






'''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()
SCREAMING_SNAKE_CASE_				      =							logging.get_logger()




@dataclass
class 							lowerCAmelCase					:







   """simple docstring"""







   _A						=   42
   _A						=   field(default_factory=lowerCamelCase__   )
   _A						=   field(default_factory=lowerCamelCase__   )
   _A						=   field(default_factory=lowerCamelCase__   )



   def    __magic_name__    ( self   ,   _A   ,   _A   ,   _A   ,   _A			)    ->   List[str]:
    __a    :	Dict									=						len(list(m.modules()			)			) == 1 or isinstance(_A   ,   nn.Convad			) or isinstance(_A   ,   nn.BatchNormad			)
    if has_not_submodules:
     self.traced.append(_A			)
     __a    :	Dict									=						m



   def __call__( self   ,   _A			)    ->   Optional[Any]:
    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    __magic_name__    ( self			)    ->   Optional[Any]:
    # 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   ):







   """simple docstring"""







   def __init__( self   ,   _A			)    ->   Any:
    super().__init__()

    __a    :	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}'''
     __a    :	int									=						len(_A			) + 1
     feature_blocks.append((f'''res{block_index}''', v)			)

    __a    :	int									=						nn.ModuleDict(_A			)



   def    __magic_name__    ( self   ,   _A			)    ->   Optional[int]:
    return get_trunk_forward_outputs(
        _A   ,   out_feat_keys=_A   ,   feature_blocks=self._feature_blocks   ,   )




class 							lowerCAmelCase					(							lowerCamelCase__   ):







   """simple docstring"""







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

 with torch.no_grad():
  __a    :	str									=						from_model.eval()
  __a    :	Optional[int]									=						our_model.eval()

  __a    :	List[str]									=						torch.randn((1, 3, 32, 32)		)
  # trace both
  __a    :	List[str]									=						Tracker(SCREAMING_SNAKE_CASE__		)
  __a    :	Optional[int]									=						dest_tracker(SCREAMING_SNAKE_CASE__		).parametrized

  pprint(dest_tracker.nameamodule		)
  __a    :	int									=						Tracker(SCREAMING_SNAKE_CASE__		)
  __a    :	Dict									=						src_tracker(SCREAMING_SNAKE_CASE__		).parametrized

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

 __a    :	Optional[int]									=						{}

 __a    :	Dict									=						to_params_dict(SCREAMING_SNAKE_CASE__		)
 __a    :	Optional[Any]									=						to_params_dict(SCREAMING_SNAKE_CASE__		)

 for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()       ,		dst_state_dict.items()		):
  __a    :	Optional[Any]									=						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:
  __a    :	List[str]									=						'classifier.1.weight'
  __a    :	Tuple									=						'classifier.1.bias'

 return from_to_ours_keys






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__ = None       ,		SCREAMING_SNAKE_CASE__ = True		):
 __a    :	str									=						'imagenet-1k-id2label.json'
 __a    :	Optional[int]									=						1000

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

 __a    :	Optional[Any]									=						idalabel
 __a    :	Optional[Any]									=						{v: k for k, v in idalabel.items()}

 __a    :	List[Any]									=						partial(SCREAMING_SNAKE_CASE__       ,		num_labels=SCREAMING_SNAKE_CASE__       ,		idalabel=SCREAMING_SNAKE_CASE__       ,		labelaid=SCREAMING_SNAKE_CASE__		)

 __a    :	str									=						{
     '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]:
  __a    :	int									=						torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__       ,		model_dir=str(SCREAMING_SNAKE_CASE__		)       ,		map_location='cpu'		)
  # check if we have a head, if yes add it
  __a    :	str									=						files['classy_state_dict']['base_model']['model']
  return model_state_dict["trunk"], model_state_dict["heads"]

 __a    :	Union[str, Any]									=						{
     'regnet-y-10b-seer': partial(
         SCREAMING_SNAKE_CASE__       ,		'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'       ,		),
     'regnet-y-10b-seer-in1k': partial(
         SCREAMING_SNAKE_CASE__       ,		'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'       ,		),
 }

 __a    :	Optional[Any]									=						get_from_to_our_keys(SCREAMING_SNAKE_CASE__		)

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

  __a    :	str									=						{}

  __a    :	Optional[Any]									=						list(from_state_dict.keys()		)
  __a    :	Any									=						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
   __a    :	Union[str, Any]									=						re.sub(SCREAMING_SNAKE_CASE__       ,		''       ,		SCREAMING_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
   __a    :	Any									=						from_to_ours_keys[src_key]
   # store the parameter with our key
   __a    :	Optional[Any]									=						from_state_dict[key]
   not_used_keys.remove(SCREAMING_SNAKE_CASE__		)
  # check that all keys have been updated
  assert len(SCREAMING_SNAKE_CASE__		) == 0, f'''Some keys where not used {','.join(SCREAMING_SNAKE_CASE__		)}'''

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

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

  SCREAMING_SNAKE_CASE_				      =							parser.parse_args()

  SCREAMING_SNAKE_CASE_				      =							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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_				      =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				      =							{
    "squeezebert/squeezebert-uncased": (
        "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"
    ),
    "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json",
    "squeezebert/squeezebert-mnli-headless": (
        "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"
    ),
}




class 							lowerCAmelCase					(							lowerCamelCase__   ):







   """simple docstring"""







   _A						=   SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
   _A						=   'squeezebert'



   def __init__( self   ,   _A=30522   ,   _A=768   ,   _A=12   ,   _A=12   ,   _A=3072   ,   _A="gelu"   ,   _A=0.1   ,   _A=0.1   ,   _A=512   ,   _A=2   ,   _A=0.02   ,   _A=1E-1_2   ,   _A=0   ,   _A=768   ,   _A=4   ,   _A=4   ,   _A=4   ,   _A=1   ,   _A=4   ,   _A=4   ,   **_A   ,   )    ->   List[Any]:
    super().__init__(pad_token_id=_A   ,   **_A			)

    __a    :	Optional[int]									=						vocab_size
    __a    :	Optional[int]									=						hidden_size
    __a    :	int									=						num_hidden_layers
    __a    :	int									=						num_attention_heads
    __a    :	Tuple									=						hidden_act
    __a    :	Optional[Any]									=						intermediate_size
    __a    :	Any									=						hidden_dropout_prob
    __a    :	List[Any]									=						attention_probs_dropout_prob
    __a    :	List[str]									=						max_position_embeddings
    __a    :	Tuple									=						type_vocab_size
    __a    :	Tuple									=						initializer_range
    __a    :	List[str]									=						layer_norm_eps
    __a    :	str									=						embedding_size
    __a    :	Any									=						q_groups
    __a    :	Any									=						k_groups
    __a    :	Union[str, Any]									=						v_groups
    __a    :	List[str]									=						post_attention_groups
    __a    :	Dict									=						intermediate_groups
    __a    :	Optional[Any]									=						output_groups




class 							lowerCAmelCase					(							lowerCamelCase__   ):







   """simple docstring"""







   @property
   def    __magic_name__    ( self			)    ->   Mapping[str, Mapping[int, str]]:
    if self.task == "multiple-choice":
     __a    :	Tuple									=						{0: 'batch', 1: 'choice', 2: 'sequence'}
    else:
     __a    :	List[Any]									=						{0: 'batch', 1: 'sequence'}
    return OrderedDict(
        [
            ('input_ids', dynamic_axis),
            ('attention_mask', dynamic_axis),
            ('token_type_ids', dynamic_axis),
        ]			)




