

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 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   List[str]					  =							np.inf

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

	_visit(_a				,      _a)

	return None if batch_size is np.inf else batch_size






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			def __init__(      self				:			Union[str, Any]   ,   a				:			NestedDataStructureLike[PathLike]   ,   a				:			Optional[NamedSplit] = None   ,   a				:			Optional[Features] = None   ,   a				:			str = None   ,   a				:			bool = False   ,   a				:			bool = False   ,   a				:			Optional[int] = None   ,   **a				:			str   ,   )				->							int:

				"""simple docstring"""

				super().__init__(
				    a   ,   split=a   ,   features=a   ,   cache_dir=a   ,   keep_in_memory=a   ,   streaming=a   ,   num_proc=a   ,   **a   ,   )
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							path_or_paths if isinstance(a   ,   a						) else {self.split: path_or_paths}
				SCREAMING_SNAKE_CASE :   Any					  =							_PACKAGED_DATASETS_MODULES["parquet"][1]
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							Parquet(
				    cache_dir=a   ,   data_files=a   ,   features=a   ,   hash=a   ,   **a   ,   )






			def 				__UpperCamelCase			(      self				:			int						)				->							int:

				"""simple docstring"""

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

					self.builder.download_and_prepare(
					    download_config=a   ,   download_mode=a   ,   verification_mode=a   ,   base_path=a   ,   num_proc=self.num_proc   ,   )
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.builder.as_dataset(
					    split=self.split   ,   verification_mode=a   ,   in_memory=self.keep_in_memory						)
				return dataset






class _UpperCamelCase				:
			'''simple docstring'''







			def __init__(      self				:			Union[str, Any]   ,   a				:			Dataset   ,   a				:			Union[PathLike, BinaryIO]   ,   a				:			Optional[int] = None   ,   **a				:			Optional[Any]   ,   )				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							dataset
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							path_or_buf
				SCREAMING_SNAKE_CASE :   str					  =							batch_size or get_writer_batch_size(dataset.features						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							parquet_writer_kwargs
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							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=a   ,   batch_size=a   ,   **self.parquet_writer_kwargs						)
				else:
					SCREAMING_SNAKE_CASE :   Dict					  =							self._write(file_obj=self.path_or_buf   ,   batch_size=a   ,   **self.parquet_writer_kwargs						)
				return written






			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			BinaryIO   ,   a				:			int   ,   **a				:			str						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							0
				SCREAMING_SNAKE_CASE :   Tuple					  =							parquet_writer_kwargs.pop("path_or_buf"   ,   a						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							self.dataset.features.arrow_schema

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							pq.ParquetWriter(a   ,   schema=a   ,   **a						)

				for offset in logging.tqdm(
				    range(0   ,   len(self.dataset						)   ,   a						)   ,   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(a   ,   offset + batch_size						)   ,   indices=self.dataset._indices if self.dataset._indices is not None else None   ,   )
					writer.write_table(a						)
					written += batch.nbytes
				writer.close()
				return written


import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
			import onnxruntime as ort






class _UpperCamelCase				(				__A				,						unittest.TestCase ):
			'''simple docstring'''







			pass






@nightly
@require_onnxruntime
@require_torch_gpu
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			@property
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							List[str]:

				"""simple docstring"""

				return (
				    "CUDAExecutionProvider",
				    {
				        "gpu_mem_limit": "15000000000",  # 15GB
				        "arena_extend_strategy": "kSameAsRequested",
				    },
				)
			@property
			def 				__UpperCamelCase			(      self				:			int						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							ort.SessionOptions()
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							False
				return options
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
				SCREAMING_SNAKE_CASE :   int					  =							OnnxStableDiffusionInpaintPipeline.from_pretrained(
				    "runwayml/stable-diffusion-inpainting"   ,   revision="onnx"   ,   safety_checker=a   ,   feature_extractor=a   ,   provider=self.gpu_provider   ,   sess_options=self.gpu_options   ,   )
				pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							"A red cat sitting on a park bench"

				SCREAMING_SNAKE_CASE :   Tuple					  =							np.random.RandomState(0						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							pipe(
				    prompt=a   ,   image=a   ,   mask_image=a   ,   guidance_scale=7.5   ,   num_inference_steps=10   ,   generator=a   ,   output_type="np"   ,   )
				SCREAMING_SNAKE_CASE :   List[Any]					  =							output.images
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							images[0, 255:258, 255:258, -1]

				assert images.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   int					  =							np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3






			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							LMSDiscreteScheduler.from_pretrained(
				    "runwayml/stable-diffusion-inpainting"   ,   subfolder="scheduler"   ,   revision="onnx"						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							OnnxStableDiffusionInpaintPipeline.from_pretrained(
				    "runwayml/stable-diffusion-inpainting"   ,   revision="onnx"   ,   scheduler=a   ,   safety_checker=a   ,   feature_extractor=a   ,   provider=self.gpu_provider   ,   sess_options=self.gpu_options   ,   )
				pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							"A red cat sitting on a park bench"

				SCREAMING_SNAKE_CASE :   Dict					  =							np.random.RandomState(0						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							pipe(
				    prompt=a   ,   image=a   ,   mask_image=a   ,   guidance_scale=7.5   ,   num_inference_steps=20   ,   generator=a   ,   output_type="np"   ,   )
				SCREAMING_SNAKE_CASE :   List[str]					  =							output.images
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							images[0, 255:258, 255:258, -1]

				assert images.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   Any					  =							np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3