
import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast


@dataclass
class 						__lowerCAmelCase	(	datasets.BuilderConfig      ):



      """simple docstring"""







      A__	:			Optional[datasets.Features]        =    None






class 						__lowerCAmelCase	(	datasets.ArrowBasedBuilder      ):



      """simple docstring"""







      A__	:			List[str]        =    PandasConfig




      def 		snake_case_			(      self		:       int				):
          return datasets.DatasetInfo(features=self.config.features				)




      def 		snake_case_			(      self		:       int				,       _snake_case		:       Optional[int]				):
          if not self.config.data_files:
              raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}'				)
          __lowercase		:			Any	    =       dl_manager.download_and_extract(self.config.data_files				)
          if isinstance(_snake_case				,       (str, list, tuple)				):
              __lowercase		:			str	    =       data_files
              if isinstance(_snake_case				,       _snake_case				):
                  __lowercase		:			Any	    =       [files]
              # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
              __lowercase		:			Optional[int]	    =       [dl_manager.iter_files(_snake_case				) for file in files]
              return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,       gen_kwargs={'''files''': files}				)]
          __lowercase		:			str	    =       []
          for split_name, files in data_files.items():
              if isinstance(_snake_case				,       _snake_case				):
                  __lowercase		:			str	    =       [files]
              # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
              __lowercase		:			int	    =       [dl_manager.iter_files(_snake_case				) for file in files]
              splits.append(datasets.SplitGenerator(name=_snake_case				,       gen_kwargs={'''files''': files}				)				)
          return splits




      def 		snake_case_			(      self		:       Optional[int]				,       _snake_case		:       pa.Table				):
          if self.config.features is not None:
              # more expensive cast to support nested features with keys in a different order
              # allows str <-> int/float or str to Audio for example
              __lowercase		:			List[str]	    =       table_cast(_snake_case				,       self.config.features.arrow_schema				)
          return pa_table




      def 		snake_case_			(      self		:       int				,       _snake_case		:       Union[str, Any]				):
          for i, file in enumerate(itertools.chain.from_iterable(_snake_case				)				):
              with open(_snake_case				,       '''rb'''				) as f:
                  __lowercase		:			Dict	    =       pa.Table.from_pandas(pd.read_pickle(_snake_case				)				)
                  yield i, self._cast_table(_snake_case				)




import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
 import onnxruntime as ort


@nightly
@require_onnxruntime
@require_torch_gpu
class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      @property
      def 		snake_case_			(      self		:       Union[str, Any]				):
          return (
              "CUDAExecutionProvider",
              {
                  "gpu_mem_limit": "15000000000",  # 15GB
                  "arena_extend_strategy": "kSameAsRequested",
              },
          )




      @property
      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			Tuple	    =       ort.SessionOptions()
          __lowercase		:			List[Any]	    =       False
          return options




      def 		snake_case_			(      self		:       Dict				):
          __lowercase		:			int	    =       load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/in_paint/overture-creations-5sI6fQgYIuo.png'''				)
          __lowercase		:			int	    =       load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''				)
          __lowercase		:			Optional[Any]	    =       load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'''				)

          # using the PNDM scheduler by default
          __lowercase		:			Union[str, Any]	    =       OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
              '''CompVis/stable-diffusion-v1-4'''				,       revision='''onnx'''				,       safety_checker=_snake_case				,       feature_extractor=_snake_case				,       provider=self.gpu_provider				,       sess_options=self.gpu_options				,       )
          pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			Tuple	    =       '''A red cat sitting on a park bench'''

          __lowercase		:			int	    =       np.random.RandomState(0				)
          __lowercase		:			int	    =       pipe(
              prompt=_snake_case				,       image=_snake_case				,       mask_image=_snake_case				,       strength=0.75				,       guidance_scale=7.5				,       num_inference_steps=15				,       generator=_snake_case				,       output_type='''np'''				,       )

          __lowercase		:			Dict	    =       output.images[0]

          assert image.shape == (512, 512, 3)
          assert np.abs(expected_image - image				).max() < 1E-2


