



def   UpperCAmelCase							(		A__     )							->					int:
	if not isinstance(a_				,  a_     ):
		raise ValueError("""double_factorial() only accepts integral values"""     )
	if n < 0:
		raise ValueError("""double_factorial() not defined for negative values"""     )
	return 1 if n <= 1 else n * double_factorial(n - 2     )


if __name__ == "__main__":
						import doctest

						doctest.testmod()







"""simple docstring"""







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      __a      (       lowerCAmelCase_					,						unittest.TestCase      ):
       _lowerCamelCase				:				int   			=      UnCLIPPipeline
       _lowerCamelCase				:				Any   			=      TEXT_TO_IMAGE_PARAMS - {
           'negative_prompt',
           'height',
           'width',
           'negative_prompt_embeds',
           'guidance_scale',
           'prompt_embeds',
           'cross_attention_kwargs',
       }
       _lowerCamelCase				:				Optional[int]   			=      TEXT_TO_IMAGE_BATCH_PARAMS
       _lowerCamelCase				:				Dict   			=      [
           'generator',
           'return_dict',
           'prior_num_inference_steps',
           'decoder_num_inference_steps',
           'super_res_num_inference_steps',
       ]
       _lowerCamelCase				:				Any   			=      False







       @property
       def  _snake_case      (       self		):



          '''simple docstring'''

          return 32







       @property
       def  _snake_case      (       self		):



          '''simple docstring'''

          return 32







       @property
       def  _snake_case      (       self		):



          '''simple docstring'''

          return self.time_input_dim







       @property
       def  _snake_case      (       self		):



          '''simple docstring'''

          return self.time_input_dim * 4







       @property
       def  _snake_case      (       self		):



          '''simple docstring'''

          return 100







       @property
       def  _snake_case      (       self		):



          '''simple docstring'''

          _UpperCAmelCase 						=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)
          return tokenizer







       @property
       def  _snake_case      (       self		):



          '''simple docstring'''

          torch.manual_seed(0		)
          _UpperCAmelCase 						=       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=1000				,)
          return CLIPTextModelWithProjection(lowerCamelCase_		)







       @property
       def  _snake_case      (       self		):



          '''simple docstring'''

          torch.manual_seed(0		)

          _UpperCAmelCase 						=       {
              "num_attention_heads": 2,
              "attention_head_dim": 12,
              "embedding_dim": self.text_embedder_hidden_size,
              "num_layers": 1,
          }

          _UpperCAmelCase 						=       PriorTransformer(**lowerCamelCase_		)
          return model







       @property
       def  _snake_case      (       self		):



          '''simple docstring'''

          torch.manual_seed(0		)

          _UpperCAmelCase 						=       {
              "clip_embeddings_dim": self.text_embedder_hidden_size,
              "time_embed_dim": self.time_embed_dim,
              "cross_attention_dim": self.cross_attention_dim,
          }

          _UpperCAmelCase 						=       UnCLIPTextProjModel(**lowerCamelCase_		)
          return model







       @property
       def  _snake_case      (       self		):



          '''simple docstring'''

          torch.manual_seed(0		)

          _UpperCAmelCase 						=       {
              "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",
          }

          _UpperCAmelCase 						=       UNetaDConditionModel(**lowerCamelCase_		)
          return model







       @property
       def  _snake_case      (       self		):



          '''simple docstring'''

          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  _snake_case      (       self		):



          '''simple docstring'''

          torch.manual_seed(0		)

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







       @property
       def  _snake_case      (       self		):



          '''simple docstring'''

          torch.manual_seed(1		)

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







       def  _snake_case      (       self		):



          '''simple docstring'''

          _UpperCAmelCase 						=       self.dummy_prior
          _UpperCAmelCase 						=       self.dummy_decoder
          _UpperCAmelCase 						=       self.dummy_text_proj
          _UpperCAmelCase 						=       self.dummy_text_encoder
          _UpperCAmelCase 						=       self.dummy_tokenizer
          _UpperCAmelCase 						=       self.dummy_super_res_first
          _UpperCAmelCase 						=       self.dummy_super_res_last

          _UpperCAmelCase 						=       UnCLIPScheduler(
              variance_type="fixed_small_log"				,prediction_type="sample"				,num_train_timesteps=1000				,clip_sample_range=5.0				,)

          _UpperCAmelCase 						=       UnCLIPScheduler(
              variance_type="learned_range"				,prediction_type="epsilon"				,num_train_timesteps=1000				,)

          _UpperCAmelCase 						=       UnCLIPScheduler(
              variance_type="fixed_small_log"				,prediction_type="epsilon"				,num_train_timesteps=1000				,)

          _UpperCAmelCase 						=       {
              "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				,lowerCamelCase_				,lowerCamelCase_=0		):



          '''simple docstring'''

          if str(lowerCamelCase_		).startswith("mps"		):
             _UpperCAmelCase 						=       torch.manual_seed(lowerCamelCase_		)
          else:
             _UpperCAmelCase 						=       torch.Generator(device=lowerCamelCase_		).manual_seed(lowerCamelCase_		)
          _UpperCAmelCase 						=       {
              "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		):



          '''simple docstring'''

          _UpperCAmelCase 						=       "cpu"

          _UpperCAmelCase 						=       self.get_dummy_components()

          _UpperCAmelCase 						=       self.pipeline_class(**lowerCamelCase_		)
          _UpperCAmelCase 						=       pipe.to(lowerCamelCase_		)

          pipe.set_progress_bar_config(disable=lowerCamelCase_		)

          _UpperCAmelCase 						=       pipe(**self.get_dummy_inputs(lowerCamelCase_		)		)
          _UpperCAmelCase 						=       output.images

          _UpperCAmelCase 						=       pipe(
              **self.get_dummy_inputs(lowerCamelCase_		)				,return_dict=lowerCamelCase_				,)[0]

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

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

          _UpperCAmelCase 						=       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		):



          '''simple docstring'''

          _UpperCAmelCase 						=       torch.device("cpu"		)






          class      __a      :
                 _lowerCamelCase				:				Optional[Any]   			=      1



          _UpperCAmelCase 						=       self.get_dummy_components()

          _UpperCAmelCase 						=       self.pipeline_class(**lowerCamelCase_		)
          _UpperCAmelCase 						=       pipe.to(lowerCamelCase_		)

          _UpperCAmelCase 						=       components["prior"]
          _UpperCAmelCase 						=       components["decoder"]
          _UpperCAmelCase 						=       components["super_res_first"]
          _UpperCAmelCase 						=       components["tokenizer"]
          _UpperCAmelCase 						=       components["text_encoder"]

          _UpperCAmelCase 						=       torch.Generator(device=lowerCamelCase_		).manual_seed(0		)
          _UpperCAmelCase 						=       prior.dtype
          _UpperCAmelCase 						=       1

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

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

          pipe.set_progress_bar_config(disable=lowerCamelCase_		)

          _UpperCAmelCase 						=       "this is a prompt example"

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

          _UpperCAmelCase 						=       tokenizer(
              lowerCamelCase_				,padding="max_length"				,max_length=tokenizer.model_max_length				,return_tensors="pt"				,)
          _UpperCAmelCase 						=       text_encoder(text_inputs.input_ids		)
          _UpperCAmelCase 						=       text_inputs.attention_mask

          _UpperCAmelCase 						=       torch.Generator(device=lowerCamelCase_		).manual_seed(0		)
          _UpperCAmelCase 						=       pipe(
              generator=lowerCamelCase_				,prior_num_inference_steps=2				,decoder_num_inference_steps=2				,super_res_num_inference_steps=2				,prior_latents=lowerCamelCase_				,decoder_latents=lowerCamelCase_				,super_res_latents=lowerCamelCase_				,text_model_output=lowerCamelCase_				,text_attention_mask=lowerCamelCase_				,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		):



          '''simple docstring'''

          _UpperCAmelCase 						=       torch_device == "cpu"

          self._test_attention_slicing_forward_pass(test_max_difference=lowerCamelCase_				,expected_max_diff=0.0_1		)







       @skip_mps
       def  _snake_case      (       self		):



          '''simple docstring'''

          _UpperCAmelCase 						=       torch_device == "cpu"
          _UpperCAmelCase 						=       True
          _UpperCAmelCase 						=       [
              "prior_num_inference_steps",
              "decoder_num_inference_steps",
              "super_res_num_inference_steps",
          ]

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







       def  _snake_case      (       self		):



          '''simple docstring'''

          _UpperCAmelCase 						=       [
              "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 						=       [2, 3]
             self._test_inference_batch_consistent(
                 batch_sizes=lowerCamelCase_				,additional_params_copy_to_batched_inputs=lowerCamelCase_				,)
          else:
             self._test_inference_batch_consistent(
                 additional_params_copy_to_batched_inputs=lowerCamelCase_		)







       @skip_mps
       def  _snake_case      (       self		):



          '''simple docstring'''

          return super().test_dict_tuple_outputs_equivalent()







       @skip_mps
       def  _snake_case      (       self		):



          '''simple docstring'''

          return super().test_save_load_local()







       @skip_mps
       def  _snake_case      (       self		):



          '''simple docstring'''

          return super().test_save_load_optional_components()






@nightly
class      __a      (       unittest.TestCase      ):







       def  _snake_case      (       self		):



          '''simple docstring'''

          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







       def  _snake_case      (       self		):



          '''simple docstring'''

          _UpperCAmelCase 						=       load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/unclip/karlo_v1_alpha_horse_cpu.npy"		)

          _UpperCAmelCase 						=       UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"		)
          pipeline.set_progress_bar_config(disable=lowerCamelCase_		)

          _UpperCAmelCase 						=       torch.manual_seed(0		)
          _UpperCAmelCase 						=       pipeline(
              "horse"				,num_images_per_prompt=1				,generator=lowerCamelCase_				,output_type="np"				,)

          _UpperCAmelCase 						=       output.images[0]

          assert image.shape == (256, 256, 3)
          assert np.abs(expected_image - image		).max() < 1e-1






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







       def  _snake_case      (       self		):



          '''simple docstring'''

          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







       def  _snake_case      (       self		):



          '''simple docstring'''

          _UpperCAmelCase 						=       load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/unclip/karlo_v1_alpha_horse_fp16.npy"		)

          _UpperCAmelCase 						=       UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"				,torch_dtype=torch.floataa		)
          _UpperCAmelCase 						=       pipeline.to(lowerCamelCase_		)
          pipeline.set_progress_bar_config(disable=lowerCamelCase_		)

          _UpperCAmelCase 						=       torch.Generator(device="cpu"		).manual_seed(0		)
          _UpperCAmelCase 						=       pipeline(
              "horse"				,generator=lowerCamelCase_				,output_type="np"				,)

          _UpperCAmelCase 						=       output.images[0]

          assert image.shape == (256, 256, 3)

          assert_mean_pixel_difference(lowerCamelCase_				,lowerCamelCase_		)







       def  _snake_case      (       self		):



          '''simple docstring'''

          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()

          _UpperCAmelCase 						=       UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"				,torch_dtype=torch.floataa		)
          _UpperCAmelCase 						=       pipe.to(lowerCamelCase_		)
          pipe.set_progress_bar_config(disable=lowerCamelCase_		)
          pipe.enable_attention_slicing()
          pipe.enable_sequential_cpu_offload()

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

