



'''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      (			lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Optional[int]     =					np.inf

			def set_batch_size(lowerCamelCase__   ) -> None:
						nonlocal batch_size
						if isinstance(lowerCamelCase__						,				lowerCamelCase__   ):
									A_     :						Optional[int]     =					min(lowerCamelCase__						,				config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS   )
						elif isinstance(lowerCamelCase__						,				lowerCamelCase__   ):
									A_     :						Dict     =					min(lowerCamelCase__						,				config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS   )
						elif isinstance(lowerCamelCase__						,				lowerCamelCase__   ) and feature.dtype == "binary":
									A_     :						Optional[int]     =					min(lowerCamelCase__						,				config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS   )

			_visit(lowerCamelCase__						,				lowerCamelCase__   )

			return None if batch_size is np.inf else batch_size







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	def __init__(self	,				lowercase	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = False	,				lowercase = False	,				lowercase = None	,				**lowercase	,				):
				super().__init__(
				    lowercase	,				split=lowercase	,				features=lowercase	,				cache_dir=lowercase	,				keep_in_memory=lowercase	,				streaming=lowercase	,				num_proc=lowercase	,				**lowercase	,				)
				A_     :						Dict     =					path_or_paths if isinstance(lowercase	,				lowercase							) else {self.split: path_or_paths}
				A_     :						Tuple     =					_PACKAGED_DATASETS_MODULES["""parquet"""][1]
				A_     :						Optional[Any]     =					Parquet(
				    cache_dir=lowercase	,				data_files=lowercase	,				features=lowercase	,				hash=lowercase	,				**lowercase	,				)


	def    _a     (self							):
				# Build iterable dataset
				if self.streaming:
							A_     :						Dict     =					self.builder.as_streaming_dataset(split=self.split							)
				# Build regular (map-style) dataset
				else:
							A_     :						List[str]     =					None
							A_     :						Optional[Any]     =					None
							A_     :						int     =					None
							A_     :						Any     =					None

							self.builder.download_and_prepare(
							    download_config=lowercase	,				download_mode=lowercase	,				verification_mode=lowercase	,				base_path=lowercase	,				num_proc=self.num_proc	,				)
							A_     :						Optional[Any]     =					self.builder.as_dataset(
							    split=self.split	,				verification_mode=lowercase	,				in_memory=self.keep_in_memory							)
				return dataset







class      _lowerCAmelCase							:


	def __init__(self	,				lowercase	,				lowercase	,				lowercase = None	,				**lowercase	,				):
				A_     :						Tuple     =					dataset
				A_     :						Dict     =					path_or_buf
				A_     :						Dict     =					batch_size or get_writer_batch_size(dataset.features							)
				A_     :						Dict     =					parquet_writer_kwargs


	def    _a     (self							):
				A_     :						Dict     =					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:
										A_     :						int     =					self._write(file_obj=lowercase	,				batch_size=lowercase	,				**self.parquet_writer_kwargs							)
				else:
							A_     :						List[str]     =					self._write(file_obj=self.path_or_buf	,				batch_size=lowercase	,				**self.parquet_writer_kwargs							)
				return written


	def    _a     (self	,				lowercase	,				lowercase	,				**lowercase							):
				A_     :						List[str]     =					0
				A_     :						Optional[Any]     =					parquet_writer_kwargs.pop("""path_or_buf"""	,				lowercase							)
				A_     :						List[str]     =					self.dataset.features.arrow_schema

				A_     :						List[str]     =					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"""	,				):
							A_     :						Tuple     =					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,
    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      _lowerCAmelCase							(      __UpperCAmelCase				,      unittest.TestCase			):
	__SCREAMING_SNAKE_CASE       :		Tuple		      = KandinskyVaaInpaintPipeline
	__SCREAMING_SNAKE_CASE       :		int		      = ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
	__SCREAMING_SNAKE_CASE       :		str		      = [
	    'image_embeds',
	    'negative_image_embeds',
	    'image',
	    'mask_image',
	]
	__SCREAMING_SNAKE_CASE       :		Optional[Any]		      = [
	    'generator',
	    'height',
	    'width',
	    'latents',
	    'guidance_scale',
	    'num_inference_steps',
	    'return_dict',
	    'guidance_scale',
	    'num_images_per_prompt',
	    'output_type',
	    'return_dict',
	]
	__SCREAMING_SNAKE_CASE       :		List[str]		      = 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_     :						str     =					{
				    """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_     :						str     =					UNetaDConditionModel(**lowercase							)
				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_     :						int     =					VQModel(**self.dummy_movq_kwargs							)
				return model


	def    _a     (self							):
				A_     :						Optional[Any]     =					self.dummy_unet
				A_     :						Dict     =					self.dummy_movq

				A_     :						List[Any]     =					DDIMScheduler(
				    num_train_timesteps=1000	,				beta_schedule="""linear"""	,				beta_start=0.0_00_85	,				beta_end=0.0_12	,				clip_sample=lowercase	,				set_alpha_to_one=lowercase	,				steps_offset=1	,				prediction_type="""epsilon"""	,				thresholding=lowercase	,				)

				A_     :						Any     =					{
				    """unet""": unet,
				    """scheduler""": scheduler,
				    """movq""": movq,
				}

				return components


	def    _a     (self	,				lowercase	,				lowercase=0							):
				A_     :						Optional[Any]     =					floats_tensor((1, self.text_embedder_hidden_size)	,				rng=random.Random(lowercase							)							).to(lowercase							)
				A_     :						List[Any]     =					floats_tensor((1, self.text_embedder_hidden_size)	,				rng=random.Random(seed + 1							)							).to(
				    lowercase							)
				# create init_image
				A_     :						Any     =					floats_tensor((1, 3, 64, 64)	,				rng=random.Random(lowercase							)							).to(lowercase							)
				A_     :						Union[str, Any]     =					image.cpu().permute(0	,				2	,				3	,				1							)[0]
				A_     :						Tuple     =					Image.fromarray(np.uinta(lowercase							)							).convert("""RGB"""							).resize((256, 256)							)
				# create mask
				A_     :						List[str]     =					np.ones((64, 64)	,				dtype=np.floataa							)
				A_     :						Any     =					0

				if str(lowercase							).startswith("""mps"""							):
							A_     :						int     =					torch.manual_seed(lowercase							)
				else:
							A_     :						Dict     =					torch.Generator(device=lowercase							).manual_seed(lowercase							)
				A_     :						Any     =					{
				    """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    _a     (self							):
				A_     :						List[str]     =					"""cpu"""

				A_     :						str     =					self.get_dummy_components()

				A_     :						Any     =					self.pipeline_class(**lowercase							)
				A_     :						List[Any]     =					pipe.to(lowercase							)

				pipe.set_progress_bar_config(disable=lowercase							)

				A_     :						Union[str, Any]     =					pipe(**self.get_dummy_inputs(lowercase							)							)
				A_     :						List[str]     =					output.images

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

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

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

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

				A_     :						Optional[int]     =					np.array(
				    [0.50_77_59_03, 0.49_52_71_95, 0.48_82_45_43, 0.50_19_22_37, 0.48_64_49_06, 0.49_37_38_14, 0.4_78_05_98, 0.47_23_48_27, 0.48_32_78_48]							)

				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    _a     (self							):
				super().test_inference_batch_single_identical(expected_max_diff=3E-3							)







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


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


	def    _a     (self							):
				A_     :						Optional[Any]     =					load_numpy(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
				    """/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"""							)

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

				A_     :						str     =					"""a hat"""

				A_     :						Union[str, Any]     =					KandinskyVaaPriorPipeline.from_pretrained(
				    """kandinsky-community/kandinsky-2-2-prior"""	,				torch_dtype=torch.floataa							)
				pipe_prior.to(lowercase							)

				A_     :						Union[str, Any]     =					KandinskyVaaInpaintPipeline.from_pretrained(
				    """kandinsky-community/kandinsky-2-2-decoder-inpaint"""	,				torch_dtype=torch.floataa							)
				A_     :						str     =					pipeline.to(lowercase							)
				pipeline.set_progress_bar_config(disable=lowercase							)

				A_     :						int     =					torch.Generator(device="""cpu"""							).manual_seed(0							)
				A_,   A_     :						Dict     =					pipe_prior(
				    lowercase	,				generator=lowercase	,				num_inference_steps=5	,				negative_prompt=""""""	,				).to_tuple()

				A_     :						List[Any]     =					pipeline(
				    image=lowercase	,				mask_image=lowercase	,				image_embeds=lowercase	,				negative_image_embeds=lowercase	,				generator=lowercase	,				num_inference_steps=100	,				height=768	,				width=768	,				output_type="""np"""	,				)

				A_     :						Tuple     =					output.images[0]

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

				assert_mean_pixel_difference(lowercase	,				lowercase							)