

'''simple docstring'''

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import 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    ):
   _A							:  Dict      =       ShapEPipeline
   _A							:  List[str]      =       ['prompt']
   _A							:  Dict      =       ['prompt']
   _A							:  Dict      =       [
       'num_images_per_prompt',
       'num_inference_steps',
       'generator',
       'latents',
       'guidance_scale',
       'frame_size',
       'output_type',
       'return_dict',
   ]
   _A							:  Dict      =       False
   @property
   def      __UpperCamelCase    (			self      :   Any				):
     return 32
   @property
   def      __UpperCamelCase    (			self      :   Tuple				):
     return 32
   @property
   def      __UpperCamelCase    (			self      :   Tuple				):
     return self.time_input_dim * 4
   @property
   def      __UpperCamelCase    (			self      :   Optional[int]				):
     return 8
   @property
   def      __UpperCamelCase    (			self      :   List[str]				):
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)
     return tokenizer
   @property
   def      __UpperCamelCase    (			self      :   Any				):
     torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE:Optional[Any]       =       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=1_000  ,)
     return CLIPTextModelWithProjection(lowerCAmelCase_				)
   @property
   def      __UpperCamelCase    (			self      :   Tuple				):
     torch.manual_seed(0				)

     SCREAMING_SNAKE_CASE:Dict       =       {
         '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',
         'encoder_hid_proj_type': None,
         'added_emb_type': None,
     }

     SCREAMING_SNAKE_CASE:Union[str, Any]       =       PriorTransformer(**lowerCAmelCase_				)
     return model
   @property
   def      __UpperCamelCase    (			self      :   Dict				):
     torch.manual_seed(0				)

     SCREAMING_SNAKE_CASE:List[str]       =       {
         '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:List[Any]       =       ShapERenderer(**lowerCAmelCase_				)
     return model
   def      __UpperCamelCase    (			self      :   str				):
     SCREAMING_SNAKE_CASE:List[Any]       =       self.dummy_prior
     SCREAMING_SNAKE_CASE:Optional[Any]       =       self.dummy_text_encoder
     SCREAMING_SNAKE_CASE:Dict       =       self.dummy_tokenizer
     SCREAMING_SNAKE_CASE:List[Any]       =       self.dummy_renderer

     SCREAMING_SNAKE_CASE:List[str]       =       HeunDiscreteScheduler(
         beta_schedule="exp"  ,num_train_timesteps=1_024  ,prediction_type="sample"  ,use_karras_sigmas=lowerCAmelCase_  ,clip_sample=lowerCAmelCase_  ,clip_sample_range=1.0  ,)
     SCREAMING_SNAKE_CASE:List[str]       =       {
         'prior': prior,
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'renderer': renderer,
         'scheduler': scheduler,
     }

     return components
   def      __UpperCamelCase    (			self      :   Tuple  ,SCREAMING_SNAKE_CASE__      :   int  ,SCREAMING_SNAKE_CASE__      :   List[Any]=0				):
     if str(lowerCAmelCase_				).startswith("mps"				):
       SCREAMING_SNAKE_CASE:Any       =       torch.manual_seed(lowerCAmelCase_				)
     else:
       SCREAMING_SNAKE_CASE:Union[str, Any]       =       torch.Generator(device=lowerCAmelCase_				).manual_seed(lowerCAmelCase_				)
     SCREAMING_SNAKE_CASE:str       =       {
         'prompt': 'horse',
         'generator': generator,
         'num_inference_steps': 1,
         'frame_size': 32,
         'output_type': 'np',
     }
     return inputs
   def      __UpperCamelCase    (			self      :   int				):
     SCREAMING_SNAKE_CASE:Dict       =       'cpu'

     SCREAMING_SNAKE_CASE:Tuple       =       self.get_dummy_components()

     SCREAMING_SNAKE_CASE:Tuple       =       self.pipeline_class(**lowerCAmelCase_				)
     SCREAMING_SNAKE_CASE:List[str]       =       pipe.to(lowerCAmelCase_				)

     pipe.set_progress_bar_config(disable=lowerCAmelCase_				)

     SCREAMING_SNAKE_CASE:Any       =       pipe(**self.get_dummy_inputs(lowerCAmelCase_				)				)
     SCREAMING_SNAKE_CASE:List[Any]       =       output.images[0]
     SCREAMING_SNAKE_CASE:str       =       image[0, -3:, -3:, -1]

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

     SCREAMING_SNAKE_CASE:int       =       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      :   str				):
     # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
     self._test_inference_batch_consistent(batch_sizes=[1, 2]				)
   def      __UpperCamelCase    (			self      :   Optional[Any]				):
     SCREAMING_SNAKE_CASE:Dict       =       torch_device == 'cpu'
     SCREAMING_SNAKE_CASE:str       =       True

     self._test_inference_batch_single_identical(
         batch_size=2  ,test_max_difference=lowerCAmelCase_  ,relax_max_difference=lowerCAmelCase_  ,)







   def      __UpperCamelCase    (			self      :   str				):
     SCREAMING_SNAKE_CASE:Optional[Any]       =       self.get_dummy_components()
     SCREAMING_SNAKE_CASE:List[Any]       =       self.pipeline_class(**lowerCAmelCase_				)
     SCREAMING_SNAKE_CASE:int       =       pipe.to(lowerCAmelCase_				)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_				)

     SCREAMING_SNAKE_CASE:Union[str, Any]       =       1
     SCREAMING_SNAKE_CASE:Optional[int]       =       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:List[str]       =       batch_size * [inputs[key]]

     SCREAMING_SNAKE_CASE:Tuple       =       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      __UpperCamelCase    (			self      :   Optional[Any]				):
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







   def      __UpperCamelCase    (			self      :   int				):
     SCREAMING_SNAKE_CASE:str       =       load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/shap_e/test_shap_e_np_out.npy"				)
     SCREAMING_SNAKE_CASE:str       =       ShapEPipeline.from_pretrained("openai/shap-e"				)
     SCREAMING_SNAKE_CASE:Optional[int]       =       pipe.to(lowerCAmelCase_				)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_				)

     SCREAMING_SNAKE_CASE:int       =       torch.Generator(device=lowerCAmelCase_				).manual_seed(0				)

     SCREAMING_SNAKE_CASE:int       =       pipe(
         "a shark"  ,generator=lowerCAmelCase_  ,guidance_scale=15.0  ,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_				)














"""simple docstring"""
class _lowerCAmelCase						:







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_=None   ,				lowerCAmelCase_=None							)		->							int:
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    data
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    previous
										_SCREAMING_SNAKE_CASE   :		Any       		=    next_node







						def __str__(    self							)		->							str:
										return F"""{self.data}"""







						def 			A      (    self							)		->							int:
										return self.data







						def 			A      (    self							)		->							Dict:
										return self.next







						def 			A      (    self							)		->							Union[str, Any]:
										return self.previous







class _lowerCAmelCase						:







						def __init__(    self   ,				lowerCAmelCase_							)		->							List[str]:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    head







						def __iter__(    self							)		->							Any:
										return self







						def 			A      (    self							)		->							Dict:
										if not self.current:
														raise StopIteration
										else:
														_SCREAMING_SNAKE_CASE   :		Any       		=    self.current.get_data()
														_SCREAMING_SNAKE_CASE   :		Dict       		=    self.current.get_next()
														return value







class _lowerCAmelCase						:







						def __init__(    self							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    None  # First node in list
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    None  # Last node in list







						def __str__(    self							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		str       		=    self.head
										_SCREAMING_SNAKE_CASE   :		int       		=    []
										while current is not None:
														nodes.append(current.get_data()							)
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    current.get_next()
										return " ".join(str(lowerCAmelCase_							) for node in nodes							)







						def __contains__(    self   ,				lowerCAmelCase_							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		Any       		=    self.head
										while current:
														if current.get_data() == value:
																		return True
														_SCREAMING_SNAKE_CASE   :		int       		=    current.get_next()
										return False







						def __iter__(    self							)		->							str:
										return LinkedListIterator(self.head							)







						def 			A      (    self							)		->							Dict:
										if self.head:
														return self.head.get_data()
										return None







						def 			A      (    self							)		->							List[Any]:
										if self.tail:
														return self.tail.get_data()
										return None







						def 			A      (    self   ,				lowerCAmelCase_							)		->							None:
										if self.head is None:
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    node
														_SCREAMING_SNAKE_CASE   :		Dict       		=    node
										else:
														self.insert_before_node(self.head   ,				lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							None:
										if self.head is None:
														self.set_head(lowerCAmelCase_							)
										else:
														self.insert_after_node(self.tail   ,				lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		Any       		=    Node(lowerCAmelCase_							)
										if self.head is None:
														self.set_head(lowerCAmelCase_							)
										else:
														self.set_tail(lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    node
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    node.previous

										if node.get_previous() is None:
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    node_to_insert
										else:
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    node_to_insert

										_SCREAMING_SNAKE_CASE   :		Any       		=    node_to_insert







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    node
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    node.next

										if node.get_next() is None:
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    node_to_insert
										else:
														_SCREAMING_SNAKE_CASE   :		Any       		=    node_to_insert

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    node_to_insert







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    1
										_SCREAMING_SNAKE_CASE   :		str       		=    Node(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.head
										while node:
														if current_position == position:
																		self.insert_before_node(lowerCAmelCase_   ,				lowerCAmelCase_							)
																		return
														current_position += 1
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    node.next
										self.insert_after_node(self.tail   ,				lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							Node:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.head
										while node:
														if node.get_data() == item:
																		return node
														_SCREAMING_SNAKE_CASE   :		Dict       		=    node.get_next()
										raise Exception('Node not found'							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							int:
										if (node := self.get_node(lowerCAmelCase_							)) is not None:
														if node == self.head:
																		_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.head.get_next()

														if node == self.tail:
																		_SCREAMING_SNAKE_CASE   :		int       		=    self.tail.get_previous()

														self.remove_node_pointers(lowerCAmelCase_							)







						@staticmethod
						def 			A      (    lowerCAmelCase_							)		->							None:
										if node.get_next():
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    node.previous

										if node.get_previous():
														_SCREAMING_SNAKE_CASE   :		str       		=    node.next

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    None
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    None







						def 			A      (    self							)		->							List[str]:
										return self.head is None







def 				lowercase__		(					):
				pass


if __name__ == "__main__":
			import doctest

			doctest.testmod()

