


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       lowercase_					(unittest.TestCase   ):





				@property
				def 						__UpperCamelCase    (				self)  -> str:
											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)  -> int:
											a__      =self.dummy_uncond_unet
											a__      =PNDMScheduler()

											a__      =PNDMPipeline(unet=lowercase_       ,  scheduler=lowercase_)
											pndm.to(lowercase_)
											pndm.set_progress_bar_config(disable=lowercase_)

											a__      =torch.manual_seed(0)
											a__      =pndm(generator=lowercase_       ,  num_inference_steps=20       ,  output_type='numpy').images

											a__      =torch.manual_seed(0)
											a__      =pndm(generator=lowercase_       ,  num_inference_steps=20       ,  output_type='numpy'       ,  return_dict=lowercase_)[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([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       lowercase_					(unittest.TestCase   ):





				def 						__UpperCamelCase    (				self)  -> str:
											a__      ='google/ddpm-cifar10-32'

											a__      =UNetaDModel.from_pretrained(lowercase_)
											a__      =PNDMScheduler()

											a__      =PNDMPipeline(unet=lowercase_       ,  scheduler=lowercase_)
											pndm.to(lowercase_)
											pndm.set_progress_bar_config(disable=lowercase_)
											a__      =torch.manual_seed(0)
											a__      =pndm(generator=lowercase_       ,  output_type='numpy').images

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

											assert image.shape == (1, 32, 32, 3)
											a__      =np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25])

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









'''simple docstring'''



__UpperCamelCase   :							List[Any]         =   """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						bytes     )	->      bytes:

   """simple docstring"""
   # Make sure the supplied data is a bytes-like object
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      __a					  =     f"""a bytes-like object is required, not '{data.__class__.__name__}'"""
      raise TypeError(SCREAMING_SNAKE_CASE__     )

   __a					  =     ''.join(bin(SCREAMING_SNAKE_CASE__     )[2:].zfill(8     ) for byte in data     )

   __a					  =     len(SCREAMING_SNAKE_CASE__     ) % 6 != 0

   if padding_needed:
      # The padding that will be added later
      __a					  =     b'=' * ((6 - len(SCREAMING_SNAKE_CASE__     ) % 6) // 2)

      # Append binary_stream with arbitrary binary digits (0's by default) to make its
      # length a multiple of 6.
      binary_stream += "0" * (6 - len(SCREAMING_SNAKE_CASE__     ) % 6)
   else:
      __a					  =     b''

   # Encode every 6 binary digits to their corresponding Base64 character
   return (
       "".join(
           B64_CHARSET[int(binary_stream[index : index + 6],       2     )]
           for index in range(0,       len(SCREAMING_SNAKE_CASE__     ),       6     )     ).encode()
       + padding
   )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      bytes:

   """simple docstring"""
   # Make sure encoded_data is either a string or a bytes-like object
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ) and not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      __a					  =     (
          'argument should be a bytes-like object or ASCII string, '
          f"""not '{encoded_data.__class__.__name__}'"""
      )
      raise TypeError(SCREAMING_SNAKE_CASE__     )

   # In case encoded_data is a bytes-like object, make sure it contains only
   # ASCII characters so we convert it to a string object
   if isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      try:
         __a					  =     encoded_data.decode('utf-8'     )
      except UnicodeDecodeError:
         raise ValueError('base64 encoded data should only contain ASCII characters'     )

   __a					  =     encoded_data.count('='     )

   # Check if the encoded string contains non base64 characters
   if padding:
      assert all(
          char in B64_CHARSET for char in encoded_data[:-padding]     ), "Invalid base64 character(s) found."
   else:
      assert all(
          char in B64_CHARSET for char in encoded_data     ), "Invalid base64 character(s) found."

   # Check the padding
   assert len(SCREAMING_SNAKE_CASE__     ) % 4 == 0 and padding < 3, "Incorrect padding"

   if padding:
      # Remove padding if there is one
      __a					  =     encoded_data[:-padding]

      __a					  =     ''.join(
          bin(B64_CHARSET.index(SCREAMING_SNAKE_CASE__     )     )[2:].zfill(6     ) for char in encoded_data     )[: -padding * 2]
   else:
      __a					  =     ''.join(
          bin(B64_CHARSET.index(SCREAMING_SNAKE_CASE__     )     )[2:].zfill(6     ) for char in encoded_data     )

   __a					  =     [
       int(binary_stream[index : index + 8],       2     )
       for index in range(0,       len(SCREAMING_SNAKE_CASE__     ),       8     )
   ]

   return bytes(SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 import doctest

 doctest.testmod()