code: import gc
  import unittest
  
  import numpy as np
  import torch
  from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
  
  from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
  from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
  from diffusers.utils import load_numpy, nightly, slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
  
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
  
  
  enable_full_determinism()
  
  
  
  
  class    __lowerCAmelCase  (       lowerCAmelCase_,	unittest.TestCase						):
  
  
  
   '''simple docstring'''
   _lowerCamelCase						:	Union[str, Any]				    = UnCLIPPipeline
   _lowerCamelCase						:	Optional[int]				    = TEXT_TO_IMAGE_PARAMS - {
       """negative_prompt""",
       """height""",
       """width""",
       """negative_prompt_embeds""",
       """guidance_scale""",
       """prompt_embeds""",
       """cross_attention_kwargs""",
   }
   _lowerCamelCase						:	int				    = TEXT_TO_IMAGE_BATCH_PARAMS
   _lowerCamelCase						:	Optional[int]				    = [
       """generator""",
       """return_dict""",
       """prior_num_inference_steps""",
       """decoder_num_inference_steps""",
       """super_res_num_inference_steps""",
   ]
   _lowerCamelCase						:	Optional[int]				    = False
  
   @property
   def 					__snake_case					(			self       :		Union[str, Any]  ) ->							Union[str, Any]:
          return 3_2
  
   @property
   def 					__snake_case					(			self       :		List[Any]  ) ->							Optional[Any]:
          return 3_2
  
   @property
   def 					__snake_case					(			self       :		List[str]  ) ->							Optional[int]:
          return self.time_input_dim
  
   @property
   def 					__snake_case					(			self       :		Optional[int]  ) ->							Optional[Any]:
          return self.time_input_dim * 4
  
   @property
   def 					__snake_case					(			self       :		Optional[int]  ) ->							Any:
          return 1_0_0
  
   @property
   def 					__snake_case					(			self       :		Optional[Any]  ) ->							int:
          _UpperCamelCase     :       str  						=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )
          return tokenizer
  
   @property
   def 					__snake_case					(			self       :		Dict  ) ->							Tuple:
          torch.manual_seed(0  )
          _UpperCamelCase     :       Tuple  						=  CLIPTextConfig(
              bos_token_id=0       ,			eos_token_id=2       ,			hidden_size=self.text_embedder_hidden_size       ,			projection_dim=self.text_embedder_hidden_size       ,			intermediate_size=3_7       ,			layer_norm_eps=1e-05       ,			num_attention_heads=4       ,			num_hidden_layers=5       ,			pad_token_id=1       ,			vocab_size=1_0_0_0       ,			)
          return CLIPTextModelWithProjection(__SCREAMING_SNAKE_CASE  )
  
   @property
   def 					__snake_case					(			self       :		str  ) ->							List[Any]:
          torch.manual_seed(0  )
  
          _UpperCamelCase     :       str  						=  {
              'num_attention_heads': 2,
              'attention_head_dim': 1_2,
              'embedding_dim': self.text_embedder_hidden_size,
              'num_layers': 1,
          }
  
          _UpperCamelCase     :       Dict  						=  PriorTransformer(**__SCREAMING_SNAKE_CASE  )
          return model
  
   @property
   def 					__snake_case					(			self       :		str  ) ->							Union[str, Any]:
          torch.manual_seed(0  )
  
          _UpperCamelCase     :       int  						=  {
              'clip_embeddings_dim': self.text_embedder_hidden_size,
              'time_embed_dim': self.time_embed_dim,
              'cross_attention_dim': self.cross_attention_dim,
          }
  
          _UpperCamelCase     :       Dict  						=  UnCLIPTextProjModel(**__SCREAMING_SNAKE_CASE  )
          return model
  
   @property
   def 					__snake_case					(			self       :		Dict  ) ->							Dict:
          torch.manual_seed(0  )
  
          _UpperCamelCase     :       Optional[Any]  						=  {
              'sample_size': 3_2,
              # RGB in channels
              'in_channels': 3,
              # Out channels is double in channels because predicts mean and variance
              'out_channels': 6,
              'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
              'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
              'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
              'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
              'layers_per_block': 1,
              'cross_attention_dim': self.cross_attention_dim,
              'attention_head_dim': 4,
              'resnet_time_scale_shift': 'scale_shift',
              'class_embed_type': 'identity',
          }
  
          _UpperCamelCase     :       List[str]  						=  UNetaDConditionModel(**__SCREAMING_SNAKE_CASE  )
          return model
  
   @property
   def 					__snake_case					(			self       :		Tuple  ) ->							Tuple:
          return {
              "sample_size": 6_4,
              "layers_per_block": 1,
              "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
              "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
              "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
              "in_channels": 6,
              "out_channels": 3,
          }
  
   @property
   def 					__snake_case					(			self       :		Optional[Any]  ) ->							List[Any]:
          torch.manual_seed(0  )
  
          _UpperCamelCase     :       str  						=  UNetaDModel(**self.dummy_super_res_kwargs  )
          return model
  
   @property
   def 					__snake_case					(			self       :		str  ) ->							Tuple:
          torch.manual_seed(1  )
  
          _UpperCamelCase     :       Tuple  						=  UNetaDModel(**self.dummy_super_res_kwargs  )
          return model
  
   def 					__snake_case					(			self       :		int  ) ->							Optional[Any]:
          _UpperCamelCase     :       Union[str, Any]  						=  self.dummy_prior
          _UpperCamelCase     :       str  						=  self.dummy_decoder
          _UpperCamelCase     :       Optional[int]  						=  self.dummy_text_proj
          _UpperCamelCase     :       List[Any]  						=  self.dummy_text_encoder
          _UpperCamelCase     :       str  						=  self.dummy_tokenizer
          _UpperCamelCase     :       List[str]  						=  self.dummy_super_res_first
          _UpperCamelCase     :       Dict  						=  self.dummy_super_res_last
  
          _UpperCamelCase     :       Optional[int]  						=  UnCLIPScheduler(
              variance_type='fixed_small_log'       ,			prediction_type='sample'       ,			num_train_timesteps=1_0_0_0       ,			clip_sample_range=5.0       ,			)
  
          _UpperCamelCase     :       int  						=  UnCLIPScheduler(
              variance_type='learned_range'       ,			prediction_type='epsilon'       ,			num_train_timesteps=1_0_0_0       ,			)
  
          _UpperCamelCase     :       Dict  						=  UnCLIPScheduler(
              variance_type='fixed_small_log'       ,			prediction_type='epsilon'       ,			num_train_timesteps=1_0_0_0       ,			)
  
          _UpperCamelCase     :       Optional[Any]  						=  {
              'prior': prior,
              'decoder': decoder,
              'text_proj': text_proj,
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'super_res_first': super_res_first,
              'super_res_last': super_res_last,
              'prior_scheduler': prior_scheduler,
              'decoder_scheduler': decoder_scheduler,
              'super_res_scheduler': super_res_scheduler,
          }
  
          return components
  
   def 					__snake_case					(			self       :		Union[str, Any]       ,			lowerCamelCase       :		Any       ,			lowerCamelCase       :		List[Any]=0  ) ->							Union[str, Any]:
  
          if str(__SCREAMING_SNAKE_CASE  ).startswith('mps'  ):
                 _UpperCamelCase     :       Any  						=  torch.manual_seed(__SCREAMING_SNAKE_CASE  )
          else:
                 _UpperCamelCase     :       Union[str, Any]  						=  torch.Generator(device=__SCREAMING_SNAKE_CASE  ).manual_seed(__SCREAMING_SNAKE_CASE  )
          _UpperCamelCase     :       int  						=  {
              'prompt': 'horse',
              'generator': generator,
              'prior_num_inference_steps': 2,
              'decoder_num_inference_steps': 2,
              'super_res_num_inference_steps': 2,
              'output_type': 'numpy',
          }
          return inputs
  
   def 					__snake_case					(			self       :		Any  ) ->							int:
          _UpperCamelCase     :       Optional[int]  						=  'cpu'
  
          _UpperCamelCase     :       Dict  						=  self.get_dummy_components()
  
          _UpperCamelCase     :       Any  						=  self.pipeline_class(**__SCREAMING_SNAKE_CASE  )
          _UpperCamelCase     :       Union[str, Any]  						=  pipe.to(__SCREAMING_SNAKE_CASE  )
  
          pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )
  
          _UpperCamelCase     :       Optional[Any]  						=  pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE  )  )
          _UpperCamelCase     :       List[Any]  						=  output.images
  
          _UpperCamelCase     :       Union[str, Any]  						=  pipe(
              **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE  )       ,			return_dict=__SCREAMING_SNAKE_CASE       ,			)[0]
  
          _UpperCamelCase     :       Tuple  						=  image[0, -3:, -3:, -1]
          _UpperCamelCase     :       str  						=  image_from_tuple[0, -3:, -3:, -1]
  
          assert image.shape == (1, 6_4, 6_4, 3)
  
          _UpperCamelCase     :       int  						=  np.array(
              [
                  0.9_9_9_7,
                  0.9_9_8_8,
                  0.0_0_2_8,
                  0.9_9_9_7,
                  0.9_9_8_4,
                  0.9_9_6_5,
                  0.0_0_2_9,
                  0.9_9_8_6,
                  0.0_0_2_5,
              ]  )
  
          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 					__snake_case					(			self       :		List[str]  ) ->							Tuple:
          _UpperCamelCase     :       List[Any]  						=  torch.device('cpu'  )
  
  
  
  
          class    __lowerCAmelCase  :
  
  
  
           '''simple docstring'''
           _lowerCamelCase						:	Optional[Any]				    = 1
  
          _UpperCamelCase     :       Tuple  						=  self.get_dummy_components()
  
          _UpperCamelCase     :       List[Any]  						=  self.pipeline_class(**__SCREAMING_SNAKE_CASE  )
          _UpperCamelCase     :       Any  						=  pipe.to(__SCREAMING_SNAKE_CASE  )
  
          _UpperCamelCase     :       Tuple  						=  components['prior']
          _UpperCamelCase     :       Optional[int]  						=  components['decoder']
          _UpperCamelCase     :       Union[str, Any]  						=  components['super_res_first']
          _UpperCamelCase     :       int  						=  components['tokenizer']
          _UpperCamelCase     :       Tuple  						=  components['text_encoder']
  
          _UpperCamelCase     :       List[str]  						=  torch.Generator(device=__SCREAMING_SNAKE_CASE  ).manual_seed(0  )
          _UpperCamelCase     :       Union[str, Any]  						=  prior.dtype
          _UpperCamelCase     :       Tuple  						=  1
  
          _UpperCamelCase     :       Optional[Any]  						=  (batch_size, prior.config.embedding_dim)
          _UpperCamelCase     :       Union[str, Any]  						=  pipe.prepare_latents(
              __SCREAMING_SNAKE_CASE       ,			dtype=__SCREAMING_SNAKE_CASE       ,			device=__SCREAMING_SNAKE_CASE       ,			generator=__SCREAMING_SNAKE_CASE       ,			latents=__SCREAMING_SNAKE_CASE       ,			scheduler=DummyScheduler()  )
          _UpperCamelCase     :       Optional[int]  						=  (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
          _UpperCamelCase     :       Optional[int]  						=  pipe.prepare_latents(
              __SCREAMING_SNAKE_CASE       ,			dtype=__SCREAMING_SNAKE_CASE       ,			device=__SCREAMING_SNAKE_CASE       ,			generator=__SCREAMING_SNAKE_CASE       ,			latents=__SCREAMING_SNAKE_CASE       ,			scheduler=DummyScheduler()  )
  
          _UpperCamelCase     :       Optional[Any]  						=  (
              batch_size,
              super_res_first.config.in_channels // 2,
              super_res_first.config.sample_size,
              super_res_first.config.sample_size,
          )
          _UpperCamelCase     :       Dict  						=  pipe.prepare_latents(
              __SCREAMING_SNAKE_CASE       ,			dtype=__SCREAMING_SNAKE_CASE       ,			device=__SCREAMING_SNAKE_CASE       ,			generator=__SCREAMING_SNAKE_CASE       ,			latents=__SCREAMING_SNAKE_CASE       ,			scheduler=DummyScheduler()  )
  
          pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )
  
          _UpperCamelCase     :       List[str]  						=  'this is a prompt example'
  
          _UpperCamelCase     :       Tuple  						=  torch.Generator(device=__SCREAMING_SNAKE_CASE  ).manual_seed(0  )
          _UpperCamelCase     :       Optional[Any]  						=  pipe(
              [prompt]       ,			generator=__SCREAMING_SNAKE_CASE       ,			prior_num_inference_steps=2       ,			decoder_num_inference_steps=2       ,			super_res_num_inference_steps=2       ,			prior_latents=__SCREAMING_SNAKE_CASE       ,			decoder_latents=__SCREAMING_SNAKE_CASE       ,			super_res_latents=__SCREAMING_SNAKE_CASE       ,			output_type='np'       ,			)
          _UpperCamelCase     :       Tuple  						=  output.images
  
          _UpperCamelCase     :       Tuple  						=  tokenizer(
              __SCREAMING_SNAKE_CASE       ,			padding='max_length'       ,			max_length=tokenizer.model_max_length       ,			return_tensors='pt'       ,			)
          _UpperCamelCase     :       List[str]  						=  text_encoder(text_inputs.input_ids  )
          _UpperCamelCase     :       Optional[int]  						=  text_inputs.attention_mask
  
          _UpperCamelCase     :       Tuple  						=  torch.Generator(device=__SCREAMING_SNAKE_CASE  ).manual_seed(0  )
          _UpperCamelCase     :       Any  						=  pipe(
              generator=__SCREAMING_SNAKE_CASE       ,			prior_num_inference_steps=2       ,			decoder_num_inference_steps=2       ,			super_res_num_inference_steps=2       ,			prior_latents=__SCREAMING_SNAKE_CASE       ,			decoder_latents=__SCREAMING_SNAKE_CASE       ,			super_res_latents=__SCREAMING_SNAKE_CASE       ,			text_model_output=__SCREAMING_SNAKE_CASE       ,			text_attention_mask=__SCREAMING_SNAKE_CASE       ,			output_type='np'       ,			)[0]
  
          # make sure passing text embeddings manually is identical
          assert np.abs(image - image_from_text  ).max() < 1e-4
  
   @skip_mps
   def 					__snake_case					(			self       :		Tuple  ) ->							Optional[Any]:
          _UpperCamelCase     :       Union[str, Any]  						=  torch_device == 'cpu'
  
          self._test_attention_slicing_forward_pass(test_max_difference=__SCREAMING_SNAKE_CASE       ,			expected_max_diff=0.0_1  )
  
   @skip_mps
   def 					__snake_case					(			self       :		List[Any]  ) ->							Union[str, Any]:
          _UpperCamelCase     :       Union[str, Any]  						=  torch_device == 'cpu'
          _UpperCamelCase     :       Optional[int]  						=  True
          _UpperCamelCase     :       int  						=  [
              'prior_num_inference_steps',
              'decoder_num_inference_steps',
              'super_res_num_inference_steps',
          ]
  
          self._test_inference_batch_single_identical(
              test_max_difference=__SCREAMING_SNAKE_CASE       ,			relax_max_difference=__SCREAMING_SNAKE_CASE       ,			additional_params_copy_to_batched_inputs=__SCREAMING_SNAKE_CASE       ,			)
  
   def 					__snake_case					(			self       :		Dict  ) ->							List[Any]:
          _UpperCamelCase     :       Optional[int]  						=  [
              'prior_num_inference_steps',
              'decoder_num_inference_steps',
              'super_res_num_inference_steps',
          ]
  
          if torch_device == "mps":
                 # TODO: MPS errors with larger batch sizes
                 _UpperCamelCase     :       Optional[Any]  						=  [2, 3]
                 self._test_inference_batch_consistent(
                     batch_sizes=__SCREAMING_SNAKE_CASE       ,			additional_params_copy_to_batched_inputs=__SCREAMING_SNAKE_CASE       ,			)
          else:
                 self._test_inference_batch_consistent(
                     additional_params_copy_to_batched_inputs=__SCREAMING_SNAKE_CASE  )
  
   @skip_mps
   def 					__snake_case					(			self       :		List[str]  ) ->							Tuple:
          return super().test_dict_tuple_outputs_equivalent()
  
   @skip_mps
   def 					__snake_case					(			self       :		Optional[Any]  ) ->							str:
          return super().test_save_load_local()
  
   @skip_mps
   def 					__snake_case					(			self       :		List[Any]  ) ->							int:
          return super().test_save_load_optional_components()
  
  
  
  
  @nightly
  class    __lowerCAmelCase  (       unittest.TestCase						):
  
  
  
   '''simple docstring'''
  
   def 					__snake_case					(			self       :		Dict  ) ->							int:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
  
   def 					__snake_case					(			self       :		Tuple  ) ->							int:
          _UpperCamelCase     :       Dict  						=  load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/unclip/karlo_v1_alpha_horse_cpu.npy'  )
  
          _UpperCamelCase     :       Dict  						=  UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'  )
          pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )
  
          _UpperCamelCase     :       int  						=  torch.manual_seed(0  )
          _UpperCamelCase     :       str  						=  pipeline(
              'horse'       ,			num_images_per_prompt=1       ,			generator=__SCREAMING_SNAKE_CASE       ,			output_type='np'       ,			)
  
          _UpperCamelCase     :       Union[str, Any]  						=  output.images[0]
  
          assert image.shape == (2_5_6, 2_5_6, 3)
          assert np.abs(expected_image - image  ).max() < 1e-1
  
  
  
  
  @slow
  @require_torch_gpu
  class    __lowerCAmelCase  (       unittest.TestCase						):
  
  
  
   '''simple docstring'''
  
   def 					__snake_case					(			self       :		List[Any]  ) ->							Optional[Any]:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
  
   def 					__snake_case					(			self       :		List[str]  ) ->							Dict:
          _UpperCamelCase     :       List[str]  						=  load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/unclip/karlo_v1_alpha_horse_fp16.npy'  )
  
          _UpperCamelCase     :       List[Any]  						=  UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'       ,			torch_dtype=torch.floataa  )
          _UpperCamelCase     :       List[str]  						=  pipeline.to(__SCREAMING_SNAKE_CASE  )
          pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )
  
          _UpperCamelCase     :       List[Any]  						=  torch.Generator(device='cpu'  ).manual_seed(0  )
          _UpperCamelCase     :       Optional[int]  						=  pipeline(
              'horse'       ,			generator=__SCREAMING_SNAKE_CASE       ,			output_type='np'       ,			)
  
          _UpperCamelCase     :       Tuple  						=  output.images[0]
  
          assert image.shape == (2_5_6, 2_5_6, 3)
  
          assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE  )
  
   def 					__snake_case					(			self       :		List[Any]  ) ->							int:
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()
  
          _UpperCamelCase     :       Tuple  						=  UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'       ,			torch_dtype=torch.floataa  )
          _UpperCamelCase     :       Dict  						=  pipe.to(__SCREAMING_SNAKE_CASE  )
          pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )
          pipe.enable_attention_slicing()
          pipe.enable_sequential_cpu_offload()
  
          _UpperCamelCase     :       Any  						=  pipe(
              'horse'       ,			num_images_per_prompt=1       ,			prior_num_inference_steps=2       ,			decoder_num_inference_steps=2       ,			super_res_num_inference_steps=2       ,			output_type='np'       ,			)
  
          _UpperCamelCase     :       str  						=  torch.cuda.max_memory_allocated()
          # make sure that less than 7 GB is allocated
          assert mem_bytes < 7 * 1_0**9
  
  
  
  
style_context: from __future__ import annotations
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	int		,						lowercase_:	int				):
         assert (
             c % greatest_common_divisor(lowercase_		,						lowercase_				) == 0
         )  # greatest_common_divisor(a,b) function implemented below
         ((_UpperCamelCase)						,      (_UpperCamelCase)						,      (_UpperCamelCase))     :       int  						=  extended_gcd(lowercase_		,						lowercase_				)  # extended_gcd(a,b) function implemented below
         _UpperCamelCase     :       List[Any]  						=  c / d
         return (r * x, r * y)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	int		,						lowercase_:	int		,						lowercase_:	int = 2				):
         ((_UpperCamelCase)						,      (_UpperCamelCase))     :       Tuple  						=  diophantine(lowercase_		,						lowercase_		,						lowercase_				)  # Initial value
         _UpperCamelCase     :       List[Any]  						=  greatest_common_divisor(lowercase_		,						lowercase_				)
         _UpperCamelCase     :       Optional[int]  						=  a // d
         _UpperCamelCase     :       Optional[Any]  						=  b // d
  
         for i in range(lowercase_				):
                _UpperCamelCase     :       Union[str, Any]  						=  xa + i * q
                _UpperCamelCase     :       List[Any]  						=  ya - i * p
                print(lowercase_		,						lowercase_				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	int				):
         if a < b:
                _UpperCamelCase						,      _UpperCamelCase     :       Optional[Any]  						=  b, a
  
         while a % b != 0:
                _UpperCamelCase						,      _UpperCamelCase     :       Union[str, Any]  						=  b, a % b
  
         return b
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	int				):
         assert a >= 0 and b >= 0
  
         if b == 0:
                _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase     :       Optional[Any]  						=  a, 1, 0
         else:
                ((_UpperCamelCase)						,      (_UpperCamelCase)						,      (_UpperCamelCase))     :       Any  						=  extended_gcd(lowercase_		,						a % b				)
                _UpperCamelCase     :       Any  						=  q
                _UpperCamelCase     :       str  						=  p - q * (a // b)
  
         assert a % d == 0 and b % d == 0
         assert d == a * x + b * y
  
         return (d, x, y)
  
  
  if __name__ == "__main__":
   from doctest import testmod
  
   testmod(name='''diophantine''', verbose=True)
   testmod(name='''diophantine_all_soln''', verbose=True)
   testmod(name='''extended_gcd''', verbose=True)
   testmod(name='''greatest_common_divisor''', verbose=True)
  
  
  
  