





from dataclasses import dataclass

lowerCAmelCase_        =			"""0.1"""
lowerCAmelCase_        =			"""Lucia Harcekova"""



@dataclass
class 				_lowerCAmelCase			:


  '''simple docstring'''
  a_							:				int
  a_							:				int
  a_							:				str







  def __repr__(					self					:    Any							):


    '''simple docstring'''





    return f"""({self.offset}, {self.length}, {self.indicator})"""




class 				_lowerCAmelCase			:


  '''simple docstring'''







  def __init__(					self					:    Any					, UpperCamelCase					:    int = 13					, UpperCamelCase					:    int = 6							):


    '''simple docstring'''





    _snake_case      : Tuple														=		window_size
    _snake_case      : Optional[Any]														=		lookahead_buffer_size
    _snake_case      : Any														=		self.window_size - self.lookahead_buffer_size







  def UpperCamelCase_       (					self					:    Dict					, UpperCamelCase					:    str							):


    '''simple docstring'''





    _snake_case      : List[str]														=		[]
    _snake_case      : Union[str, Any]														=		''

    # while there are still characters in text to compress
    while text:
      # find the next encoding phrase
      # - triplet with offset, length, indicator (the next encoding character)
      _snake_case      : List[Any]														=		self._find_encoding_token(UpperCamelCase					, UpperCamelCase							)

      # update the search buffer:
      # - add new characters from text into it
      # - check if size exceed the max search buffer size, if so, drop the
      #   oldest elements
      search_buffer += text[: token.length + 1]
      if len(UpperCamelCase							) > self.search_buffer_size:
        _snake_case      : List[Any]														=		search_buffer[-self.search_buffer_size :]

      # update the text
      _snake_case      : int														=		text[token.length + 1 :]

      # append the token to output
      output.append(UpperCamelCase							)

    return output







  def UpperCamelCase_       (					self					:    Optional[int]					, UpperCamelCase					:    list[Token]							):


    '''simple docstring'''





    _snake_case      : List[str]														=		''

    for token in tokens:
      for _ in range(token.length							):
        output += output[-token.offset]
      output += token.indicator

    return output







  def UpperCamelCase_       (					self					:    str					, UpperCamelCase					:    str					, UpperCamelCase					:    str							):


    '''simple docstring'''





    if not text:
      raise ValueError('We need some text to work with.'							)

    # Initialise result parameters to default values
    _snake_case							,    _snake_case      : Union[str, Any]														=		0, 0

    if not search_buffer:
      return Token(UpperCamelCase					, UpperCamelCase					, text[length]							)

    for i, character in enumerate(UpperCamelCase							):
      _snake_case      : Tuple														=		len(UpperCamelCase							) - i
      if character == text[0]:
        _snake_case      : int														=		self._match_length_from_index(UpperCamelCase					, UpperCamelCase					, 0					, UpperCamelCase							)
        # if the found length is bigger than the current or if it's equal,
        # which means it's offset is smaller: update offset and length
        if found_length >= length:
          _snake_case							,    _snake_case      : List[Any]														=		found_offset, found_length

    return Token(UpperCamelCase					, UpperCamelCase					, text[length]							)







  def UpperCamelCase_       (					self					:    str					, UpperCamelCase					:    str					, UpperCamelCase					:    str					, UpperCamelCase					:    int					, UpperCamelCase					:    int							):


    '''simple docstring'''





    if not text or text[text_index] != window[window_index]:
      return 0
    return 1 + self._match_length_from_index(
        UpperCamelCase					, window + text[text_index]					, text_index + 1					, window_index + 1							)


if __name__ == "__main__":
      from doctest import testmod

      testmod()
      # Initialize compressor class
      lowerCAmelCase_        =			LZaaCompressor(window_size=13, lookahead_buffer_size=6)

      # Example
      lowerCAmelCase_        =			"""cabracadabrarrarrad"""
      lowerCAmelCase_        =			lzaa_compressor.compress(TEXT)
      print(lzaa_compressor.compress("""ababcbababaa"""))
      lowerCAmelCase_        =			lzaa_compressor.decompress(compressed_text)
      assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."












"""simple docstring"""







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()
_UpperCamelCase     			=  logging.get_logger()







@dataclass
class 					lowerCamelCase__					:
    SCREAMING_SNAKE_CASE		=			42
    SCREAMING_SNAKE_CASE		=			field(default_factory=snake_case )
    SCREAMING_SNAKE_CASE		=			field(default_factory=snake_case )
    SCREAMING_SNAKE_CASE		=			field(default_factory=snake_case )






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






    def __call__( self	,A					):
          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					):
          # 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 					lowerCamelCase__					(       nn.Module ):






    def __init__( self	,A					):
          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(A					) + 1
                feature_blocks.append((F'''res{block_index}''', v)					)

          UpperCAmelCase							=       nn.ModuleDict(A					)






    def 				_UpperCamelCase							( self	,A					):
          return get_trunk_forward_outputs(
              A	,out_feat_keys=A	,feature_blocks=self._feature_blocks	,)







class 					lowerCamelCase__					(       snake_case ):






    def 				_UpperCamelCase							( self					):
          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 (		_snake_case							):







      """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(_snake_case							)
      else:
            UpperCAmelCase							=       RegNetModel(_snake_case							)
      # create from model (with small weights)
      UpperCAmelCase							=       FakeRegNetVisslWrapper(
          RegNet(FakeRegNetParams(depth=27      ,       group_width=1010      ,       w_a=1744      ,       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(_snake_case							)
            UpperCAmelCase							=       dest_tracker(_snake_case							).parametrized

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

      # convert the keys -> module dict to keys -> params
      def to_params_dict(_snake_case							):
            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(_snake_case							)
      UpperCAmelCase							=       to_params_dict(_snake_case							)

      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 _a (		_snake_case      ,       _snake_case = None      ,       _snake_case = True							):







      """simple docstring"""






      UpperCAmelCase							=       """imagenet-1k-id2label.json"""
      UpperCAmelCase							=       1000

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

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

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

      UpperCAmelCase							=       {
          """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]      ,       hidden_sizes=[2020, 4040, 1_1110, 2_8280]      ,       groups_width=1010							),
          # finetuned on imagenet
          """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]      ,       hidden_sizes=[2020, 4040, 1_1110, 2_8280]      ,       groups_width=1010							),
      }

      # add seer weights logic
      def load_using_classy_vision(_snake_case							) -> Tuple[Dict, Dict]:
            UpperCAmelCase							=       torch.hub.load_state_dict_from_url(_snake_case      ,       model_dir=str(_snake_case							)      ,       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(
              _snake_case      ,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""      ,       ),
          """regnet-y-10b-seer-in1k""": partial(
              _snake_case      ,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""      ,       ),
      }

      UpperCAmelCase							=       get_from_to_our_keys(_snake_case							)

      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(_snake_case      ,       """"""      ,       _snake_case							)
                  # 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(_snake_case							)
            # check that all keys have been updated
            assert len(_snake_case							) == 0, F'''Some keys where not used {','.join(_snake_case							)}'''

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

            # save our state dict to disk
            torch.save(_snake_case      ,       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(_snake_case							)
            # 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(
                _snake_case      ,       _snake_case      ,       [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=_snake_case							)
            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__":
  _UpperCamelCase     			=  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.""",
  )

  _UpperCamelCase     			=  parser.parse_args()

  _UpperCamelCase     			=  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)



