


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 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						ShapEPipeline
						UpperCAmelCase             =						["prompt"]
						UpperCAmelCase             =						["prompt"]
						UpperCAmelCase             =						[
						    "num_images_per_prompt",
						    "num_inference_steps",
						    "generator",
						    "latents",
						    "guidance_scale",
						    "frame_size",
						    "output_type",
						    "return_dict",
						]
						UpperCAmelCase             =						False




						@property
						def 							__UpperCamelCase				(       self					:      Tuple  )							->     str:


										"""simple docstring"""





										return 32




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


										"""simple docstring"""





										return 32




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


										"""simple docstring"""





										return self.time_input_dim * 4




						@property
						def 							__UpperCamelCase				(       self					:      List[str]  )							->     Tuple:


										"""simple docstring"""





										return 8




						@property
						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     Dict:


										"""simple docstring"""





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




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


										"""simple docstring"""





										torch.manual_seed(0  )
										_SCREAMING_SNAKE_CASE			  =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(_a  )




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


										"""simple docstring"""





										torch.manual_seed(0  )

										_SCREAMING_SNAKE_CASE			  ={
										    '''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			  =PriorTransformer(**_a  )
										return model




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


										"""simple docstring"""





										torch.manual_seed(0  )

										_SCREAMING_SNAKE_CASE			  ={
										    '''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			  =ShapERenderer(**_a  )
										return model




						def 							__UpperCamelCase				(       self					:      Any  )							->     Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.dummy_prior
										_SCREAMING_SNAKE_CASE			  =self.dummy_text_encoder
										_SCREAMING_SNAKE_CASE			  =self.dummy_tokenizer
										_SCREAMING_SNAKE_CASE			  =self.dummy_renderer

										_SCREAMING_SNAKE_CASE			  =HeunDiscreteScheduler(
										    beta_schedule='''exp'''				,    num_train_timesteps=1024				,    prediction_type='''sample'''				,    use_karras_sigmas=_a				,    clip_sample=_a				,    clip_sample_range=1.0				,    )
										_SCREAMING_SNAKE_CASE			  ={
										    '''prior''': prior,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''renderer''': renderer,
										    '''scheduler''': scheduler,
										}

										return components




						def 							__UpperCamelCase				(       self					:      Dict				,    _a					:      List[Any]				,    _a					:      Optional[int]=0  )							->     Optional[Any]:


										"""simple docstring"""





										if str(_a  ).startswith('''mps'''  ):
														_SCREAMING_SNAKE_CASE			  =torch.manual_seed(_a  )
										else:
														_SCREAMING_SNAKE_CASE			  =torch.Generator(device=_a  ).manual_seed(_a  )
										_SCREAMING_SNAKE_CASE			  ={
										    '''prompt''': '''horse''',
										    '''generator''': generator,
										    '''num_inference_steps''': 1,
										    '''frame_size''': 32,
										    '''output_type''': '''np''',
										}
										return inputs




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ='''cpu'''

										_SCREAMING_SNAKE_CASE			  =self.get_dummy_components()

										_SCREAMING_SNAKE_CASE			  =self.pipeline_class(**_a  )
										_SCREAMING_SNAKE_CASE			  =pipe.to(_a  )

										pipe.set_progress_bar_config(disable=_a  )

										_SCREAMING_SNAKE_CASE			  =pipe(**self.get_dummy_inputs(_a  )  )
										_SCREAMING_SNAKE_CASE			  =output.images[0]
										_SCREAMING_SNAKE_CASE			  =image[0, -3:, -3:, -1]

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

										_SCREAMING_SNAKE_CASE			  =np.array(
										    [
										        0.00_03_92_16,
										        0.00_03_92_16,
										        0.00_03_92_16,
										        0.00_03_92_16,
										        0.00_03_92_16,
										        0.00_03_92_16,
										        0.00_03_92_16,
										        0.00_03_92_16,
										        0.00_03_92_16,
										    ]  )

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




						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     Dict:


										"""simple docstring"""





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




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =torch_device == '''cpu'''
										_SCREAMING_SNAKE_CASE			  =True

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




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.get_dummy_components()
										_SCREAMING_SNAKE_CASE			  =self.pipeline_class(**_a  )
										_SCREAMING_SNAKE_CASE			  =pipe.to(_a  )
										pipe.set_progress_bar_config(disable=_a  )

										_SCREAMING_SNAKE_CASE			  =1
										_SCREAMING_SNAKE_CASE			  =2

										_SCREAMING_SNAKE_CASE			  =self.get_dummy_inputs(_a  )

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

										_SCREAMING_SNAKE_CASE			  =pipe(**_a				,    num_images_per_prompt=_a  )[0]

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

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




						def 							__UpperCamelCase				(       self					:      Union[str, Any]  )							->     List[str]:


										"""simple docstring"""





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




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/shap_e/test_shap_e_np_out.npy'''  )
										_SCREAMING_SNAKE_CASE			  =ShapEPipeline.from_pretrained('''openai/shap-e'''  )
										_SCREAMING_SNAKE_CASE			  =pipe.to(_a  )
										pipe.set_progress_bar_config(disable=_a  )

										_SCREAMING_SNAKE_CASE			  =torch.Generator(device=_a  ).manual_seed(0  )

										_SCREAMING_SNAKE_CASE			  =pipe(
										    '''a shark'''				,    generator=_a				,    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(_a				,    _a  )



from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_						:			int													=							logging.get_logger(__name__)

snake_case_						:			Optional[Any]													=							{
    '''SCUT-DLVCLab/lilt-roberta-en-base''': (
        '''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'''
    ),
}

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						"lilt"




						def __init__(       self					:      int				,    _a					:      Dict=3_0522				,    _a					:      str=768				,    _a					:      Optional[int]=12				,    _a					:      str=12				,    _a					:      List[str]=3072				,    _a					:      Any="gelu"				,    _a					:      int=0.1				,    _a					:      Any=0.1				,    _a					:      Union[str, Any]=512				,    _a					:      str=2				,    _a					:      List[Any]=0.02				,    _a					:      str=1E-12				,    _a					:      str=0				,    _a					:      str="absolute"				,    _a					:      Any=None				,    _a					:      Dict=4				,    _a					:      int=1024				,    **_a					:      List[Any]				,    )							->     Any:


										"""simple docstring"""





										super().__init__(pad_token_id=_a				,    **_a  )

										_SCREAMING_SNAKE_CASE			  =vocab_size
										_SCREAMING_SNAKE_CASE			  =hidden_size
										_SCREAMING_SNAKE_CASE			  =num_hidden_layers
										_SCREAMING_SNAKE_CASE			  =num_attention_heads
										_SCREAMING_SNAKE_CASE			  =hidden_act
										_SCREAMING_SNAKE_CASE			  =intermediate_size
										_SCREAMING_SNAKE_CASE			  =hidden_dropout_prob
										_SCREAMING_SNAKE_CASE			  =attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE			  =max_position_embeddings
										_SCREAMING_SNAKE_CASE			  =type_vocab_size
										_SCREAMING_SNAKE_CASE			  =initializer_range
										_SCREAMING_SNAKE_CASE			  =layer_norm_eps
										_SCREAMING_SNAKE_CASE			  =position_embedding_type
										_SCREAMING_SNAKE_CASE			  =classifier_dropout
										_SCREAMING_SNAKE_CASE			  =channel_shrink_ratio
										_SCREAMING_SNAKE_CASE			  =max_ad_position_embeddings