


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):
     SCREAMING_SNAKE_CASE__				      =      UnCLIPPipeline
     SCREAMING_SNAKE_CASE__				      =      TEXT_TO_IMAGE_PARAMS - {
         '''negative_prompt''',
         '''height''',
         '''width''',
         '''negative_prompt_embeds''',
         '''guidance_scale''',
         '''prompt_embeds''',
         '''cross_attention_kwargs''',
     }
     SCREAMING_SNAKE_CASE__				      =      TEXT_TO_IMAGE_BATCH_PARAMS
     SCREAMING_SNAKE_CASE__				      =      [
         '''generator''',
         '''return_dict''',
         '''prior_num_inference_steps''',
         '''decoder_num_inference_steps''',
         '''super_res_num_inference_steps''',
     ]
     SCREAMING_SNAKE_CASE__				      =      False





     @property
     def 					__A (self	)					->       int:
      return 3_2





     @property
     def 					__A (self	)					->       List[str]:
      return 3_2





     @property
     def 					__A (self	)					->       Optional[int]:
      return self.time_input_dim





     @property
     def 					__A (self	)					->       List[str]:
      return self.time_input_dim * 4





     @property
     def 					__A (self	)					->       Tuple:
      return 1_0_0





     @property
     def 					__A (self	)					->       List[str]:
      _lowercase											=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)
      return tokenizer





     @property
     def 					__A (self	)					->       Optional[int]:
      torch.manual_seed(0	)
      _lowercase											=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(UpperCAmelCase	)





     @property
     def 					__A (self	)					->       List[Any]:
      torch.manual_seed(0	)

      _lowercase											={
          '''num_attention_heads''': 2,
          '''attention_head_dim''': 1_2,
          '''embedding_dim''': self.text_embedder_hidden_size,
          '''num_layers''': 1,
      }

      _lowercase											=PriorTransformer(**UpperCAmelCase	)
      return model





     @property
     def 					__A (self	)					->       Optional[int]:
      torch.manual_seed(0	)

      _lowercase											={
          '''clip_embeddings_dim''': self.text_embedder_hidden_size,
          '''time_embed_dim''': self.time_embed_dim,
          '''cross_attention_dim''': self.cross_attention_dim,
      }

      _lowercase											=UnCLIPTextProjModel(**UpperCAmelCase	)
      return model





     @property
     def 					__A (self	)					->       Tuple:
      torch.manual_seed(0	)

      _lowercase											={
          '''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''',
      }

      _lowercase											=UNetaDConditionModel(**UpperCAmelCase	)
      return model





     @property
     def 					__A (self	)					->       List[Any]:
      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 					__A (self	)					->       Optional[Any]:
      torch.manual_seed(0	)

      _lowercase											=UNetaDModel(**self.dummy_super_res_kwargs	)
      return model





     @property
     def 					__A (self	)					->       List[str]:
      # seeded differently to get different unet than `self.dummy_super_res_first`
      torch.manual_seed(1	)

      _lowercase											=UNetaDModel(**self.dummy_super_res_kwargs	)
      return model





     def 					__A (self	)					->       Union[str, Any]:
      _lowercase											=self.dummy_prior
      _lowercase											=self.dummy_decoder
      _lowercase											=self.dummy_text_proj
      _lowercase											=self.dummy_text_encoder
      _lowercase											=self.dummy_tokenizer
      _lowercase											=self.dummy_super_res_first
      _lowercase											=self.dummy_super_res_last

      _lowercase											=UnCLIPScheduler(
          variance_type='''fixed_small_log'''		,					prediction_type='''sample'''		,					num_train_timesteps=1_0_0_0		,					clip_sample_range=5.0		,					)

      _lowercase											=UnCLIPScheduler(
          variance_type='''learned_range'''		,					prediction_type='''epsilon'''		,					num_train_timesteps=1_0_0_0		,					)

      _lowercase											=UnCLIPScheduler(
          variance_type='''fixed_small_log'''		,					prediction_type='''epsilon'''		,					num_train_timesteps=1_0_0_0		,					)

      _lowercase											={
          '''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 					__A (self		,					UpperCAmelCase		,					UpperCAmelCase=0	)					->       Dict:
      if str(UpperCAmelCase	).startswith('''mps'''	):
       _lowercase											=torch.manual_seed(UpperCAmelCase	)
      else:
       _lowercase											=torch.Generator(device=UpperCAmelCase	).manual_seed(UpperCAmelCase	)
      _lowercase											={
          '''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 					__A (self	)					->       Optional[Any]:
      _lowercase											='''cpu'''

      _lowercase											=self.get_dummy_components()

      _lowercase											=self.pipeline_class(**UpperCAmelCase	)
      _lowercase											=pipe.to(UpperCAmelCase	)

      pipe.set_progress_bar_config(disable=UpperCAmelCase	)

      _lowercase											=pipe(**self.get_dummy_inputs(UpperCAmelCase	)	)
      _lowercase											=output.images

      _lowercase											=pipe(
          **self.get_dummy_inputs(UpperCAmelCase	)		,					return_dict=UpperCAmelCase		,					)[0]

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

      assert image.shape == (1, 6_4, 6_4, 3)

      _lowercase											=np.array(
          [
              0.9997,
              0.9988,
              0.0028,
              0.9997,
              0.9984,
              0.9965,
              0.0029,
              0.9986,
              0.0025,
          ]	)

      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 					__A (self	)					->       str:
      _lowercase											=torch.device('''cpu'''	)







      class 				lowerCamelCase__       :
           SCREAMING_SNAKE_CASE__				      =      1



      _lowercase											=self.get_dummy_components()

      _lowercase											=self.pipeline_class(**UpperCAmelCase	)
      _lowercase											=pipe.to(UpperCAmelCase	)

      _lowercase											=components['''prior''']
      _lowercase											=components['''decoder''']
      _lowercase											=components['''super_res_first''']
      _lowercase											=components['''tokenizer''']
      _lowercase											=components['''text_encoder''']

      _lowercase											=torch.Generator(device=UpperCAmelCase	).manual_seed(0	)
      _lowercase											=prior.dtype
      _lowercase											=1

      _lowercase											=(batch_size, prior.config.embedding_dim)
      _lowercase											=pipe.prepare_latents(
          UpperCAmelCase		,					dtype=UpperCAmelCase		,					device=UpperCAmelCase		,					generator=UpperCAmelCase		,					latents=UpperCAmelCase		,					scheduler=DummyScheduler()	)
      _lowercase											=(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
      _lowercase											=pipe.prepare_latents(
          UpperCAmelCase		,					dtype=UpperCAmelCase		,					device=UpperCAmelCase		,					generator=UpperCAmelCase		,					latents=UpperCAmelCase		,					scheduler=DummyScheduler()	)

      _lowercase											=(
          batch_size,
          super_res_first.config.in_channels // 2,
          super_res_first.config.sample_size,
          super_res_first.config.sample_size,
      )
      _lowercase											=pipe.prepare_latents(
          UpperCAmelCase		,					dtype=UpperCAmelCase		,					device=UpperCAmelCase		,					generator=UpperCAmelCase		,					latents=UpperCAmelCase		,					scheduler=DummyScheduler()	)

      pipe.set_progress_bar_config(disable=UpperCAmelCase	)

      _lowercase											='''this is a prompt example'''

      _lowercase											=torch.Generator(device=UpperCAmelCase	).manual_seed(0	)
      _lowercase											=pipe(
          [prompt]		,					generator=UpperCAmelCase		,					prior_num_inference_steps=2		,					decoder_num_inference_steps=2		,					super_res_num_inference_steps=2		,					prior_latents=UpperCAmelCase		,					decoder_latents=UpperCAmelCase		,					super_res_latents=UpperCAmelCase		,					output_type='''np'''		,					)
      _lowercase											=output.images

      _lowercase											=tokenizer(
          UpperCAmelCase		,					padding='''max_length'''		,					max_length=tokenizer.model_max_length		,					return_tensors='''pt'''		,					)
      _lowercase											=text_encoder(text_inputs.input_ids	)
      _lowercase											=text_inputs.attention_mask

      _lowercase											=torch.Generator(device=UpperCAmelCase	).manual_seed(0	)
      _lowercase											=pipe(
          generator=UpperCAmelCase		,					prior_num_inference_steps=2		,					decoder_num_inference_steps=2		,					super_res_num_inference_steps=2		,					prior_latents=UpperCAmelCase		,					decoder_latents=UpperCAmelCase		,					super_res_latents=UpperCAmelCase		,					text_model_output=UpperCAmelCase		,					text_attention_mask=UpperCAmelCase		,					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 					__A (self	)					->       Optional[int]:
      _lowercase											=torch_device == '''cpu'''

      self._test_attention_slicing_forward_pass(test_max_difference=UpperCAmelCase		,					expected_max_diff=0.01	)





     @skip_mps
     def 					__A (self	)					->       Optional[int]:
      _lowercase											=torch_device == '''cpu'''
      _lowercase											=True
      _lowercase											=[
          '''prior_num_inference_steps''',
          '''decoder_num_inference_steps''',
          '''super_res_num_inference_steps''',
      ]

      self._test_inference_batch_single_identical(
          test_max_difference=UpperCAmelCase		,					relax_max_difference=UpperCAmelCase		,					additional_params_copy_to_batched_inputs=UpperCAmelCase		,					)





     def 					__A (self	)					->       Optional[Any]:
      _lowercase											=[
          '''prior_num_inference_steps''',
          '''decoder_num_inference_steps''',
          '''super_res_num_inference_steps''',
      ]

      if torch_device == "mps":
       # TODO: MPS errors with larger batch sizes
       _lowercase											=[2, 3]
       self._test_inference_batch_consistent(
           batch_sizes=UpperCAmelCase		,					additional_params_copy_to_batched_inputs=UpperCAmelCase		,					)
      else:
       self._test_inference_batch_consistent(
           additional_params_copy_to_batched_inputs=UpperCAmelCase	)





     @skip_mps
     def 					__A (self	)					->       Union[str, Any]:
      return super().test_dict_tuple_outputs_equivalent()





     @skip_mps
     def 					__A (self	)					->       Any:
      return super().test_save_load_local()





     @skip_mps
     def 					__A (self	)					->       Tuple:
      return super().test_save_load_optional_components()







@nightly
class 				lowerCamelCase__       (	unittest.TestCase):





     def 					__A (self	)					->       List[Any]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()





     def 					__A (self	)					->       str:
      _lowercase											=load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/unclip/karlo_v1_alpha_horse_cpu.npy'''	)

      _lowercase											=UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''	)
      pipeline.set_progress_bar_config(disable=UpperCAmelCase	)

      _lowercase											=torch.manual_seed(0	)
      _lowercase											=pipeline(
          '''horse'''		,					num_images_per_prompt=1		,					generator=UpperCAmelCase		,					output_type='''np'''		,					)

      _lowercase											=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):





     def 					__A (self	)					->       Union[str, Any]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()





     def 					__A (self	)					->       Optional[int]:
      _lowercase											=load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/unclip/karlo_v1_alpha_horse_fp16.npy'''	)

      _lowercase											=UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''		,					torch_dtype=torch.floataa	)
      _lowercase											=pipeline.to(UpperCAmelCase	)
      pipeline.set_progress_bar_config(disable=UpperCAmelCase	)

      _lowercase											=torch.Generator(device='''cpu'''	).manual_seed(0	)
      _lowercase											=pipeline(
          '''horse'''		,					generator=UpperCAmelCase		,					output_type='''np'''		,					)

      _lowercase											=output.images[0]

      assert image.shape == (2_5_6, 2_5_6, 3)

      assert_mean_pixel_difference(UpperCAmelCase		,					UpperCAmelCase	)





     def 					__A (self	)					->       Union[str, Any]:
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      _lowercase											=UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''		,					torch_dtype=torch.floataa	)
      _lowercase											=pipe.to(UpperCAmelCase	)
      pipe.set_progress_bar_config(disable=UpperCAmelCase	)
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      _lowercase											=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'''		,					)

      _lowercase											=torch.cuda.max_memory_allocated()
      # make sure that less than 7 GB is allocated
      assert mem_bytes < 7 * 1_0**9





from math import sqrt
def     UpperCAmelCase_ (							__snake_case						)					->      list[int]:







 """simple docstring"""


 _lowercase											=[1]
 for i in range(2					,						int(sqrt(__snake_case						)						) + 1					,						1						):
  if number % i == 0:
   values.append(__snake_case						)
   if int(number // i						) != i:
    values.append(int(number // i						)						)
 return sorted(__snake_case						)
def     UpperCAmelCase_ (							__snake_case						)					->      bool:







 """simple docstring"""


 return sum(factors(__snake_case						)						) > n
def     UpperCAmelCase_ (							__snake_case						)					->      bool:







 """simple docstring"""


 _lowercase											=factors(__snake_case						)
 _lowercase											=len(__snake_case						)
 _lowercase											=[[0 for i in range(number + 1						)] for j in range(r + 1						)]
 for i in range(r + 1						):
  _lowercase											=True

 for i in range(1					,						number + 1						):
  _lowercase											=False

 for i in range(1					,						r + 1						):
  for j in range(1					,						number + 1						):
   if j < values[i - 1]:
    _lowercase											=subset[i - 1][j]
   else:
    _lowercase											=subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

 return subset[r][number] != 0
def     UpperCAmelCase_ (							__snake_case						)					->      bool:







 """simple docstring"""


 return abundant(__snake_case						) and not semi_perfect(__snake_case						)


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)
  for number in (69, 70, 71):
    print(f'''{number} is {'' if weird(number) else 'not '}weird.''')

