






'''simple docstring'''


import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


lowerCamelCase_				: int    	=							False
class    _SCREAMING_SNAKE_CASE							(		unittest.TestCase	):


			'''simple docstring'''





			pass
@nightly
@require_torch_gpu
class    _SCREAMING_SNAKE_CASE							(		unittest.TestCase	):


			'''simple docstring'''





			def 				A      (   self							:						str			)							->    int:

									'''simple docstring'''







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






			def 				A      (   self							:						str			)							->    Dict:

									'''simple docstring'''







									UpperCamelCase__         = VersatileDiffusionTextToImagePipeline.from_pretrained("""shi-labs/versatile-diffusion"""			)
									# remove text_unet
									pipe.remove_unused_weights()
									pipe.to(lowercase			)
									pipe.set_progress_bar_config(disable=lowercase			)

									UpperCamelCase__         = """A painting of a squirrel eating a burger """
									UpperCamelCase__         = torch.manual_seed(0			)
									UpperCamelCase__         = pipe(
									    prompt=lowercase ,      generator=lowercase ,      guidance_scale=7.5 ,      num_inference_steps=2 ,      output_type="""numpy"""			).images

									with tempfile.TemporaryDirectory() as tmpdirname:
															pipe.save_pretrained(lowercase			)
															UpperCamelCase__         = VersatileDiffusionTextToImagePipeline.from_pretrained(lowercase			)
									pipe.to(lowercase			)
									pipe.set_progress_bar_config(disable=lowercase			)

									UpperCamelCase__         = generator.manual_seed(0			)
									UpperCamelCase__         = pipe(
									    prompt=lowercase ,      generator=lowercase ,      guidance_scale=7.5 ,      num_inference_steps=2 ,      output_type="""numpy"""			).images

									assert np.abs(image - new_image			).sum() < 1e-5, "Models don't have the same forward pass"







			def 				A      (   self							:						Optional[Any]			)							->    int:

									'''simple docstring'''







									UpperCamelCase__         = VersatileDiffusionTextToImagePipeline.from_pretrained(
									    """shi-labs/versatile-diffusion""" ,      torch_dtype=torch.floataa			)
									pipe.to(lowercase			)
									pipe.set_progress_bar_config(disable=lowercase			)

									UpperCamelCase__         = """A painting of a squirrel eating a burger """
									UpperCamelCase__         = torch.manual_seed(0			)
									UpperCamelCase__         = pipe(
									    prompt=lowercase ,      generator=lowercase ,      guidance_scale=7.5 ,      num_inference_steps=5_0 ,      output_type="""numpy"""			).images

									UpperCamelCase__         = image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									UpperCamelCase__         = np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]			)

									assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2








'''simple docstring'''


from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			def __init__(   self							:						Tuple ,      lowercase							:						pyspark.sql.DataFrame ,      lowercase							:						Optional[NamedSplit] = None ,      lowercase							:						Optional[Features] = None ,      lowercase							:						bool = True ,      lowercase							:						str = None ,      lowercase							:						bool = False ,      lowercase							:						str = None ,      lowercase							:						bool = True ,      lowercase							:						str = "arrow" ,      **lowercase							:						Any ,      )							->    Optional[int]:

									'''simple docstring'''







									super().__init__(
									    split=lowercase ,      features=lowercase ,      cache_dir=lowercase ,      keep_in_memory=lowercase ,      streaming=lowercase ,      **lowercase ,      )
									UpperCamelCase__         = load_from_cache_file
									UpperCamelCase__         = file_format
									UpperCamelCase__         = Spark(
									    df=lowercase ,      features=lowercase ,      cache_dir=lowercase ,      working_dir=lowercase ,      **lowercase ,      )







			def 				A      (   self							:						int			)							->    Any:

									'''simple docstring'''







									if self.streaming:
															return self.builder.as_streaming_dataset(split=self.split			)
									UpperCamelCase__         = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
									self.builder.download_and_prepare(
									    download_mode=lowercase ,      file_format=self._file_format ,      )
									return self.builder.as_dataset(split=self.split			)
