





import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__   :				int						=	logging.get_logger()





@dataclass
class A_   :
   lowerCAmelCase__  	=					42
   lowerCAmelCase__  	=					field(default_factory=_lowerCamelCase  )
   lowerCAmelCase__  	=					field(default_factory=_lowerCamelCase  )
   lowerCAmelCase__  	=					field(default_factory=_lowerCamelCase  )




   def _lowerCAmelCase (self      :List[str]       ,  _UpperCamelCase      :Union[str, Any]       ,  _UpperCamelCase      :Tensor       ,  _UpperCamelCase      :Tensor       ,  _UpperCamelCase      :str					)->   Dict:
         __A			  =							len(list(m.modules()					)					) == 1 or isinstance(_UpperCamelCase       ,  nn.Convad					) or isinstance(_UpperCamelCase       ,  nn.BatchNormad					)
         if has_not_submodules:
               self.traced.append(_UpperCamelCase					)
               __A			  =							m




   def __call__(self      :Dict       ,  _UpperCamelCase      :Tensor					)->   str:
         for name, m in self.module.named_modules():
               self.handles.append(m.register_forward_hook(partial(self._forward_hook       ,  name=_UpperCamelCase					)					)					)
         self.module(_UpperCamelCase					)
         [x.remove() for x in self.handles]
         return self






   @property
   def _lowerCAmelCase (self      :List[Any]					)->   int:
         # check the len of the state_dict keys to see if we have learnable params
         return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()					)					) > 0}





class A_   (     nn.Module  ):




   def __init__(self      :Tuple       ,  _UpperCamelCase      :nn.Module					)->   Optional[Any]:
         super().__init__()

         __A			  =							[]
         # - get the stem
         feature_blocks.append(('''conv1''', model.stem)					)
         # - get all the feature blocks
         for k, v in model.trunk_output.named_children():
               assert k.startswith('''block'''					), f"""Unexpected layer name {k}"""
               __A			  =							len(_UpperCamelCase					) + 1
               feature_blocks.append((f"""res{block_index}""", v)					)

         __A			  =							nn.ModuleDict(_UpperCamelCase					)






   def _lowerCAmelCase (self      :Union[str, Any]       ,  _UpperCamelCase      :Tensor					)->   Dict:
         return get_trunk_forward_outputs(
             _UpperCamelCase       ,  out_feat_keys=_UpperCamelCase       ,  feature_blocks=self._feature_blocks       ,  )





class A_   (     _lowerCamelCase  ):




   def _lowerCAmelCase (self      :List[str]					)->   Tuple:
         return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]





def _a							(      lowerCamelCase:  str					)      ->		Dict[str, str]:







      '''simple docstring'''






      __A			  =							RegNetConfig(depths=[2, 7, 17, 1]   ,							hidden_sizes=[8, 8, 8, 8]   ,							groups_width=8					)
      if "in1k" in model_name:
            __A			  =							RegNetForImageClassification(lowerCamelCase					)
      else:
            __A			  =							RegNetModel(lowerCamelCase					)
      # create from model (with small weights)
      __A			  =							FakeRegNetVisslWrapper(
          RegNet(FakeRegNetParams(depth=27   ,							group_width=10_10   ,							w_a=17_44   ,							w_a=620.83   ,							w_m=2.52					)					)					)

      with torch.no_grad():
            __A			  =							from_model.eval()
            __A			  =							our_model.eval()

            __A			  =							torch.randn((1, 3, 32, 32)					)
            # trace both
            __A			  =							Tracker(lowerCamelCase					)
            __A			  =							dest_tracker(lowerCamelCase					).parametrized

            pprint(dest_tracker.nameamodule					)
            __A			  =							Tracker(lowerCamelCase					)
            __A			  =							src_tracker(lowerCamelCase					).parametrized

      # convert the keys -> module dict to keys -> params
      def to_params_dict(lowerCamelCase:  Optional[Any]					):
            __A			  =							OrderedDict()
            for name, module in dict_with_modules.items():
                  for param_name, param in module.state_dict().items():
                        __A			  =							param
            return params_dict

      __A			  =							{}

      __A			  =							to_params_dict(lowerCamelCase					)
      __A			  =							to_params_dict(lowerCamelCase					)

      for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()   ,							dst_state_dict.items()					):
            __A			  =							dest_key
            logger.info(F"""{src_key} -> {dest_key}"""					)
      # if "in1k" was in the model_name it means it must have a classification head (was finetuned)
      if "in1k" in model_name:
            __A			  =							'''classifier.1.weight'''
            __A			  =							'''classifier.1.bias'''

      return from_to_ours_keys



def _a							(      lowerCamelCase:  Path   ,							lowerCamelCase:  str = None   ,							lowerCamelCase:  bool = True					)      ->		Optional[int]:







      '''simple docstring'''






      __A			  =							'''imagenet-1k-id2label.json'''
      __A			  =							10_00

      __A			  =							'''huggingface/label-files'''
      __A			  =							num_labels
      __A			  =							json.load(open(cached_download(hf_hub_url(lowerCamelCase   ,							lowerCamelCase   ,							repo_type='''dataset'''					)					)   ,							'''r'''					)					)
      __A			  =							{int(lowerCamelCase					): v for k, v in idalabel.items()}

      __A			  =							idalabel
      __A			  =							{v: k for k, v in idalabel.items()}

      __A			  =							partial(lowerCamelCase   ,							num_labels=lowerCamelCase   ,							idalabel=lowerCamelCase   ,							labelaid=lowerCamelCase					)

      __A			  =							{
          '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]   ,							hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]   ,							groups_width=10_10					),
          # finetuned on imagenet
          '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]   ,							hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]   ,							groups_width=10_10					),
      }

      # add seer weights logic
      def load_using_classy_vision(lowerCamelCase:  str					) -> Tuple[Dict, Dict]:
            __A			  =							torch.hub.load_state_dict_from_url(lowerCamelCase   ,							model_dir=str(lowerCamelCase					)   ,							map_location='''cpu'''					)
            # check if we have a head, if yes add it
            __A			  =							files['''classy_state_dict''']['''base_model''']['''model''']
            return model_state_dict["trunk"], model_state_dict["heads"]

      __A			  =							{
          '''regnet-y-10b-seer''': partial(
              lowerCamelCase   ,							'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''   ,							),
          '''regnet-y-10b-seer-in1k''': partial(
              lowerCamelCase   ,							'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'''   ,							),
      }

      __A			  =							get_from_to_our_keys(lowerCamelCase					)

      if not (save_directory / F"""{model_name}.pth""").exists():
            logger.info('''Loading original state_dict.'''					)
            __A  ,		__A			  =							names_to_from_model[model_name]()
            __A			  =							from_state_dict_trunk
            if "in1k" in model_name:
                  # add the head
                  __A			  =							{**from_state_dict_trunk, **from_state_dict_head}
            logger.info('''Done!'''					)

            __A			  =							{}

            __A			  =							list(from_state_dict.keys()					)
            __A			  =							r'''\.block.-part.'''
            # this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
            for key in from_state_dict.keys():
                  # remove the weird "block[0,1]-part" from the key
                  __A			  =							re.sub(lowerCamelCase   ,							''''''   ,							lowerCamelCase					)
                  # now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
                  __A			  =							from_to_ours_keys[src_key]
                  # store the parameter with our key
                  __A			  =							from_state_dict[key]
                  not_used_keys.remove(lowerCamelCase					)
            # check that all keys have been updated
            assert len(lowerCamelCase					) == 0, F"""Some keys where not used {','.join(lowerCamelCase					)}"""

            logger.info(F"""The following keys were not used: {','.join(lowerCamelCase					)}"""					)

            # save our state dict to disk
            torch.save(lowerCamelCase   ,							save_directory / F"""{model_name}.pth"""					)

            del converted_state_dict
      else:
            logger.info('''The state_dict was already stored on disk.'''					)
      if push_to_hub:
            logger.info(F"""Token is {os.environ['HF_TOKEN']}"""					)
            logger.info('''Loading our model.'''					)
            # create our model
            __A			  =							names_to_config[model_name]
            __A			  =							RegNetModel
            if "in1k" in model_name:
                  __A			  =							RegNetForImageClassification
            __A			  =							our_model_func(lowerCamelCase					)
            # place our model to the meta device (so remove all the weights)
            our_model.to(torch.device('''meta'''					)					)
            logger.info('''Loading state_dict in our model.'''					)
            # load state dict
            __A			  =							our_model.state_dict().keys()
            PreTrainedModel._load_pretrained_model_low_mem(
                lowerCamelCase   ,							lowerCamelCase   ,							[save_directory / F"""{model_name}.pth"""]					)
            logger.info('''Finally, pushing!'''					)
            # push it to hub
            our_model.push_to_hub(
                repo_path_or_name=save_directory / model_name   ,							commit_message='''Add model'''   ,							output_dir=save_directory / model_name   ,							)
            __A			  =							3_84
            # we can use the convnext one
            __A			  =							AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''   ,							size=lowerCamelCase					)
            image_processor.push_to_hub(
                repo_path_or_name=save_directory / model_name   ,							commit_message='''Add image processor'''   ,							output_dir=save_directory / model_name   ,							)


if __name__ == "__main__":
       snake_case__   :				Optional[Any]						=	argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--model_name',
           default=None,
           type=str,
           help=(
               'The name of the model you wish to convert, it must be one of the supported regnet* architecture,'
               ' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'
           ),
       )
       parser.add_argument(
           '--pytorch_dump_folder_path',
           default=None,
           type=Path,
           required=True,
           help='Path to the output PyTorch model directory.',
       )
       parser.add_argument(
           '--push_to_hub',
           default=True,
           type=bool,
           required=False,
           help='If True, push model and image processor to the hub.',
       )

       snake_case__   :				Tuple						=	parser.parse_args()

       snake_case__   :				Path						=	args.pytorch_dump_folder_path
       pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
       convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)












import math
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

snake_case__   :				Any						=	'\n    Examples:\n        ```py\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif\n\n        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n        >>> repo = "openai/shap-e"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 15.0\n        >>> prompt = "a shark"\n\n        >>> images = pipe(\n        ...     prompt,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], "shark_3d.gif")\n        ```\n'





@dataclass
class A_   (     _lowerCamelCase  ):
   lowerCAmelCase__  	=					42





class A_   (     _lowerCamelCase  ):




   def __init__(self      :List[str]       ,  _UpperCamelCase      :PriorTransformer       ,  _UpperCamelCase      :CLIPTextModelWithProjection       ,  _UpperCamelCase      :CLIPTokenizer       ,  _UpperCamelCase      :HeunDiscreteScheduler       ,  _UpperCamelCase      :ShapERenderer       ,  )->   Tuple:
         super().__init__()

         self.register_modules(
             prior=_UpperCamelCase       ,  text_encoder=_UpperCamelCase       ,  tokenizer=_UpperCamelCase       ,  scheduler=_UpperCamelCase       ,  renderer=_UpperCamelCase       ,  )




   def _lowerCAmelCase (self      :Any       ,  _UpperCamelCase      :str       ,  _UpperCamelCase      :Optional[int]       ,  _UpperCamelCase      :Union[str, Any]       ,  _UpperCamelCase      :Optional[int]       ,  _UpperCamelCase      :str       ,  _UpperCamelCase      :Union[str, Any]					)->   Any:
         if latents is None:
               __A			  =							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}"""					)
               __A			  =							latents.to(_UpperCamelCase					)

         __A			  =							latents * scheduler.init_noise_sigma
         return latents




   def _lowerCAmelCase (self      :Union[str, Any]       ,  _UpperCamelCase      :Dict=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.text_encoder, self.prior]
         for cpu_offloaded_model in models:
               if cpu_offloaded_model is not None:
                     cpu_offload(_UpperCamelCase       ,  _UpperCamelCase					)




   def _lowerCAmelCase (self      :Tuple       ,  _UpperCamelCase      :Dict=0					)->   Tuple:
         if is_accelerate_available() and is_accelerate_version('''>='''       ,  '''0.17.0.dev0'''					):
               from accelerate import cpu_offload_with_hook
         else:
               raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''					)

         __A			  =							torch.device(f"""cuda:{gpu_id}"""					)

         if self.device.type != "cpu":
               self.to('''cpu'''       ,  silence_dtype_warnings=_UpperCamelCase					)
               torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

         __A			  =							None
         for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
               __A  ,		__A			  =							cpu_offload_with_hook(_UpperCamelCase       ,  _UpperCamelCase       ,  prev_module_hook=_UpperCamelCase					)

         if self.safety_checker is not None:
               __A  ,		__A			  =							cpu_offload_with_hook(self.safety_checker       ,  _UpperCamelCase       ,  prev_module_hook=_UpperCamelCase					)

         # We'll offload the last model manually.
         __A			  =							hook




   @property
   def _lowerCAmelCase (self      :int					)->   int:
         if self.device != torch.device('''meta'''					) or not hasattr(self.text_encoder       ,  '''_hf_hook'''					):
               return self.device
         for module in self.text_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 _lowerCAmelCase (self      :str       ,  _UpperCamelCase      :Optional[Any]       ,  _UpperCamelCase      :Optional[int]       ,  _UpperCamelCase      :Any       ,  _UpperCamelCase      :int       ,  )->   Dict:
         len(_UpperCamelCase					) if isinstance(_UpperCamelCase       ,  _UpperCamelCase					) else 1

         # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
         __A			  =							0
         # get prompt text embeddings
         __A			  =							self.tokenizer(
             _UpperCamelCase       ,  padding='''max_length'''       ,  max_length=self.tokenizer.model_max_length       ,  truncation=_UpperCamelCase       ,  return_tensors='''pt'''       ,  )
         __A			  =							text_inputs.input_ids
         __A			  =							self.tokenizer(_UpperCamelCase       ,  padding='''longest'''       ,  return_tensors='''pt'''					).input_ids

         if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(_UpperCamelCase       ,  _UpperCamelCase					):
               __A			  =							self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]					)
               logger.warning(
                   '''The following part of your input was truncated because CLIP can only handle sequences up to'''
                   f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""					)

         __A			  =							self.text_encoder(text_input_ids.to(_UpperCamelCase					)					)
         __A			  =							text_encoder_output.text_embeds

         __A			  =							prompt_embeds.repeat_interleave(_UpperCamelCase       ,  dim=0					)
         # in Shap-E it normalize the prompt_embeds and then later rescale it
         __A			  =							prompt_embeds / torch.linalg.norm(_UpperCamelCase       ,  dim=-1       ,  keepdim=_UpperCamelCase					)

         if do_classifier_free_guidance:
               __A			  =							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
               __A			  =							torch.cat([negative_prompt_embeds, prompt_embeds]					)

         # Rescale the features to have unit variance
         __A			  =							math.sqrt(prompt_embeds.shape[1]					) * prompt_embeds

         return prompt_embeds






   @torch.no_grad()
   @replace_example_docstring(_UpperCamelCase					)
   def __call__(self      :Optional[Any]       ,  _UpperCamelCase      :str       ,  _UpperCamelCase      :int = 1       ,  _UpperCamelCase      :int = 25       ,  _UpperCamelCase      :Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,  _UpperCamelCase      :Optional[torch.FloatTensor] = None       ,  _UpperCamelCase      :float = 4.0       ,  _UpperCamelCase      :int = 64       ,  _UpperCamelCase      :Optional[str] = "pil"       ,  _UpperCamelCase      :bool = True       ,  )->   Optional[Any]:

         if isinstance(_UpperCamelCase       ,  _UpperCamelCase					):
               __A			  =							1
         elif isinstance(_UpperCamelCase       ,  _UpperCamelCase					):
               __A			  =							len(_UpperCamelCase					)
         else:
               raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(_UpperCamelCase					)}"""					)

         __A			  =							self._execution_device

         __A			  =							batch_size * num_images_per_prompt

         __A			  =							guidance_scale > 1.0
         __A			  =							self._encode_prompt(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)

         # prior

         self.scheduler.set_timesteps(_UpperCamelCase       ,  device=_UpperCamelCase					)
         __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)       ,  prompt_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
         __A			  =							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
               __A			  =							torch.cat([latents] * 2					) if do_classifier_free_guidance else latents
               __A			  =							self.scheduler.scale_model_input(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							self.prior(
                   _UpperCamelCase       ,  timestep=_UpperCamelCase       ,  proj_embedding=_UpperCamelCase       ,  ).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(
                   _UpperCamelCase       ,  timestep=_UpperCamelCase       ,  sample=_UpperCamelCase       ,  ).prev_sample

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

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

         __A			  =							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}"""					)

         __A			  =							images.cpu().numpy()

         if output_type == "pil":
               __A			  =							[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					)





