



import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference







class 		snake_case    (						__UpperCamelCase       ,unittest.TestCase	):



   '''simple docstring'''






   snake_case_       :			Any        =		ShapEImgaImgPipeline
   snake_case_       :			Optional[Any]        =		["""image"""]
   snake_case_       :			Any        =		["""image"""]
   snake_case_       :			List[Any]        =		[
       """num_images_per_prompt""",
       """num_inference_steps""",
       """generator""",
       """latents""",
       """guidance_scale""",
       """frame_size""",
       """output_type""",
       """return_dict""",
   ]
   snake_case_       :			Tuple        =		False

   @property
   def   UpperCamelCase_	(       self				:      Union[str, Any])					->					List[str]:

    """simple docstring"""







    return 32

   @property
   def   UpperCamelCase_	(       self				:      List[Any])					->					Union[str, Any]:

    """simple docstring"""







    return 32

   @property
   def   UpperCamelCase_	(       self				:      Optional[int])					->					Tuple:

    """simple docstring"""







    return self.time_input_dim * 4

   @property
   def   UpperCamelCase_	(       self				:      Dict)					->					List[str]:

    """simple docstring"""







    return 8

   @property
   def   UpperCamelCase_	(       self				:      Union[str, Any])					->					int:

    """simple docstring"""







    torch.manual_seed(0)
    _snake_case     :	Tuple											=       CLIPVisionConfig(
        hidden_size=self.text_embedder_hidden_size					,		image_size=64					,		projection_dim=self.text_embedder_hidden_size					,		intermediate_size=37					,		num_attention_heads=4					,		num_channels=3					,		num_hidden_layers=5					,		patch_size=1					,		)

    _snake_case     :	str											=       CLIPVisionModel(__UpperCAmelCase)
    return model

   @property
   def   UpperCamelCase_	(       self				:      int)					->					Optional[int]:

    """simple docstring"""







    _snake_case     :	Dict											=       CLIPImageProcessor(
        crop_size=224					,		do_center_crop=__UpperCAmelCase					,		do_normalize=__UpperCAmelCase					,		do_resize=__UpperCAmelCase					,		image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]					,		image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]					,		resample=3					,		size=224					,		)

    return image_processor

   @property
   def   UpperCamelCase_	(       self				:      Union[str, Any])					->					Tuple:

    """simple docstring"""







    torch.manual_seed(0)

    _snake_case     :	str											=       {
        "num_attention_heads": 2,
        "attention_head_dim": 16,
        "embedding_dim": self.time_input_dim,
        "num_embeddings": 32,
        "embedding_proj_dim": self.text_embedder_hidden_size,
        "time_embed_dim": self.time_embed_dim,
        "num_layers": 1,
        "clip_embed_dim": self.time_input_dim * 2,
        "additional_embeddings": 0,
        "time_embed_act_fn": "gelu",
        "norm_in_type": "layer",
        "embedding_proj_norm_type": "layer",
        "encoder_hid_proj_type": None,
        "added_emb_type": None,
    }

    _snake_case     :	List[Any]											=       PriorTransformer(**__UpperCAmelCase)
    return model

   @property
   def   UpperCamelCase_	(       self				:      Dict)					->					int:

    """simple docstring"""







    torch.manual_seed(0)

    _snake_case     :	Any											=       {
        "param_shapes": (
            (self.renderer_dim, 93),
            (self.renderer_dim, 8),
            (self.renderer_dim, 8),
            (self.renderer_dim, 8),
        ),
        "d_latent": self.time_input_dim,
        "d_hidden": self.renderer_dim,
        "n_output": 12,
        "background": (
            0.1,
            0.1,
            0.1,
        ),
    }
    _snake_case     :	Union[str, Any]											=       ShapERenderer(**__UpperCAmelCase)
    return model

   def   UpperCamelCase_	(       self				:      str)					->					Tuple:

    """simple docstring"""







    _snake_case     :	List[str]											=       self.dummy_prior
    _snake_case     :	Tuple											=       self.dummy_image_encoder
    _snake_case     :	Union[str, Any]											=       self.dummy_image_processor
    _snake_case     :	Optional[Any]											=       self.dummy_renderer

    _snake_case     :	Optional[int]											=       HeunDiscreteScheduler(
        beta_schedule="""exp"""					,		num_train_timesteps=1024					,		prediction_type="""sample"""					,		use_karras_sigmas=__UpperCAmelCase					,		clip_sample=__UpperCAmelCase					,		clip_sample_range=1.0					,		)
    _snake_case     :	str											=       {
        "prior": prior,
        "image_encoder": image_encoder,
        "image_processor": image_processor,
        "renderer": renderer,
        "scheduler": scheduler,
    }

    return components

   def   UpperCamelCase_	(       self				:      Tuple					,		lowerCAmelCase				:      Union[str, Any]					,		lowerCAmelCase				:      Any=0)					->					str:

    """simple docstring"""







    _snake_case     :	int											=       floats_tensor((1, 3, 64, 64)					,		rng=random.Random(__UpperCAmelCase)).to(__UpperCAmelCase)

    if str(__UpperCAmelCase).startswith("""mps"""):
     _snake_case     :	Any											=       torch.manual_seed(__UpperCAmelCase)
    else:
     _snake_case     :	Any											=       torch.Generator(device=__UpperCAmelCase).manual_seed(__UpperCAmelCase)
    _snake_case     :	Optional[Any]											=       {
        "image": input_image,
        "generator": generator,
        "num_inference_steps": 1,
        "frame_size": 32,
        "output_type": "np",
    }
    return inputs

   def   UpperCamelCase_	(       self				:      Optional[int])					->					List[Any]:

    """simple docstring"""







    _snake_case     :	List[str]											=       "cpu"

    _snake_case     :	Optional[int]											=       self.get_dummy_components()

    _snake_case     :	Dict											=       self.pipeline_class(**__UpperCAmelCase)
    _snake_case     :	Union[str, Any]											=       pipe.to(__UpperCAmelCase)

    pipe.set_progress_bar_config(disable=__UpperCAmelCase)

    _snake_case     :	List[Any]											=       pipe(**self.get_dummy_inputs(__UpperCAmelCase))
    _snake_case     :	int											=       output.images[0]
    _snake_case     :	Union[str, Any]											=       image[0, -3:, -3:, -1]

    assert image.shape == (20, 32, 32, 3)

    _snake_case     :	List[str]											=       np.array(
        [
            0.00_039_216,
            0.00_039_216,
            0.00_039_216,
            0.00_039_216,
            0.00_039_216,
            0.00_039_216,
            0.00_039_216,
            0.00_039_216,
            0.00_039_216,
        ])

    assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2

   def   UpperCamelCase_	(       self				:      Optional[int])					->					int:

    """simple docstring"""







    self._test_inference_batch_consistent(batch_sizes=[1, 2])

   def   UpperCamelCase_	(       self				:      Any)					->					Optional[int]:

    """simple docstring"""







    _snake_case     :	Union[str, Any]											=       torch_device == "cpu"
    _snake_case     :	Optional[Any]											=       True
    self._test_inference_batch_single_identical(
        batch_size=2					,		test_max_difference=__UpperCAmelCase					,		relax_max_difference=__UpperCAmelCase					,		)


   def   UpperCamelCase_	(       self				:      Any)					->					List[Any]:

    """simple docstring"""







    _snake_case     :	Optional[int]											=       self.get_dummy_components()
    _snake_case     :	Dict											=       self.pipeline_class(**__UpperCAmelCase)
    _snake_case     :	List[str]											=       pipe.to(__UpperCAmelCase)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase)

    _snake_case     :	str											=       1
    _snake_case     :	Dict											=       2

    _snake_case     :	Union[str, Any]											=       self.get_dummy_inputs(__UpperCAmelCase)

    for key in inputs.keys():
     if key in self.batch_params:
      _snake_case     :	str											=       batch_size * [inputs[key]]

    _snake_case     :	Dict											=       pipe(**__UpperCAmelCase					,		num_images_per_prompt=__UpperCAmelCase)[0]

    assert images.shape[0] == batch_size * num_images_per_prompt







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



   '''simple docstring'''






   def   UpperCamelCase_	(       self				:      List[Any])					->					str:

    """simple docstring"""







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


   def   UpperCamelCase_	(       self				:      Tuple)					->					Union[str, Any]:

    """simple docstring"""







    _snake_case     :	Tuple											=       load_image(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png""")
    _snake_case     :	Optional[Any]											=       load_numpy(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/shap_e/test_shap_e_img2img_out.npy""")
    _snake_case     :	Union[str, Any]											=       ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img""")
    _snake_case     :	Dict											=       pipe.to(__UpperCAmelCase)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase)

    _snake_case     :	int											=       torch.Generator(device=__UpperCAmelCase).manual_seed(0)

    _snake_case     :	Tuple											=       pipe(
        __UpperCAmelCase					,		generator=__UpperCAmelCase					,		guidance_scale=3.0					,		num_inference_steps=64					,		frame_size=64					,		output_type="""np"""					,		).images[0]

    assert images.shape == (20, 64, 64, 3)

    assert_mean_pixel_difference(__UpperCAmelCase					,		__UpperCAmelCase)








from abc import ABC, abstractmethod
from argparse import ArgumentParser




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						@staticmethod
						@abstractmethod
						def        UpperCAmelCase__					(__UpperCAmelCase:					ArgumentParser   )    ->     Tuple:




											'''simple docstring'''



											raise NotImplementedError()



						@abstractmethod
						def        UpperCAmelCase__					(self:					List[str]   )    ->     List[str]:




											'''simple docstring'''



											raise NotImplementedError()





