






'''simple docstring'''
import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader





def  _a(  UpperCamelCase__				:						Features							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				List[str]									=np.inf

      def set_batch_size(UpperCamelCase__				:						FeatureType							) -> None:
            nonlocal batch_size
            if isinstance(UpperCamelCase__,	UpperCamelCase__							):
                  SCREAMING_SNAKE_CASE__     :				str									=min(UpperCamelCase__,	config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS							)
            elif isinstance(UpperCamelCase__,	UpperCamelCase__							):
                  SCREAMING_SNAKE_CASE__     :				int									=min(UpperCamelCase__,	config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS							)
            elif isinstance(UpperCamelCase__,	UpperCamelCase__							) and feature.dtype == "binary":
                  SCREAMING_SNAKE_CASE__     :				Dict									=min(UpperCamelCase__,	config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS							)

      _visit(UpperCamelCase__,	UpperCamelCase__							)

      return None if batch_size is np.inf else batch_size






class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):




 def __init__(      self	:							Any					,      __lowercase	:							NestedDataStructureLike[PathLike]					,      __lowercase	:							Optional[NamedSplit] = None					,      __lowercase	:							Optional[Features] = None					,      __lowercase	:							str = None					,      __lowercase	:							bool = False					,      __lowercase	:							bool = False					,      __lowercase	:							Optional[int] = None					,      **__lowercase	:							str					,      )	->						Optional[int]:
       super().__init__(
           __lowercase					,      split=__lowercase					,      features=__lowercase					,      cache_dir=__lowercase					,      keep_in_memory=__lowercase					,      streaming=__lowercase					,      num_proc=__lowercase					,      **__lowercase					,      )
       SCREAMING_SNAKE_CASE__     :				Dict									=path_or_paths if isinstance(__lowercase					,      __lowercase      ) else {self.split: path_or_paths}
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=_PACKAGED_DATASETS_MODULES['''parquet'''][1]
       SCREAMING_SNAKE_CASE__     :				List[str]									=Parquet(
           cache_dir=__lowercase					,      data_files=__lowercase					,      features=__lowercase					,      hash=__lowercase					,      **__lowercase					,      )




 def      __magic_name__	(      self	:							Optional[Any]      )	->						List[Any]:
       # Build iterable dataset
       if self.streaming:
             SCREAMING_SNAKE_CASE__     :				Tuple									=self.builder.as_streaming_dataset(split=self.split      )
       # Build regular (map-style) dataset
       else:
             SCREAMING_SNAKE_CASE__     :				Any									=None
             SCREAMING_SNAKE_CASE__     :				Any									=None
             SCREAMING_SNAKE_CASE__     :				Tuple									=None
             SCREAMING_SNAKE_CASE__     :				Tuple									=None

             self.builder.download_and_prepare(
                 download_config=__lowercase					,      download_mode=__lowercase					,      verification_mode=__lowercase					,      base_path=__lowercase					,      num_proc=self.num_proc					,      )
             SCREAMING_SNAKE_CASE__     :				Dict									=self.builder.as_dataset(
                 split=self.split					,      verification_mode=__lowercase					,      in_memory=self.keep_in_memory      )
       return dataset






class      __SCREAMING_SNAKE_CASE      :




 def __init__(      self	:							Dict					,      __lowercase	:							Dataset					,      __lowercase	:							Union[PathLike, BinaryIO]					,      __lowercase	:							Optional[int] = None					,      **__lowercase	:							int					,      )	->						Optional[int]:
       SCREAMING_SNAKE_CASE__     :				List[Any]									=dataset
       SCREAMING_SNAKE_CASE__     :				str									=path_or_buf
       SCREAMING_SNAKE_CASE__     :				int									=batch_size or get_writer_batch_size(dataset.features      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=parquet_writer_kwargs




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						int:
       SCREAMING_SNAKE_CASE__     :				Tuple									=self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

       if isinstance(self.path_or_buf					,      (str, bytes, os.PathLike)      ):
             with open(self.path_or_buf					,      '''wb+'''      ) as buffer:
                   SCREAMING_SNAKE_CASE__     :				Optional[int]									=self._write(file_obj=__lowercase					,      batch_size=__lowercase					,      **self.parquet_writer_kwargs      )
       else:
             SCREAMING_SNAKE_CASE__     :				int									=self._write(file_obj=self.path_or_buf					,      batch_size=__lowercase					,      **self.parquet_writer_kwargs      )
       return written




 def      __magic_name__	(      self	:							List[Any]					,      __lowercase	:							BinaryIO					,      __lowercase	:							int					,      **__lowercase	:							Tuple      )	->						int:
       SCREAMING_SNAKE_CASE__     :				int									=0
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=parquet_writer_kwargs.pop('''path_or_buf'''					,      __lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.dataset.features.arrow_schema

       SCREAMING_SNAKE_CASE__     :				List[Any]									=pq.ParquetWriter(__lowercase					,      schema=__lowercase					,      **__lowercase      )

       for offset in logging.tqdm(
           range(0					,      len(self.dataset      )					,      __lowercase      )					,      unit='''ba'''					,      disable=not logging.is_progress_bar_enabled()					,      desc='''Creating parquet from Arrow format'''					,      ):
             SCREAMING_SNAKE_CASE__     :				Dict									=query_table(
                 table=self.dataset._data					,      key=slice(__lowercase					,      offset + batch_size      )					,      indices=self.dataset._indices if self.dataset._indices is not None else None					,      )
             writer.write_table(__lowercase      )
             written += batch.nbytes
       writer.close()
       return written







'''simple docstring'''
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      __SCREAMING_SNAKE_CASE      (			lowerCamelCase	,				unittest.TestCase				):
 snake_case_  							=							KandinskyVaaImgaImgPipeline
 snake_case_  							=							["""image_embeds""", """negative_image_embeds""", """image"""]
 snake_case_  							=							[
     """image_embeds""",
     """negative_image_embeds""",
     """image""",
 ]
 snake_case_  							=							[
     """generator""",
     """height""",
     """width""",
     """strength""",
     """guidance_scale""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 snake_case_  							=							False




 @property
 def      __magic_name__	(      self	:							List[str]      )	->						Tuple:
       return 32




 @property
 def      __magic_name__	(      self	:							List[str]      )	->						str:
       return 32




 @property
 def      __magic_name__	(      self	:							Any      )	->						Optional[int]:
       return self.time_input_dim




 @property
 def      __magic_name__	(      self	:							List[Any]      )	->						int:
       return self.time_input_dim * 4




 @property
 def      __magic_name__	(      self	:							Tuple      )	->						Optional[int]:
       return 1_00




 @property
 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Tuple:
       torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									={
           '''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,
       }

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=UNetaDConditionModel(**__lowercase      )
       return model




 @property
 def      __magic_name__	(      self	:							Dict      )	->						Any:
       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      __magic_name__	(      self	:							Tuple      )	->						Optional[Any]:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=VQModel(**self.dummy_movq_kwargs      )
       return model




 def      __magic_name__	(      self	:							str      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				List[str]									=self.dummy_unet
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.dummy_movq

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									={
           '''num_train_timesteps''': 10_00,
           '''beta_schedule''': '''linear''',
           '''beta_start''': 0.00085,
           '''beta_end''': 0.012,
           '''clip_sample''': False,
           '''set_alpha_to_one''': False,
           '''steps_offset''': 0,
           '''prediction_type''': '''epsilon''',
           '''thresholding''': False,
       }

       SCREAMING_SNAKE_CASE__     :				str									=DDIMScheduler(**__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Any									={
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''movq''': movq,
       }

       return components




 def      __magic_name__	(      self	:							str					,      __lowercase	:							Optional[Any]					,      __lowercase	:							Any=0      )	->						int:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=floats_tensor((1, self.text_embedder_hidden_size)					,      rng=random.Random(__lowercase      )      ).to(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=floats_tensor((1, self.text_embedder_hidden_size)					,      rng=random.Random(seed + 1      )      ).to(
           __lowercase      )
       # create init_image
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=floats_tensor((1, 3, 64, 64)					,      rng=random.Random(__lowercase      )      ).to(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Dict									=image.cpu().permute(0					,      2					,      3					,      1      )[0]
       SCREAMING_SNAKE_CASE__     :				Any									=Image.fromarray(np.uinta(__lowercase      )      ).convert('''RGB'''      ).resize((2_56, 2_56)      )

       if str(__lowercase      ).startswith('''mps'''      ):
             SCREAMING_SNAKE_CASE__     :				Dict									=torch.manual_seed(__lowercase      )
       else:
             SCREAMING_SNAKE_CASE__     :				Tuple									=torch.Generator(device=__lowercase      ).manual_seed(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				str									={
           '''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      __magic_name__	(      self	:							int      )	->						Union[str, Any]:
       SCREAMING_SNAKE_CASE__     :				List[Any]									='''cpu'''

       SCREAMING_SNAKE_CASE__     :				Tuple									=self.get_dummy_components()

       SCREAMING_SNAKE_CASE__     :				Dict									=self.pipeline_class(**__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=pipe.to(__lowercase      )

       pipe.set_progress_bar_config(disable=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Dict									=pipe(**self.get_dummy_inputs(__lowercase      )      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=output.images

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=pipe(
           **self.get_dummy_inputs(__lowercase      )					,      return_dict=__lowercase					,      )[0]

       SCREAMING_SNAKE_CASE__     :				List[Any]									=image[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE__     :				List[str]									=image_from_tuple[0, -3:, -3:, -1]

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

       SCREAMING_SNAKE_CASE__     :				Tuple									=np.array(
           [0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263]      )
       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      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):




 def      __magic_name__	(      self	:							int      )	->						Any:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				str									=load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinskyv22/kandinskyv22_img2img_frog.npy'''      )

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

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=KandinskyVaaPriorPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-prior'''					,      torch_dtype=torch.floataa      )
       pipe_prior.to(__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Any									=KandinskyVaaImgaImgPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-decoder'''					,      torch_dtype=torch.floataa      )
       SCREAMING_SNAKE_CASE__     :				Dict									=pipeline.to(__lowercase      )

       pipeline.set_progress_bar_config(disable=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Tuple									=torch.Generator(device='''cpu'''      ).manual_seed(0      )
       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				List[str]									=pipe_prior(
           __lowercase					,      generator=__lowercase					,      num_inference_steps=5					,      negative_prompt=''''''					,      ).to_tuple()

       SCREAMING_SNAKE_CASE__     :				List[Any]									=pipeline(
           image=__lowercase					,      image_embeds=__lowercase					,      negative_image_embeds=__lowercase					,      generator=__lowercase					,      num_inference_steps=1_00					,      height=7_68					,      width=7_68					,      strength=0.2					,      output_type='''np'''					,      )

       SCREAMING_SNAKE_CASE__     :				int									=output.images[0]

       assert image.shape == (7_68, 7_68, 3)

       assert_mean_pixel_difference(__lowercase					,      __lowercase      )