


"""simple docstring"""







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


lowerCamelCase_       :       List[str]      						=  logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCamelCase_       :       Any      						=  """
    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 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  42






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 def __init__(		self			,   __A			,   __A			,   __A			,   __A			,   __A			,   ) ->					Union[str, Any]:
     super().__init__()

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


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A			,   __A			,   __A						) ->					Union[str, Any]:
     if latents is None:
         a					=randn_tensor(__A			,   generator=__A			,   device=__A			,   dtype=__A						)
     else:
         if latents.shape != shape:
             raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''						)
         a					=latents.to(__A						)

     a					=latents * scheduler.init_noise_sigma
     return latents


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A=0						) ->					Optional[Any]:
     if is_accelerate_available():
         from accelerate import cpu_offload
     else:
         raise ImportError('''Please install accelerate via `pip install accelerate`'''						)

     a					=torch.device(f'''cuda:{gpu_id}'''						)

     a					=[self.image_encoder, self.prior]
     for cpu_offloaded_model in models:
         if cpu_offloaded_model is not None:
             cpu_offload(__A			,   __A						)


 @property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[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(__A			,   '''_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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A			,   ) ->					Optional[Any]:
     if isinstance(__A			,   __A						) and isinstance(image[0]			,   torch.Tensor						):
         a					=torch.cat(__A			,   axis=0						) if image[0].ndim == 4 else torch.stack(__A			,   axis=0						)

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

     a					=image.to(dtype=self.image_encoder.dtype			,   device=__A						)

     a					=self.image_encoder(__A						)['''last_hidden_state''']
     a					=image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

     a					=image_embeds.repeat_interleave(__A			,   dim=0						)

     if do_classifier_free_guidance:
         a					=torch.zeros_like(__A						)

         # 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
         a					=torch.cat([negative_image_embeds, image_embeds]						)

     return image_embeds





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

     if isinstance(__A			,   PIL.Image.Image						):
         a					=1
     elif isinstance(__A			,   torch.Tensor						):
         a					=image.shape[0]
     elif isinstance(__A			,   __A						) and isinstance(image[0]			,   (torch.Tensor, PIL.Image.Image)						):
         a					=len(__A						)
     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(__A						)}'''						)

     a					=self._execution_device

     a					=batch_size * num_images_per_prompt

     a					=guidance_scale > 1.0
     a					=self._encode_image(__A			,   __A			,   __A			,   __A						)

     # prior

     self.scheduler.set_timesteps(__A			,   device=__A						)
     a					=self.scheduler.timesteps

     a					=self.prior.config.num_embeddings
     a					=self.prior.config.embedding_dim

     a					=self.prepare_latents(
         (batch_size, num_embeddings * embedding_dim)			,   image_embeds.dtype			,   __A			,   __A			,   __A			,   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
     a					=latents.reshape(latents.shape[0]			,   __A			,   __A						)

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

         a					=self.prior(
             __A			,   timestep=__A			,   proj_embedding=__A			,   ).predicted_image_embedding

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

         if do_classifier_free_guidance is not None:
             a		,				a					=noise_pred.chunk(2						)
             a					=noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

         a					=self.scheduler.step(
             __A			,   timestep=__A			,   sample=__A			,   ).prev_sample

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

     a					=[]
     for i, latent in enumerate(__A						):
         print()
         a					=self.renderer.decode(
             latent[None, :]			,   __A			,   size=__A			,   ray_batch_size=4096			,   n_coarse_samples=64			,   n_fine_samples=128			,   )

         images.append(__A						)

     a					=torch.stack(__A						)

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

     a					=images.cpu().numpy()

     if output_type == "pil":
         a					=[self.numpy_to_pil(__A						) 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=__A						)



"""simple docstring"""







import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_rjieba
@require_tokenizers
class 				__A    (	_SCREAMING_SNAKE_CASE,   unittest.TestCase      ):






 """simple docstring"""




 __lowerCAmelCase			 =  RoFormerTokenizer
 __lowerCAmelCase			 =  RoFormerTokenizerFast
 __lowerCAmelCase			 =  True
 __lowerCAmelCase			 =  True


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     super().setUp()


 def 				SCREAMING_SNAKE_CASE							(		self			,   **__A						) ->					Optional[int]:
     return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''			,   **__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   **__A						) ->					List[Any]:
     return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''			,   **__A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     a					='''永和服装饰品有限公司,今天天气非常好'''
     a					='''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
     return input_text, output_text


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     a					=self.get_tokenizer()
     a		,				a					=self.get_chinese_input_output_texts()
     a					=tokenizer.tokenize(__A						)

     self.assertListEqual(__A			,   output_text.split()						)

     a					=tokens + [tokenizer.unk_token]
     a					=[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__A						)			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     a					=self.get_rust_tokenizer()
     a		,				a					=self.get_chinese_input_output_texts()
     a					=tokenizer.tokenize(__A						)
     self.assertListEqual(__A			,   output_text.split()						)
     a					=tokens + [tokenizer.unk_token]
     a					=[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__A						)			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     pass


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     pass





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     pass