


import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class       _UpperCamelCase    (					unittest.TestCase				):

				"""simple docstring"""







				@property
				def      _UpperCAmelCase					(						self   )	->				Any:
					torch.manual_seed(0   )
					A								=				UNetaDModel(
					    block_out_channels=(32, 64)				,   layers_per_block=2				,   sample_size=32				,   in_channels=3				,   out_channels=3				,   down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")				,   up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")				,   )
					return model

				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					A								=				self.dummy_uncond_unet
					A								=				ScoreSdeVeScheduler()

					A								=				ScoreSdeVePipeline(unet=a__				,   scheduler=a__   )
					sde_ve.to(a__   )
					sde_ve.set_progress_bar_config(disable=a__   )

					A								=				torch.manual_seed(0   )
					A								=				sde_ve(num_inference_steps=2				,   output_type="""numpy"""				,   generator=a__   ).images

					A								=				torch.manual_seed(0   )
					A								=				sde_ve(num_inference_steps=2				,   output_type="""numpy"""				,   generator=a__				,   return_dict=a__   )[
					    0
					]

					A								=				image[0, -3:, -3:, -1]
					A								=				image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					A								=				np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

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






@slow
@require_torch
class       _UpperCamelCase    (					unittest.TestCase				):

				"""simple docstring"""







				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					A								=				"""google/ncsnpp-church-256"""
					A								=				UNetaDModel.from_pretrained(a__   )

					A								=				ScoreSdeVeScheduler.from_pretrained(a__   )

					A								=				ScoreSdeVePipeline(unet=a__				,   scheduler=a__   )
					sde_ve.to(a__   )
					sde_ve.set_progress_bar_config(disable=a__   )

					A								=				torch.manual_seed(0   )
					A								=				sde_ve(num_inference_steps=10				,   output_type="""numpy"""				,   generator=a__   ).images

					A								=				image[0, -3:, -3:, -1]

					assert image.shape == (1, 256, 256, 3)

					A								=				np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]   )

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







import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= ['image_processor', 'tokenizer']
				lowerCAmelCase 		= 'ViltImageProcessor'
				lowerCAmelCase 		= ('BertTokenizer', 'BertTokenizerFast')

				def __init__(						self				,   a__=None				,   a__=None				,   **a__   )	->				Any:
					A								=				None
					if "feature_extractor" in kwargs:
						warnings.warn(
						    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
						    """ instead."""				,   a__				,   )
						A								=				kwargs.pop("""feature_extractor"""   )

					A								=				image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
						raise ValueError("""You need to specify an `image_processor`."""   )
					if tokenizer is None:
						raise ValueError("""You need to specify a `tokenizer`."""   )

					super().__init__(a__				,   a__   )
					A								=				self.image_processor

				def __call__(						self				,   a__				,   a__ = None				,   a__ = True				,   a__ = False				,   a__ = None				,   a__ = None				,   a__ = 0				,   a__ = None				,   a__ = None				,   a__ = None				,   a__ = False				,   a__ = False				,   a__ = False				,   a__ = False				,   a__ = True				,   a__ = None				,   **a__				,   )	->				BatchEncoding:
					A								=				self.tokenizer(
					    text=a__				,   add_special_tokens=a__				,   padding=a__				,   truncation=a__				,   max_length=a__				,   stride=a__				,   pad_to_multiple_of=a__				,   return_token_type_ids=a__				,   return_attention_mask=a__				,   return_overflowing_tokens=a__				,   return_special_tokens_mask=a__				,   return_offsets_mapping=a__				,   return_length=a__				,   verbose=a__				,   return_tensors=a__				,   **a__				,   )
					# add pixel_values + pixel_mask
					A								=				self.image_processor(a__				,   return_tensors=a__   )
					encoding.update(a__   )

					return encoding

				def      _UpperCAmelCase					(						self				,   *a__				,   **a__   )	->				Union[str, Any]:
					return self.tokenizer.batch_decode(*a__				,   **a__   )

				def      _UpperCAmelCase					(						self				,   *a__				,   **a__   )	->				Any:
					return self.tokenizer.decode(*a__				,   **a__   )

				@property
				def      _UpperCAmelCase					(						self   )	->				int:
					A								=				self.tokenizer.model_input_names
					A								=				self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )

				@property
				def      _UpperCAmelCase					(						self   )	->				str:
					warnings.warn(
					    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,   a__				,   )
					return self.image_processor_class

				@property
				def      _UpperCAmelCase					(						self   )	->				str:
					warnings.warn(
					    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""				,   a__				,   )
					return self.image_processor



