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 , )       ->	Optional[Any]:
        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)       ->	List[Any]:
        if latents is None:
            __UpperCamelCase     :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     :List[Any]			  =		latents.to(__lowercase)

        __UpperCamelCase     :int			  =		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     :str			  =		[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)       ->	Tuple:
        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 , )       ->	str:
        if isinstance(__lowercase , __lowercase) and isinstance(image[0] , torch.Tensor):
            __UpperCamelCase     :Union[str, Any]			  =		torch.cat(__lowercase , axis=0) if image[0].ndim == 4 else torch.stack(__lowercase , axis=0)

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

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

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

        __UpperCamelCase     :Any			  =		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     :Any			  =		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     :List[Any]			  =		1
        elif isinstance(__lowercase , torch.Tensor):
            __UpperCamelCase     :str			  =		image.shape[0]
        elif isinstance(__lowercase , __lowercase) and isinstance(image[0] , (torch.Tensor, PIL.Image.Image)):
            __UpperCamelCase     :Dict			  =		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     :Tuple			  =		self._execution_device

        __UpperCamelCase     :List[Any]			  =		batch_size * num_images_per_prompt

        __UpperCamelCase     :List[Any]			  =		guidance_scale > 1.0
        __UpperCamelCase     :str			  =		self._encode_image(__lowercase , __lowercase , __lowercase , __lowercase)

        # prior

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

        __UpperCamelCase     :str			  =		self.prior.config.num_embeddings
        __UpperCamelCase     :Optional[Any]			  =		self.prior.config.embedding_dim

        __UpperCamelCase     :List[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     :List[Any]			  =		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     :int			  =		torch.cat([latents] * 2) if do_classifier_free_guidance else latents
            __UpperCamelCase     :Dict			  =		self.scheduler.scale_model_input(__lowercase , __lowercase)

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

            # remove the variance
            __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		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     :Union[str, Any]			  =		noise_pred.chunk(2)
                __UpperCamelCase     :Optional[Any]			  =		noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

            __UpperCamelCase     :Optional[int]			  =		self.scheduler.step(
                __lowercase , timestep=__lowercase , sample=__lowercase , ).prev_sample

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

        __UpperCamelCase     :List[Any]			  =		[]
        for i, latent in enumerate(__lowercase):
            print()
            __UpperCamelCase     :Any			  =		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     :List[str]			  =		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     :Optional[int]			  =		images.cpu().numpy()

        if output_type == "pil":
            __UpperCamelCase     :Optional[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 glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
__lowercase							 =					(720, 1280)  # Height, Width
__lowercase							 =					(0.4, 0.6)  # if height or width lower than this scale, drop it.
__lowercase							 =					1 / 100
__lowercase							 =					''''''
__lowercase							 =					''''''
__lowercase							 =					''''''
__lowercase							 =					250



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







    __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		get_dataset(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    for index in range(SCREAMING_SNAKE_CASE	):
        __UpperCamelCase     :Optional[Any]			  =		random.sample(range(len(SCREAMING_SNAKE_CASE	)	)  ,			4	)
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :str			  =		update_image_and_anno(
            SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			filter_scale=SCREAMING_SNAKE_CASE  ,			)

        # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
        __UpperCamelCase     :List[Any]			  =		random_chars(32	)
        __UpperCamelCase     :List[str]			  =		path.split(os.sep	)[-1].rsplit('''.'''  ,			1	)[0]
        __UpperCamelCase     :Tuple			  =		f"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
        cva.imwrite(f"""{file_root}.jpg"""  ,			SCREAMING_SNAKE_CASE  ,			[cva.IMWRITE_JPEG_QUALITY, 85]	)
        print(f"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"""	)
        __UpperCamelCase     :Optional[Any]			  =		[]
        for anno in new_annos:
            __UpperCamelCase     :int			  =		anno[3] - anno[1]
            __UpperCamelCase     :Optional[int]			  =		anno[4] - anno[2]
            __UpperCamelCase     :int			  =		anno[1] + width / 2
            __UpperCamelCase     :List[str]			  =		anno[2] + height / 2
            __UpperCamelCase     :str			  =		f"""{anno[0]} {x_center} {y_center} {width} {height}"""
            annos_list.append(SCREAMING_SNAKE_CASE	)
        with open(f"""{file_root}.txt"""  ,			'''w'''	) as outfile:
            outfile.write('''\n'''.join(line for line in annos_list	)	)



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







    __UpperCamelCase     :str			  =		[]
    __UpperCamelCase     :str			  =		[]
    for label_file in glob.glob(os.path.join(SCREAMING_SNAKE_CASE  ,			'''*.txt'''	)	):
        __UpperCamelCase     :Any			  =		label_file.split(os.sep	)[-1].rsplit('''.'''  ,			1	)[0]
        with open(SCREAMING_SNAKE_CASE	) as in_file:
            __UpperCamelCase     :str			  =		in_file.readlines()
        __UpperCamelCase     :Optional[int]			  =		os.path.join(SCREAMING_SNAKE_CASE  ,			f"""{label_name}.jpg"""	)

        __UpperCamelCase     :int			  =		[]
        for obj_list in obj_lists:
            __UpperCamelCase     :Optional[int]			  =		obj_list.rstrip('''\n'''	).split(''' '''	)
            __UpperCamelCase     :Any			  =		float(obj[1]	) - float(obj[3]	) / 2
            __UpperCamelCase     :List[str]			  =		float(obj[2]	) - float(obj[4]	) / 2
            __UpperCamelCase     :Dict			  =		float(obj[1]	) + float(obj[3]	) / 2
            __UpperCamelCase     :List[str]			  =		float(obj[2]	) + float(obj[4]	) / 2

            boxes.append([int(obj[0]	), xmin, ymin, xmax, ymax]	)
        if not boxes:
            continue
        img_paths.append(SCREAMING_SNAKE_CASE	)
        labels.append(SCREAMING_SNAKE_CASE	)
    return img_paths, labels



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = 0.0  ,			):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		np.zeros([output_size[0], output_size[1], 3]  ,			dtype=np.uinta	)
    __UpperCamelCase     :List[Any]			  =		scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
    __UpperCamelCase     :int			  =		scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
    __UpperCamelCase     :Optional[int]			  =		int(scale_x * output_size[1]	)
    __UpperCamelCase     :Any			  =		int(scale_y * output_size[0]	)

    __UpperCamelCase     :List[str]			  =		[]
    __UpperCamelCase     :Dict			  =		[]
    for i, index in enumerate(SCREAMING_SNAKE_CASE	):
        __UpperCamelCase     :Any			  =		all_img_list[index]
        path_list.append(SCREAMING_SNAKE_CASE	)
        __UpperCamelCase     :Any			  =		all_annos[index]
        __UpperCamelCase     :Union[str, Any]			  =		cva.imread(SCREAMING_SNAKE_CASE	)
        if i == 0:  # top-left
            __UpperCamelCase     :str			  =		cva.resize(SCREAMING_SNAKE_CASE  ,			(divid_point_x, divid_point_y)	)
            __UpperCamelCase     :Union[str, Any]			  =		img
            for bbox in img_annos:
                __UpperCamelCase     :Union[str, Any]			  =		bbox[1] * scale_x
                __UpperCamelCase     :Optional[Any]			  =		bbox[2] * scale_y
                __UpperCamelCase     :int			  =		bbox[3] * scale_x
                __UpperCamelCase     :Union[str, Any]			  =		bbox[4] * scale_y
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
        elif i == 1:  # top-right
            __UpperCamelCase     :str			  =		cva.resize(SCREAMING_SNAKE_CASE  ,			(output_size[1] - divid_point_x, divid_point_y)	)
            __UpperCamelCase     :List[str]			  =		img
            for bbox in img_annos:
                __UpperCamelCase     :str			  =		scale_x + bbox[1] * (1 - scale_x)
                __UpperCamelCase     :Dict			  =		bbox[2] * scale_y
                __UpperCamelCase     :Optional[Any]			  =		scale_x + bbox[3] * (1 - scale_x)
                __UpperCamelCase     :List[Any]			  =		bbox[4] * scale_y
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
        elif i == 2:  # bottom-left
            __UpperCamelCase     :str			  =		cva.resize(SCREAMING_SNAKE_CASE  ,			(divid_point_x, output_size[0] - divid_point_y)	)
            __UpperCamelCase     :Optional[int]			  =		img
            for bbox in img_annos:
                __UpperCamelCase     :Tuple			  =		bbox[1] * scale_x
                __UpperCamelCase     :Optional[Any]			  =		scale_y + bbox[2] * (1 - scale_y)
                __UpperCamelCase     :Tuple			  =		bbox[3] * scale_x
                __UpperCamelCase     :Dict			  =		scale_y + bbox[4] * (1 - scale_y)
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
        else:  # bottom-right
            __UpperCamelCase     :Optional[int]			  =		cva.resize(
                SCREAMING_SNAKE_CASE  ,			(output_size[1] - divid_point_x, output_size[0] - divid_point_y)	)
            __UpperCamelCase     :Optional[int]			  =		img
            for bbox in img_annos:
                __UpperCamelCase     :Optional[Any]			  =		scale_x + bbox[1] * (1 - scale_x)
                __UpperCamelCase     :Optional[int]			  =		scale_y + bbox[2] * (1 - scale_y)
                __UpperCamelCase     :Optional[Any]			  =		scale_x + bbox[3] * (1 - scale_x)
                __UpperCamelCase     :int			  =		scale_y + bbox[4] * (1 - scale_y)
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)

    # Remove bounding box small than scale of filter
    if filter_scale > 0:
        __UpperCamelCase     :List[Any]			  =		[
            anno
            for anno in new_anno
            if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
        ]

    return output_img, new_anno, path_list[0]



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







    assert number_char > 1, "The number of character should greater than 1"
    __UpperCamelCase     :Optional[Any]			  =		ascii_lowercase + digits
    return "".join(random.choice(SCREAMING_SNAKE_CASE	) for _ in range(SCREAMING_SNAKE_CASE	)	)


if __name__ == "__main__":
    main()
    print('''DONE ✅''')





