

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()
__A					=	logging.get_logger()



@dataclass
class   lowercase_    :
      UpperCamelCase_			:		Any						      =				4_2
      UpperCamelCase_			:		str						      =				field(default_factory=snake_case_     )
      UpperCamelCase_			:		Dict						      =				field(default_factory=snake_case_     )
      UpperCamelCase_			:		Union[str, Any]						      =				field(default_factory=snake_case_     )



      def 						UpperCamelCase_		(			self      :				List[str]			,					A__      :				int			,					A__      :				List[str]			,					A__      :				int			,					A__      :				List[str]		)    ->     str:
        _snake_case						=							len(list(m.modules()		)		) == 1 or isinstance(A_			,					nn.Convad		) or isinstance(A_			,					nn.BatchNormad		)
        if has_not_submodules:
          self.traced.append(A_		)
          _snake_case						=							m



      def __call__(			self      :				Union[str, Any]			,					A__      :				Union[str, Any]		)    ->     int:






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







      @property
      def 						UpperCamelCase_		(			self      :				Dict		)    ->     Union[str, Any]:
        return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()		)		) > 0}



class   lowercase_    (  nn.Module     ):



      def __init__(			self      :				Any			,					A__      :				Tuple		)    ->     List[Any]:
        super().__init__()

        _snake_case						=							[]
        # - 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}"""
          _snake_case						=							len(A_		) + 1
          feature_blocks.append((f"""res{block_index}""", v)		)

        _snake_case						=							nn.ModuleDict(A_		)







      def 						UpperCamelCase_		(			self      :				str			,					A__      :				Dict		)    ->     Union[str, Any]:
        return get_trunk_forward_outputs(
            A_			,					out_feat_keys=A_			,					feature_blocks=self._feature_blocks			,					)



class   lowercase_    (  snake_case_     ):



      def 						UpperCamelCase_		(			self      :				int		)    ->     List[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       snake_case_(_UpperCamelCase     )							->	Union[str, Any]:



  """simple docstring"""






  _snake_case						=							RegNetConfig(depths=[2, 7, 17, 1] ,       hidden_sizes=[8, 8, 8, 8] ,       groups_width=8     )
  if "in1k" in model_name:
    _snake_case						=							RegNetForImageClassification(_UpperCamelCase     )
  else:
    _snake_case						=							RegNetModel(_UpperCamelCase     )
  # create from model (with small weights)
  _snake_case						=							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():
    _snake_case						=							from_model.eval()
    _snake_case						=							our_model.eval()

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

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

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

  _snake_case						=							{}

  _snake_case						=							to_params_dict(_UpperCamelCase     )
  _snake_case						=							to_params_dict(_UpperCamelCase     )

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

  return from_to_ours_keys



def       snake_case_(_UpperCamelCase ,       _UpperCamelCase = None ,       _UpperCamelCase = True     )							->	str:



  """simple docstring"""






  _snake_case						=							'''imagenet-1k-id2label.json'''
  _snake_case						=							1_000

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

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

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

  _snake_case						=							{
      '''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(_UpperCamelCase     ) -> Tuple[Dict, Dict]:
    _snake_case						=							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
    _snake_case						=							files['''classy_state_dict''']['''base_model''']['''model''']
    return model_state_dict["trunk"], model_state_dict["heads"]

  _snake_case						=							{
      '''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''' ,       ),
  }

  _snake_case						=							get_from_to_our_keys(_UpperCamelCase     )

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

    _snake_case						=							{}

    _snake_case						=							list(from_state_dict.keys()     )
    _snake_case						=							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
      _snake_case						=							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
      _snake_case						=							from_to_ours_keys[src_key]
      # store the parameter with our key
      _snake_case						=							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
    _snake_case						=							names_to_config[model_name]
    _snake_case						=							RegNetModel
    if "in1k" in model_name:
      _snake_case						=							RegNetForImageClassification
    _snake_case						=							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
    _snake_case						=							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 ,       )
    _snake_case						=							384
    # we can use the convnext one
    _snake_case						=							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__":
 __A					=	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.''',
 )

 __A					=	parser.parse_args()

 __A					=	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 typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve







def 						A_(			A		:						np.ndarray						,			A		:						np.ndarray						,			A		:						np.ndarray):
							UpperCamelCase					       =			np.stack((horizontal_flow, vertical_flow)						,			2)

							# Create a grid of all pixel coordinates and subtract the flow to get the
							# target pixels coordinates
							UpperCamelCase					       =			np.stack(
							    np.meshgrid(np.arange(0						,			image.shape[1])						,			np.arange(0						,			image.shape[0]))						,			2)
							UpperCamelCase					       =			np.round(grid - flow).astype(np.intaa)

							# Find the locations outside of the original image
							UpperCamelCase					       =			(grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]))
							UpperCamelCase					       =			0

							UpperCamelCase					       =			image[grid[:, :, 1], grid[:, :, 0]]

							# Set pixels at invalid locations to 0
							UpperCamelCase					       =			0

							return warped







def 						A_(			A		:						np.ndarray						,			A		:						np.ndarray						,			A		:						SupportsIndex						,			A		:						float | None = None						,			):
							if alpha is None:
														UpperCamelCase					       =			0.1

							# Initialize flow
							UpperCamelCase					       =			np.zeros_like(A)
							UpperCamelCase					       =			np.zeros_like(A)

							# Prepare kernels for the calculation of the derivatives and the average velocity
							UpperCamelCase					       =			np.array([[-1, 1], [-1, 1]]) * 0.25
							UpperCamelCase					       =			np.array([[-1, -1], [1, 1]]) * 0.25
							UpperCamelCase					       =			np.array([[1, 1], [1, 1]]) * 0.25
							UpperCamelCase					       =			np.array(
							    [[1 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]])

							# Iteratively refine the flow
							for _ in range(A):
														UpperCamelCase					       =			warp(A						,			A						,			A)
														UpperCamelCase					       =			convolve(A						,			A) + convolve(A						,			A)
														UpperCamelCase					       =			convolve(A						,			A) + convolve(A						,			A)
														UpperCamelCase					       =			convolve(A						,			A) + convolve(A						,			-kernel_t)

														UpperCamelCase					       =			convolve(A						,			A)
														UpperCamelCase					       =			convolve(A						,			A)

														# This updates the flow as proposed in the paper (Step 12)
														UpperCamelCase					       =			(
														    derivative_x * avg_horizontal_velocity
														    + derivative_y * avg_vertical_velocity
														    + derivative_t
														)
														UpperCamelCase					       =			update / (alpha**2 + derivative_x**2 + derivative_y**2)

														UpperCamelCase					       =			avg_horizontal_velocity - derivative_x * update
														UpperCamelCase					       =			avg_vertical_velocity - derivative_y * update

							return horizontal_flow, vertical_flow


if __name__ == "__main__":
						import doctest

						doctest.testmod()




