





# 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


__lowerCamelCase :  Union[str, Any]										=  get_logger(__name__)



class       A__     :
      _UpperCAmelCase			:Union[str, Any]							=     'dummy_data'
      _UpperCAmelCase			:Optional[int]							=     'datasets'
      _UpperCAmelCase			:Dict							=     False





      def __init__(				self ,							A_ ,							A_ ,							A_ ,							A_ = None ,							A_ = False ,							A_ = True ,							A_ = None ,							):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     0
       UpperCamelCase			:      List[Any] 			=     dataset_name
       UpperCamelCase			:      List[str] 			=     cache_dir
       UpperCamelCase			:      Optional[int] 			=     use_local_dummy_data
       UpperCamelCase			:      Union[str, Any] 			=     config
       # download_callbacks take a single url as input
       UpperCamelCase			:      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
       UpperCamelCase			:      Optional[int] 			=     load_existing_dummy_data

       # TODO(PVP, QL) might need to make this more general
       UpperCamelCase			:      Optional[Any] 			=     str(A_     )
       # to be downloaded
       UpperCamelCase			:      List[Any] 			=     None
       UpperCamelCase			:      Tuple 			=     None





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       if self._dummy_file is None:
        UpperCamelCase			:      int 			=     self.download_dummy_data()
       return self._dummy_file





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return os.path.join(self.dummy_data_folder ,							"dummy_data.zip"     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Union[str, Any] 			=     (
           self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
       )

       UpperCamelCase			:      List[Any] 			=     cached_path(
           A_ ,							cache_dir=self.cache_dir ,							extract_compressed_file=A_ ,							force_extract=A_     )

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





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return os.path.join(self.datasets_scripts_dir ,							self.dataset_name ,							self.dummy_zip_file     )





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       if self._bucket_url is None:
        UpperCamelCase			:      List[str] 			=     hf_github_url(self.dataset_name ,							self.dummy_zip_file.replace(os.sep ,							"/"     )     )
       return self._bucket_url





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       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  __UpperCamelCase(				self ,							A_ ,							*A_     ):
       '''simple docstring'''




       if self.load_existing_dummy_data:
        # dummy data is downloaded and tested
        UpperCamelCase			:      Dict 			=     self.dummy_file
       else:
        # dummy data cannot be downloaded and only the path to dummy file is returned
        UpperCamelCase			:      List[str] 			=     self.dummy_file_name

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





      def  __UpperCamelCase(				self ,							A_ ,							*A_     ):
       '''simple docstring'''




       return self.download_and_extract(A_     )





      def  __UpperCamelCase(				self ,							A_ ,							A_     ):
       '''simple docstring'''




       return self.download_and_extract(A_     )





      def  __UpperCamelCase(				self ,							A_ ,							*A_ ,							**A_     ):
       '''simple docstring'''




       return path





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return {}





      def  __UpperCamelCase(				self ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     {}
       for key, single_urls in data_url.items():
        for download_callback in self.download_callbacks:
         if isinstance(A_ ,							A_     ):
          for single_url in single_urls:
           download_callback(A_     )
         else:
          UpperCamelCase			:      int 			=     single_urls
          download_callback(A_     )
            # 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(A_ ,							A_     ):
         UpperCamelCase			:      Optional[Any] 			=     [os.path.join(A_ ,							urllib.parse.quote_plus(Path(A_     ).name     )     ) for x in single_urls]
        else:
         UpperCamelCase			:      List[str] 			=     single_urls
         UpperCamelCase			:      int 			=     os.path.join(A_ ,							urllib.parse.quote_plus(Path(A_     ).name     )     )
        UpperCamelCase			:      Optional[int] 			=     value

       # make sure that values are unique
       if all(isinstance(A_ ,							A_     ) 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
        UpperCamelCase			:      List[Any] 			=     {key: value + key for key, value in dummy_data_dict.items()}

       return dummy_data_dict





      def  __UpperCamelCase(				self ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Any 			=     []
       # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
       UpperCamelCase			:      str 			=     all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}" ,							A_     )     ) for url in data_url     )
       UpperCamelCase			:      Union[str, Any] 			=     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):
        UpperCamelCase			:      List[Any] 			=     [data_url[0]] * len(A_     )
       for single_url in data_url:
        for download_callback in self.download_callbacks:
         download_callback(A_     )
        # 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
        UpperCamelCase			:      int 			=     os.path.join(A_ ,							urllib.parse.quote_plus(single_url.split("/"     )[-1]     )     )
        dummy_data_list.append(A_     )
       return dummy_data_list





      def  __UpperCamelCase(				self ,							A_ ,							A_     ):
       '''simple docstring'''




       for download_callback in self.download_callbacks:
        download_callback(A_     )
       # 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
       UpperCamelCase			:      List[str] 			=     os.path.join(A_ ,							urllib.parse.quote_plus(data_url.split("/"     )[-1]     )     )
       if os.path.exists(A_     ) 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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       pass





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       pass





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




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

       UpperCamelCase			:      Tuple 			=     Path(A_     )
       UpperCamelCase			:      Any 			=     _iter_archive_members(A_     ) 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(A_     ).as_posix(), file_path.open("rb"     )





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       if not isinstance(A_ ,							A_     ):
        UpperCamelCase			:      Optional[int] 			=     [paths]
       for path in paths:
        if os.path.isfile(A_     ):
         if os.path.basename(A_     ).startswith((".", "__")     ):
          return
         yield path
        else:
         for dirpath, dirnames, filenames in os.walk(A_     ):
          if os.path.basename(A_     ).startswith((".", "__")     ):
           continue
          dirnames.sort()
          for filename in sorted(A_     ):
           if filename.startswith((".", "__")     ):
            continue
           yield os.path.join(A_ ,							A_     )







import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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__     ( __snake_case ,     unittest.TestCase    ):
      _UpperCAmelCase			:str							=     KandinskyVaaPipeline
      _UpperCAmelCase			:str							=     [
          'image_embeds',
          'negative_image_embeds',
      ]
      _UpperCAmelCase			:str							=     ['image_embeds', 'negative_image_embeds']
      _UpperCAmelCase			:List[str]							=     [
          'generator',
          'height',
          'width',
          'latents',
          'guidance_scale',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      _UpperCAmelCase			:List[str]							=     False





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return 32





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return 32





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self.time_input_dim





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self.time_input_dim * 4





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return 100





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       torch.manual_seed(0     )

       UpperCamelCase			:      List[str] 			=     {
           "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,
       }

       UpperCamelCase			:      Dict 			=     UNetaDConditionModel(**A_     )
       return model





      @property
      def  __UpperCamelCase(				self     ):
       '''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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       torch.manual_seed(0     )
       UpperCamelCase			:      Optional[Any] 			=     VQModel(**self.dummy_movq_kwargs     )
       return model





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     self.dummy_unet
       UpperCamelCase			:      Optional[Any] 			=     self.dummy_movq

       UpperCamelCase			:      Dict 			=     DDIMScheduler(
           num_train_timesteps=1000 ,							beta_schedule="linear" ,							beta_start=0.0_00_85 ,							beta_end=0.0_12 ,							clip_sample=A_ ,							set_alpha_to_one=A_ ,							steps_offset=1 ,							prediction_type="epsilon" ,							thresholding=A_ ,							)

       UpperCamelCase			:      Tuple 			=     {
           "unet": unet,
           "scheduler": scheduler,
           "movq": movq,
       }
       return components





      def  __UpperCamelCase(				self ,							A_ ,							A_=0     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     floats_tensor((1, self.text_embedder_hidden_size) ,							rng=random.Random(A_     )     ).to(A_     )
       UpperCamelCase			:      Optional[Any] 			=     floats_tensor((1, self.text_embedder_hidden_size) ,							rng=random.Random(seed + 1     )     ).to(
           A_     )
       if str(A_     ).startswith("mps"     ):
        UpperCamelCase			:      Optional[Any] 			=     torch.manual_seed(A_     )
       else:
        UpperCamelCase			:      List[Any] 			=     torch.Generator(device=A_     ).manual_seed(A_     )
       UpperCamelCase			:      Optional[int] 			=     {
           "image_embeds": image_embeds,
           "negative_image_embeds": negative_image_embeds,
           "generator": generator,
           "height": 64,
           "width": 64,
           "guidance_scale": 4.0,
           "num_inference_steps": 2,
           "output_type": "np",
       }
       return inputs





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     "cpu"

       UpperCamelCase			:      List[str] 			=     self.get_dummy_components()

       UpperCamelCase			:      Tuple 			=     self.pipeline_class(**A_     )
       UpperCamelCase			:      List[str] 			=     pipe.to(A_     )

       pipe.set_progress_bar_config(disable=A_     )

       UpperCamelCase			:      Dict 			=     pipe(**self.get_dummy_inputs(A_     )     )
       UpperCamelCase			:      Optional[int] 			=     output.images

       UpperCamelCase			:      int 			=     pipe(
           **self.get_dummy_inputs(A_     ) ,							return_dict=A_ ,							)[0]

       UpperCamelCase			:      Tuple 			=     image[0, -3:, -3:, -1]
       UpperCamelCase			:      List[Any] 			=     image_from_tuple[0, -3:, -3:, -1]

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

       UpperCamelCase			:      int 			=     np.array(
           [0.6_23_79_76, 1.0, 0.36_44_13_32, 1.0, 0.70_63_96_34, 0.29_87_71_86, 0.85_65_21_25, 0.5_21_68_43, 0.54_45_40_46]     )

       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  __UpperCamelCase(				self     ):
       '''simple docstring'''




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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"     )

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

       UpperCamelCase			:      Dict 			=     KandinskyVaaPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-decoder" ,							torch_dtype=torch.floataa     )
       UpperCamelCase			:      Tuple 			=     pipeline.to(A_     )
       pipeline.set_progress_bar_config(disable=A_     )

       UpperCamelCase			:      str 			=     "red cat, 4k photo"

       UpperCamelCase			:      str 			=     torch.Generator(device="cuda"     ).manual_seed(0     )
       UpperCamelCase			,					UpperCamelCase			:      Tuple 			=     pipe_prior(
           A_ ,							generator=A_ ,							num_inference_steps=5 ,							negative_prompt="" ,							).to_tuple()

       UpperCamelCase			:      int 			=     torch.Generator(device="cuda"     ).manual_seed(0     )
       UpperCamelCase			:      Tuple 			=     pipeline(
           image_embeds=A_ ,							negative_image_embeds=A_ ,							generator=A_ ,							num_inference_steps=100 ,							output_type="np" ,							)

       UpperCamelCase			:      Union[str, Any] 			=     output.images[0]

       assert image.shape == (512, 512, 3)

       assert_mean_pixel_difference(A_ ,							A_     )
