
from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand


def 		UpperCAmelCase     (      a_		)						->      Union[str, Any]:







				"""simple docstring"""



				return ConvertCommand(
				    args.model_type						,  args.tf_checkpoint						,  args.pytorch_dump_output						,  args.config						,  args.finetuning_task_name		)


SCREAMING_SNAKE_CASE				:List[Any]									=							'\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n'





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	@staticmethod
	def 							UpperCamelCase_    (			A   :					ArgumentParser					):
					__A       =					parser.add_parser(
					    "convert"				,help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."				,)
					train_parser.add_argument("--model_type"				,type=A				,required=A				,help="Model's type."					)
					train_parser.add_argument(
					    "--tf_checkpoint"				,type=A				,required=A				,help="TensorFlow checkpoint path or folder."					)
					train_parser.add_argument(
					    "--pytorch_dump_output"				,type=A				,required=A				,help="Path to the PyTorch saved model output."					)
					train_parser.add_argument("--config"				,type=A				,default=""				,help="Configuration file path or folder."					)
					train_parser.add_argument(
					    "--finetuning_task_name"				,type=A				,default=A				,help="Optional fine-tuning task name if the TF model was a finetuned model."				,)
					train_parser.set_defaults(func=A					)






	def __init__(			self   :					Optional[int]				,A   :					str				,A   :					str				,A   :					str				,A   :					str				,A   :					str				,*A   :					Any				,):
					__A       =					logging.get_logger("transformers-cli/converting"					)

					self._logger.info(f'''Loading model {model_type}'''					)
					__A       =					model_type
					__A       =					tf_checkpoint
					__A       =					pytorch_dump_output
					__A       =					config
					__A       =					finetuning_task_name







	def 							UpperCamelCase_    (			self   :					Tuple					):
					if self._model_type == "albert":
									try:
													from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
													    convert_tf_checkpoint_to_pytorch,
													)
									except ImportError:
													raise ImportError(A					)

									convert_tf_checkpoint_to_pytorch(self._tf_checkpoint				,self._config				,self._pytorch_dump_output					)
					elif self._model_type == "bert":
									try:
													from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
													    convert_tf_checkpoint_to_pytorch,
													)
									except ImportError:
													raise ImportError(A					)

									convert_tf_checkpoint_to_pytorch(self._tf_checkpoint				,self._config				,self._pytorch_dump_output					)
					elif self._model_type == "funnel":
									try:
													from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
													    convert_tf_checkpoint_to_pytorch,
													)
									except ImportError:
													raise ImportError(A					)

									convert_tf_checkpoint_to_pytorch(self._tf_checkpoint				,self._config				,self._pytorch_dump_output					)
					elif self._model_type == "t5":
									try:
													from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
									except ImportError:
													raise ImportError(A					)

									convert_tf_checkpoint_to_pytorch(self._tf_checkpoint				,self._config				,self._pytorch_dump_output					)
					elif self._model_type == "gpt":
									from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
									    convert_openai_checkpoint_to_pytorch,
									)

									convert_openai_checkpoint_to_pytorch(self._tf_checkpoint				,self._config				,self._pytorch_dump_output					)
					elif self._model_type == "transfo_xl":
									try:
													from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
													    convert_transfo_xl_checkpoint_to_pytorch,
													)
									except ImportError:
													raise ImportError(A					)

									if "ckpt" in self._tf_checkpoint.lower():
													__A       =					self._tf_checkpoint
													__A       =					""
									else:
													__A       =					self._tf_checkpoint
													__A       =					""
									convert_transfo_xl_checkpoint_to_pytorch(
									    A				,self._config				,self._pytorch_dump_output				,A					)
					elif self._model_type == "gpt2":
									try:
													from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
													    convert_gpta_checkpoint_to_pytorch,
													)
									except ImportError:
													raise ImportError(A					)

									convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint				,self._config				,self._pytorch_dump_output					)
					elif self._model_type == "xlnet":
									try:
													from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
													    convert_xlnet_checkpoint_to_pytorch,
													)
									except ImportError:
													raise ImportError(A					)

									convert_xlnet_checkpoint_to_pytorch(
									    self._tf_checkpoint				,self._config				,self._pytorch_dump_output				,self._finetuning_task_name					)
					elif self._model_type == "xlm":
									from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
									    convert_xlm_checkpoint_to_pytorch,
									)

									convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint				,self._pytorch_dump_output					)
					elif self._model_type == "lxmert":
									from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
									    convert_lxmert_checkpoint_to_pytorch,
									)

									convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint				,self._pytorch_dump_output					)
					elif self._model_type == "rembert":
									from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
									    convert_rembert_tf_checkpoint_to_pytorch,
									)

									convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint				,self._config				,self._pytorch_dump_output					)
					else:
									raise ValueError(
									    "--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"					)




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


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

__a	 =      """
    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 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							42






class 	__lowercase					(	__snake_case	):





      def __init__(		self    :       Any       ,							__lowerCamelCase    :       PriorTransformer       ,							__lowerCamelCase    :       CLIPVisionModel       ,							__lowerCamelCase    :       CLIPImageProcessor       ,							__lowerCamelCase    :       HeunDiscreteScheduler       ,							__lowerCamelCase    :       ShapERenderer       ,							)     -> str:






         """simple docstring"""







         super().__init__()

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





      def 							_lowercase					(		self    :       Dict       ,							__lowerCamelCase    :       Optional[int]       ,							__lowerCamelCase    :       Tuple       ,							__lowerCamelCase    :       Dict       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       str       ,							__lowerCamelCase    :       str							)     -> int:






         """simple docstring"""







         if latents is None:
            UpperCAmelCase      					=  randn_tensor(__lowerCamelCase       ,							generator=__lowerCamelCase       ,							device=__lowerCamelCase       ,							dtype=__lowerCamelCase							)
         else:
            if latents.shape != shape:
               raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""							)
            UpperCAmelCase      					=  latents.to(__lowerCamelCase							)

         UpperCAmelCase      					=  latents * scheduler.init_noise_sigma
         return latents





      def 							_lowercase					(		self    :       Optional[Any]       ,							__lowerCamelCase    :       Dict=0							)     -> Optional[int]:






         """simple docstring"""







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

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

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





      @property
      def 							_lowercase					(		self    :       List[str]							)     -> int:






         """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(__lowerCamelCase       ,							"""_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 							_lowercase					(		self    :       List[Any]       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       List[str]       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       Union[str, Any]       ,							)     -> List[str]:






         """simple docstring"""







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

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

         UpperCAmelCase      					=  image.to(dtype=self.image_encoder.dtype       ,							device=__lowerCamelCase							)

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

         UpperCAmelCase      					=  image_embeds.repeat_interleave(__lowerCamelCase       ,							dim=0							)

         if do_classifier_free_guidance:
            UpperCAmelCase      					=  torch.zeros_like(__lowerCamelCase							)

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

         return image_embeds





      @torch.no_grad()
      @replace_example_docstring(__lowerCamelCase							)
      def __call__(		self    :       str       ,							__lowerCamelCase    :       Union[PIL.Image.Image, List[PIL.Image.Image]]       ,							__lowerCamelCase    :       int = 1       ,							__lowerCamelCase    :       int = 2_5       ,							__lowerCamelCase    :       Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,							__lowerCamelCase    :       Optional[torch.FloatTensor] = None       ,							__lowerCamelCase    :       float = 4.0       ,							__lowerCamelCase    :       int = 6_4       ,							__lowerCamelCase    :       Optional[str] = "pil"       ,							__lowerCamelCase    :       bool = True       ,							)     -> Tuple:






         """simple docstring"""







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

         UpperCAmelCase      					=  self._execution_device

         UpperCAmelCase      					=  batch_size * num_images_per_prompt

         UpperCAmelCase      					=  guidance_scale > 1.0
         UpperCAmelCase      					=  self._encode_image(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							)

         # prior

         self.scheduler.set_timesteps(__lowerCamelCase       ,							device=__lowerCamelCase							)
         UpperCAmelCase      					=  self.scheduler.timesteps

         UpperCAmelCase      					=  self.prior.config.num_embeddings
         UpperCAmelCase      					=  self.prior.config.embedding_dim

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

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

            UpperCAmelCase      					=  self.prior(
                __lowerCamelCase       ,							timestep=__lowerCamelCase       ,							proj_embedding=__lowerCamelCase       ,							).predicted_image_embedding

            # remove the variance
            UpperCAmelCase					,	UpperCAmelCase      					=  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      					=  noise_pred.chunk(2							)
               UpperCAmelCase      					=  noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

            UpperCAmelCase      					=  self.scheduler.step(
                __lowerCamelCase       ,							timestep=__lowerCamelCase       ,							sample=__lowerCamelCase       ,							).prev_sample

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

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

            images.append(__lowerCamelCase							)

         UpperCAmelCase      					=  torch.stack(__lowerCamelCase							)

         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      					=  images.cpu().numpy()

         if output_type == "pil":
            UpperCAmelCase      					=  [self.numpy_to_pil(__lowerCamelCase							) 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=__lowerCamelCase							)



