




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   UpperCAmelCase    (      A ,       unittest.TestCase ):
   __SCREAMING_SNAKE_CASE		:				List[str]      					=       UnCLIPPipeline
   __SCREAMING_SNAKE_CASE		:				Dict      					=       TEXT_TO_IMAGE_PARAMS - {
       "negative_prompt",
       "height",
       "width",
       "negative_prompt_embeds",
       "guidance_scale",
       "prompt_embeds",
       "cross_attention_kwargs",
   }
   __SCREAMING_SNAKE_CASE		:				str      					=       TEXT_TO_IMAGE_BATCH_PARAMS
   __SCREAMING_SNAKE_CASE		:				List[Any]      					=       [
       "generator",
       "return_dict",
       "prior_num_inference_steps",
       "decoder_num_inference_steps",
       "super_res_num_inference_steps",
   ]
   __SCREAMING_SNAKE_CASE		:				str      					=       False






   @property
   def 			_a    (						self :							int  )->      Dict:
     return 32






   @property
   def 			_a    (						self :							Optional[Any]  )->      List[str]:
     return 32






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






   @property
   def 			_a    (						self :							int  )->      int:
     return self.time_input_dim * 4






   @property
   def 			_a    (						self :							List[str]  )->      str:
     return 1_00






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






   @property
   def 			_a    (						self :							Tuple  )->      List[str]:
     torch.manual_seed(0  )
     _lowercase:			Dict      =     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=37    ,       layer_norm_eps=1E-05    ,       num_attention_heads=4    ,       num_hidden_layers=5    ,       pad_token_id=1    ,       vocab_size=10_00    ,       )
     return CLIPTextModelWithProjection(UpperCamelCase_  )






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

     _lowercase:			List[Any]      =     {
         'num_attention_heads': 2,
         'attention_head_dim': 12,
         'embedding_dim': self.text_embedder_hidden_size,
         'num_layers': 1,
     }

     _lowercase:			Dict      =     PriorTransformer(**UpperCamelCase_  )
     return model






   @property
   def 			_a    (						self :							Dict  )->      Any:
     torch.manual_seed(0  )

     _lowercase:			List[str]      =     {
         'clip_embeddings_dim': self.text_embedder_hidden_size,
         'time_embed_dim': self.time_embed_dim,
         'cross_attention_dim': self.cross_attention_dim,
     }

     _lowercase:			Any      =     UnCLIPTextProjModel(**UpperCamelCase_  )
     return model






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

     _lowercase:			Tuple      =     {
         'sample_size': 32,
         # 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:			Optional[Any]      =     UNetaDConditionModel(**UpperCamelCase_  )
     return model






   @property
   def 			_a    (						self :							Optional[int]  )->      List[Any]:
     return {
         "sample_size": 64,
         "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 :							Any  )->      Union[str, Any]:
     torch.manual_seed(0  )

     _lowercase:			List[str]      =     UNetaDModel(**self.dummy_super_res_kwargs  )
     return model






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

     _lowercase:			List[Any]      =     UNetaDModel(**self.dummy_super_res_kwargs  )
     return model






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

     _lowercase:			Union[str, Any]      =     UnCLIPScheduler(
         variance_type='fixed_small_log'    ,       prediction_type='sample'    ,       num_train_timesteps=10_00    ,       clip_sample_range=5.0    ,       )

     _lowercase:			Optional[int]      =     UnCLIPScheduler(
         variance_type='learned_range'    ,       prediction_type='epsilon'    ,       num_train_timesteps=10_00    ,       )

     _lowercase:			Dict      =     UnCLIPScheduler(
         variance_type='fixed_small_log'    ,       prediction_type='epsilon'    ,       num_train_timesteps=10_00    ,       )

     _lowercase:			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 			_a    (						self :							Optional[int]    ,       UpperCamelCase_ :							List[str]    ,       UpperCamelCase_ :							Dict=0  )->      Optional[Any]:
     if str(UpperCamelCase_  ).startswith('mps'  ):
       _lowercase:			Dict      =     torch.manual_seed(UpperCamelCase_  )
     else:
       _lowercase:			Optional[int]      =     torch.Generator(device=UpperCamelCase_  ).manual_seed(UpperCamelCase_  )
     _lowercase:			List[Any]      =     {
         '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 :							Union[str, Any]  )->      Tuple:
     _lowercase:			List[str]      =     'cpu'

     _lowercase:			Union[str, Any]      =     self.get_dummy_components()

     _lowercase:			Dict      =     self.pipeline_class(**UpperCamelCase_  )
     _lowercase:			Optional[Any]      =     pipe.to(UpperCamelCase_  )

     pipe.set_progress_bar_config(disable=UpperCamelCase_  )

     _lowercase:			Optional[int]      =     pipe(**self.get_dummy_inputs(UpperCamelCase_  )  )
     _lowercase:			Optional[int]      =     output.images

     _lowercase:			Any      =     pipe(
         **self.get_dummy_inputs(UpperCamelCase_  )    ,       return_dict=UpperCamelCase_    ,       )[0]

     _lowercase:			List[Any]      =     image[0, -3:, -3:, -1]
     _lowercase:			Union[str, Any]      =     image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)

     _lowercase:			List[Any]      =     np.array(
         [
             0.9_997,
             0.9_988,
             0.0_028,
             0.9_997,
             0.9_984,
             0.9_965,
             0.0_029,
             0.9_986,
             0.0_025,
         ]  )

     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 :							Any  )->      Optional[int]:
     _lowercase:			List[str]      =     torch.device('cpu'  )

     class   UpperCAmelCase    :
        __SCREAMING_SNAKE_CASE		:				List[str]      					=       1
     _lowercase:			Tuple      =     self.get_dummy_components()

     _lowercase:			Tuple      =     self.pipeline_class(**UpperCamelCase_  )
     _lowercase:			List[str]      =     pipe.to(UpperCamelCase_  )

     _lowercase:			List[Any]      =     components['prior']
     _lowercase:			Any      =     components['decoder']
     _lowercase:			Tuple      =     components['super_res_first']
     _lowercase:			Dict      =     components['tokenizer']
     _lowercase:			Dict      =     components['text_encoder']

     _lowercase:			str      =     torch.Generator(device=UpperCamelCase_  ).manual_seed(0  )
     _lowercase:			Dict      =     prior.dtype
     _lowercase:			Optional[int]      =     1

     _lowercase:			Dict      =     (batch_size, prior.config.embedding_dim)
     _lowercase:			List[Any]      =     pipe.prepare_latents(
         UpperCamelCase_    ,       dtype=UpperCamelCase_    ,       device=UpperCamelCase_    ,       generator=UpperCamelCase_    ,       latents=UpperCamelCase_    ,       scheduler=DummyScheduler()  )
     _lowercase:			Tuple      =     (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
     _lowercase:			Dict      =     pipe.prepare_latents(
         UpperCamelCase_    ,       dtype=UpperCamelCase_    ,       device=UpperCamelCase_    ,       generator=UpperCamelCase_    ,       latents=UpperCamelCase_    ,       scheduler=DummyScheduler()  )

     _lowercase:			int      =     (
         batch_size,
         super_res_first.config.in_channels // 2,
         super_res_first.config.sample_size,
         super_res_first.config.sample_size,
     )
     _lowercase:			Optional[Any]      =     pipe.prepare_latents(
         UpperCamelCase_    ,       dtype=UpperCamelCase_    ,       device=UpperCamelCase_    ,       generator=UpperCamelCase_    ,       latents=UpperCamelCase_    ,       scheduler=DummyScheduler()  )

     pipe.set_progress_bar_config(disable=UpperCamelCase_  )

     _lowercase:			Tuple      =     'this is a prompt example'

     _lowercase:			str      =     torch.Generator(device=UpperCamelCase_  ).manual_seed(0  )
     _lowercase:			List[Any]      =     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:			Tuple      =     output.images

     _lowercase:			List[str]      =     tokenizer(
         UpperCamelCase_    ,       padding='max_length'    ,       max_length=tokenizer.model_max_length    ,       return_tensors='pt'    ,       )
     _lowercase:			Any      =     text_encoder(text_inputs.input_ids  )
     _lowercase:			Tuple      =     text_inputs.attention_mask

     _lowercase:			Tuple      =     torch.Generator(device=UpperCamelCase_  ).manual_seed(0  )
     _lowercase:			str      =     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 :							Tuple  )->      List[str]:
     _lowercase:			Dict      =     torch_device == 'cpu'

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






   @skip_mps
   def 			_a    (						self :							List[Any]  )->      Any:
     _lowercase:			str      =     torch_device == 'cpu'
     _lowercase:			Any      =     True
     _lowercase:			int      =     [
         '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 :							List[str]  )->      List[Any]:
     _lowercase:			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
       _lowercase:			int      =     [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 :							Dict  )->      List[str]:
     return super().test_dict_tuple_outputs_equivalent()






   @skip_mps
   def 			_a    (						self :							List[Any]  )->      str:
     return super().test_save_load_local()






   @skip_mps
   def 			_a    (						self :							List[str]  )->      int:
     return super().test_save_load_optional_components()

@nightly
class   UpperCAmelCase    (      unittest.TestCase ):






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






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

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

     _lowercase:			List[Any]      =     torch.manual_seed(0  )
     _lowercase:			Tuple      =     pipeline(
         'horse'    ,       num_images_per_prompt=1    ,       generator=UpperCamelCase_    ,       output_type='np'    ,       )

     _lowercase:			int      =     output.images[0]

     assert image.shape == (2_56, 2_56, 3)
     assert np.abs(expected_image - image  ).max() < 1E-1

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






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






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

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

     _lowercase:			List[str]      =     torch.Generator(device='cpu'  ).manual_seed(0  )
     _lowercase:			Optional[int]      =     pipeline(
         'horse'    ,       generator=UpperCamelCase_    ,       output_type='np'    ,       )

     _lowercase:			List[str]      =     output.images[0]

     assert image.shape == (2_56, 2_56, 3)

     assert_mean_pixel_difference(UpperCamelCase_    ,       UpperCamelCase_  )






   def 			_a    (						self :							List[str]  )->      Any:
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     _lowercase:			List[str]      =     UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'    ,       torch_dtype=torch.floataa  )
     _lowercase:			List[Any]      =     pipe.to(UpperCamelCase_  )
     pipe.set_progress_bar_config(disable=UpperCamelCase_  )
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     _lowercase:			int      =     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:			int      =     torch.cuda.max_memory_allocated()
     # make sure that less than 7 GB is allocated
     assert mem_bytes < 7 * 10**9






from math import sqrt


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		list[int]:





  """simple docstring"""





  _lowercase:			Dict      =     [1]
  for i in range(2       ,  int(sqrt(UpperCamelCase				)				) + 1       ,  1				):
    if number % i == 0:
      values.append(UpperCamelCase				)
      if int(number // i				) != i:
        values.append(int(number // i				)				)
  return sorted(UpperCamelCase				)


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		bool:





  """simple docstring"""





  return sum(factors(UpperCamelCase				)				) > n


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		bool:





  """simple docstring"""





  _lowercase:			Dict      =     factors(UpperCamelCase				)
  _lowercase:			int      =     len(UpperCamelCase				)
  _lowercase:			Optional[int]      =     [[0 for i in range(number + 1				)] for j in range(r + 1				)]
  for i in range(r + 1				):
    _lowercase:			List[Any]      =     True

  for i in range(1       ,  number + 1				):
    _lowercase:			Optional[int]      =     False

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

  return subset[r][number] != 0


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		bool:





  """simple docstring"""





  return abundant(UpperCamelCase				) and not semi_perfect(UpperCamelCase				)


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.''')
