





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       :     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__   (       __snake_case			):
      _UpperCAmelCase	:Union[PIL.Image.Image, np.ndarray]



class 					A__   (       __snake_case			):





      def __init__( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							):
       '''simple docstring'''




       super().__init__()

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





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       if latents is None:
        UpperCamelCase	:					str       	=   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}"""   )
        UpperCamelCase	:					Dict       	=   latents.to(A_   )

       UpperCamelCase	:					Tuple       	=   latents * scheduler.init_noise_sigma
       return latents





      def        __UpperCamelCase( self       ,							A_=0   ):
       '''simple docstring'''




       if is_accelerate_available():
        from accelerate import cpu_offload
       else:
        raise ImportError("Please install accelerate via `pip install accelerate`"   )

       UpperCamelCase	:					List[Any]       	=   torch.device(F"""cuda:{gpu_id}"""   )

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





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       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        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							):
       '''simple docstring'''




       if isinstance(A_       ,							A_   ) and isinstance(image[0]       ,							torch.Tensor   ):
        UpperCamelCase	:					Any       	=   torch.cat(A_       ,							axis=0   ) if image[0].ndim == 4 else torch.stack(A_       ,							axis=0   )

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

       UpperCamelCase	:					str       	=   image.to(dtype=self.image_encoder.dtype       ,							device=A_   )

       UpperCamelCase	:					Union[str, Any]       	=   self.image_encoder(A_   )["last_hidden_state"]
       UpperCamelCase	:					Union[str, Any]       	=   image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

       UpperCamelCase	:					Dict       	=   image_embeds.repeat_interleave(A_       ,							dim=0   )

       if do_classifier_free_guidance:
        UpperCamelCase	:					List[str]       	=   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
        UpperCamelCase	:					Dict       	=   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       ,							):
       '''simple docstring'''




       if isinstance(A_       ,							PIL.Image.Image   ):
        UpperCamelCase	:					Union[str, Any]       	=   1
       elif isinstance(A_       ,							torch.Tensor   ):
        UpperCamelCase	:					str       	=   image.shape[0]
       elif isinstance(A_       ,							A_   ) and isinstance(image[0]       ,							(torch.Tensor, PIL.Image.Image)   ):
        UpperCamelCase	:					Optional[Any]       	=   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_   )}"""   )

       UpperCamelCase	:					Dict       	=   self._execution_device

       UpperCamelCase	:					Dict       	=   batch_size * num_images_per_prompt

       UpperCamelCase	:					str       	=   guidance_scale > 1.0
       UpperCamelCase	:					str       	=   self._encode_image(A_       ,							A_       ,							A_       ,							A_   )

       # prior

       self.scheduler.set_timesteps(A_       ,							device=A_   )
       UpperCamelCase	:					Optional[Any]       	=   self.scheduler.timesteps

       UpperCamelCase	:					List[str]       	=   self.prior.config.num_embeddings
       UpperCamelCase	:					Union[str, Any]       	=   self.prior.config.embedding_dim

       UpperCamelCase	:					int       	=   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
       UpperCamelCase	:					Optional[int]       	=   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
        UpperCamelCase	:					Union[str, Any]       	=   torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents
        UpperCamelCase	:					List[str]       	=   self.scheduler.scale_model_input(A_       ,							A_   )

        UpperCamelCase	:					List[str]       	=   self.prior(
            A_       ,							timestep=A_       ,							proj_embedding=A_       ,							).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	:					Optional[int]       	=   noise_pred.chunk(2   )
         UpperCamelCase	:					int       	=   noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

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

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

       UpperCamelCase	:					Any       	=   []
       for i, latent in enumerate(A_   ):
        print()
        UpperCamelCase	:					Union[str, Any]       	=   self.renderer.decode(
            latent[None, :]       ,							A_       ,							size=A_       ,							ray_batch_size=4096       ,							n_coarse_samples=64       ,							n_fine_samples=128       ,							)

        images.append(A_   )

       UpperCamelCase	:					List[str]       	=   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}"""   )

       UpperCamelCase	:					str       	=   images.cpu().numpy()

       if output_type == "pil":
        UpperCamelCase	:					List[str]       	=   [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"""





from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


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


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str			):







      '''simple docstring'''




      if not path:
            return "pipe"

      for ext in PipelineDataFormat.SUPPORTED_FORMATS:
            if path.endswith(SCREAMING_SNAKE_CASE			):
                  return ext

      raise Exception(
          F'Unable to determine file format from file extension {path}. '
          F'Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}'			)


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Optional[Any]			):







      '''simple docstring'''




      lowerCAmelCase  						=       pipeline(
          task=args.task							,						model=args.model if args.model else None							,						config=args.config							,						tokenizer=args.tokenizer							,						device=args.device							,						)
      lowerCAmelCase  						=       try_infer_format_from_ext(args.input			) if args.format == """infer""" else args.format
      lowerCAmelCase  						=       PipelineDataFormat.from_str(
          format=SCREAMING_SNAKE_CASE							,						output_path=args.output							,						input_path=args.input							,						column=args.column if args.column else nlp.default_input_names							,						overwrite=args.overwrite							,						)
      return RunCommand(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)






class 		lowercase					(					_UpperCAmelCase					):





      def __init__(       self      ,						lowercase      ,						lowercase )		->     Union[str, Any]:
            lowerCAmelCase  						=       nlp
            lowerCAmelCase  						=       reader





      @staticmethod
      def     _snake_case			(       lowercase )		->     Optional[int]:
            lowerCAmelCase  						=       parser.add_parser("""run"""      ,						help="""Run a pipeline through the CLI""" )
            run_parser.add_argument("""--task"""      ,						choices=get_supported_tasks()      ,						help="""Task to run""" )
            run_parser.add_argument("""--input"""      ,						type=lowercase      ,						help="""Path to the file to use for inference""" )
            run_parser.add_argument("""--output"""      ,						type=lowercase      ,						help="""Path to the file that will be used post to write results.""" )
            run_parser.add_argument("""--model"""      ,						type=lowercase      ,						help="""Name or path to the model to instantiate.""" )
            run_parser.add_argument("""--config"""      ,						type=lowercase      ,						help="""Name or path to the model's config to instantiate.""" )
            run_parser.add_argument(
                """--tokenizer"""      ,						type=lowercase      ,						help="""Name of the tokenizer to use. (default: same as the model name)""" )
            run_parser.add_argument(
                """--column"""      ,						type=lowercase      ,						help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)"""      ,						)
            run_parser.add_argument(
                """--format"""      ,						type=lowercase      ,						default="""infer"""      ,						choices=PipelineDataFormat.SUPPORTED_FORMATS      ,						help="""Input format to read from"""      ,						)
            run_parser.add_argument(
                """--device"""      ,						type=lowercase      ,						default=-1      ,						help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"""      ,						)
            run_parser.add_argument("""--overwrite"""      ,						action="""store_true"""      ,						help="""Allow overwriting the output file.""" )
            run_parser.set_defaults(func=lowercase )





      def     _snake_case			(       self )		->     int:
            lowerCAmelCase						,		lowerCAmelCase  						=       self._nlp, []

            for entry in self._reader:
                  lowerCAmelCase  						=       nlp(**lowercase ) if self._reader.is_multi_columns else nlp(lowercase )
                  if isinstance(lowercase      ,						lowercase ):
                        outputs.append(lowercase )
                  else:
                        outputs += output

        # Saving data
            if self._nlp.binary_output:
                  lowerCAmelCase  						=       self._reader.save_binary(lowercase )
                  logger.warning(f'Current pipeline requires output to be in binary format, saving at {binary_path}' )
            else:
                  self._reader.save(lowercase )






