

"""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        __lowercase     :
  snake_case_			    =    42
  snake_case_			    =    field(default_factory=lowerCamelCase_   )
  snake_case_			    =    field(default_factory=lowerCamelCase_   )
  snake_case_			    =    field(default_factory=lowerCamelCase_   )

  def 				__lowercase    (					self						:     int   ,A						:     Any   ,A						:     Optional[int]   ,A						:     Dict   ,A						:     List[str]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Any   				=   len(list(m.modules()      )      ) == 1 or isinstance(A   ,nn.Convad      ) or isinstance(A   ,nn.BatchNormad      )
   if has_not_submodules:
    self.traced.append(A      )
    UpperCAmelCase__   :					List[str]   				=   m

  def __call__(					self						:     Optional[int]   ,A						:     List[Any]      ):






   '''simple docstring'''







   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 				__lowercase    (					self						:     int      ):






   '''simple docstring'''
   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						:     int   ,A						:     Any      ):






   '''simple docstring'''
   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(A      ) + 1
    feature_blocks.append((f"res{block_index}", v)      )

   UpperCAmelCase__   :					int   				=   nn.ModuleDict(A      )




  def 				__lowercase    (					self						:     Dict   ,A						:     Any      ):






   '''simple docstring'''
   return get_trunk_forward_outputs(
       A   ,out_feat_keys=A   ,feature_blocks=self._feature_blocks   ,)







class        __lowercase     (      lowerCamelCase_   ):

  def 				__lowercase    (					self						:     Tuple      ):






   '''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  (   __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					Union[str, Any]   				=   RegNetConfig(depths=[2, 7, 17, 1]							,      hidden_sizes=[8, 8, 8, 8]							,      groups_width=8 )
 if "in1k" in model_name:
  UpperCAmelCase__   :					str   				=   RegNetForImageClassification(lowercase__ )
 else:
  UpperCAmelCase__   :					Optional[Any]   				=   RegNetModel(lowercase__ )
 # create from model (with small weights)
 UpperCAmelCase__   :					str   				=   FakeRegNetVisslWrapper(
     RegNet(FakeRegNetParams(depth=27							,      group_width=1010							,      w_a=1744							,      w_a=620.83							,      w_m=2.52 ) ) )

 with torch.no_grad():
  UpperCAmelCase__   :					int   				=   from_model.eval()
  UpperCAmelCase__   :					Optional[Any]   				=   our_model.eval()

  UpperCAmelCase__   :					List[Any]   				=   torch.randn((1, 3, 32, 32) )
  # trace both
  UpperCAmelCase__   :					Tuple   				=   Tracker(lowercase__ )
  UpperCAmelCase__   :					List[str]   				=   dest_tracker(lowercase__ ).parametrized

  pprint(dest_tracker.nameamodule )
  UpperCAmelCase__   :					Optional[int]   				=   Tracker(lowercase__ )
  UpperCAmelCase__   :					Optional[Any]   				=   src_tracker(lowercase__ ).parametrized

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

 UpperCAmelCase__   :					Optional[Any]   				=   {}

 UpperCAmelCase__   :					str   				=   to_params_dict(lowercase__ )
 UpperCAmelCase__   :					List[Any]   				=   to_params_dict(lowercase__ )

 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__   :					Optional[Any]   				=   "classifier.1.weight"
  UpperCAmelCase__   :					int   				=   "classifier.1.bias"

 return from_to_ours_keys
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase = None							,      __UpperCamelCase = True ):
 '''simple docstring'''





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

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

 UpperCAmelCase__   :					int   				=   idalabel
 UpperCAmelCase__   :					Tuple   				=   {v: k for k, v in idalabel.items()}

 UpperCAmelCase__   :					Tuple   				=   partial(lowercase__							,      num_labels=lowercase__							,      idalabel=lowercase__							,      labelaid=lowercase__ )

 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(__UpperCamelCase ) -> Tuple[Dict, Dict]:
  UpperCAmelCase__   :					List[Any]   				=   torch.hub.load_state_dict_from_url(lowercase__							,      model_dir=str(lowercase__ )							,      map_location="""cpu""" )
  # check if we have a head, if yes add it
  UpperCAmelCase__   :					Optional[Any]   				=   files["classy_state_dict"]["base_model"]["model"]
  return model_state_dict["trunk"], model_state_dict["heads"]

 UpperCAmelCase__   :					Optional[Any]   				=   {
     "regnet-y-10b-seer": partial(
         lowercase__							,      """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""							,      ),
     "regnet-y-10b-seer-in1k": partial(
         lowercase__							,      """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""							,      ),
 }

 UpperCAmelCase__   :					List[str]   				=   get_from_to_our_keys(lowercase__ )

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

  UpperCAmelCase__   :					Any   				=   {}

  UpperCAmelCase__   :					Dict   				=   list(from_state_dict.keys() )
  UpperCAmelCase__   :					Optional[int]   				=   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__   :					Optional[Any]   				=   re.sub(lowercase__							,      """"""							,      lowercase__ )
   # 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__   :					Any   				=   from_state_dict[key]
   not_used_keys.remove(lowercase__ )
  # check that all keys have been updated
  assert len(lowercase__ ) == 0, F"Some keys where not used {','.join(lowercase__ )}"

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

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








"""simple docstring"""




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation


__UpperCAmelCase	=			logging.get_logger(__name__)

__UpperCAmelCase	=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__UpperCAmelCase	=			{
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

__UpperCAmelCase	=			{
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

__UpperCAmelCase	=			{
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}







class        __lowercase     (      __lowerCamelCase   ):
  snake_case_			    =    VOCAB_FILES_NAMES
  snake_case_			    =    PRETRAINED_VOCAB_FILES_MAP
  snake_case_			    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  snake_case_			    =    ["""input_ids""", """attention_mask""", """token_type_ids"""]
  snake_case_			    =    DebertaTokenizer

  def __init__(					self						:     Optional[Any]   ,A						:     Any=None   ,A						:     Any=None   ,A						:     Dict=None   ,A						:     Optional[int]="replace"   ,A						:     Optional[int]="[CLS]"   ,A						:     str="[SEP]"   ,A						:     Optional[int]="[SEP]"   ,A						:     List[Any]="[CLS]"   ,A						:     Dict="[UNK]"   ,A						:     List[str]="[PAD]"   ,A						:     Optional[Any]="[MASK]"   ,A						:     str=False   ,**A						:     Dict   ,):






   '''simple docstring'''
   super().__init__(
       A   ,A   ,tokenizer_file=A   ,errors=A   ,bos_token=A   ,eos_token=A   ,unk_token=A   ,sep_token=A   ,cls_token=A   ,pad_token=A   ,mask_token=A   ,add_prefix_space=A   ,**A   ,)
   UpperCAmelCase__   :					Tuple   				=   kwargs.pop("""add_bos_token"""   ,A      )

   UpperCAmelCase__   :					Tuple   				=   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
   if pre_tok_state.get("""add_prefix_space"""   ,A      ) != add_prefix_space:
    UpperCAmelCase__   :					List[Any]   				=   getattr(A   ,pre_tok_state.pop("""type"""      )      )
    UpperCAmelCase__   :					Any   				=   add_prefix_space
    UpperCAmelCase__   :					List[Any]   				=   pre_tok_class(**A      )

   UpperCAmelCase__   :					str   				=   add_prefix_space

  @property
  def 				__lowercase    (					self						:     str      ):






   '''simple docstring'''
   if self._mask_token is None:
    if self.verbose:
     logger.error("""Using mask_token, but it is not set yet."""      )
    return None
   return str(self._mask_token      )

  @mask_token.setter
  def 				__lowercase    (					self						:     Dict   ,A						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					str   				=   AddedToken(A   ,lstrip=A   ,rstrip=A      ) if isinstance(A   ,A      ) else value
   UpperCAmelCase__   :					List[str]   				=   value

  def 				__lowercase    (					self						:     Tuple   ,A						:     List[int]   ,A						:     Optional[List[int]] = None      ):






   '''simple docstring'''
   if token_ids_a is None:
    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   UpperCAmelCase__   :					Dict   				=   [self.cls_token_id]
   UpperCAmelCase__   :					str   				=   [self.sep_token_id]
   return cls + token_ids_a + sep + token_ids_a + sep

  def 				__lowercase    (					self						:     Tuple   ,A						:     List[int]   ,A						:     Optional[List[int]] = None      ):






   '''simple docstring'''
   UpperCAmelCase__   :					List[str]   				=   [self.sep_token_id]
   UpperCAmelCase__   :					str   				=   [self.cls_token_id]

   if token_ids_a is None:
    return len(cls + token_ids_a + sep      ) * [0]
   return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]

  def 				__lowercase    (					self						:     int   ,*A						:     Tuple   ,**A						:     Optional[int]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					int   				=   kwargs.get("""is_split_into_words"""   ,A      )
   assert self.add_prefix_space or not is_split_into_words, (
       f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
       "to use it with pretokenized inputs."
   )

   return super()._batch_encode_plus(*A   ,**A      )

  def 				__lowercase    (					self						:     str   ,*A						:     List[str]   ,**A						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   kwargs.get("""is_split_into_words"""   ,A      )

   assert self.add_prefix_space or not is_split_into_words, (
       f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
       "to use it with pretokenized inputs."
   )

   return super()._encode_plus(*A   ,**A      )

  def 				__lowercase    (					self						:     Tuple   ,A						:     str   ,A						:     Optional[str] = None      ):






   '''simple docstring'''
   UpperCAmelCase__   :					int   				=   self._tokenizer.model.save(A   ,name=A      )
   return tuple(A      )




  def 				__lowercase    (					self						:     List[str]   ,A						:     "Conversation"      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Dict   				=   []
   for is_user, text in conversation.iter_texts():
    input_ids.extend(self.encode(A   ,add_special_tokens=A      ) + [self.eos_token_id]      )

   if len(A      ) > self.model_max_length:
    UpperCAmelCase__   :					Optional[int]   				=   input_ids[-self.model_max_length :]
   return input_ids





