


from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
		from .features import FeatureType


@dataclass
class 					lowerCAmelCase_ :
						lowerCamelCase_         =     42
						lowerCamelCase_         =     None
						# Automatically constructed
						lowerCamelCase_         =     'dict'
						lowerCamelCase_         =     None
						lowerCamelCase_         =     field(default='Translation'			,init=_a			,repr=_a)




						def __call__(	self						:   Union[str, Any]						)      ->Dict:






									"""simple docstring"""






									return pa.struct({lang: pa.string() for lang in sorted(self.languages						)}						)






						def  _snake_case					(	self						:   Tuple						)      ->Union["FeatureType", Dict[str, "FeatureType"]]:






									"""simple docstring"""






									from .features import Value

									return {k: Value("string"						) for k in sorted(self.languages						)}






@dataclass
class 					lowerCAmelCase_ :
						lowerCamelCase_         =     None
						lowerCamelCase_         =     None
						lowerCamelCase_         =     None
						# Automatically constructed
						lowerCamelCase_         =     'dict'
						lowerCamelCase_         =     None
						lowerCamelCase_         =     field(default='TranslationVariableLanguages'			,init=_a			,repr=_a)




						def  _snake_case					(	self						:   Optional[int]						)      ->Tuple:






									"""simple docstring"""






									a__						  =    sorted(set(self.languages						)						) if self.languages else None
									a__						  =    len(self.languages						) if self.languages else None




						def __call__(	self						:   Dict						)      ->Union[str, Any]:






									"""simple docstring"""






									return pa.struct({"language": pa.list_(pa.string()						), "translation": pa.list_(pa.string()						)}						)




						def  _snake_case					(	self						:   Optional[int]   ,     __A						:   Union[str, Any]						)      ->int:






									"""simple docstring"""






									a__						  =    set(self.languages						)
									if self.languages and set(__A						) - lang_set:
												raise ValueError(
												    F'''Some languages in example ({', '.join(sorted(set(__A						) - lang_set						)						)}) are not in valid set ({', '.join(__A						)}).'''						)

									# Convert dictionary into tuples, splitting out cases where there are
									# multiple translations for a single language.
									a__						  =    []
									for lang, text in translation_dict.items():
												if isinstance(__A   ,     __A						):
															translation_tuples.append((lang, text)						)
												else:
															translation_tuples.extend([(lang, el) for el in text]						)

        # Ensure translations are in ascending order by language code.
									a__						  =    zip(*sorted(__A						)						)

									return {"language": languages, "translation": translations}






						def  _snake_case					(	self						:   int						)      ->Union["FeatureType", Dict[str, "FeatureType"]]:






									"""simple docstring"""






									from .features import Sequence, Value

									return {
									    "language": Sequence(Value("string"						)						),
									    "translation": Sequence(Value("string"						)						),
									}






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


snake_case__			 =						False


class 					lowerCAmelCase_ (       unittest.TestCase):
						pass






@nightly
@require_torch_gpu
class 					lowerCAmelCase_ (       unittest.TestCase):




						def  _snake_case					(	self						:   Tuple						)      ->Optional[int]:






									"""simple docstring"""






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




						def  _snake_case					(	self						:   Dict						)      ->Any:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"						)
									# remove text_unet
									pipe.remove_unused_weights()
									pipe.to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

									a__ :List[Any]						  =    "A painting of a squirrel eating a burger "
									a__ :Optional[Any]						  =    torch.manual_seed(0						)
									a__ :List[Any]						  =    pipe(
									    prompt=__A   ,     generator=__A   ,     guidance_scale=7.5   ,     num_inference_steps=2   ,     output_type="numpy"						).images

									with tempfile.TemporaryDirectory() as tmpdirname:
												pipe.save_pretrained(__A						)
												a__ :List[Any]						  =    VersatileDiffusionTextToImagePipeline.from_pretrained(__A						)
									pipe.to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

									a__ :Optional[int]						  =    generator.manual_seed(0						)
									a__ :List[Any]						  =    pipe(
									    prompt=__A   ,     generator=__A   ,     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  _snake_case					(	self						:   Optional[Any]						)      ->List[Any]:






									"""simple docstring"""






									a__ :Tuple						  =    VersatileDiffusionTextToImagePipeline.from_pretrained(
									    "shi-labs/versatile-diffusion"   ,     torch_dtype=torch.floataa						)
									pipe.to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

									a__ :Tuple						  =    "A painting of a squirrel eating a burger "
									a__ :Tuple						  =    torch.manual_seed(0						)
									a__ :Optional[Any]						  =    pipe(
									    prompt=__A   ,     generator=__A   ,     guidance_scale=7.5   ,     num_inference_steps=50   ,     output_type="numpy"						).images

									a__ :Tuple						  =    image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									a__ :Tuple						  =    np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]						)

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


