


'''simple docstring'''






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase       			= ['image_processor', 'tokenizer']
    lowercase       			= 'CLIPImageProcessor'
    lowercase       			= ('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')

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

      lowerCAmelCase_      :				List[str]      =       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__(lowerCamelCase   ,						lowerCamelCase		)

    def __call__(       self   :  Optional[Any]   ,						lowerCamelCase   :  List[Any]=None   ,						lowerCamelCase   :  Tuple=None   ,						lowerCamelCase   :  int=None   ,						**lowerCamelCase   :  int		)       ->  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:
        lowerCAmelCase_      :				int      =       self.tokenizer(lowerCamelCase   ,						return_tensors=lowerCamelCase   ,						**lowerCamelCase		)

      if images is not None:
        lowerCAmelCase_      :				int      =       self.image_processor(lowerCamelCase   ,						return_tensors=lowerCamelCase   ,						**lowerCamelCase		)

      if text is not None and images is not None:
        lowerCAmelCase_      :				Optional[int]      =       image_features.pixel_values
        return encoding
      elif text is not None:
        return encoding
      else:
        return BatchEncoding(data=dict(**lowerCamelCase		)   ,						tensor_type=lowerCamelCase		)

    def __lowercase       (       self   :  int   ,						*lowerCamelCase   :  Optional[Any]   ,						**lowerCamelCase   :  Union[str, Any]		)       ->  List[str]:
      return self.tokenizer.batch_decode(*lowerCamelCase   ,						**lowerCamelCase		)

    def __lowercase       (       self   :  Optional[int]   ,						*lowerCamelCase   :  str   ,						**lowerCamelCase   :  Union[str, Any]		)       ->  List[str]:
      return self.tokenizer.decode(*lowerCamelCase   ,						**lowerCamelCase		)





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







'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    @property
    def __lowercase       (       self   :  str		)       ->  List[Any]:
      torch.manual_seed(0		)
      lowerCAmelCase_      :				Optional[Any]      =       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 __lowercase       (       self   :  Tuple		)       ->  Optional[Any]:
      lowerCAmelCase_      :				Optional[int]      =       self.dummy_uncond_unet
      lowerCAmelCase_      :				Tuple      =       PNDMScheduler()

      lowerCAmelCase_      :				List[Any]      =       PNDMPipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pndm.to(lowerCamelCase		)
      pndm.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				Dict      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				List[Any]      =       pndm(generator=lowerCamelCase   ,						num_inference_steps=20   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				str      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				int      =       pndm(generator=lowerCamelCase   ,						num_inference_steps=20   ,						output_type="""numpy"""   ,						return_dict=lowerCamelCase		)[0]

      lowerCAmelCase_      :				Optional[int]      =       image[0, -3:, -3:, -1]
      lowerCAmelCase_      :				Optional[Any]      =       image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				int      =       np.array([1.0, 1.0, 0.0, 1.0, 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        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    def __lowercase       (       self   :  str		)       ->  Tuple:
      lowerCAmelCase_      :				str      =       """google/ddpm-cifar10-32"""

      lowerCAmelCase_      :				Dict      =       UNetaDModel.from_pretrained(lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       PNDMScheduler()

      lowerCAmelCase_      :				Union[str, Any]      =       PNDMPipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pndm.to(lowerCamelCase		)
      pndm.set_progress_bar_config(disable=lowerCamelCase		)
      lowerCAmelCase_      :				Any      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				Union[str, Any]      =       pndm(generator=lowerCamelCase   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				Optional[int]      =       image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				List[Any]      =       np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]		)

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



