

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     a__  :
			"""simple docstring"""







			__lowerCamelCase       				=				42
			__lowerCamelCase       				=				None
			# Automatically constructed
			__lowerCamelCase       				=				"dict"
			__lowerCamelCase       				=				None
			__lowerCamelCase       				=				field(default='Translation'							,		init=snake_case							,		repr=snake_case	)


			def __call__(			self				)     ->  int:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self				)     ->  Union["FeatureType", Dict[str, "FeatureType"]]:
							'''simple docstring'''






							from .features import Value

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

@dataclass
class     a__  :
			"""simple docstring"""







			__lowerCamelCase       				=				None
			__lowerCamelCase       				=				None
			__lowerCamelCase       				=				None
			# Automatically constructed
			__lowerCamelCase       				=				"dict"
			__lowerCamelCase       				=				None
			__lowerCamelCase       				=				field(default='TranslationVariableLanguages'							,		init=snake_case							,		repr=snake_case	)


			def    UpperCamelCase						(			self				)     ->  str:
							'''simple docstring'''






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


			def __call__(			self				)     ->  Any:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self					,					lowercase				)     ->  Union[str, Any]:
							'''simple docstring'''






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

							# 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(lowercase					,					lowercase				):
															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__			, A__						=	zip(*sorted(lowercase				)				)

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


			def    UpperCamelCase						(			self				)     ->  Union["FeatureType", Dict[str, "FeatureType"]]:
							'''simple docstring'''






							from .features import Sequence, Value

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




import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_lowerCAmelCase					:			Optional[Any]  				=			False






class 	__magic_name__    (			unittest.TestCase      ):
			pass






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





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						__a        =VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion'					)
						pipe.to(__snake_case					)
						pipe.set_progress_bar_config(disable=__snake_case					)

						__a        =load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'					)
						__a        =torch.manual_seed(0					)
						__a        =pipe(
						    image=__snake_case					,  generator=__snake_case					,  guidance_scale=7.5					,  num_inference_steps=50					,  output_type='numpy'					,  ).images

						__a        =image[0, 253:256, 253:256, -1]

						assert image.shape == (1, 512, 512, 3)
						__a        =np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945]					)

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






