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

lowerCamelCase_  :	List[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     _UpperCamelCase			(   _A					):






			'''simple docstring'''

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




class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			def __init__(    self	:    Dict  ,		snake_case_	:    PriorTransformer  ,		snake_case_	:    CLIPVisionModel  ,		snake_case_	:    CLIPImageProcessor  ,		snake_case_	:    HeunDiscreteScheduler  ,		snake_case_	:    ShapERenderer  ,		):
							super().__init__()

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

			def  lowerCAmelCase__		(    self	:    Optional[int]  ,		snake_case_	:    Optional[Any]  ,		snake_case_	:    List[str]  ,		snake_case_	:    List[Any]  ,		snake_case_	:    Tuple  ,		snake_case_	:    Dict  ,		snake_case_	:    Optional[int] ):
							if latents is None:
											UpperCamelCase_:							List[Any]		=  randn_tensor(snake_case_  ,		generator=snake_case_  ,		device=snake_case_  ,		dtype=snake_case_ )
							else:
											if latents.shape != shape:
															raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
											UpperCamelCase_:							Union[str, Any]		=  latents.to(snake_case_ )

							UpperCamelCase_:							str		=  latents * scheduler.init_noise_sigma
							return latents

			def  lowerCAmelCase__		(    self	:    int  ,		snake_case_	:    List[str]=0 ):
							if is_accelerate_available():
											from accelerate import cpu_offload
							else:
											raise ImportError("""Please install accelerate via `pip install accelerate`""" )

							UpperCamelCase_:							Optional[Any]		=  torch.device(f'''cuda:{gpu_id}''' )

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

			@property
			def  lowerCAmelCase__		(    self	:    Dict ):
							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(snake_case_  ,		"""_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  lowerCAmelCase__		(    self	:    Tuple  ,		snake_case_	:    Union[str, Any]  ,		snake_case_	:    str  ,		snake_case_	:    Dict  ,		snake_case_	:    Optional[int]  ,		):
							if isinstance(snake_case_  ,		snake_case_ ) and isinstance(image[0]  ,		torch.Tensor ):
											UpperCamelCase_:							Tuple		=  torch.cat(snake_case_  ,		axis=0 ) if image[0].ndim == 4 else torch.stack(snake_case_  ,		axis=0 )

							if not isinstance(snake_case_  ,		torch.Tensor ):
											UpperCamelCase_:							Union[str, Any]		=  self.image_processor(snake_case_  ,		return_tensors="""pt""" ).pixel_values[0].unsqueeze(0 )

							UpperCamelCase_:							Any		=  image.to(dtype=self.image_encoder.dtype  ,		device=snake_case_ )

							UpperCamelCase_:							List[Any]		=  self.image_encoder(snake_case_ )["""last_hidden_state"""]
							UpperCamelCase_:							str		=  image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

							UpperCamelCase_:							List[str]		=  image_embeds.repeat_interleave(snake_case_  ,		dim=0 )

							if do_classifier_free_guidance:
											UpperCamelCase_:							List[Any]		=  torch.zeros_like(snake_case_ )

											# 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(snake_case_ )
			def __call__(    self	:    Union[str, Any]  ,		snake_case_	:    Union[PIL.Image.Image, List[PIL.Image.Image]]  ,		snake_case_	:    int = 1  ,		snake_case_	:    int = 25  ,		snake_case_	:    Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,		snake_case_	:    Optional[torch.FloatTensor] = None  ,		snake_case_	:    float = 4.0  ,		snake_case_	:    int = 64  ,		snake_case_	:    Optional[str] = "pil"  ,		snake_case_	:    bool = True  ,		):

							if isinstance(snake_case_  ,		PIL.Image.Image ):
											UpperCamelCase_:							Any		=  1
							elif isinstance(snake_case_  ,		torch.Tensor ):
											UpperCamelCase_:							str		=  image.shape[0]
							elif isinstance(snake_case_  ,		snake_case_ ) and isinstance(image[0]  ,		(torch.Tensor, PIL.Image.Image) ):
											UpperCamelCase_:							List[Any]		=  len(snake_case_ )
							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(snake_case_ )}''' )

							UpperCamelCase_:							Any		=  self._execution_device

							UpperCamelCase_:							Optional[Any]		=  batch_size * num_images_per_prompt

							UpperCamelCase_:							List[Any]		=  guidance_scale > 1.0
							UpperCamelCase_:							Union[str, Any]		=  self._encode_image(snake_case_  ,		snake_case_  ,		snake_case_  ,		snake_case_ )

							# prior

							self.scheduler.set_timesteps(snake_case_  ,		device=snake_case_ )
							UpperCamelCase_:							Optional[Any]		=  self.scheduler.timesteps

							UpperCamelCase_:							Tuple		=  self.prior.config.num_embeddings
							UpperCamelCase_:							str		=  self.prior.config.embedding_dim

							UpperCamelCase_:							Union[str, Any]		=  self.prepare_latents(
							    (batch_size, num_embeddings * embedding_dim)  ,		image_embeds.dtype  ,		snake_case_  ,		snake_case_  ,		snake_case_  ,		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]  ,		snake_case_  ,		snake_case_ )

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

											UpperCamelCase_:							Dict		=  self.prior(
											    snake_case_  ,		timestep=snake_case_  ,		proj_embedding=snake_case_  ,		).predicted_image_embedding

											# remove the variance
											UpperCamelCase_,				UpperCamelCase_:							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_:							int		=  noise_pred.chunk(2 )
															UpperCamelCase_:							List[str]		=  noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

											UpperCamelCase_:							Optional[Any]		=  self.scheduler.step(
											    snake_case_  ,		timestep=snake_case_  ,		sample=snake_case_  ,		).prev_sample

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

							UpperCamelCase_:							Optional[int]		=  []
							for i, latent in enumerate(snake_case_ ):
											print()
											UpperCamelCase_:							str		=  self.renderer.decode(
											    latent[None, :]  ,		snake_case_  ,		size=snake_case_  ,		ray_batch_size=4096  ,		n_coarse_samples=64  ,		n_fine_samples=128  ,		)

											images.append(snake_case_ )

							UpperCamelCase_:							Tuple		=  torch.stack(snake_case_ )

							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_:							Tuple		=  images.cpu().numpy()

							if output_type == "pil":
											UpperCamelCase_:							Tuple		=  [self.numpy_to_pil(snake_case_ ) 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=snake_case_ )








import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class     _UpperCamelCase			(   _A       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  Tuple    =	CTRLTokenizer
			__UpperCamelCase :  int    =	False
			__UpperCamelCase :  List[str]    =	False

			def  lowerCAmelCase__		(    self	:    List[str] ):
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							UpperCamelCase_:							int		=  ["""adapt""", """re@@""", """a@@""", """apt""", """c@@""", """t""", """<unk>"""]
							UpperCamelCase_:							int		=  dict(zip(snake_case_  ,		range(len(snake_case_ ) ) ) )
							UpperCamelCase_:							Union[str, Any]		=  ["""#version: 0.2""", """a p""", """ap t</w>""", """r e""", """a d""", """ad apt</w>""", """"""]
							UpperCamelCase_:							Tuple		=  {"""unk_token""": """<unk>"""}

							UpperCamelCase_:							Optional[Any]		=  os.path.join(self.tmpdirname  ,		VOCAB_FILES_NAMES["""vocab_file"""] )
							UpperCamelCase_:							Dict		=  os.path.join(self.tmpdirname  ,		VOCAB_FILES_NAMES["""merges_file"""] )
							with open(self.vocab_file  ,		"""w"""  ,		encoding="""utf-8""" ) as fp:
											fp.write(json.dumps(snake_case_ ) + """\n""" )
							with open(self.merges_file  ,		"""w"""  ,		encoding="""utf-8""" ) as fp:
											fp.write("""\n""".join(snake_case_ ) )

			def  lowerCAmelCase__		(    self	:    Optional[int]  ,		**snake_case_	:    int ):
							kwargs.update(self.special_tokens_map )
							return CTRLTokenizer.from_pretrained(self.tmpdirname  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Optional[int]  ,		snake_case_	:    List[str] ):
							UpperCamelCase_:							Dict		=  """adapt react readapt apt"""
							UpperCamelCase_:							List[str]		=  """adapt react readapt apt"""
							return input_text, output_text




			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							str		=  CTRLTokenizer(self.vocab_file  ,		self.merges_file  ,		**self.special_tokens_map )
							UpperCamelCase_:							List[Any]		=  """adapt react readapt apt"""
							UpperCamelCase_:							Optional[int]		=  """adapt re@@ a@@ c@@ t re@@ adapt apt""".split()
							UpperCamelCase_:							int		=  tokenizer.tokenize(snake_case_ )
							self.assertListEqual(snake_case_  ,		snake_case_ )

							UpperCamelCase_:							List[Any]		=  tokens + [tokenizer.unk_token]

							UpperCamelCase_:							Union[str, Any]		=  [0, 1, 2, 4, 5, 1, 0, 3, 6]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_ )  ,		snake_case_ )






