





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


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



									"""simple docstring"""



									return 32


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



									"""simple docstring"""



									return 32


							@property
							def   _snake_case	(				self     :Tuple		) -> Dict:



									"""simple docstring"""



									return self.time_input_dim * 4


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



									"""simple docstring"""



									return 8


							@property
							def   _snake_case	(				self     :Any		) -> int:



									"""simple docstring"""



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


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



									"""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   _snake_case	(				self     :Tuple		) -> Any:



									"""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   _snake_case	(				self     :str		) -> 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   _snake_case	(				self     :int		) -> List[str]:



									"""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   _snake_case	(				self     :List[str]				,					__A     :List[Any]				,					__A     :List[Any]=0		) -> str:



									"""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   _snake_case	(				self     :Dict		) -> 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(
									    [
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									    ]		)

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


							def   _snake_case	(				self     :Any		) -> Dict:



									"""simple docstring"""



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


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



									"""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   _snake_case	(				self     :Union[str, Any]		) -> List[str]:



									"""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 	UpperCamelCase_							(  unittest.TestCase     ):


							def   _snake_case	(				self     :Tuple		) -> Optional[int]:



									"""simple docstring"""



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


							def   _snake_case	(				self     :str		) -> int:



									"""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=1_5.0				,					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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_													=      logging.get_logger(__name__)

lowercase_													=      {
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}






class     __a			(  SCREAMING_SNAKE_CASE					):
	SCREAMING_SNAKE_CASE  	=							"distilbert"
	SCREAMING_SNAKE_CASE  	=							{
	    "hidden_size": "dim",
	    "num_attention_heads": "n_heads",
	    "num_hidden_layers": "n_layers",
	}



	def __init__(    self				:     Optional[Any]					,						snake_case_				:     Tuple=3_05_22					,						snake_case_				:     Any=5_12					,						snake_case_				:     Dict=False					,						snake_case_				:     Tuple=6					,						snake_case_				:     Union[str, Any]=12					,						snake_case_				:     List[str]=7_68					,						snake_case_				:     Optional[Any]=4 * 7_68					,						snake_case_				:     Optional[Any]=0.1					,						snake_case_				:     Dict=0.1					,						snake_case_				:     Union[str, Any]="gelu"					,						snake_case_				:     Any=0.0_2					,						snake_case_				:     Optional[Any]=0.1					,						snake_case_				:     List[Any]=0.2					,						snake_case_				:     Optional[Any]=0					,						**snake_case_				:     List[Any]					,						)->				Tuple:
						__lowerCAmelCase						 =vocab_size
						__lowerCAmelCase						 =max_position_embeddings
						__lowerCAmelCase						 =sinusoidal_pos_embds
						__lowerCAmelCase						 =n_layers
						__lowerCAmelCase						 =n_heads
						__lowerCAmelCase						 =dim
						__lowerCAmelCase						 =hidden_dim
						__lowerCAmelCase						 =dropout
						__lowerCAmelCase						 =attention_dropout
						__lowerCAmelCase						 =activation
						__lowerCAmelCase						 =initializer_range
						__lowerCAmelCase						 =qa_dropout
						__lowerCAmelCase						 =seq_classif_dropout
						super().__init__(**snake_case_					,						pad_token_id=snake_case_)






class     __a			(  SCREAMING_SNAKE_CASE					):



	@property
	def 		UpperCamelCase				(    self				:     Union[str, Any])->				Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
											__lowerCAmelCase						 ={0: """batch""", 1: """choice""", 2: """sequence"""}
						else:
											__lowerCAmelCase						 ={0: """batch""", 1: """sequence"""}
						return OrderedDict(
						    [
						        ("""input_ids""", dynamic_axis),
						        ("""attention_mask""", dynamic_axis),
						    ])




