


"""simple docstring"""





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_(       a__							,					unittest.TestCase			):
					__UpperCamelCase  =					ShapEImgaImgPipeline
					__UpperCamelCase  =					['''image''']
					__UpperCamelCase  =					['''image''']
					__UpperCamelCase  =					[
					    '''num_images_per_prompt''',
					    '''num_inference_steps''',
					    '''generator''',
					    '''latents''',
					    '''guidance_scale''',
					    '''frame_size''',
					    '''output_type''',
					    '''return_dict''',
					]
					__UpperCamelCase  =					False






					@property
					def      lowerCamelCase__     (	self  :					Dict				):
											return 3_2






					@property
					def      lowerCamelCase__     (	self  :					List[str]				):
											return 3_2






					@property
					def      lowerCamelCase__     (	self  :					Optional[int]				):
											return self.time_input_dim * 4






					@property
					def      lowerCamelCase__     (	self  :					Union[str, Any]				):
											return 8






					@property
					def      lowerCamelCase__     (	self  :					Dict				):
											torch.manual_seed(0				)
											lowerCAmelCase					:	Tuple       =   CLIPVisionConfig(
											    hidden_size=self.text_embedder_hidden_size					,				image_size=6_4					,				projection_dim=self.text_embedder_hidden_size					,				intermediate_size=3_7					,				num_attention_heads=4					,				num_channels=3					,				num_hidden_layers=5					,				patch_size=1					,				)

											lowerCAmelCase					:	Optional[Any]       =   CLIPVisionModel(UpperCamelCase_				)
											return model






					@property
					def      lowerCamelCase__     (	self  :					Union[str, Any]				):
											lowerCAmelCase					:	List[str]       =   CLIPImageProcessor(
											    crop_size=2_2_4					,				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=2_2_4					,				)

											return image_processor






					@property
					def      lowerCamelCase__     (	self  :					Optional[Any]				):
											torch.manual_seed(0				)

											lowerCAmelCase					:	List[str]       =   {
											    '''num_attention_heads''': 2,
											    '''attention_head_dim''': 1_6,
											    '''embedding_dim''': self.time_input_dim,
											    '''num_embeddings''': 3_2,
											    '''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,
											}

											lowerCAmelCase					:	List[Any]       =   PriorTransformer(**UpperCamelCase_				)
											return model






					@property
					def      lowerCamelCase__     (	self  :					int				):
											torch.manual_seed(0				)

											lowerCAmelCase					:	Union[str, Any]       =   {
											    '''param_shapes''': (
											        (self.renderer_dim, 9_3),
											        (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''': 1_2,
											    '''background''': (
											        0.1,
											        0.1,
											        0.1,
											    ),
											}
											lowerCAmelCase					:	Any       =   ShapERenderer(**UpperCamelCase_				)
											return model






					def      lowerCamelCase__     (	self  :					List[str]				):
											lowerCAmelCase					:	Optional[int]       =   self.dummy_prior
											lowerCAmelCase					:	str       =   self.dummy_image_encoder
											lowerCAmelCase					:	Optional[Any]       =   self.dummy_image_processor
											lowerCAmelCase					:	Tuple       =   self.dummy_renderer

											lowerCAmelCase					:	List[str]       =   HeunDiscreteScheduler(
											    beta_schedule='''exp'''					,				num_train_timesteps=1_0_2_4					,				prediction_type='''sample'''					,				use_karras_sigmas=UpperCamelCase_					,				clip_sample=UpperCamelCase_					,				clip_sample_range=1.0					,				)
											lowerCAmelCase					:	Optional[Any]       =   {
											    '''prior''': prior,
											    '''image_encoder''': image_encoder,
											    '''image_processor''': image_processor,
											    '''renderer''': renderer,
											    '''scheduler''': scheduler,
											}

											return components






					def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Optional[int]=0				):
											lowerCAmelCase					:	Any       =   floats_tensor((1, 3, 6_4, 6_4)					,				rng=random.Random(UpperCamelCase_				)				).to(UpperCamelCase_				)

											if str(UpperCamelCase_				).startswith('''mps'''				):
																	lowerCAmelCase					:	str       =   torch.manual_seed(UpperCamelCase_				)
											else:
																	lowerCAmelCase					:	Optional[int]       =   torch.Generator(device=UpperCamelCase_				).manual_seed(UpperCamelCase_				)
											lowerCAmelCase					:	int       =   {
											    '''image''': input_image,
											    '''generator''': generator,
											    '''num_inference_steps''': 1,
											    '''frame_size''': 3_2,
											    '''output_type''': '''np''',
											}
											return inputs






					def      lowerCamelCase__     (	self  :					List[Any]				):
											lowerCAmelCase					:	int       =   '''cpu'''

											lowerCAmelCase					:	List[str]       =   self.get_dummy_components()

											lowerCAmelCase					:	str       =   self.pipeline_class(**UpperCamelCase_				)
											lowerCAmelCase					:	Any       =   pipe.to(UpperCamelCase_				)

											pipe.set_progress_bar_config(disable=UpperCamelCase_				)

											lowerCAmelCase					:	int       =   pipe(**self.get_dummy_inputs(UpperCamelCase_				)				)
											lowerCAmelCase					:	Tuple       =   output.images[0]
											lowerCAmelCase					:	List[Any]       =   image[0, -3:, -3:, -1]

											assert image.shape == (2_0, 3_2, 3_2, 3)

											lowerCAmelCase					:	List[Any]       =   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      lowerCamelCase__     (	self  :					List[Any]				):
											# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
											self._test_inference_batch_consistent(batch_sizes=[1, 2]				)






					def      lowerCamelCase__     (	self  :					Union[str, Any]				):
											lowerCAmelCase					:	List[Any]       =   torch_device == '''cpu'''
											lowerCAmelCase					:	List[str]       =   True
											self._test_inference_batch_single_identical(
											    batch_size=2					,				test_max_difference=UpperCamelCase_					,				relax_max_difference=UpperCamelCase_					,				)






					def      lowerCamelCase__     (	self  :					Union[str, Any]				):
											lowerCAmelCase					:	Union[str, Any]       =   self.get_dummy_components()
											lowerCAmelCase					:	int       =   self.pipeline_class(**UpperCamelCase_				)
											lowerCAmelCase					:	Optional[Any]       =   pipe.to(UpperCamelCase_				)
											pipe.set_progress_bar_config(disable=UpperCamelCase_				)

											lowerCAmelCase					:	Dict       =   1
											lowerCAmelCase					:	List[Any]       =   2

											lowerCAmelCase					:	Union[str, Any]       =   self.get_dummy_inputs(UpperCamelCase_				)

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

											lowerCAmelCase					:	int       =   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			):






					def      lowerCamelCase__     (	self  :					List[Any]				):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






					def      lowerCamelCase__     (	self  :					Tuple				):
											lowerCAmelCase					:	Dict       =   load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''				)
											lowerCAmelCase					:	Union[str, Any]       =   load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/shap_e/test_shap_e_img2img_out.npy'''				)
											lowerCAmelCase					:	int       =   ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''				)
											lowerCAmelCase					:	Any       =   pipe.to(UpperCamelCase_				)
											pipe.set_progress_bar_config(disable=UpperCamelCase_				)

											lowerCAmelCase					:	Any       =   torch.Generator(device=UpperCamelCase_				).manual_seed(0				)

											lowerCAmelCase					:	str       =   pipe(
											    UpperCamelCase_					,				generator=UpperCamelCase_					,				guidance_scale=3.0					,				num_inference_steps=6_4					,				frame_size=6_4					,				output_type='''np'''					,				).images[0]

											assert images.shape == (2_0, 6_4, 6_4, 3)

											assert_mean_pixel_difference(UpperCamelCase_					,				UpperCamelCase_				)






"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from packaging import version

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


snake_case__		:     Tuple    				=				logging.get_logger(__name__)

snake_case__		:     int    				=				{
    '''facebook/levit-128S''': '''https://huggingface.co/facebook/levit-128S/resolve/main/config.json''',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}



class 		snake_case_(       a__			):
     __UpperCamelCase  =					'''levit'''






     def __init__(	self  :					str					,				UpperCamelCase_  :					Union[str, Any]=2_2_4					,				UpperCamelCase_  :					Union[str, Any]=3					,				UpperCamelCase_  :					Union[str, Any]=3					,				UpperCamelCase_  :					int=2					,				UpperCamelCase_  :					Union[str, Any]=1					,				UpperCamelCase_  :					Tuple=1_6					,				UpperCamelCase_  :					Dict=[1_2_8, 2_5_6, 3_8_4]					,				UpperCamelCase_  :					Optional[Any]=[4, 8, 1_2]					,				UpperCamelCase_  :					Dict=[4, 4, 4]					,				UpperCamelCase_  :					Any=[1_6, 1_6, 1_6]					,				UpperCamelCase_  :					str=0					,				UpperCamelCase_  :					int=[2, 2, 2]					,				UpperCamelCase_  :					Optional[Any]=[2, 2, 2]					,				UpperCamelCase_  :					str=0.02					,				**UpperCamelCase_  :					List[str]					,				):
           super().__init__(**UpperCamelCase_				)
           lowerCAmelCase					:	Tuple       =   image_size
           lowerCAmelCase					:	int       =   num_channels
           lowerCAmelCase					:	Optional[int]       =   kernel_size
           lowerCAmelCase					:	Dict       =   stride
           lowerCAmelCase					:	List[Any]       =   padding
           lowerCAmelCase					:	Dict       =   hidden_sizes
           lowerCAmelCase					:	List[str]       =   num_attention_heads
           lowerCAmelCase					:	Tuple       =   depths
           lowerCAmelCase					:	Dict       =   key_dim
           lowerCAmelCase					:	Union[str, Any]       =   drop_path_rate
           lowerCAmelCase					:	List[Any]       =   patch_size
           lowerCAmelCase					:	Tuple       =   attention_ratio
           lowerCAmelCase					:	Optional[int]       =   mlp_ratio
           lowerCAmelCase					:	Union[str, Any]       =   initializer_range
           lowerCAmelCase					:	List[str]       =   [
               ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
               ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
           ]



class 		snake_case_(       a__			):
     __UpperCamelCase  =					version.parse('''1.11'''			)






     @property
     def      lowerCamelCase__     (	self  :					Tuple				):
           return OrderedDict(
               [
                   ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
               ]				)






     @property
     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           return 1E-4


