

"""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       _lowerCAmelCase						(			lowercase  ,unittest.TestCase     ):







 """simple docstring"""







 __UpperCAmelCase			:      Optional[int]     	=			UnCLIPPipeline
 __UpperCAmelCase			:      int     	=			TEXT_TO_IMAGE_PARAMS - {
     "negative_prompt",
     "height",
     "width",
     "negative_prompt_embeds",
     "guidance_scale",
     "prompt_embeds",
     "cross_attention_kwargs",
 }
 __UpperCAmelCase			:      Optional[Any]     	=			TEXT_TO_IMAGE_BATCH_PARAMS
 __UpperCAmelCase			:      List[Any]     	=			[
     "generator",
     "return_dict",
     "prior_num_inference_steps",
     "decoder_num_inference_steps",
     "super_res_num_inference_steps",
 ]
 __UpperCAmelCase			:      Optional[int]     	=			False


 @property
 def       _lowercase				(    self							:    str			):
    return 3_2


 @property
 def       _lowercase				(    self							:    Tuple			):
    return 3_2


 @property
 def       _lowercase				(    self							:    Optional[Any]			):
    return self.time_input_dim


 @property
 def       _lowercase				(    self							:    Any			):
    return self.time_input_dim * 4


 @property
 def       _lowercase				(    self							:    List[Any]			):
    return 1_0_0


 @property
 def       _lowercase				(    self							:    Dict			):
    __lowercase			=		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)
    return tokenizer


 @property
 def       _lowercase				(    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       _lowercase				(    self							:    Dict			):
    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       _lowercase				(    self							:    Any			):
    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       _lowercase				(    self							:    Optional[int]			):
    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       _lowercase				(    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       _lowercase				(    self							:    Any			):
    torch.manual_seed(0			)

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


 @property
 def       _lowercase				(    self							:    List[Any]			):
    # 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       _lowercase				(    self							:    Optional[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       _lowercase				(    self							:    Optional[int],      UpperCAmelCase__							:    Dict,      UpperCAmelCase__							:    Dict=0			):
    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       _lowercase				(    self							:    Optional[int]			):
    __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(
        [
            0.9_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       _lowercase				(    self							:    Tuple			):
    __lowercase			=		torch.device("cpu"			)




    class       _lowerCAmelCase						:







     """simple docstring"""







     __UpperCAmelCase			:      Dict     	=			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       _lowercase				(    self							:    List[str]			):
    __lowercase			=		torch_device == "cpu"

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


 @skip_mps
 def       _lowercase				(    self							:    str			):
    __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       _lowercase				(    self							:    List[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       _lowercase				(    self							:    List[str]			):
    return super().test_dict_tuple_outputs_equivalent()


 @skip_mps
 def       _lowercase				(    self							:    Optional[Any]			):
    return super().test_save_load_local()



 @skip_mps
 def       _lowercase				(    self							:    int			):
    return super().test_save_load_optional_components()







@nightly
class       _lowerCAmelCase						(			unittest.TestCase     ):







 """simple docstring"""







 def       _lowercase				(    self							:    Tuple			):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()



 def       _lowercase				(    self							:    Dict			):
    __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     ):







 """simple docstring"""







 def       _lowercase				(    self							:    Tuple			):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()


 def       _lowercase				(    self							:    Any			):
    __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       _lowercase				(    self							:    Optional[int]			):
    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







"""simple docstring"""





def 						_A						(						UpperCamelCase_  : list,    UpperCamelCase_  : list,    UpperCamelCase_  : list,    UpperCamelCase_  : int,    UpperCamelCase_  : int)			->		None:
   '''simple docstring'''


   if target == 0:
      answer.append(path.copy())
   else:
      for index in range(UpperCamelCase_,    len(UpperCamelCase_)):
         if target >= candidates[index]:
            path.append(candidates[index])
            backtrack(UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_,    target - candidates[index],    UpperCamelCase_)
            path.pop(len(UpperCamelCase_) - 1)




def 						_A						(						UpperCamelCase_  : list,    UpperCamelCase_  : int)			->		list:
   '''simple docstring'''


   __lowercase			=		[]  # type: list[int]
   __lowercase			=		[]  # type: list[int]
   backtrack(UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_,    0)
   return answer




def 						_A						(						)			->		None:
   '''simple docstring'''


   print(combination_sum([-8, 2.3, 0],    1))


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()




