from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


__lowercase											=      logging.get_logger(__name__)  # pylint: disable=invalid-name

__lowercase											=      '''
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        >>> repo = "openai/shap-e-img2img"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"
        >>> image = load_image(image_url).convert("RGB")

        >>> images = pipe(
        ...     image,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], "corgi_3d.gif")
        ```
'''


@dataclass
class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		Union[PIL.Image.Image, np.ndarray]


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    def __init__(			self				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				) ->			Tuple:
        super().__init__()

        self.register_modules(
            prior=__lowercase				,				image_encoder=__lowercase				,				image_processor=__lowercase				,				scheduler=__lowercase				,				renderer=__lowercase				,				)







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase) ->			Dict:
        if latents is None:
            __UpperCamelCase  :List[Any]    						=	randn_tensor(__lowercase				,				generator=__lowercase				,				device=__lowercase				,				dtype=__lowercase)
        else:
            if latents.shape != shape:
                raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}""")
            __UpperCamelCase  :Dict    						=	latents.to(__lowercase)

        __UpperCamelCase  :List[str]    						=	latents * scheduler.init_noise_sigma
        return latents







    def     UpperCamelCase__     (			self				,				__lowercase=0) ->			Any:
        if is_accelerate_available():
            from accelerate import cpu_offload
        else:
            raise ImportError('''Please install accelerate via `pip install accelerate`''')

        __UpperCamelCase  :Tuple    						=	torch.device(f"""cuda:{gpu_id}""")

        __UpperCamelCase  :Optional[int]    						=	[self.image_encoder, self.prior]
        for cpu_offloaded_model in models:
            if cpu_offloaded_model is not None:
                cpu_offload(__lowercase				,				__lowercase)







    @property
    def     UpperCamelCase__     (			self) ->			Any:
        if self.device != torch.device('''meta''') or not hasattr(self.image_encoder				,				'''_hf_hook'''):
            return self.device
        for module in self.image_encoder.modules():
            if (
                hasattr(__lowercase				,				'''_hf_hook''')
                and hasattr(module._hf_hook				,				'''execution_device''')
                and module._hf_hook.execution_device is not None
            ):
                return torch.device(module._hf_hook.execution_device)
        return self.device







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				) ->			Optional[Any]:
        if isinstance(__lowercase				,				__lowercase) and isinstance(image[0]				,				torch.Tensor):
            __UpperCamelCase  :str    						=	torch.cat(__lowercase				,				axis=0) if image[0].ndim == 4 else torch.stack(__lowercase				,				axis=0)

        if not isinstance(__lowercase				,				torch.Tensor):
            __UpperCamelCase  :Optional[int]    						=	self.image_processor(__lowercase				,				return_tensors='''pt''').pixel_values[0].unsqueeze(0)

        __UpperCamelCase  :Any    						=	image.to(dtype=self.image_encoder.dtype				,				device=__lowercase)

        __UpperCamelCase  :List[Any]    						=	self.image_encoder(__lowercase)['''last_hidden_state''']
        __UpperCamelCase  :List[Any]    						=	image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

        __UpperCamelCase  :str    						=	image_embeds.repeat_interleave(__lowercase				,				dim=0)

        if do_classifier_free_guidance:
            __UpperCamelCase  :Dict    						=	torch.zeros_like(__lowercase)

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
            __UpperCamelCase  :List[str]    						=	torch.cat([negative_image_embeds, image_embeds])

        return image_embeds







    @torch.no_grad()
    @replace_example_docstring(__lowercase)
    def __call__(			self				,				__lowercase				,				__lowercase = 1				,				__lowercase = 25				,				__lowercase = None				,				__lowercase = None				,				__lowercase = 4.0				,				__lowercase = 64				,				__lowercase = "pil"				,				__lowercase = True				,				) ->			List[Any]:

        if isinstance(__lowercase				,				PIL.Image.Image):
            __UpperCamelCase  :Dict    						=	1
        elif isinstance(__lowercase				,				torch.Tensor):
            __UpperCamelCase  :List[str]    						=	image.shape[0]
        elif isinstance(__lowercase				,				__lowercase) and isinstance(image[0]				,				(torch.Tensor, PIL.Image.Image)):
            __UpperCamelCase  :Any    						=	len(__lowercase)
        else:
            raise ValueError(
                f"""`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(__lowercase)}""")

        __UpperCamelCase  :Optional[int]    						=	self._execution_device

        __UpperCamelCase  :Tuple    						=	batch_size * num_images_per_prompt

        __UpperCamelCase  :str    						=	guidance_scale > 1.0
        __UpperCamelCase  :Tuple    						=	self._encode_image(__lowercase				,				__lowercase				,				__lowercase				,				__lowercase)

        # prior

        self.scheduler.set_timesteps(__lowercase				,				device=__lowercase)
        __UpperCamelCase  :Any    						=	self.scheduler.timesteps

        __UpperCamelCase  :int    						=	self.prior.config.num_embeddings
        __UpperCamelCase  :str    						=	self.prior.config.embedding_dim

        __UpperCamelCase  :str    						=	self.prepare_latents(
            (batch_size, num_embeddings * embedding_dim)				,				image_embeds.dtype				,				__lowercase				,				__lowercase				,				__lowercase				,				self.scheduler				,				)

        # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
        __UpperCamelCase  :Tuple    						=	latents.reshape(latents.shape[0]				,				__lowercase				,				__lowercase)

        for i, t in enumerate(self.progress_bar(__lowercase)):
            # expand the latents if we are doing classifier free guidance
            __UpperCamelCase  :List[Any]    						=	torch.cat([latents] * 2) if do_classifier_free_guidance else latents
            __UpperCamelCase  :Dict    						=	self.scheduler.scale_model_input(__lowercase				,				__lowercase)

            __UpperCamelCase  :Union[str, Any]    						=	self.prior(
                __lowercase				,				timestep=__lowercase				,				proj_embedding=__lowercase				,				).predicted_image_embedding

            # remove the variance
            __UpperCamelCase						, __UpperCamelCase  :Optional[int]    						=	noise_pred.split(
                scaled_model_input.shape[2]				,				dim=2)  # batch_size, num_embeddings, embedding_dim

            if do_classifier_free_guidance is not None:
                __UpperCamelCase						, __UpperCamelCase  :Optional[int]    						=	noise_pred.chunk(2)
                __UpperCamelCase  :str    						=	noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

            __UpperCamelCase  :str    						=	self.scheduler.step(
                __lowercase				,				timestep=__lowercase				,				sample=__lowercase				,				).prev_sample

        if output_type == "latent":
            return ShapEPipelineOutput(images=__lowercase)

        __UpperCamelCase  :Union[str, Any]    						=	[]
        for i, latent in enumerate(__lowercase):
            print()
            __UpperCamelCase  :Optional[int]    						=	self.renderer.decode(
                latent[None, :]				,				__lowercase				,				size=__lowercase				,				ray_batch_size=4_096				,				n_coarse_samples=64				,				n_fine_samples=128				,				)

            images.append(__lowercase)

        __UpperCamelCase  :Optional[Any]    						=	torch.stack(__lowercase)

        if output_type not in ["np", "pil"]:
            raise ValueError(f"""Only the output types `pil` and `np` are supported not output_type={output_type}""")

        __UpperCamelCase  :Dict    						=	images.cpu().numpy()

        if output_type == "pil":
            __UpperCamelCase  :Union[str, Any]    						=	[self.numpy_to_pil(__lowercase) for image in images]

        # Offload last model to CPU
        if hasattr(self				,				'''final_offload_hook''') and self.final_offload_hook is not None:
            self.final_offload_hook.offload()

        if not return_dict:
            return (images,)

        return ShapEPipelineOutput(images=__lowercase)






import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

__lowercase											=      imread(r'''digital_image_processing/image_data/lena_small.jpg''')
__lowercase											=      cvtColor(img, COLOR_BGR2GRAY)



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Optional[Any]    						=	cn.convert_to_negative(SCREAMING_SNAKE_CASE			)
    # assert negative_img array for at least one True
    assert negative_img.any()



def lowerCamelCase						(     ):
    '''simple docstring'''







    with Image.open('''digital_image_processing/image_data/lena_small.jpg'''			) as img:
        # Work around assertion for response
        assert str(cc.change_contrast(SCREAMING_SNAKE_CASE						,    110			)			).startswith(
            '''<PIL.Image.Image image mode=RGB size=100x100 at'''			)



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :List[Any]    						=	canny.gen_gaussian_kernel(9						,    sigma=1.4			)
    # Assert ambiguous array
    assert resp.all()



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :List[str]    						=	imread('''digital_image_processing/image_data/lena_small.jpg'''						,    0			)
    # assert ambiguous array for all == True
    assert canny_img.all()
    __UpperCamelCase  :str    						=	canny.canny(SCREAMING_SNAKE_CASE			)
    # assert canny array for at least one True
    assert canny_array.any()



def lowerCamelCase						(     ):
    '''simple docstring'''







    assert gg.gaussian_filter(SCREAMING_SNAKE_CASE						,    5						,    sigma=0.9			).all()



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :List[str]    						=	array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]			)
    __UpperCamelCase  :Optional[Any]    						=	conv.img_convolve(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			).astype(SCREAMING_SNAKE_CASE			)
    assert res.any()



def lowerCamelCase						(     ):
    '''simple docstring'''







    assert med.median_filter(SCREAMING_SNAKE_CASE						,    3			).any()



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase						, __UpperCamelCase  :Dict    						=	sob.sobel_filter(SCREAMING_SNAKE_CASE			)
    assert grad.any() and theta.any()



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	sp.make_sepia(SCREAMING_SNAKE_CASE						,    20			)
    assert sepia.all()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE = "digital_image_processing/image_data/lena_small.jpg"			):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	bs.Burkes(imread(SCREAMING_SNAKE_CASE						,    1			)						,    120			)
    burkes.process()
    assert burkes.output_img.any()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE = "digital_image_processing/image_data/lena_small.jpg"						,    ):
    '''simple docstring'''







    __UpperCamelCase  :List[str]    						=	rs.NearestNeighbour(imread(SCREAMING_SNAKE_CASE						,    1			)						,    400						,    200			)
    nn.process()
    assert nn.output.any()



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Union[str, Any]    						=	'''digital_image_processing/image_data/lena.jpg'''

    # Reading the image and converting it to grayscale.
    __UpperCamelCase  :Optional[int]    						=	imread(SCREAMING_SNAKE_CASE						,    0			)

    # Test for get_neighbors_pixel function() return not None
    __UpperCamelCase  :Optional[int]    						=	0
    __UpperCamelCase  :List[Any]    						=	0
    __UpperCamelCase  :Dict    						=	image[x_coordinate][y_coordinate]

    __UpperCamelCase  :int    						=	lbp.get_neighbors_pixel(
        SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    assert neighbors_pixels is not None

    # Test for local_binary_pattern function()
    # Create a numpy array as the same height and width of read image
    __UpperCamelCase  :Tuple    						=	np.zeros((image.shape[0], image.shape[1])			)

    # Iterating through the image and calculating the local binary pattern value
    # for each pixel.
    for i in range(0						,    image.shape[0]			):
        for j in range(0						,    image.shape[1]			):
            __UpperCamelCase  :Optional[int]    						=	lbp.local_binary_value(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    assert lbp_image.any()





