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						):
  lowercase_			=     ShapEImgaImgPipeline
  lowercase_			=     ["image"]
  lowercase_			=     ["image"]
  lowercase_			=     [
      "num_images_per_prompt",
      "num_inference_steps",
      "generator",
      "latents",
      "guidance_scale",
      "frame_size",
      "output_type",
      "return_dict",
  ]
  lowercase_			=     False




  @property
  def 	__lowercase(      self     :	Any				)->       List[str]:




       """simple docstring"""
       return 32




  @property
  def 	__lowercase(      self     :	str				)->       str:




       """simple docstring"""
       return 32




  @property
  def 	__lowercase(      self     :	Optional[Any]				)->       Optional[Any]:




       """simple docstring"""
       return self.time_input_dim * 4




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




       """simple docstring"""
       return 8




  @property
  def 	__lowercase(      self     :	Tuple				)->       Any:




       """simple docstring"""
       torch.manual_seed(0				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		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     , )

       SCREAMING_SNAKE_CASE__				:				List[str]			=		CLIPVisionModel(lowerCamelCase__				)
       return model




  @property
  def 	__lowercase(      self     :	Optional[Any]				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		CLIPImageProcessor(
           crop_size=224     , do_center_crop=lowerCamelCase__     , do_normalize=lowerCamelCase__     , do_resize=lowerCamelCase__     , image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]     , image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]     , resample=3     , size=224     , )

       return image_processor




  @property
  def 	__lowercase(      self     :	Optional[Any]				)->       List[str]:




       """simple docstring"""
       torch.manual_seed(0				)

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		{
           "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,
       }

       SCREAMING_SNAKE_CASE__				:				Tuple			=		PriorTransformer(**lowerCamelCase__				)
       return model




  @property
  def 	__lowercase(      self     :	List[Any]				)->       Optional[Any]:




       """simple docstring"""
       torch.manual_seed(0				)

       SCREAMING_SNAKE_CASE__				:				Union[str, 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,
           ),
       }
       SCREAMING_SNAKE_CASE__				:				Dict			=		ShapERenderer(**lowerCamelCase__				)
       return model




  def 	__lowercase(      self     :	int				)->       List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		self.dummy_prior
       SCREAMING_SNAKE_CASE__				:				str			=		self.dummy_image_encoder
       SCREAMING_SNAKE_CASE__				:				Any			=		self.dummy_image_processor
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.dummy_renderer

       SCREAMING_SNAKE_CASE__				:				str			=		HeunDiscreteScheduler(
           beta_schedule='exp'     , num_train_timesteps=1024     , prediction_type='sample'     , use_karras_sigmas=lowerCamelCase__     , clip_sample=lowerCamelCase__     , clip_sample_range=1.0     , )
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		{
           "prior": prior,
           "image_encoder": image_encoder,
           "image_processor": image_processor,
           "renderer": renderer,
           "scheduler": scheduler,
       }

       return components




  def 	__lowercase(      self     :	List[str]     , a_     :	Optional[Any]     , a_     :	Optional[int]=0				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		floats_tensor((1, 3, 64, 64)     , rng=random.Random(lowerCamelCase__				)				).to(lowerCamelCase__				)

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




  def 	__lowercase(      self     :	List[str]				)->       List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		"cpu"

       SCREAMING_SNAKE_CASE__				:				int			=		self.get_dummy_components()

       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.pipeline_class(**lowerCamelCase__				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		pipe.to(lowerCamelCase__				)

       pipe.set_progress_bar_config(disable=lowerCamelCase__				)

       SCREAMING_SNAKE_CASE__				:				Tuple			=		pipe(**self.get_dummy_inputs(lowerCamelCase__				)				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		output.images[0]
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		image[0, -3:, -3:, -1]

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

       SCREAMING_SNAKE_CASE__				:				Dict			=		np.array(
           [
               0.0003_9216,
               0.0003_9216,
               0.0003_9216,
               0.0003_9216,
               0.0003_9216,
               0.0003_9216,
               0.0003_9216,
               0.0003_9216,
               0.0003_9216,
           ]				)

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




  def 	__lowercase(      self     :	Optional[Any]				)->       str:




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




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		torch_device == "cpu"
       SCREAMING_SNAKE_CASE__				:				Dict			=		True
       self._test_inference_batch_single_identical(
           batch_size=2     , test_max_difference=lowerCamelCase__     , relax_max_difference=lowerCamelCase__     , )




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.get_dummy_components()
       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.pipeline_class(**lowerCamelCase__				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		pipe.to(lowerCamelCase__				)
       pipe.set_progress_bar_config(disable=lowerCamelCase__				)

       SCREAMING_SNAKE_CASE__				:				Tuple			=		1
       SCREAMING_SNAKE_CASE__				:				Tuple			=		2

       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.get_dummy_inputs(lowerCamelCase__				)

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

       SCREAMING_SNAKE_CASE__				:				List[str]			=		pipe(**lowerCamelCase__     , num_images_per_prompt=lowerCamelCase__				)[0]

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




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




  def 	__lowercase(      self     :	Union[str, Any]				)->       Optional[int]:




       """simple docstring"""
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png'				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/shap_e/test_shap_e_img2img_out.npy'				)
       SCREAMING_SNAKE_CASE__				:				str			=		ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img'				)
       SCREAMING_SNAKE_CASE__				:				str			=		pipe.to(lowerCamelCase__				)
       pipe.set_progress_bar_config(disable=lowerCamelCase__				)

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.Generator(device=lowerCamelCase__				).manual_seed(0				)

       SCREAMING_SNAKE_CASE__				:				Tuple			=		pipe(
           lowerCamelCase__     , generator=lowerCamelCase__     , 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(lowerCamelCase__     , lowerCamelCase__				)




class       snake_case					(      UpperCamelCase_						):
  pass




class       snake_case					(      UpperCamelCase_						):
  pass




class       snake_case					:




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		[
           [],
           [],
           [],
       ]




  def 	__lowercase(      self     :	int     , a_     :	int     , a_     :	int				)->       None:




       """simple docstring"""
       try:
            if len(self.queues[priority]				) >= 100:
                 raise OverflowError('Maximum queue size is 100'				)
            self.queues[priority].append(a_				)
       except IndexError:
            raise ValueError('Valid priorities are 0, 1, and 2'				)




  def 	__lowercase(      self     :	int				)->       int:




       """simple docstring"""
       for queue in self.queues:
            if queue:
                 return queue.pop(0				)
       raise UnderFlowError('All queues are empty'				)




  def __str__(      self     :	Any				)->       str:




       """simple docstring"""
       return "\n".join(F'''Priority {i}: {q}''' for i, q in enumerate(self.queues				)				)




class       snake_case					:




  def __init__(      self     :	Union[str, Any]				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		[]




  def 	__lowercase(      self     :	List[str]     , a_     :	int				)->       None:




       """simple docstring"""
       if len(self.queue				) == 100:
            raise OverFlowError('Maximum queue size is 100'				)
       self.queue.append(a_				)




  def 	__lowercase(      self     :	int				)->       int:




       """simple docstring"""
       if not self.queue:
            raise UnderFlowError('The queue is empty'				)
       else:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		min(self.queue				)
            self.queue.remove(a_				)
            return data




  def __str__(      self     :	List[str]				)->       str:




       """simple docstring"""
       return str(self.queue				)







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		FixedPriorityQueue()
     fpq.enqueue(0     ,  10 )
     fpq.enqueue(1     ,  70 )
     fpq.enqueue(0     ,  1_00 )
     fpq.enqueue(2     ,  1 )
     fpq.enqueue(2     ,  5 )
     fpq.enqueue(1     ,  7 )
     fpq.enqueue(2     ,  4 )
     fpq.enqueue(1     ,  64 )
     fpq.enqueue(0     ,  1_28 )
     print(lowercase__ )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(lowercase__ )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Dict			=		ElementPriorityQueue()
     epq.enqueue(10 )
     epq.enqueue(70 )
     epq.enqueue(1_00 )
     epq.enqueue(1 )
     epq.enqueue(5 )
     epq.enqueue(7 )
     epq.enqueue(4 )
     epq.enqueue(64 )
     epq.enqueue(1_28 )
     print(lowercase__ )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(lowercase__ )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )


if __name__ == "__main__":
      fixed_priority_queue()
      element_priority_queue()



