





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 					lowercase__ (			__lowerCamelCase    ,       unittest.TestCase       ):

			'''simple docstring'''


			a		:							str     	=	ShapEImgaImgPipeline
			a		:							Dict     	=	["image"]
			a		:							Union[str, Any]     	=	["image"]
			a		:							Optional[int]     	=	[
			    "num_images_per_prompt",
			    "num_inference_steps",
			    "generator",
			    "latents",
			    "guidance_scale",
			    "frame_size",
			    "output_type",
			    "return_dict",
			]
			a		:							Any     	=	False




			@property
			def    UpperCamelCase__						(					self							)				->   Any:






							"""simple docstring"""
							return 32




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






							"""simple docstring"""
							return 32




			@property
			def    UpperCamelCase__						(					self							)				->   str:






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




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






							"""simple docstring"""
							return 8




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






							"""simple docstring"""
							torch.manual_seed(0							)
							UpperCamelCase__							:       Optional[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,	)

							UpperCamelCase__							:       str      =   CLIPVisionModel(__magic_name__							)
							return model




			@property
			def    UpperCamelCase__						(					self							)				->   Tuple:






							"""simple docstring"""
							UpperCamelCase__							:       Union[str, Any]      =   CLIPImageProcessor(
							    crop_size=224,	do_center_crop=__magic_name__,	do_normalize=__magic_name__,	do_resize=__magic_name__,	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    UpperCamelCase__						(					self							)				->   Dict:






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

							UpperCamelCase__							:       List[str]      =   {
							    '''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,
							}

							UpperCamelCase__							:       Union[str, Any]      =   PriorTransformer(**__magic_name__							)
							return model




			@property
			def    UpperCamelCase__						(					self							)				->   str:






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

							UpperCamelCase__							:       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,
							    ),
							}
							UpperCamelCase__							:       Optional[Any]      =   ShapERenderer(**__magic_name__							)
							return model




			def    UpperCamelCase__						(					self							)				->   str:






							"""simple docstring"""
							UpperCamelCase__							:       Dict      =   self.dummy_prior
							UpperCamelCase__							:       List[str]      =   self.dummy_image_encoder
							UpperCamelCase__							:       Dict      =   self.dummy_image_processor
							UpperCamelCase__							:       List[Any]      =   self.dummy_renderer

							UpperCamelCase__							:       int      =   HeunDiscreteScheduler(
							    beta_schedule='''exp''',	num_train_timesteps=1024,	prediction_type='''sample''',	use_karras_sigmas=__magic_name__,	clip_sample=__magic_name__,	clip_sample_range=1.0,	)
							UpperCamelCase__							:       str      =   {
							    '''prior''': prior,
							    '''image_encoder''': image_encoder,
							    '''image_processor''': image_processor,
							    '''renderer''': renderer,
							    '''scheduler''': scheduler,
							}

							return components




			def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__=0							)				->   Dict:






							"""simple docstring"""
							UpperCamelCase__							:       Optional[Any]      =   floats_tensor((1, 3, 64, 64),	rng=random.Random(__magic_name__							)							).to(__magic_name__							)

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




			def    UpperCamelCase__						(					self							)				->   Any:






							"""simple docstring"""
							UpperCamelCase__							:       Union[str, Any]      =   '''cpu'''

							UpperCamelCase__							:       Optional[Any]      =   self.get_dummy_components()

							UpperCamelCase__							:       List[str]      =   self.pipeline_class(**__magic_name__							)
							UpperCamelCase__							:       Any      =   pipe.to(__magic_name__							)

							pipe.set_progress_bar_config(disable=__magic_name__							)

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

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

							UpperCamelCase__							:       List[Any]      =   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    UpperCamelCase__						(					self							)				->   Optional[int]:






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




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






							"""simple docstring"""
							UpperCamelCase__							:       str      =   torch_device == '''cpu'''
							UpperCamelCase__							:       int      =   True
							self._test_inference_batch_single_identical(
							    batch_size=2,	test_max_difference=__magic_name__,	relax_max_difference=__magic_name__,	)




			def    UpperCamelCase__						(					self							)				->   Any:






							"""simple docstring"""
							UpperCamelCase__							:       Union[str, Any]      =   self.get_dummy_components()
							UpperCamelCase__							:       Union[str, Any]      =   self.pipeline_class(**__magic_name__							)
							UpperCamelCase__							:       Union[str, Any]      =   pipe.to(__magic_name__							)
							pipe.set_progress_bar_config(disable=__magic_name__							)

							UpperCamelCase__							:       Dict      =   1
							UpperCamelCase__							:       Any      =   2

							UpperCamelCase__							:       Any      =   self.get_dummy_inputs(__magic_name__							)

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

							UpperCamelCase__							:       List[str]      =   pipe(**__magic_name__,	num_images_per_prompt=__magic_name__							)[0]

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






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

			'''simple docstring'''


			def    UpperCamelCase__						(					self							)				->   Dict:






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




			def    UpperCamelCase__						(					self							)				->   Optional[int]:






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

							UpperCamelCase__							:       Optional[Any]      =   torch.Generator(device=__magic_name__							).manual_seed(0							)

							UpperCamelCase__							:       List[Any]      =   pipe(
							    __magic_name__,	generator=__magic_name__,	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(__magic_name__,	__magic_name__							)








from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_  							=				{
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_  							=				[
      'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'InformerForPrediction',
      'InformerModel',
      'InformerPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_informer import (
        INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        InformerForPrediction,
        InformerModel,
        InformerPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_  							=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

