dummy_file.replace(os.sep   ,    '''/'''       ).split('''/'''       )[:-1]       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    *_lowerCamelCase       ):
       if self.load_existing_dummy_data:
             # dummy data is downloaded and tested
             a		:Dict									=			self.dummy_file
       else:
             # dummy data cannot be downloaded and only the path to dummy file is returned
             a		:Union[str, Any]									=			self.dummy_file_name

       # special case when data_url is a dict
       if isinstance(_lowerCamelCase   ,    _lowerCamelCase       ):
             return self.create_dummy_data_dict(_lowerCamelCase   ,    _lowerCamelCase       )
       elif isinstance(_lowerCamelCase   ,    (list, tuple)       ):
             return self.create_dummy_data_list(_lowerCamelCase   ,    _lowerCamelCase       )
       else:
             return self.create_dummy_data_single(_lowerCamelCase   ,    _lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    *_lowerCamelCase       ):
       return self.download_and_extract(_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       return self.download_and_extract(_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    *_lowerCamelCase   ,    **_lowerCamelCase       ):
       return path




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return {}




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       a		:Dict									=			{}
       for key, single_urls in data_url.items():
             for download_callback in self.download_callbacks:
                   if isinstance(_lowerCamelCase   ,    _lowerCamelCase       ):
                         for single_url in single_urls:
                               download_callback(_lowerCamelCase       )
                   else:
                         a		:Any									=			single_urls
                         download_callback(_lowerCamelCase       )
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
             if isinstance(_lowerCamelCase   ,    _lowerCamelCase       ):
                   a		:Optional[Any]									=			[os.path.join(_lowerCamelCase   ,    urllib.parse.quote_plus(Path(_lowerCamelCase       ).name       )       ) for x in single_urls]
             else:
                   a		:Optional[int]									=			single_urls
                   a		:Union[str, Any]									=			os.path.join(_lowerCamelCase   ,    urllib.parse.quote_plus(Path(_lowerCamelCase       ).name       )       )
             a		:Dict									=			value

       # make sure that values are unique
       if all(isinstance(_lowerCamelCase   ,    _lowerCamelCase       ) for i in dummy_data_dict.values()       ) and len(set(dummy_data_dict.values()       )       ) < len(
           dummy_data_dict.values()       ):
             # append key to value to make its name unique
             a		:List[str]									=			{key: value + key for key, value in dummy_data_dict.items()}

       return dummy_data_dict




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       a		:Dict									=			[]
       # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
       a		:Optional[int]									=			all(bool(re.findall('''[0-9]{3,}-of-[0-9]{3,}'''   ,    _lowerCamelCase       )       ) for url in data_url       )
       a		:Tuple									=			all(
           url.startswith('''https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed'''       ) for url in data_url       )
       if data_url and (is_tf_records or is_pubmed_records):
             a		:Union[str, Any]									=			[data_url[0]] * len(_lowerCamelCase       )
       for single_url in data_url:
             for download_callback in self.download_callbacks:
                   download_callback(_lowerCamelCase       )
             # we force the name of each key to be the last file / folder name of the url path
             # if the url has arguments, we need to encode them with urllib.parse.quote_plus
             a		:str									=			os.path.join(_lowerCamelCase   ,    urllib.parse.quote_plus(single_url.split('''/'''       )[-1]       )       )
             dummy_data_list.append(_lowerCamelCase       )
       return dummy_data_list




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       for download_callback in self.download_callbacks:
             download_callback(_lowerCamelCase       )
       # we force the name of each key to be the last file / folder name of the url path
       # if the url has arguments, we need to encode them with urllib.parse.quote_plus
       a		:List[str]									=			os.path.join(_lowerCamelCase   ,    urllib.parse.quote_plus(data_url.split('''/'''       )[-1]       )       )
       if os.path.exists(_lowerCamelCase       ) or not self.load_existing_dummy_data:
             return value
       else:
             # Backward compatibility, maybe deprecate at one point.
             # For many datasets with single url calls to dl_manager.download_and_extract,
             # the dummy_data.zip file is actually the zipped downloaded file
             # while now we expected the dummy_data.zip file to be a directory containing
             # the downloaded file.
             return path_to_dummy_data




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       def _iter_archive_members(_lowerCamelCase       ):
             # this preserves the order of the members inside the ZIP archive
             a		:List[str]									=			Path(self.dummy_file       ).parent
             a		:Any									=			path.relative_to(_lowerCamelCase       )
             with ZipFile(self.local_path_to_dummy_data       ) as zip_file:
                   a		:int									=			zip_file.namelist()
             for member in members:
                   if member.startswith(relative_path.as_posix()       ):
                         yield dummy_parent_path.joinpath(_lowerCamelCase       )

       a		:Any									=			Path(_lowerCamelCase       )
       a		:Union[str, Any]									=			_iter_archive_members(_lowerCamelCase       ) if self.use_local_dummy_data else path.rglob('''*'''       )
       for file_path in file_paths:
             if file_path.is_file() and not file_path.name.startswith(('''.''', '''__''')       ):
                   yield file_path.relative_to(_lowerCamelCase       ).as_posix(), file_path.open('''rb'''       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       if not isinstance(_lowerCamelCase   ,    _lowerCamelCase       ):
             a		:Tuple									=			[paths]
       for path in paths:
             if os.path.isfile(_lowerCamelCase       ):
                   if os.path.basename(_lowerCamelCase       ).startswith(('''.''', '''__''')       ):
                         return
                   yield path
             else:
                   for dirpath, dirnames, filenames in os.walk(_lowerCamelCase       ):
                         if os.path.basename(_lowerCamelCase       ).startswith(('''.''', '''__''')       ):
                               continue
                         dirnames.sort()
                         for filename in sorted(_lowerCamelCase       ):
                               if filename.startswith(('''.''', '''__''')       ):
                                     continue
                               yield os.path.join(_lowerCamelCase   ,    _lowerCamelCase       )









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    ( _snake_case		,       unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			KandinskyVaaInpaintPipeline
 SCREAMING_SNAKE_CASE__ =			['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
 SCREAMING_SNAKE_CASE__ =			[
     'image_embeds',
     'negative_image_embeds',
     'image',
     'mask_image',
 ]
 SCREAMING_SNAKE_CASE__ =			[
     'generator',
     'height',
     'width',
     'latents',
     'guidance_scale',
     'num_inference_steps',
     'return_dict',
     'guidance_scale',
     'num_images_per_prompt',
     'output_type',
     'return_dict',
 ]
 SCREAMING_SNAKE_CASE__ =			False




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return 32




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return 32




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




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




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return 100




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

       a		:Tuple									=			{
           '''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		:int									=			UNetaDConditionModel(**_lowerCamelCase       )
       return model




 @property
 def 			SCREAMING_SNAKE_CASE__							(						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 			SCREAMING_SNAKE_CASE__							(						self       ):
       torch.manual_seed(0       )
       a		:Any									=			VQModel(**self.dummy_movq_kwargs       )
       return model




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, Any]									=			self.dummy_unet
       a		:Any									=			self.dummy_movq

       a		:Dict									=			DDIMScheduler(
           num_train_timesteps=1000   ,    beta_schedule='''linear'''   ,    beta_start=0.0_0085   ,    beta_end=0.012   ,    clip_sample=_lowerCamelCase   ,    set_alpha_to_one=_lowerCamelCase   ,    steps_offset=1   ,    prediction_type='''epsilon'''   ,    thresholding=_lowerCamelCase   ,    )

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

       return components




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase=0       ):
       a		:Tuple									=			floats_tensor((1, self.text_embedder_hidden_size)   ,    rng=random.Random(_lowerCamelCase       )       ).to(_lowerCamelCase       )
       a		:int									=			floats_tensor((1, self.text_embedder_hidden_size)   ,    rng=random.Random(seed + 1       )       ).to(
           _lowerCamelCase       )
       # create init_image
       a		:int									=			floats_tensor((1, 3, 64, 64)   ,    rng=random.Random(_lowerCamelCase       )       ).to(_lowerCamelCase       )
       a		:Union[str, Any]									=			image.cpu().permute(0   ,    2   ,    3   ,    1       )[0]
       a		:Any									=			Image.fromarray(np.uinta(_lowerCamelCase       )       ).convert('''RGB'''       ).resize((256, 256)       )
       # create mask
       a		:Dict									=			np.ones((64, 64)   ,    dtype=np.floataa       )
       a		:Tuple									=			0

       if str(_lowerCamelCase       ).startswith('''mps'''       ):
             a		:Dict									=			torch.manual_seed(_lowerCamelCase       )
       else:
             a		:Union[str, Any]									=			torch.Generator(device=_lowerCamelCase       ).manual_seed(_lowerCamelCase       )
       a		:Tuple									=			{
           '''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 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[Any]									=			'''cpu'''

       a		:List[Any]									=			self.get_dummy_components()

       a		:int									=			self.pipeline_class(**_lowerCamelCase       )
       a		:Tuple									=			pipe.to(_lowerCamelCase       )

       pipe.set_progress_bar_config(disable=_lowerCamelCase       )

       a		:str									=			pipe(**self.get_dummy_inputs(_lowerCamelCase       )       )
       a		:Optional[int]									=			output.images

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

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

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

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

       a		:str									=			np.array(
           [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848]       )

       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 			SCREAMING_SNAKE_CASE__							(						self       ):
       super().test_inference_batch_single_identical(expected_max_diff=3e-3       )







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




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




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[int]									=			load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''       )

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

       a		:List[Any]									=			'''a hat'''

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

       a		:List[Any]									=			KandinskyVaaInpaintPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''   ,    torch_dtype=torch.floataa       )
       a		:str									=			pipeline.to(_lowerCamelCase       )
       pipeline.set_progress_bar_config(disable=_lowerCamelCase       )

       a		:Union[str, Any]									=			torch.Generator(device='''cpu'''       ).manual_seed(0       )
       a				,							a		:Optional[Any]									=			pipe_prior(
           _lowerCamelCase   ,    generator=_lowerCamelCase   ,    num_inference_steps=5   ,    negative_prompt=''''''   ,    ).to_tuple()

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

       a		:Any									=			output.images[0]

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

       assert_mean_pixel_difference(_lowerCamelCase   ,    _lowerCamelCase       )





