


'''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()
lowerCAmelCase  =				logging.get_logger()






@dataclass
class 					lowerCAmelCase_     :
       __UpperCAmelCase				=42
       __UpperCAmelCase				=field(default_factory=UpperCAmelCase_  )
       __UpperCAmelCase				=field(default_factory=UpperCAmelCase_  )
       __UpperCAmelCase				=field(default_factory=UpperCAmelCase_  )






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )->			Optional[int]:
            _A				=       len(list(m.modules()      )      ) == 1 or isinstance(__UpperCamelCase ,    nn.Convad      ) or isinstance(__UpperCamelCase ,    nn.BatchNormad      )
            if has_not_submodules:
                 self.traced.append(__UpperCamelCase      )
                 _A				=       m






       def __call__(   self ,    _UpperCamelCase      )->			int:
            for name, m in self.module.named_modules():
                 self.handles.append(m.register_forward_hook(partial(self._forward_hook ,    name=__UpperCamelCase      )      )      )
            self.module(__UpperCamelCase      )
            [x.remove() for x in self.handles]
            return self






       @property
       def      UpperCamelCase	(   self      )->			Union[str, 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  ):






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

            _A				=       []
            # - 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				=       len(__UpperCamelCase      ) + 1
                 feature_blocks.append((F'res{block_index}', v)      )

            _A				=       nn.ModuleDict(__UpperCamelCase      )






       def      UpperCamelCase	(   self ,    _UpperCamelCase      )->			Tuple:
            return get_trunk_forward_outputs(
                __UpperCamelCase ,    out_feat_keys=__UpperCamelCase ,    feature_blocks=self._feature_blocks ,    )






class 					lowerCAmelCase_     (    UpperCAmelCase_  ):






       def      UpperCamelCase	(   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_   (   __UpperCamelCase				:					Dict		)				->  Optional[Any]:







     """simple docstring"""
     _A				=       RegNetConfig(depths=[2, 7, 1_7, 1]       ,						hidden_sizes=[8, 8, 8, 8]       ,						groups_width=8		)
     if "in1k" in model_name:
          _A				=       RegNetForImageClassification(lowercase_		)
     else:
          _A				=       RegNetModel(lowercase_		)
     # create from model (with small weights)
     _A				=       FakeRegNetVisslWrapper(
         RegNet(FakeRegNetParams(depth=2_7       ,						group_width=1_0_1_0       ,						w_a=1_7_4_4       ,						w_a=6_20.83       ,						w_m=2.52		)		)		)

     with torch.no_grad():
          _A				=       from_model.eval()
          _A				=       our_model.eval()

          _A				=       torch.randn((1, 3, 3_2, 3_2)		)
          # trace both
          _A				=       Tracker(lowercase_		)
          _A				=       dest_tracker(lowercase_		).parametrized

          pprint(dest_tracker.nameamodule		)
          _A				=       Tracker(lowercase_		)
          _A				=       src_tracker(lowercase_		).parametrized

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

     _A				=       {}

     _A				=       to_params_dict(lowercase_		)
     _A				=       to_params_dict(lowercase_		)

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

     return from_to_ours_keys

def 				lowerCamelCase_   (   __UpperCamelCase				:					Tuple       ,						__UpperCamelCase				:					Dict = None       ,						__UpperCamelCase				:					Optional[int] = True		)				->  Any:







     """simple docstring"""
     _A				=       'imagenet-1k-id2label.json'
     _A				=       1_0_0_0

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

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

     _A				=       partial(lowercase_       ,						num_labels=lowercase_       ,						idalabel=lowercase_       ,						labelaid=lowercase_		)

     _A				=       {
         'regnet-y-10b-seer': ImageNetPreTrainedConfig(
             depths=[2, 7, 1_7, 1]       ,						hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]       ,						groups_width=1_0_1_0		),
         # finetuned on imagenet
         'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
             depths=[2, 7, 1_7, 1]       ,						hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]       ,						groups_width=1_0_1_0		),
     }

     # add seer weights logic
     def load_using_classy_vision(__UpperCamelCase				:					List[Any]		) -> Tuple[Dict, Dict]:
          _A				=       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
          _A				=       files['classy_state_dict']['base_model']['model']
          return model_state_dict["trunk"], model_state_dict["heads"]

     _A				=       {
         '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'       ,						),
     }

     _A				=       get_from_to_our_keys(lowercase_		)

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

          _A				=       {}

          _A				=       list(from_state_dict.keys()		)
          _A				=       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				=       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
               _A				=       from_to_ours_keys[src_key]
               # store the parameter with our key
               _A				=       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
          _A				=       names_to_config[model_name]
          _A				=       RegNetModel
          if "in1k" in model_name:
               _A				=       RegNetForImageClassification
          _A				=       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
          _A				=       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       ,						)
          _A				=       3_8_4
          # we can use the convnext one
          _A				=       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__":
      lowerCAmelCase  =				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.""",
      )

      lowerCAmelCase  =				parser.parse_args()

      lowerCAmelCase  =				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 argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()

def 				lowerCamelCase_   (   __UpperCamelCase				:					Tuple       ,						__UpperCamelCase				:					Any       ,						__UpperCamelCase				:					Dict       ,						__UpperCamelCase				:					List[str]       ,						__UpperCamelCase				:					str		)				->  List[str]:







     """simple docstring"""
     # Initialise PyTorch model
     _A				=       BertConfig.from_pretrained(
         'bert-large-cased'       ,						vocab_size=__UpperCamelCase       ,						max_position_embeddings=5_1_2       ,						is_decoder=__UpperCamelCase       ,						add_cross_attention=__UpperCamelCase       ,						)
     _A				=       bert_config.to_dict()
     del bert_config_dict["type_vocab_size"]
     _A				=       BertGenerationConfig(**__UpperCamelCase		)
     if is_encoder:
          _A				=       BertGenerationEncoder(__UpperCamelCase		)
     else:
          _A				=       BertGenerationDecoder(__UpperCamelCase		)
     print(F'Building PyTorch model from configuration: {config}'		)

     # Load weights from tf checkpoint
     load_tf_weights_in_bert_generation(
         __UpperCamelCase       ,						__UpperCamelCase       ,						model_class='bert'       ,						is_encoder_named_decoder=__UpperCamelCase       ,						is_encoder=__UpperCamelCase       ,						)

     # Save pytorch-model
     print(F'Save PyTorch model and config to {pytorch_dump_path}'		)
     model.save_pretrained(__UpperCamelCase		)


if __name__ == "__main__":
    lowerCAmelCase  =				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--tf_hub_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--is_encoder_named_decoder""",
        action="""store_true""",
        help="""If decoder has to be renamed to encoder in PyTorch model.""",
    )
    parser.add_argument("""--is_encoder""", action="""store_true""", help="""If model is an encoder.""")
    parser.add_argument("""--vocab_size""", default=50_358, type=int, help="""Vocab size of model""")
    lowerCAmelCase  =				parser.parse_args()
    convert_tf_checkpoint_to_pytorch(
        args.tf_hub_path,
        args.pytorch_dump_path,
        args.is_encoder_named_decoder,
        args.vocab_size,
        is_encoder=args.is_encoder,
    )





