






import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

lowerCamelCase			:					Any         =	logging.get_logger(__name__)


lowerCamelCase			:					List[str]         =	{name: getattr(transformers, name + '''Fast''') for name in SLOW_TO_FAST_CONVERTERS}





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case					, __snake_case       ):
     if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
          raise ValueError(F"""Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()       )}."""       )

     if tokenizer_name is None:
          __lowerCAmelCase								=					TOKENIZER_CLASSES
     else:
          __lowerCAmelCase								=					{tokenizer_name: getattr(__snake_case					, tokenizer_name + "Fast"       )}

     logger.info(F"""Loading tokenizer classes: {tokenizer_names}"""       )

     for tokenizer_name in tokenizer_names:
          __lowerCAmelCase								=					TOKENIZER_CLASSES[tokenizer_name]

          __lowerCAmelCase								=					True
          if checkpoint_name is None:
               __lowerCAmelCase								=					list(tokenizer_class.max_model_input_sizes.keys()       )
          else:
               __lowerCAmelCase								=					[checkpoint_name]

          logger.info(F"""For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}"""       )

          for checkpoint in checkpoint_names:
               logger.info(F"""Loading {tokenizer_class.__class__.__name__} {checkpoint}"""       )

               # Load tokenizer
               __lowerCAmelCase								=					tokenizer_class.from_pretrained(__snake_case					, force_download=__snake_case       )

               # Save fast tokenizer
               logger.info(F"""Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}"""       )

               # For organization names we create sub-directories
               if "/" in checkpoint:
                    __lowerCAmelCase			, __lowerCAmelCase								=					checkpoint.split("/"       )
                    __lowerCAmelCase								=					os.path.join(__snake_case					, __snake_case       )
               elif add_prefix:
                    __lowerCAmelCase								=					checkpoint
                    __lowerCAmelCase								=					dump_path
               else:
                    __lowerCAmelCase								=					None
                    __lowerCAmelCase								=					dump_path

               logger.info(F"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""       )

               if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()       )[0]:
                    __lowerCAmelCase								=					list(tokenizer.pretrained_vocab_files_map.values()       )[0][checkpoint]
                    __lowerCAmelCase								=					file_path.split(__snake_case       )[-1][0]
                    if next_char == "/":
                         __lowerCAmelCase								=					os.path.join(__snake_case					, __snake_case       )
                         __lowerCAmelCase								=					None

                    logger.info(F"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""       )

               __lowerCAmelCase								=					tokenizer.save_pretrained(
                   __snake_case					, legacy_format=__snake_case					, filename_prefix=__snake_case       )
               logger.info(F"""=> File names {file_names}"""       )

               for file_name in file_names:
                    if not file_name.endswith("tokenizer.json"       ):
                         os.remove(__snake_case       )
                         logger.info(F"""=> removing {file_name}"""       )


if __name__ == "__main__":
 lowerCamelCase			:					Optional[int]         =	argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--dump_path''', default=None, type=str, required=True, help='''Path to output generated fast tokenizer files.'''
 )
 parser.add_argument(
     '''--tokenizer_name''',
     default=None,
     type=str,
     help=(
         F'''Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will '''
         '''download and convert all the checkpoints from AWS.'''
     ),
 )
 parser.add_argument(
     '''--checkpoint_name''',
     default=None,
     type=str,
     help='''Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.''',
 )
 parser.add_argument(
     '''--force_download''',
     action='''store_true''',
     help='''Re-download checkpoints.''',
 )
 lowerCamelCase			:					Union[str, Any]         =	parser.parse_args()

 convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)














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

lowerCamelCase			:					str         =	'''
    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_			):
      snake_case_  =							42



class 							_UpperCamelCase			(a_			):


      def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						)->	Dict:
           super().__init__()

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


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	Union[str, Any]:
           if latents is None:
                __lowerCAmelCase								=					randn_tensor(__UpperCamelCase					,						generator=__UpperCamelCase					,						device=__UpperCamelCase					,						dtype=__UpperCamelCase	)
           else:
                if latents.shape != shape:
                     raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""	)
                __lowerCAmelCase								=					latents.to(__UpperCamelCase	)

           __lowerCAmelCase								=					latents * scheduler.init_noise_sigma
           return latents


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase=0	)->	str:
           if is_accelerate_available():
                from accelerate import cpu_offload
           else:
                raise ImportError("Please install accelerate via `pip install accelerate`"	)

           __lowerCAmelCase								=					torch.device(F"""cuda:{gpu_id}"""	)

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


      @property
      def  __UpperCAmelCase		(	self	)->	List[str]:
           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(__UpperCamelCase					,						"_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					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						)->	Union[str, Any]:
           if isinstance(__UpperCamelCase					,						__UpperCamelCase	) and isinstance(image[0]					,						torch.Tensor	):
                __lowerCAmelCase								=					torch.cat(__UpperCamelCase					,						axis=0	) if image[0].ndim == 4 else torch.stack(__UpperCamelCase					,						axis=0	)

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

           __lowerCAmelCase								=					image.to(dtype=self.image_encoder.dtype					,						device=__UpperCamelCase	)

           __lowerCAmelCase								=					self.image_encoder(__UpperCamelCase	)["last_hidden_state"]
           __lowerCAmelCase								=					image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

           __lowerCAmelCase								=					image_embeds.repeat_interleave(__UpperCamelCase					,						dim=0	)

           if do_classifier_free_guidance:
                __lowerCAmelCase								=					torch.zeros_like(__UpperCamelCase	)

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

           return image_embeds


      @torch.no_grad()
      @replace_example_docstring(__UpperCamelCase	)
      def __call__(	self					,						__UpperCamelCase					,						__UpperCamelCase = 1					,						__UpperCamelCase = 2_5					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = 4.0					,						__UpperCamelCase = 6_4					,						__UpperCamelCase = "pil"					,						__UpperCamelCase = True					,						)->	Dict:

           if isinstance(__UpperCamelCase					,						PIL.Image.Image	):
                __lowerCAmelCase								=					1
           elif isinstance(__UpperCamelCase					,						torch.Tensor	):
                __lowerCAmelCase								=					image.shape[0]
           elif isinstance(__UpperCamelCase					,						__UpperCamelCase	) and isinstance(image[0]					,						(torch.Tensor, PIL.Image.Image)	):
                __lowerCAmelCase								=					len(__UpperCamelCase	)
           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(__UpperCamelCase	)}"""	)

           __lowerCAmelCase								=					self._execution_device

           __lowerCAmelCase								=					batch_size * num_images_per_prompt

           __lowerCAmelCase								=					guidance_scale > 1.0
           __lowerCAmelCase								=					self._encode_image(__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)

           # prior

           self.scheduler.set_timesteps(__UpperCamelCase					,						device=__UpperCamelCase	)
           __lowerCAmelCase								=					self.scheduler.timesteps

           __lowerCAmelCase								=					self.prior.config.num_embeddings
           __lowerCAmelCase								=					self.prior.config.embedding_dim

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

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

                __lowerCAmelCase								=					self.prior(
                    __UpperCamelCase					,						timestep=__UpperCamelCase					,						proj_embedding=__UpperCamelCase					,						).predicted_image_embedding

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

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

                __lowerCAmelCase								=					self.scheduler.step(
                    __UpperCamelCase					,						timestep=__UpperCamelCase					,						sample=__UpperCamelCase					,						).prev_sample

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

           __lowerCAmelCase								=					[]
           for i, latent in enumerate(__UpperCamelCase	):
                print()
                __lowerCAmelCase								=					self.renderer.decode(
                    latent[None, :]					,						__UpperCamelCase					,						size=__UpperCamelCase					,						ray_batch_size=4_0_9_6					,						n_coarse_samples=6_4					,						n_fine_samples=1_2_8					,						)

                images.append(__UpperCamelCase	)

           __lowerCAmelCase								=					torch.stack(__UpperCamelCase	)

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

           __lowerCAmelCase								=					images.cpu().numpy()

           if output_type == "pil":
                __lowerCAmelCase								=					[self.numpy_to_pil(__UpperCamelCase	) 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=__UpperCamelCase	)






