


'''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 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE  , unittest.TestCase    ):


  '''simple docstring'''







  a_  					=							ShapEImgaImgPipeline
  a_  					=							["""image"""]
  a_  					=							["""image"""]
  a_  					=							[
      """num_images_per_prompt""",
      """num_inference_steps""",
      """generator""",
      """latents""",
      """guidance_scale""",
      """frame_size""",
      """output_type""",
      """return_dict""",
  ]
  a_  					=							False






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        return 32






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        return 32






  @property
  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        return self.time_input_dim * 4






  @property
  def        _a							(							self :       List[str] ):

        '''simple docstring'''




        return 8






  @property
  def        _a							(							self :       Dict ):

        '''simple docstring'''




        torch.manual_seed(0 )
        A_						:  Tuple          =				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	,)

        A_						:  Optional[Any]          =				CLIPVisionModel(_a )
        return model






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        A_						:  Tuple          =				CLIPImageProcessor(
            crop_size=224	,do_center_crop=_a	,do_normalize=_a	,do_resize=_a	,image_mean=[0.48145466, 0.4578275, 0.40821073]	,image_std=[0.26862954, 0.26130258, 0.27577711]	,resample=3	,size=224	,)

        return image_processor






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        torch.manual_seed(0 )

        A_						:  Tuple          =				{
            """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,
        }

        A_						:  Optional[Any]          =				PriorTransformer(**_a )
        return model






  @property
  def        _a							(							self :       Union[str, Any] ):

        '''simple docstring'''




        torch.manual_seed(0 )

        A_						:  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,
            ),
        }
        A_						:  List[Any]          =				ShapERenderer(**_a )
        return model






  def        _a							(							self :       List[str] ):

        '''simple docstring'''




        A_						:  str          =				self.dummy_prior
        A_						:  Union[str, Any]          =				self.dummy_image_encoder
        A_						:  List[Any]          =				self.dummy_image_processor
        A_						:  Union[str, Any]          =				self.dummy_renderer

        A_						:  List[Any]          =				HeunDiscreteScheduler(
            beta_schedule="""exp"""	,num_train_timesteps=1024	,prediction_type="""sample"""	,use_karras_sigmas=_a	,clip_sample=_a	,clip_sample_range=1.0	,)
        A_						:  Tuple          =				{
            """prior""": prior,
            """image_encoder""": image_encoder,
            """image_processor""": image_processor,
            """renderer""": renderer,
            """scheduler""": scheduler,
        }

        return components






  def        _a							(							self :       int	,_a :       Dict	,_a :       List[str]=0 ):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				floats_tensor((1, 3, 64, 64)	,rng=random.Random(_a ) ).to(_a )

        if str(_a ).startswith("""mps""" ):
              A_						:  Optional[int]          =				torch.manual_seed(_a )
        else:
              A_						:  List[str]          =				torch.Generator(device=_a ).manual_seed(_a )
        A_						:  Tuple          =				{
            """image""": input_image,
            """generator""": generator,
            """num_inference_steps""": 1,
            """frame_size""": 32,
            """output_type""": """np""",
        }
        return inputs






  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        A_						:  Optional[Any]          =				"""cpu"""

        A_						:  Tuple          =				self.get_dummy_components()

        A_						:  Optional[int]          =				self.pipeline_class(**_a )
        A_						:  Optional[int]          =				pipe.to(_a )

        pipe.set_progress_bar_config(disable=_a )

        A_						:  Optional[Any]          =				pipe(**self.get_dummy_inputs(_a ) )
        A_						:  Tuple          =				output.images[0]
        A_						:  List[Any]          =				image[0, -3:, -3:, -1]

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

        A_						:  Dict          =				np.array(
            [
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
            ] )

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






  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




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






  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        A_						:  int          =				torch_device == """cpu"""
        A_						:  List[Any]          =				True
        self._test_inference_batch_single_identical(
            batch_size=2	,test_max_difference=_a	,relax_max_difference=_a	,)






  def        _a							(							self :       Union[str, Any] ):

        '''simple docstring'''




        A_						:  str          =				self.get_dummy_components()
        A_						:  Optional[int]          =				self.pipeline_class(**_a )
        A_						:  List[str]          =				pipe.to(_a )
        pipe.set_progress_bar_config(disable=_a )

        A_						:  str          =				1
        A_						:  str          =				2

        A_						:  List[Any]          =				self.get_dummy_inputs(_a )

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

        A_						:  Dict          =				pipe(**_a	,num_images_per_prompt=_a )[0]

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







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


  '''simple docstring'''







  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




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






  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




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

        A_						:  Dict          =				torch.Generator(device=_a ).manual_seed(0 )

        A_						:  Optional[Any]          =				pipe(
            _a	,generator=_a	,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(_a	,_a )




'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__magic_name__			=      logging.get_logger(__name__)


if is_vision_available():
     import PIL



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""pixel_values"""]






  def __init__(							self :       Optional[Any]	,_a :       bool = True	,_a :       Dict[str, int] = None	,_a :       PILImageResampling = PILImageResampling.BICUBIC	,_a :       bool = True	,_a :       Dict[str, int] = None	,_a :       bool = True	,_a :       Union[int, float] = 1 / 255	,_a :       bool = True	,_a :       Optional[Union[float, List[float]]] = None	,_a :       Optional[Union[float, List[float]]] = None	,_a :       bool = True	,**_a :       Dict	,):

        '''simple docstring'''




        super().__init__(**_a )
        A_						:  Tuple          =				size if size is not None else {"""shortest_edge""": 224}
        A_						:  Optional[Any]          =				get_size_dict(_a	,default_to_square=_a )
        A_						:  Tuple          =				crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        A_						:  Optional[Any]          =				get_size_dict(_a	,default_to_square=_a	,param_name="""crop_size""" )

        A_						:  Any          =				do_resize
        A_						:  List[str]          =				size
        A_						:  Union[str, Any]          =				resample
        A_						:  Dict          =				do_center_crop
        A_						:  List[str]          =				crop_size
        A_						:  Any          =				do_rescale
        A_						:  Union[str, Any]          =				rescale_factor
        A_						:  Any          =				do_normalize
        A_						:  List[str]          =				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        A_						:  List[Any]          =				image_std if image_std is not None else OPENAI_CLIP_STD
        A_						:  Tuple          =				do_convert_rgb






  def        _a							(							self :       Optional[int]	,_a :       np.ndarray	,_a :       Dict[str, int]	,_a :       PILImageResampling = PILImageResampling.BICUBIC	,_a :       Optional[Union[str, ChannelDimension]] = None	,**_a :       Optional[Any]	,):

        '''simple docstring'''




        A_						:  Optional[Any]          =				get_size_dict(_a	,default_to_square=_a )
        if "shortest_edge" not in size:
              raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
        A_						:  Tuple          =				get_resize_output_image_size(_a	,size=size["""shortest_edge"""]	,default_to_square=_a )
        return resize(_a	,size=_a	,resample=_a	,data_format=_a	,**_a )






  def        _a							(							self :       List[Any]	,_a :       np.ndarray	,_a :       Dict[str, int]	,_a :       Optional[Union[str, ChannelDimension]] = None	,**_a :       Optional[int]	,):

        '''simple docstring'''




        A_						:  Optional[int]          =				get_size_dict(_a )
        if "height" not in size or "width" not in size:
              raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}' )
        return center_crop(_a	,size=(size["""height"""], size["""width"""])	,data_format=_a	,**_a )






  def        _a							(							self :       Any	,_a :       np.ndarray	,_a :       Union[int, float]	,_a :       Optional[Union[str, ChannelDimension]] = None	,**_a :       Any	,):

        '''simple docstring'''




        return rescale(_a	,scale=_a	,data_format=_a	,**_a )






  def        _a							(							self :       Any	,_a :       np.ndarray	,_a :       Union[float, List[float]]	,_a :       Union[float, List[float]]	,_a :       Optional[Union[str, ChannelDimension]] = None	,**_a :       List[str]	,):

        '''simple docstring'''




        return normalize(_a	,mean=_a	,std=_a	,data_format=_a	,**_a )






  def        _a							(							self :       Optional[Any]	,_a :       ImageInput	,_a :       bool = None	,_a :       Dict[str, int] = None	,_a :       PILImageResampling = None	,_a :       bool = None	,_a :       int = None	,_a :       bool = None	,_a :       float = None	,_a :       bool = None	,_a :       Optional[Union[float, List[float]]] = None	,_a :       Optional[Union[float, List[float]]] = None	,_a :       bool = None	,_a :       Optional[Union[str, TensorType]] = None	,_a :       Optional[ChannelDimension] = ChannelDimension.FIRST	,**_a :       int	,):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				do_resize if do_resize is not None else self.do_resize
        A_						:  Tuple          =				size if size is not None else self.size
        A_						:  Optional[int]          =				get_size_dict(_a	,param_name="""size"""	,default_to_square=_a )
        A_						:  List[str]          =				resample if resample is not None else self.resample
        A_						:  int          =				do_center_crop if do_center_crop is not None else self.do_center_crop
        A_						:  Any          =				crop_size if crop_size is not None else self.crop_size
        A_						:  int          =				get_size_dict(_a	,param_name="""crop_size"""	,default_to_square=_a )
        A_						:  List[Any]          =				do_rescale if do_rescale is not None else self.do_rescale
        A_						:  int          =				rescale_factor if rescale_factor is not None else self.rescale_factor
        A_						:  Any          =				do_normalize if do_normalize is not None else self.do_normalize
        A_						:  int          =				image_mean if image_mean is not None else self.image_mean
        A_						:  int          =				image_std if image_std is not None else self.image_std
        A_						:  List[str]          =				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        A_						:  int          =				make_list_of_images(_a )

        if not valid_images(_a ):
              raise ValueError(
                  """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                  """torch.Tensor, tf.Tensor or jax.ndarray.""" )

        if do_resize and size is None:
              raise ValueError("""Size must be specified if do_resize is True.""" )

        if do_center_crop and crop_size is None:
              raise ValueError("""Crop size must be specified if do_center_crop is True.""" )

        if do_rescale and rescale_factor is None:
              raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )

        if do_normalize and (image_mean is None or image_std is None):
              raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
              A_						:  Optional[int]          =				[convert_to_rgb(_a ) for image in images]

        # All transformations expect numpy arrays.
        A_						:  Dict          =				[to_numpy_array(_a ) for image in images]

        if do_resize:
              A_						:  int          =				[self.resize(image=_a	,size=_a	,resample=_a ) for image in images]

        if do_center_crop:
              A_						:  Tuple          =				[self.center_crop(image=_a	,size=_a ) for image in images]

        if do_rescale:
              A_						:  List[str]          =				[self.rescale(image=_a	,scale=_a ) for image in images]

        if do_normalize:
              A_						:  Any          =				[self.normalize(image=_a	,mean=_a	,std=_a ) for image in images]

        A_						:  List[str]          =				[to_channel_dimension_format(_a	,_a ) for image in images]

        A_						:  List[str]          =				{"""pixel_values""": images}
        return BatchFeature(data=_a	,tensor_type=_a )
