
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					:							Optional[int]           =							logging.get_logger()


@dataclass
class 	UpperCamelCase__       :

						'''simple docstring'''



						__a     :    nn.Module
						__a     :    List[nn.Module]												=			field(default_factory=snake_case__)
						__a     :    list												=			field(default_factory=snake_case__)
						__a     :    Dict[str, nn.Module]												=			field(default_factory=snake_case__)


						def    A__       (  self  , A  , A  , A  , A	)  ->Optional[int]:
									UpperCAmelCase__		:Optional[Any] 		=    len(list(m.modules()	)	) == 1 or isinstance(__lowerCAmelCase  , nn.Convad	) or isinstance(__lowerCAmelCase  , nn.BatchNormad	)
									if has_not_submodules:
												self.traced.append(__lowerCAmelCase	)
												UpperCAmelCase__		:Optional[int] 		=    m


						def __call__(  self  , A	)  ->List[Any]:


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


						@property
						def    A__       (  self	)  ->Any:
									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  , A	)  ->List[str]:
									super().__init__()

									UpperCAmelCase__		:List[Tuple[str, nn.Module]] 		=    []
									# - 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__		:Tuple 		=    len(__lowerCAmelCase	) + 1
												feature_blocks.append((F"""res{block_index}""", v)	)

									UpperCAmelCase__		:List[str] 		=    nn.ModuleDict(__lowerCAmelCase	)


						def    A__       (  self  , A	)  ->Dict:
									return get_trunk_forward_outputs(
									    __lowerCAmelCase  , out_feat_keys=__lowerCAmelCase  , feature_blocks=self._feature_blocks  , )


class 	UpperCamelCase__       (  snake_case__):

						'''simple docstring'''



						def    A__       (  self	)  ->Any:
									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						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:Any 		=    RegNetConfig(depths=[2, 7, 17, 1]     ,						hidden_sizes=[8, 8, 8, 8]     ,						groups_width=8							)
			if "in1k" in model_name:
						UpperCAmelCase__		:Dict 		=    RegNetForImageClassification(UpperCAmelCase_							)
			else:
						UpperCAmelCase__		:str 		=    RegNetModel(UpperCAmelCase_							)
			# create from model (with small weights)
			UpperCAmelCase__		:Union[str, Any] 		=    FakeRegNetVisslWrapper(
			    RegNet(FakeRegNetParams(depth=27     ,						group_width=1010     ,						w_a=1744     ,						w_a=6_20.83     ,						w_m=2.52							)							)							)

			with torch.no_grad():
						UpperCAmelCase__		:Tuple 		=    from_model.eval()
						UpperCAmelCase__		:List[str] 		=    our_model.eval()

						UpperCAmelCase__		:int 		=    torch.randn((1, 3, 32, 32)							)
						# trace both
						UpperCAmelCase__		:Union[str, Any] 		=    Tracker(UpperCAmelCase_							)
						UpperCAmelCase__		:Any 		=    dest_tracker(UpperCAmelCase_							).parametrized

						pprint(dest_tracker.nameamodule							)
						UpperCAmelCase__		:List[str] 		=    Tracker(UpperCAmelCase_							)
						UpperCAmelCase__		:Dict 		=    src_tracker(UpperCAmelCase_							).parametrized

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

			UpperCAmelCase__		:Optional[int] 		=    {}

			UpperCAmelCase__		:Optional[Any] 		=    to_params_dict(UpperCAmelCase_							)
			UpperCAmelCase__		:Tuple 		=    to_params_dict(UpperCAmelCase_							)

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

			return from_to_ours_keys




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE = None     ,						SCREAMING_SNAKE_CASE = True							):




			"""simple docstring"""


			UpperCAmelCase__		:Union[str, Any] 		=    """imagenet-1k-id2label.json"""
			UpperCAmelCase__		:Optional[Any] 		=    1000

			UpperCAmelCase__		:List[Any] 		=    """huggingface/label-files"""
			UpperCAmelCase__		:List[Any] 		=    num_labels
			UpperCAmelCase__		:Any 		=    json.load(open(cached_download(hf_hub_url(UpperCAmelCase_     ,						UpperCAmelCase_     ,						repo_type='dataset'							)							)     ,						'r'							)							)
			UpperCAmelCase__		:List[Any] 		=    {int(UpperCAmelCase_							): v for k, v in idalabel.items()}

			UpperCAmelCase__		:Optional[int] 		=    idalabel
			UpperCAmelCase__		:List[str] 		=    {v: k for k, v in idalabel.items()}

			UpperCAmelCase__		:int 		=    partial(UpperCAmelCase_     ,						num_labels=UpperCAmelCase_     ,						idalabel=UpperCAmelCase_     ,						labelaid=UpperCAmelCase_							)

			UpperCAmelCase__		:Any 		=    {
			    """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
			        depths=[2, 7, 17, 1]     ,						hidden_sizes=[2020, 4040, 11110, 28280]     ,						groups_width=1010							),
			    # finetuned on imagenet
			    """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
			        depths=[2, 7, 17, 1]     ,						hidden_sizes=[2020, 4040, 11110, 28280]     ,						groups_width=1010							),
			}

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

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

			UpperCAmelCase__		:Optional[Any] 		=    get_from_to_our_keys(UpperCAmelCase_							)

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

						UpperCAmelCase__		:List[Any] 		=    {}

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

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

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











'''simple docstring'''





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block




@dataclass
class      UpperCamelCase       (	snake_case__       ):
  a     :torch.FloatTensor






class      UpperCamelCase       (	snake_case__      ,		snake_case__       ):



  @register_to_config
  def __init__(			self		,     __lowerCAmelCase = None		,     __lowerCAmelCase = 3		,     __lowerCAmelCase = 3		,     __lowerCAmelCase = False		,     __lowerCAmelCase = "positional"		,     __lowerCAmelCase = 0		,     __lowerCAmelCase = True		,     __lowerCAmelCase = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")		,     __lowerCAmelCase = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")		,     __lowerCAmelCase = (224, 448, 672, 896)		,     __lowerCAmelCase = 2		,     __lowerCAmelCase = 1		,     __lowerCAmelCase = 1		,     __lowerCAmelCase = "conv"		,     __lowerCAmelCase = "conv"		,     __lowerCAmelCase = "silu"		,     __lowerCAmelCase = 8		,     __lowerCAmelCase = 32		,     __lowerCAmelCase = 1e-5		,     __lowerCAmelCase = "default"		,     __lowerCAmelCase = True		,     __lowerCAmelCase = None		,     __lowerCAmelCase = None		,     ):







      '''simple docstring'''


      super().__init__()

      _a				:							Tuple							       =      sample_size
      _a				:							List[str]							       =      block_out_channels[0] * 4

      # Check inputs
      if len(__lowerCAmelCase ) != len(__lowerCAmelCase ):
          raise ValueError(
              f'''Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.''' )

      if len(__lowerCAmelCase ) != len(__lowerCAmelCase ):
          raise ValueError(
              f'''Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.''' )

      # input
      _a				:							str							       =      nn.Convad(__lowerCAmelCase		,     block_out_channels[0]		,     kernel_size=3		,     padding=(1, 1) )

      # time
      if time_embedding_type == "fourier":
          _a				:							Any							       =      GaussianFourierProjection(embedding_size=block_out_channels[0]		,     scale=16 )
          _a				:							str							       =      2 * block_out_channels[0]
      elif time_embedding_type == "positional":
          _a				:							Any							       =      Timesteps(block_out_channels[0]		,     __lowerCAmelCase		,     __lowerCAmelCase )
          _a				:							Any							       =      block_out_channels[0]

      _a				:							Optional[Any]							       =      TimestepEmbedding(__lowerCAmelCase		,     __lowerCAmelCase )

      # class embedding
      if class_embed_type is None and num_class_embeds is not None:
          _a				:							Optional[Any]							       =      nn.Embedding(__lowerCAmelCase		,     __lowerCAmelCase )
      elif class_embed_type == "timestep":
          _a				:							Any							       =      TimestepEmbedding(__lowerCAmelCase		,     __lowerCAmelCase )
      elif class_embed_type == "identity":
          _a				:							str							       =      nn.Identity(__lowerCAmelCase		,     __lowerCAmelCase )
      else:
          _a				:							Dict							       =      None

      _a				:							List[Any]							       =      nn.ModuleList([] )
      _a				:							str							       =      None
      _a				:							Tuple							       =      nn.ModuleList([] )

      # down
      _a				:							Any							       =      block_out_channels[0]
      for i, down_block_type in enumerate(__lowerCAmelCase ):
          _a				:							List[str]							       =      output_channel
          _a				:							List[Any]							       =      block_out_channels[i]
          _a				:							Tuple							       =      i == len(__lowerCAmelCase ) - 1

          _a				:							int							       =      get_down_block(
              __lowerCAmelCase		,     num_layers=__lowerCAmelCase		,     in_channels=__lowerCAmelCase		,     out_channels=__lowerCAmelCase		,     temb_channels=__lowerCAmelCase		,     add_downsample=not is_final_block		,     resnet_eps=__lowerCAmelCase		,     resnet_act_fn=__lowerCAmelCase		,     resnet_groups=__lowerCAmelCase		,     attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel		,     downsample_padding=__lowerCAmelCase		,     resnet_time_scale_shift=__lowerCAmelCase		,     downsample_type=__lowerCAmelCase		,     )
          self.down_blocks.append(__lowerCAmelCase )

      # mid
      _a				:							str							       =      UNetMidBlockaD(
          in_channels=block_out_channels[-1]		,     temb_channels=__lowerCAmelCase		,     resnet_eps=__lowerCAmelCase		,     resnet_act_fn=__lowerCAmelCase		,     output_scale_factor=__lowerCAmelCase		,     resnet_time_scale_shift=__lowerCAmelCase		,     attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]		,     resnet_groups=__lowerCAmelCase		,     add_attention=__lowerCAmelCase		,     )

      # up
      _a				:							List[Any]							       =      list(reversed(__lowerCAmelCase ) )
      _a				:							Any							       =      reversed_block_out_channels[0]
      for i, up_block_type in enumerate(__lowerCAmelCase ):
          _a				:							str							       =      output_channel
          _a				:							List[Any]							       =      reversed_block_out_channels[i]
          _a				:							Optional[Any]							       =      reversed_block_out_channels[min(i + 1		,     len(__lowerCAmelCase ) - 1 )]

          _a				:							Optional[int]							       =      i == len(__lowerCAmelCase ) - 1

          _a				:							Dict							       =      get_up_block(
              __lowerCAmelCase		,     num_layers=layers_per_block + 1		,     in_channels=__lowerCAmelCase		,     out_channels=__lowerCAmelCase		,     prev_output_channel=__lowerCAmelCase		,     temb_channels=__lowerCAmelCase		,     add_upsample=not is_final_block		,     resnet_eps=__lowerCAmelCase		,     resnet_act_fn=__lowerCAmelCase		,     resnet_groups=__lowerCAmelCase		,     attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel		,     resnet_time_scale_shift=__lowerCAmelCase		,     upsample_type=__lowerCAmelCase		,     )
          self.up_blocks.append(__lowerCAmelCase )
          _a				:							str							       =      output_channel

      # out
      _a				:							List[Any]							       =      norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,     32 )
      _a				:							List[Any]							       =      nn.GroupNorm(num_channels=block_out_channels[0]		,     num_groups=__lowerCAmelCase		,     eps=__lowerCAmelCase )
      _a				:							Dict							       =      nn.SiLU()
      _a				:							Optional[int]							       =      nn.Convad(block_out_channels[0]		,     __lowerCAmelCase		,     kernel_size=3		,     padding=1 )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase = None		,     __lowerCAmelCase = True		,     ):







      '''simple docstring'''


      if self.config.center_input_sample:
          _a				:							str							       =      2 * sample - 1.0

      # 1. time
      _a				:							Dict							       =      timestep
      if not torch.is_tensor(__lowerCAmelCase ):
          _a				:							Union[str, Any]							       =      torch.tensor([timesteps]		,     dtype=torch.long		,     device=sample.device )
      elif torch.is_tensor(__lowerCAmelCase ) and len(timesteps.shape ) == 0:
          _a				:							str							       =      timesteps[None].to(sample.device )

      # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
      _a				:							Union[str, Any]							       =      timesteps * torch.ones(sample.shape[0]		,     dtype=timesteps.dtype		,     device=timesteps.device )

      _a				:							Dict							       =      self.time_proj(__lowerCAmelCase )

      # timesteps does not contain any weights and will always return f32 tensors
      # but time_embedding might actually be running in fp16. so we need to cast here.
      # there might be better ways to encapsulate this.
      _a				:							Optional[Any]							       =      t_emb.to(dtype=self.dtype )
      _a				:							List[str]							       =      self.time_embedding(__lowerCAmelCase )

      if self.class_embedding is not None:
          if class_labels is None:
              raise ValueError("""class_labels should be provided when doing class conditioning""" )

          if self.config.class_embed_type == "timestep":
              _a				:							Any							       =      self.time_proj(__lowerCAmelCase )

          _a				:							Any							       =      self.class_embedding(__lowerCAmelCase ).to(dtype=self.dtype )
          _a				:							List[Any]							       =      emb + class_emb

      # 2. pre-process
      _a				:							str							       =      sample
      _a				:							str							       =      self.conv_in(__lowerCAmelCase )

      # 3. down
      _a				:							Optional[Any]							       =      (sample,)
      for downsample_block in self.down_blocks:
          if hasattr(__lowerCAmelCase		,     """skip_conv""" ):
              _a       , _a       , _a				:							Optional[int]							       =      downsample_block(
                  hidden_states=__lowerCAmelCase		,     temb=__lowerCAmelCase		,     skip_sample=__lowerCAmelCase )
          else:
              _a       , _a				:							Union[str, Any]							       =      downsample_block(hidden_states=__lowerCAmelCase		,     temb=__lowerCAmelCase )

          down_block_res_samples += res_samples

      # 4. mid
      _a				:							Dict							       =      self.mid_block(__lowerCAmelCase		,     __lowerCAmelCase )

      # 5. up
      _a				:							List[Any]							       =      None
      for upsample_block in self.up_blocks:
          _a				:							Optional[Any]							       =      down_block_res_samples[-len(upsample_block.resnets ) :]
          _a				:							Union[str, Any]							       =      down_block_res_samples[: -len(upsample_block.resnets )]

          if hasattr(__lowerCAmelCase		,     """skip_conv""" ):
              _a       , _a				:							List[str]							       =      upsample_block(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase )
          else:
              _a				:							Tuple							       =      upsample_block(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase )

        # 6. post-process
      _a				:							List[Any]							       =      self.conv_norm_out(__lowerCAmelCase )
      _a				:							Optional[int]							       =      self.conv_act(__lowerCAmelCase )
      _a				:							Any							       =      self.conv_out(__lowerCAmelCase )

      if skip_sample is not None:
          sample += skip_sample

      if self.config.time_embedding_type == "fourier":
          _a				:							Optional[Any]							       =      timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:] ))) )
          _a				:							Dict							       =      sample / timesteps

      if not return_dict:
          return (sample,)

      return UNetaDOutput(sample=__lowerCAmelCase )


