

import warnings
from typing import List, Optional, Union

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



class        A__(_a							):
						"""simple docstring"""






						_A    :				Union[str, Any]       					=			['''image_processor''', '''tokenizer''']
						_A    :				str       					=			'''FlavaImageProcessor'''
						_A    :				Union[str, Any]       					=			('''BertTokenizer''', '''BertTokenizerFast''')





						def __init__(    self  ,      _lowercase=None  ,      _lowercase=None  ,      **_lowercase    )     -> Dict:
									a_						:						int 							= None
									if "feature_extractor" in kwargs:
												warnings.warn(
												    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
												    """ instead."""  ,      _lowercase  ,      )
												a_						:						Union[str, Any] 							= kwargs.pop("""feature_extractor"""    )

									a_						:						Any 							= 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__(_lowercase  ,      _lowercase    )
									a_						:						Dict 							= self.image_processor





						def __call__(    self  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = True  ,      _lowercase = False  ,      _lowercase = False  ,      _lowercase = None  ,      _lowercase = 0  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = False  ,      _lowercase = False  ,      _lowercase = False  ,      _lowercase = False  ,      _lowercase = True  ,      _lowercase = None  ,      **_lowercase  ,      )     -> Optional[int]:

									if text is None and images is None:
												raise ValueError("""You have to specify either text or images. Both cannot be none."""    )

									if text is not None:
												a_						:						Any 							= self.tokenizer(
												    text=_lowercase  ,      add_special_tokens=_lowercase  ,      padding=_lowercase  ,      truncation=_lowercase  ,      max_length=_lowercase  ,      stride=_lowercase  ,      pad_to_multiple_of=_lowercase  ,      return_token_type_ids=_lowercase  ,      return_attention_mask=_lowercase  ,      return_overflowing_tokens=_lowercase  ,      return_special_tokens_mask=_lowercase  ,      return_offsets_mapping=_lowercase  ,      return_length=_lowercase  ,      verbose=_lowercase  ,      return_tensors=_lowercase  ,      **_lowercase  ,      )
									if images is not None:
												a_						:						Tuple 							= self.image_processor(
												    _lowercase  ,      return_image_mask=_lowercase  ,      return_codebook_pixels=_lowercase  ,      return_tensors=_lowercase  ,      **_lowercase  ,      )

									if text is not None and images is not None:
												encoding.update(_lowercase    )
												return encoding
									elif text is not None:
												return encoding
									else:
												return BatchEncoding(data=dict(**_lowercase    )  ,      tensor_type=_lowercase    )





						def 			UpperCamelCase__					(    self  ,      *_lowercase  ,      **_lowercase    )     -> Optional[int]:
									return self.tokenizer.batch_decode(*_lowercase  ,      **_lowercase    )





						def 			UpperCamelCase__					(    self  ,      *_lowercase  ,      **_lowercase    )     -> Any:
									return self.tokenizer.decode(*_lowercase  ,      **_lowercase    )





						@property
						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						str 							= self.tokenizer.model_input_names
									a_						:						List[Any] 							= self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )





						@property
						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""  ,      _lowercase  ,      )
									return self.image_processor_class





						@property
						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""  ,      _lowercase  ,      )
									return self.image_processor










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 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       @property
       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          torch.manual_seed(0)
          _lowercase              =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	:str):



          """simple docstring"""

          _lowercase              =self.dummy_uncond_unet
          _lowercase              =ScoreSdeVeScheduler()

          _lowercase              =ScoreSdeVePipeline(unet=snake_case,      scheduler=snake_case)
          sde_ve.to(snake_case)
          sde_ve.set_progress_bar_config(disable=snake_case)

          _lowercase              =torch.manual_seed(0)
          _lowercase              =sde_ve(num_inference_steps=2,      output_type='numpy',      generator=snake_case).images

          _lowercase              =torch.manual_seed(0)
          _lowercase              =sde_ve(num_inference_steps=2,      output_type='numpy',      generator=snake_case,      return_dict=snake_case)[
              0
          ]

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

          assert image.shape == (1, 32, 32, 3)
          _lowercase              =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 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          _lowercase              ='google/ncsnpp-church-256'
          _lowercase              =UNetaDModel.from_pretrained(snake_case)

          _lowercase              =ScoreSdeVeScheduler.from_pretrained(snake_case)

          _lowercase              =ScoreSdeVePipeline(unet=snake_case,      scheduler=snake_case)
          sde_ve.to(snake_case)
          sde_ve.set_progress_bar_config(disable=snake_case)

          _lowercase              =torch.manual_seed(0)
          _lowercase              =sde_ve(num_inference_steps=10,      output_type='numpy',      generator=snake_case).images

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

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

          _lowercase              =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


