





"""simple docstring"""





from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

_lowerCamelCase       =	'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''




def 		lowerCAmelCase_	(  lowercase_   :							str ,      lowercase_   :							Tuple ,      lowercase_   :							Any=8		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			int												=       height // scale_factor**2
 if height % scale_factor**2 != 0:
  new_height += 1
 __SCREAMING_SNAKE_CASE :			List[str]												=       width // scale_factor**2
 if width % scale_factor**2 != 0:
  new_width += 1
 return new_height * scale_factor, new_width * scale_factor




def 		lowerCAmelCase_	(  lowercase_   :							str ,      lowercase_   :							int=512 ,      lowercase_   :							List[Any]=512		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			List[Any]												=       pil_image.resize((w, h) ,      resample=Image.BICUBIC ,      reducing_gap=1		)
 __SCREAMING_SNAKE_CASE :			int												=       np.array(pil_image.convert('''RGB'''		)		)
 __SCREAMING_SNAKE_CASE :			Any												=       arr.astype(np.floataa		) / 127.5 - 1
 __SCREAMING_SNAKE_CASE :			List[str]												=       np.transpose(lowercase_ ,      [2, 0, 1]		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       torch.from_numpy(lowercase_		).unsqueeze(0		)
 return image






class 			snake_case		(   __UpperCAmelCase   ):

      def __init__(			self   :Any						,							_lowerCamelCase   :UNetaDConditionModel						,							_lowerCamelCase   :DDPMScheduler						,							_lowerCamelCase   :VQModel						,							):
       super().__init__()

       self.register_modules(
           unet=_lowerCamelCase						,							scheduler=_lowerCamelCase						,							movq=_lowerCamelCase						,							)
       __SCREAMING_SNAKE_CASE :			List[Any]												=       2 ** (len(self.movq.config.block_out_channels						) - 1)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						,							_lowerCamelCase   :List[str]						,							_lowerCamelCase   :str						,							_lowerCamelCase   :List[str]						):
       # get the original timestep using init_timestep
       __SCREAMING_SNAKE_CASE :			int												=       min(int(num_inference_steps * strength						)						,							_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			Optional[int]												=       max(num_inference_steps - init_timestep						,							0						)
       __SCREAMING_SNAKE_CASE :			str												=       self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						,							_lowerCamelCase   :Union[str, Any]						,							_lowerCamelCase   :Union[str, Any]						,							_lowerCamelCase   :int						,							_lowerCamelCase   :Dict						,							_lowerCamelCase   :List[str]						,							_lowerCamelCase   :Optional[Any]						,							_lowerCamelCase   :Union[str, Any]=None						):
       if not isinstance(_lowerCamelCase						,							(torch.Tensor, PIL.Image.Image, list)						):
        raise ValueError(
            f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_lowerCamelCase						)}'''						)

       __SCREAMING_SNAKE_CASE :			Any												=       image.to(device=_lowerCamelCase						,							dtype=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       batch_size * num_images_per_prompt

       if image.shape[1] == 4:
        __SCREAMING_SNAKE_CASE :			Any												=       image

       else:
        if isinstance(_lowerCamelCase						,							_lowerCamelCase						) and len(_lowerCamelCase						) != batch_size:
         raise ValueError(
             f'''You have passed a list of generators of length {len(_lowerCamelCase						)}, but requested an effective batch'''
             f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''						)

        elif isinstance(_lowerCamelCase						,							_lowerCamelCase						):
         __SCREAMING_SNAKE_CASE :			Optional[Any]												=       [
             self.movq.encode(image[i : i + 1]						).latent_dist.sample(generator[i]						) for i in range(_lowerCamelCase						)
         ]
         __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       torch.cat(_lowerCamelCase						,							dim=0						)
        else:
         __SCREAMING_SNAKE_CASE :			List[Any]												=       self.movq.encode(_lowerCamelCase						).latent_dist.sample(_lowerCamelCase						)

        __SCREAMING_SNAKE_CASE :			str												=       self.movq.config.scaling_factor * init_latents

       __SCREAMING_SNAKE_CASE :			int												=       torch.cat([init_latents]						,							dim=0						)

       __SCREAMING_SNAKE_CASE :			Any												=       init_latents.shape
       __SCREAMING_SNAKE_CASE :			Any												=       randn_tensor(_lowerCamelCase						,							generator=_lowerCamelCase						,							device=_lowerCamelCase						,							dtype=_lowerCamelCase						)

       # get latents
       __SCREAMING_SNAKE_CASE :			List[Any]												=       self.scheduler.add_noise(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			int												=       init_latents

       return latents

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						,							_lowerCamelCase   :Tuple=0						):
       if is_accelerate_available():
        from accelerate import cpu_offload
       else:
        raise ImportError('''Please install accelerate via `pip install accelerate`'''						)

       __SCREAMING_SNAKE_CASE :			str												=       torch.device(f'''cuda:{gpu_id}'''						)

       __SCREAMING_SNAKE_CASE :			int												=       [
           self.unet,
           self.movq,
       ]
       for cpu_offloaded_model in models:
        if cpu_offloaded_model is not None:
         cpu_offload(_lowerCamelCase						,							_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :str						,							_lowerCamelCase   :Dict=0						):
       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.'''						)

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

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

       __SCREAMING_SNAKE_CASE :			Dict												=       None
       for cpu_offloaded_model in [self.unet, self.movq]:
        __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[str]												=       cpu_offload_with_hook(_lowerCamelCase						,							_lowerCamelCase						,							prev_module_hook=_lowerCamelCase						)

       # We'll offload the last model manually.
       __SCREAMING_SNAKE_CASE :			Tuple												=       hook

      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def 						SCREAMING_SNAKE_CASE_			(			self   :List[str]						):
       if not hasattr(self.unet						,							'''_hf_hook'''						):
        return self.device
       for module in self.unet.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



      @torch.no_grad()
      @replace_example_docstring(_lowerCamelCase						)
      def __call__(			self   :Dict						,							_lowerCamelCase   :Union[torch.FloatTensor, List[torch.FloatTensor]]						,							_lowerCamelCase   :Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]						,							_lowerCamelCase   :Union[torch.FloatTensor, List[torch.FloatTensor]]						,							_lowerCamelCase   :torch.FloatTensor						,							_lowerCamelCase   :int = 5_1_2						,							_lowerCamelCase   :int = 5_1_2						,							_lowerCamelCase   :int = 1_0_0						,							_lowerCamelCase   :float = 4.0						,							_lowerCamelCase   :float = 0.3						,							_lowerCamelCase   :int = 1						,							_lowerCamelCase   :Optional[Union[torch.Generator, List[torch.Generator]]] = None						,							_lowerCamelCase   :Optional[str] = "pil"						,							_lowerCamelCase   :bool = True						,							):
       __SCREAMING_SNAKE_CASE :			Any												=       self._execution_device

       __SCREAMING_SNAKE_CASE :			Dict												=       guidance_scale > 1.0

       if isinstance(_lowerCamelCase						,							_lowerCamelCase						):
        __SCREAMING_SNAKE_CASE :			List[Any]												=       torch.cat(_lowerCamelCase						,							dim=0						)
       if isinstance(_lowerCamelCase						,							_lowerCamelCase						):
        __SCREAMING_SNAKE_CASE :			int												=       torch.cat(_lowerCamelCase						,							dim=0						)
       if isinstance(_lowerCamelCase						,							_lowerCamelCase						):
        __SCREAMING_SNAKE_CASE :			Tuple												=       torch.cat(_lowerCamelCase						,							dim=0						)

       __SCREAMING_SNAKE_CASE :			Tuple												=       image_embeds.shape[0]

       if do_classifier_free_guidance:
        __SCREAMING_SNAKE_CASE :			Tuple												=       image_embeds.repeat_interleave(_lowerCamelCase						,							dim=0						)
        __SCREAMING_SNAKE_CASE :			Tuple												=       negative_image_embeds.repeat_interleave(_lowerCamelCase						,							dim=0						)
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       hint.repeat_interleave(_lowerCamelCase						,							dim=0						)

       __SCREAMING_SNAKE_CASE :			Dict												=       torch.cat([negative_image_embeds, image_embeds]						,							dim=0						).to(dtype=self.unet.dtype						,							device=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       torch.cat([hint, hint]						,							dim=0						).to(dtype=self.unet.dtype						,							device=_lowerCamelCase						)

       if not isinstance(_lowerCamelCase						,							_lowerCamelCase						):
        __SCREAMING_SNAKE_CASE :			str												=       [image]
       if not all(isinstance(_lowerCamelCase						,							(PIL.Image.Image, torch.Tensor)						) for i in image						):
        raise ValueError(
            f'''Input is in incorrect format: {[type(_lowerCamelCase						) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''						)

       __SCREAMING_SNAKE_CASE :			Tuple												=       torch.cat([prepare_image(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						) for i in image]						,							dim=0						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       image.to(dtype=image_embeds.dtype						,							device=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			int												=       self.movq.encode(_lowerCamelCase						)['''latents''']
       __SCREAMING_SNAKE_CASE :			str												=       latents.repeat_interleave(_lowerCamelCase						,							dim=0						)
       self.scheduler.set_timesteps(_lowerCamelCase						,							device=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			str												=       self.get_timesteps(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       timesteps[:1].repeat(batch_size * num_images_per_prompt						)
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Dict												=       downscale_height_and_width(_lowerCamelCase						,							_lowerCamelCase						,							self.movq_scale_factor						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       self.prepare_latents(
           _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							image_embeds.dtype						,							_lowerCamelCase						,							_lowerCamelCase						)
       for i, t in enumerate(self.progress_bar(_lowerCamelCase						)						):
        # expand the latents if we are doing classifier free guidance
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       torch.cat([latents] * 2						) if do_classifier_free_guidance else latents

        __SCREAMING_SNAKE_CASE :			Optional[Any]												=       {'''image_embeds''': image_embeds, '''hint''': hint}
        __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       self.unet(
            sample=_lowerCamelCase						,							timestep=_lowerCamelCase						,							encoder_hidden_states=_lowerCamelCase						,							added_cond_kwargs=_lowerCamelCase						,							return_dict=_lowerCamelCase						,							)[0]

        if do_classifier_free_guidance:
         __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[Any]												=       noise_pred.split(latents.shape[1]						,							dim=1						)
         __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[Any]												=       noise_pred.chunk(2						)
         __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Any												=       variance_pred.chunk(2						)
         __SCREAMING_SNAKE_CASE :			List[Any]												=       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
         __SCREAMING_SNAKE_CASE :			str												=       torch.cat([noise_pred, variance_pred_text]						,							dim=1						)

        if not (
            hasattr(self.scheduler.config						,							'''variance_type'''						)
            and self.scheduler.config.variance_type in ["learned", "learned_range"]
        ):
         __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Any												=       noise_pred.split(latents.shape[1]						,							dim=1						)

        # compute the previous noisy sample x_t -> x_t-1

        __SCREAMING_SNAKE_CASE :			List[str]												=       self.scheduler.step(
            _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							generator=_lowerCamelCase						,							)[0]

       # post-processing
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.movq.decode(_lowerCamelCase						,							force_not_quantize=_lowerCamelCase						)['''sample''']

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

       if output_type in ["np", "pil"]:
        __SCREAMING_SNAKE_CASE :			int												=       image * 0.5 + 0.5
        __SCREAMING_SNAKE_CASE :			int												=       image.clamp(0						,							1						)
        __SCREAMING_SNAKE_CASE :			List[Any]												=       image.cpu().permute(0						,							2						,							3						,							1						).float().numpy()

       if output_type == "pil":
        __SCREAMING_SNAKE_CASE :			List[str]												=       self.numpy_to_pil(_lowerCamelCase						)

       if not return_dict:
        return (image,)

       return ImagePipelineOutput(images=_lowerCamelCase						)


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()
lowerCamelCase__   = logging.get_logger()




@dataclass
class  _UpperCAmelCase       :



 '''simple docstring'''




 __A           = 42
 __A           = field(default_factory=lowerCAmelCase   )
 __A           = field(default_factory=lowerCAmelCase   )
 __A           = field(default_factory=lowerCAmelCase   )







 def 						__UpperCAmelCase							(						self							:					Optional[Any]				,      lowercase_							:					Union[str, Any]				,      lowercase_							:					Tensor				,      lowercase_							:					Tensor				,      lowercase_							:					str)		->     Tuple:


   """simple docstring"""







   _UpperCamelCase				=  len(list(m.modules())) == 1 or isinstance(lowercase_				,      nn.Convad) or isinstance(lowercase_				,      nn.BatchNormad)
   if has_not_submodules:
     self.traced.append(lowercase_)
     _UpperCamelCase				=  m







 def __call__(						self							:					Optional[Any]				,      lowercase_							:					Tensor)		->     List[str]:


   """simple docstring"""







   for name, m in self.module.named_modules():
     self.handles.append(m.register_forward_hook(partial(self._forward_hook				,      name=lowercase_)))
   self.module(lowercase_)
   [x.remove() for x in self.handles]
   return self







 @property
 def 						__UpperCAmelCase							(						self							:					Dict)		->     List[Any]:


   """simple docstring"""







   return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys())) > 0}




class  _UpperCAmelCase       (			nn.Module   ):



 '''simple docstring'''




 def __init__(						self							:					Tuple				,      lowercase_							:					nn.Module)		->     List[str]:


   """simple docstring"""







   super().__init__()

   _UpperCamelCase				=  []
   # - 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}'
     _UpperCamelCase				=  len(lowercase_) + 1
     feature_blocks.append((f'res{block_index}', v))

   _UpperCamelCase				=  nn.ModuleDict(lowercase_)







 def 						__UpperCAmelCase							(						self							:					int				,      lowercase_							:					Tensor)		->     int:


   """simple docstring"""







   return get_trunk_forward_outputs(
       lowercase_				,      out_feat_keys=lowercase_				,      feature_blocks=self._feature_blocks				,      )




class  _UpperCAmelCase       (			lowerCAmelCase   ):



 '''simple docstring'''




 def 						__UpperCAmelCase							(						self							:					List[Any])		->     List[Any]:


   """simple docstring"""







   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 							lowerCAmelCase__    (       a__      )	->Dict[str, str]:




  '''simple docstring'''

  _UpperCamelCase				=  RegNetConfig(depths=[2, 7, 17, 1]  ,			hidden_sizes=[8, 8, 8, 8]  ,			groups_width=8      )
  if "in1k" in model_name:
    _UpperCamelCase				=  RegNetForImageClassification(a__      )
  else:
    _UpperCamelCase				=  RegNetModel(a__      )
  # create from model (with small weights)
  _UpperCamelCase				=  FakeRegNetVisslWrapper(
      RegNet(FakeRegNetParams(depth=27  ,			group_width=1_010  ,			w_a=1_744  ,			w_a=620.83  ,			w_m=2.52      )      )      )

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

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

    pprint(dest_tracker.nameamodule      )
    _UpperCamelCase				=  Tracker(a__      )
    _UpperCamelCase				=  src_tracker(a__      ).parametrized

  # convert the keys -> module dict to keys -> params
  def to_params_dict(a__      ):
    _UpperCamelCase				=  OrderedDict()
    for name, module in dict_with_modules.items():
      for param_name, param in module.state_dict().items():
        _UpperCamelCase				=  param
    return params_dict

  _UpperCamelCase				=  {}

  _UpperCamelCase				=  to_params_dict(a__      )
  _UpperCamelCase				=  to_params_dict(a__      )

  for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()  ,			dst_state_dict.items()      ):
    _UpperCamelCase				=  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:
    _UpperCamelCase				=  "classifier.1.weight"
    _UpperCamelCase				=  "classifier.1.bias"

  return from_to_ours_keys


def 							lowerCAmelCase__    (       a__  ,			a__ = None  ,			a__ = True      )	->Dict:




  '''simple docstring'''

  _UpperCamelCase				=  "imagenet-1k-id2label.json"
  _UpperCamelCase				=  1_000

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

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

  _UpperCamelCase				=  partial(a__  ,			num_labels=a__  ,			idalabel=a__  ,			labelaid=a__      )

  _UpperCamelCase				=  {
      "regnet-y-10b-seer": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]  ,			hidden_sizes=[2_020, 4_040, 11_110, 28_280]  ,			groups_width=1_010      ),
      # finetuned on imagenet
      "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]  ,			hidden_sizes=[2_020, 4_040, 11_110, 28_280]  ,			groups_width=1_010      ),
  }

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

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

  _UpperCamelCase				=  get_from_to_our_keys(a__      )

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

    _UpperCamelCase				=  {}

    _UpperCamelCase				=  list(from_state_dict.keys()      )
    _UpperCamelCase				=  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
      _UpperCamelCase				=  re.sub(a__  ,			""  ,			a__      )
      # 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
      _UpperCamelCase				=  from_to_ours_keys[src_key]
      # store the parameter with our key
      _UpperCamelCase				=  from_state_dict[key]
      not_used_keys.remove(a__      )
    # check that all keys have been updated
    assert len(a__      ) == 0, f'Some keys where not used {",".join(a__      )}'

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

    # save our state dict to disk
    torch.save(a__  ,			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
    _UpperCamelCase				=  names_to_config[model_name]
    _UpperCamelCase				=  RegNetModel
    if "in1k" in model_name:
      _UpperCamelCase				=  RegNetForImageClassification
    _UpperCamelCase				=  our_model_func(a__      )
    # 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
    _UpperCamelCase				=  our_model.state_dict().keys()
    PreTrainedModel._load_pretrained_model_low_mem(
        a__  ,			a__  ,			[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  ,			)
    _UpperCamelCase				=  384
    # we can use the convnext one
    _UpperCamelCase				=  AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"  ,			size=a__      )
    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__":
   lowerCamelCase__   = 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.''',
   )

   lowerCamelCase__   = parser.parse_args()

   lowerCamelCase__   = 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)




