



import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 						snake_case  (		SCREAMING_SNAKE_CASE_					):



   '''simple docstring'''






   snake_case_   :	Any       =						["""image_processor""", """tokenizer"""]
   snake_case_   :	List[Any]       =						"""AutoImageProcessor"""
   snake_case_   :	Optional[int]       =						"""AutoTokenizer"""

   def __init__(   self							:     Optional[int]			,						lowerCAmelCase							:     Union[str, Any]=None			,						lowerCAmelCase							:     Tuple=None			,						**lowerCAmelCase							:     Dict)			->			Optional[int]:

    """simple docstring"""







    _snake_case      :					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			,						)
     _snake_case      :					Union[str, Any]					=   kwargs.pop("""feature_extractor""")

    _snake_case      :					Tuple					=   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)
    _snake_case      :					Any					=   self.image_processor
    _snake_case      :					Any					=   False

   def __call__(   self							:     Union[str, Any]			,						*lowerCAmelCase							:     List[str]			,						**lowerCAmelCase							:     Any)			->			str:

    """simple docstring"""







    if self._in_target_context_manager:
     return self.current_processor(*lowerCAmelCase			,						**lowerCAmelCase)

    _snake_case      :					List[str]					=   kwargs.pop("""images"""			,						lowerCAmelCase)
    _snake_case      :					Tuple					=   kwargs.pop("""text"""			,						lowerCAmelCase)
    if len(lowerCAmelCase) > 0:
     _snake_case      :					Dict					=   args[0]
     _snake_case      :					Any					=   args[1:]

    if images is None and text is None:
     raise ValueError("""You need to specify either an `images` or `text` input to process.""")

    if images is not None:
     _snake_case      :					List[str]					=   self.image_processor(lowerCAmelCase			,						*lowerCAmelCase			,						**lowerCAmelCase)
    if text is not None:
     _snake_case      :					int					=   self.tokenizer(lowerCAmelCase			,						**lowerCAmelCase)

    if text is None:
     return inputs
    elif images is None:
     return encodings
    else:
     _snake_case      :					Optional[int]					=   encodings["""input_ids"""]
     return inputs

   def        UpperCamelCase_					(   self							:     Optional[int]			,						*lowerCAmelCase							:     str			,						**lowerCAmelCase							:     str)			->			int:

    """simple docstring"""







    return self.tokenizer.batch_decode(*lowerCAmelCase			,						**lowerCAmelCase)

   def        UpperCamelCase_					(   self							:     List[str]			,						*lowerCAmelCase							:     List[Any]			,						**lowerCAmelCase							:     List[str])			->			int:

    """simple docstring"""







    return self.tokenizer.decode(*lowerCAmelCase			,						**lowerCAmelCase)

   @contextmanager
   def        UpperCamelCase_					(   self							:     Tuple)			->			int:

    """simple docstring"""







    warnings.warn(
        """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
        """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
        """your images inputs, or in a separate call.""")
    _snake_case      :					str					=   True
    _snake_case      :					Dict					=   self.tokenizer
    yield
    _snake_case      :					Union[str, Any]					=   self.image_processor
    _snake_case      :					int					=   False

   @property
   def        UpperCamelCase_					(   self							:     List[str])			->			Any:

    """simple docstring"""







    warnings.warn(
        """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,						lowerCAmelCase			,						)
    return self.image_processor_class


   @property
   def        UpperCamelCase_					(   self							:     List[str])			->			int:

    """simple docstring"""







    warnings.warn(
        """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,						lowerCAmelCase			,						)
    return self.image_processor





import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()







class 						snake_case  (		unittest.TestCase					):



   '''simple docstring'''






   @property
   def        UpperCamelCase_					(   self							:     Union[str, Any])			->			int:

    """simple docstring"""







    torch.manual_seed(0)
    _snake_case      :					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        UpperCamelCase_					(   self							:     List[Any])			->			Optional[Any]:

    """simple docstring"""







    _snake_case      :					List[str]					=   """cpu"""
    _snake_case      :					Union[str, Any]					=   self.dummy_uncond_unet
    _snake_case      :					Union[str, Any]					=   DDPMScheduler()

    _snake_case      :					Dict					=   DDPMPipeline(unet=lowerCAmelCase			,						scheduler=lowerCAmelCase)
    ddpm.to(lowerCAmelCase)
    ddpm.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case      :					Optional[Any]					=   torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case      :					Optional[int]					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy""").images

    _snake_case      :					Optional[int]					=   torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case      :					Union[str, Any]					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy"""			,						return_dict=lowerCAmelCase)[0]

    _snake_case      :					Any					=   image[0, -3:, -3:, -1]
    _snake_case      :					str					=   image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case      :					Optional[int]					=   np.array(
        [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.000, 1.199E-03, 2.648E-04, 5.101E-04])

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


   def        UpperCamelCase_					(   self							:     List[Any])			->			Optional[int]:

    """simple docstring"""







    _snake_case      :					int					=   self.dummy_uncond_unet
    _snake_case      :					int					=   DDPMScheduler(prediction_type="""sample""")

    _snake_case      :					Union[str, Any]					=   DDPMPipeline(unet=lowerCAmelCase			,						scheduler=lowerCAmelCase)
    ddpm.to(lowerCAmelCase)
    ddpm.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case      :					Tuple					=   torch.manual_seed(0)
    _snake_case      :					Union[str, Any]					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy""").images

    _snake_case      :					Any					=   torch.manual_seed(0)
    _snake_case      :					Dict					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy""")[0]

    _snake_case      :					str					=   image[0, -3:, -3:, -1]
    _snake_case      :					List[Any]					=   image_eps[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case      :					Any					=   1E-2 if torch_device != """mps""" else 3E-2
    assert np.abs(image_slice.flatten() - image_eps_slice.flatten()).max() < tolerance







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



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     Dict)			->			Optional[Any]:

    """simple docstring"""







    _snake_case      :					Dict					=   """google/ddpm-cifar10-32"""

    _snake_case      :					List[Any]					=   UNetaDModel.from_pretrained(lowerCAmelCase)
    _snake_case      :					Optional[Any]					=   DDPMScheduler.from_pretrained(lowerCAmelCase)

    _snake_case      :					Dict					=   DDPMPipeline(unet=lowerCAmelCase			,						scheduler=lowerCAmelCase)
    ddpm.to(lowerCAmelCase)
    ddpm.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case      :					Optional[Any]					=   torch.manual_seed(0)
    _snake_case      :					Any					=   ddpm(generator=lowerCAmelCase			,						output_type="""numpy""").images

    _snake_case      :					Optional[Any]					=   image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case      :					Union[str, Any]					=   np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385])
    assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
