
import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


__lowerCamelCase							=       logging.getLogger(__name__)


def 			UpperCamelCase					(    ):
       snake_case       :   Tuple							=      argparse.ArgumentParser(
           description="Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset."    )
       parser.add_argument(
           "--dataset_name"				,					type=__lowerCamelCase				,					default="wikitext"				,					help="Name of the training. Explore datasets at: hf.co/datasets."				,					)
       parser.add_argument(
           "--dataset_config"				,					type=__lowerCamelCase				,					default="wikitext-103-raw-v1"				,					help="Configuration name of the dataset."    )
       parser.add_argument(
           "--tokenizer_name_or_path"				,					type=__lowerCamelCase				,					default="sayakpaul/unigram-tokenizer-wikitext"				,					help="Tokenizer identifier. Can be a local filepath or a Hub identifier."				,					)
       parser.add_argument(
           "--shard_size"				,					type=__lowerCamelCase				,					default=1000				,					help="Number of entries to go in a single shard."				,					)
       parser.add_argument("--split"				,					type=__lowerCamelCase				,					default="train"				,					choices=["train", "test", "validation"]    )
       parser.add_argument(
           "--limit"				,					default=__lowerCamelCase				,					type=__lowerCamelCase				,					help="Limit the number of shards (used for debugging)."				,					)
       parser.add_argument(
           "--max_length"				,					type=__lowerCamelCase				,					default=512				,					help="Maximum sequence length. For training on TPUs, it helps to have a maximum"
           " sequence length that is a multiple of 8."				,					)
       parser.add_argument(
           "--output_dir"				,					default="tf-tpu"				,					type=__lowerCamelCase				,					help="Output directory where the TFRecord shards will be saved. If the"
           " path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord"
           " shards will be directly saved to a Google Cloud Storage bucket."				,					)

       snake_case       :   Dict							=      parser.parse_args()
       return args


def 			UpperCamelCase					(    __lowerCamelCase     : Union[str, Any]    ):
       def fn(__lowerCamelCase     : Union[str, Any]    ):
              return tokenizer(examples["text"]    )

       return fn


def 			UpperCamelCase					(    __lowerCamelCase     : Optional[int]    ):
       snake_case       :   Optional[int]							=      []
       for i in range(len(tokenized_data["input_ids"]    )    ):
              snake_case       :   str							=      {
                  "input_ids": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["input_ids"][i]    )    ),
                  "attention_mask": tf.train.Feature(
                      intaa_list=tf.train.IntaaList(value=tokenized_data["attention_mask"][i]    )    ),
              }
              snake_case       :   Optional[int]							=      tf.train.Features(feature=__lowerCamelCase    )
              snake_case       :   List[str]							=      tf.train.Example(features=__lowerCamelCase    )
              snake_case       :   Union[str, Any]							=      example.SerializeToString()
              records.append(__lowerCamelCase    )
       return records


def 			UpperCamelCase					(    __lowerCamelCase     : List[Any]    ):
       snake_case       :   Any							=      datasets.load_dataset(args.dataset_name				,					args.dataset_config				,					split=args.split    )

       if args.limit is not None:
              snake_case       :   Optional[Any]							=      min(len(__lowerCamelCase    )				,					args.limit    )
              snake_case       :   Tuple							=      dataset.select(range(__lowerCamelCase    )    )
              print(f"""Limiting the dataset to {args.limit} entries."""    )

       snake_case       :   Tuple							=      AutoTokenizer.from_pretrained(args.tokenizer_name_or_path    )

       # Handle output directory creation.
       # For serializing into a Google Cloud Storage Bucket, one needs to first
       # create a bucket.
       if "gs" not in args.output_dir:
              if not os.path.exists(args.output_dir    ):
                     os.makedirs(args.output_dir    )
              snake_case       :   List[Any]							=      os.path.join(args.output_dir				,					args.split    )
              if not os.path.exists(__lowerCamelCase    ):
                     os.makedirs(__lowerCamelCase    )
       else:
              snake_case       :   Tuple							=      os.path.join(args.output_dir				,					args.split    )

       # Tokenize the whole dataset at once.
       snake_case       :   Any							=      tokenize_function(__lowerCamelCase    )
       snake_case       :   Optional[Any]							=      dataset.map(__lowerCamelCase				,					batched=__lowerCamelCase				,					num_proc=4				,					remove_columns=["text"]    )

       # We need to concatenate all our texts together, and then split the result
       # into chunks of a fixed size, which we will call block_size. To do this, we
       # will use the map method again, with the option batched=True. When we use batched=True,
       # the function we pass to map() will be passed multiple inputs at once, allowing us
       # to group them into more or fewer examples than we had in the input.
       # This allows us to create our new fixed-length samples. The advantage of this
       # method is that we don't lose a whole lot of content from the dataset compared to the
       # case where we simply tokenize with a pre-defined max_length.

       def group_texts(__lowerCamelCase     : List[str]    ):
              # Concatenate all texts.
              snake_case       :   Optional[Any]							=      {k: sum(examples[k]				,					[]    ) for k in examples.keys()}
              snake_case       :   str							=      len(concatenated_examples[list(examples.keys()    )[0]]    )
              # We drop the small remainder, though you could add padding instead if the model supports it
              # In this, as in all things, we advise you to follow your heart 🫀
              snake_case       :   Any							=      (total_length // args.max_length) * args.max_length
              # Split by chunks of max_len.
              snake_case       :   Union[str, Any]							=      {
                  k: [t[i : i + args.max_length] for i in range(0				,					__lowerCamelCase				,					args.max_length    )]
                  for k, t in concatenated_examples.items()
              }
              return result

       snake_case       :   Any							=      dataset_tokenized.map(__lowerCamelCase				,					batched=__lowerCamelCase				,					batch_size=1000				,					num_proc=4    )

       snake_case       :   Dict							=      0
       snake_case       :   Optional[int]							=      0
       for shard in range(0				,					len(__lowerCamelCase    )				,					args.shard_size    ):
              snake_case       :   int							=      grouped_dataset[shard : shard + args.shard_size]
              snake_case       :   Any							=      len(dataset_snapshot["input_ids"]    )
              snake_case       :   List[Any]							=      os.path.join(__lowerCamelCase				,					f"""dataset-{shard_count}-{records_containing}.tfrecord"""    )
              snake_case       :   Tuple							=      get_serialized_examples(__lowerCamelCase    )

              with tf.io.TFRecordWriter(__lowerCamelCase    ) as out_file:
                     for i in range(len(__lowerCamelCase    )    ):
                            snake_case       :   Dict							=      serialized_examples[i]
                            out_file.write(__lowerCamelCase    )
                     print("Wrote file {} containing {} records".format(__lowerCamelCase				,					__lowerCamelCase    )    )

              shard_count += 1
              total_records += records_containing

       with open(f"""split-{args.split}-records-count.txt"""				,					"w"    ) as f:
              print(f"""Total {args.split} records: {total_records}"""				,					file=__lowerCamelCase    )


if __name__ == "__main__":
   __lowerCamelCase							=       parse_args()
   main(args)







import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
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.utils import logging


logging.set_verbosity_info()
__lowerCamelCase							=       logging.get_logger()






@dataclass
class   UpperCAmelCase		:
   A__					:		nn.Module
   A__					:		List[nn.Module]						     =     field(default_factory=A_ )
   A__					:		list						     =     field(default_factory=A_ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Tensor    ,						snake_case__		:					Tensor )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   List[str]							=      len(list(m.modules() ) ) == 1 or isinstance(snake_case__    ,						nn.Convad ) or isinstance(snake_case__    ,						nn.BatchNormad )
          if has_not_submodules:
                 self.traced.append(snake_case__ )



   def __call__(self		:					List[Any]    ,						snake_case__		:					Tensor )		->	List[Any]:


          '''simple docstring'''


          for m in self.module.modules():
                 self.handles.append(m.register_forward_hook(self._forward_hook ) )
          self.module(snake_case__ )
          [x.remove() for x in self.handles]
          return self



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Optional[int]:


          '''simple docstring'''


          return list(filter(lambda snake_case__ : len(list(x.state_dict().keys() ) ) > 0    ,						self.traced ) )






@dataclass
class   UpperCAmelCase		:
   A__					:		nn.Module
   A__					:		nn.Module
   A__					:		int						     =     1
   A__					:		List						     =     field(default_factory=A_ )
   A__					:		List						     =     field(default_factory=A_ )
   A__					:		bool						     =     True



   def __call__(self		:					List[Any]    ,						snake_case__		:					Tensor )		->	Any:


          '''simple docstring'''


          snake_case       :   str							=      Tracker(self.dest )(snake_case__ ).parametrized
          snake_case       :   Optional[int]							=      Tracker(self.src )(snake_case__ ).parametrized

          snake_case       :   List[str]							=      list(filter(lambda snake_case__ : type(snake_case__ ) not in self.src_skip    ,						snake_case__ ) )
          snake_case       :   Optional[Any]							=      list(filter(lambda snake_case__ : type(snake_case__ ) not in self.dest_skip    ,						snake_case__ ) )

          if len(snake_case__ ) != len(snake_case__ ) and self.raise_if_mismatch:
                 raise Exception(
                     f"""Numbers of operations are different. Source module has {len(snake_case__ )} operations while"""
                     f""" destination module has {len(snake_case__ )}.""" )

          for dest_m, src_m in zip(snake_case__    ,						snake_case__ ):
                 dest_m.load_state_dict(src_m.state_dict() )
                 if self.verbose == 1:
                        print(f"""Transfered from={src_m} to={dest_m}""" )






class   UpperCAmelCase		(						nn.Module ):



   def __init__(self		:					Tuple    ,						snake_case__		:					nn.Module )		->	Optional[Any]:


          '''simple docstring'''


          super().__init__()

          snake_case       :   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}"""
                 snake_case       :   Union[str, Any]							=      len(snake_case__ ) + 1
                 feature_blocks.append((f"""res{block_index}""", v) )

          snake_case       :   Optional[Any]							=      nn.ModuleDict(snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					Tensor )		->	Dict:


          '''simple docstring'''


          return get_trunk_forward_outputs(
              snake_case__    ,						out_feat_keys=snake_case__    ,						feature_blocks=self._feature_blocks    ,						)






class   UpperCAmelCase		(						A_ ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any    ,						snake_case__		:					str )		->	str:


          '''simple docstring'''


          snake_case       :   List[Any]							=      x.split("-" )
          return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )



   def __getitem__(self		:					Optional[int]    ,						snake_case__		:					str )		->	Callable[[], Tuple[nn.Module, Dict]]:


          '''simple docstring'''


          if x not in self:
                 snake_case       :   Dict							=      self.convert_name_to_timm(snake_case__ )
                 snake_case       :   Union[str, Any]							=      partial(lambda: (timm.create_model(snake_case__    ,						pretrained=snake_case__ ).eval(), None) )

          else:
                 snake_case       :   List[str]							=      super().__getitem__(snake_case__ )

          return val






class   UpperCAmelCase		(						A_ ):



   def __getitem__(self		:					Dict    ,						snake_case__		:					str )		->	Callable[[], nn.Module]:


          '''simple docstring'''


          if "seer" in x and "in1k" not in x:
                 snake_case       :   str							=      RegNetModel
          else:
                 snake_case       :   Optional[Any]							=      RegNetForImageClassification
          return val



def 			UpperCamelCase					(    __lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : List[Tuple[str, str]]    ):
       for from_key, to_key in keys:
              snake_case       :   str							=      from_state_dict[from_key].clone()
              print(f"""Copied key={from_key} to={to_key}"""    )
       return to_state_dict


def 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : Callable[[], nn.Module]				,					__lowerCamelCase     : Callable[[], nn.Module]				,					__lowerCamelCase     : RegNetConfig				,					__lowerCamelCase     : Path				,					__lowerCamelCase     : bool = True				,					):
       print(f"""Converting {name}..."""    )
       with torch.no_grad():
              snake_case				,					snake_case       :   int							=      from_model_func()
              snake_case       :   str							=      our_model_func(__lowerCamelCase    ).eval()
              snake_case       :   int							=      ModuleTransfer(src=__lowerCamelCase				,					dest=__lowerCamelCase				,					raise_if_mismatch=__lowerCamelCase    )
              snake_case       :   Dict							=      torch.randn((1, 3, 224, 224)    )
              module_transfer(__lowerCamelCase    )

       if from_state_dict is not None:
              snake_case       :   str							=      []
              # for seer - in1k finetuned we have to manually copy the head
              if "seer" in name and "in1k" in name:
                     snake_case       :   Tuple							=      [("0.clf.0.weight", "classifier.1.weight"), ("0.clf.0.bias", "classifier.1.bias")]
              snake_case       :   Optional[Any]							=      manually_copy_vissl_head(__lowerCamelCase				,					our_model.state_dict()				,					__lowerCamelCase    )
              our_model.load_state_dict(__lowerCamelCase    )

       snake_case       :   Any							=      our_model(__lowerCamelCase				,					output_hidden_states=__lowerCamelCase    )
       snake_case       :   Union[str, Any]							=      (
           our_outputs.logits if isinstance(__lowerCamelCase				,					__lowerCamelCase    ) else our_outputs.last_hidden_state
       )

       snake_case       :   Union[str, Any]							=      from_model(__lowerCamelCase    )
       snake_case       :   Dict							=      from_output[-1] if type(__lowerCamelCase    ) is list else from_output

       # now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
       if "seer" in name and "in1k" in name:
              snake_case       :   Any							=      our_outputs.hidden_states[-1]

       assert torch.allclose(__lowerCamelCase				,					__lowerCamelCase    ), "The model logits don't match the original one."

       if push_to_hub:
              our_model.push_to_hub(
                  repo_path_or_name=save_directory / name				,					commit_message="Add model"				,					use_temp_dir=__lowerCamelCase				,					)

              snake_case       :   List[str]							=      224 if "seer" not in name else 384
              # we can use the convnext one
              snake_case       :   int							=      AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"				,					size=__lowerCamelCase    )
              image_processor.push_to_hub(
                  repo_path_or_name=save_directory / name				,					commit_message="Add image processor"				,					use_temp_dir=__lowerCamelCase				,					)

              print(f"""Pushed {name}"""    )


def 			UpperCamelCase					(    __lowerCamelCase     : Path				,					__lowerCamelCase     : str = None				,					__lowerCamelCase     : bool = True    ):
       snake_case       :   Union[str, Any]							=      "imagenet-1k-id2label.json"
       snake_case       :   List[str]							=      1000
       snake_case       :   List[str]							=      (1, num_labels)

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

       snake_case       :   str							=      idalabel
       snake_case       :   List[Any]							=      {v: k for k, v in idalabel.items()}

       snake_case       :   Dict							=      partial(__lowerCamelCase				,					num_labels=__lowerCamelCase				,					idalabel=__lowerCamelCase				,					labelaid=__lowerCamelCase    )

       snake_case       :   Optional[Any]							=      {
           "regnet-x-002": ImageNetPreTrainedConfig(
               depths=[1, 1, 4, 7]				,					hidden_sizes=[24, 56, 152, 368]				,					groups_width=8				,					layer_type="x"    ),
           "regnet-x-004": ImageNetPreTrainedConfig(
               depths=[1, 2, 7, 12]				,					hidden_sizes=[32, 64, 160, 384]				,					groups_width=16				,					layer_type="x"    ),
           "regnet-x-006": ImageNetPreTrainedConfig(
               depths=[1, 3, 5, 7]				,					hidden_sizes=[48, 96, 240, 528]				,					groups_width=24				,					layer_type="x"    ),
           "regnet-x-008": ImageNetPreTrainedConfig(
               depths=[1, 3, 7, 5]				,					hidden_sizes=[64, 128, 288, 672]				,					groups_width=16				,					layer_type="x"    ),
           "regnet-x-016": ImageNetPreTrainedConfig(
               depths=[2, 4, 10, 2]				,					hidden_sizes=[72, 168, 408, 912]				,					groups_width=24				,					layer_type="x"    ),
           "regnet-x-032": ImageNetPreTrainedConfig(
               depths=[2, 6, 15, 2]				,					hidden_sizes=[96, 192, 432, 1008]				,					groups_width=48				,					layer_type="x"    ),
           "regnet-x-040": ImageNetPreTrainedConfig(
               depths=[2, 5, 14, 2]				,					hidden_sizes=[80, 240, 560, 1360]				,					groups_width=40				,					layer_type="x"    ),
           "regnet-x-064": ImageNetPreTrainedConfig(
               depths=[2, 4, 10, 1]				,					hidden_sizes=[168, 392, 784, 1624]				,					groups_width=56				,					layer_type="x"    ),
           "regnet-x-080": ImageNetPreTrainedConfig(
               depths=[2, 5, 15, 1]				,					hidden_sizes=[80, 240, 720, 1920]				,					groups_width=120				,					layer_type="x"    ),
           "regnet-x-120": ImageNetPreTrainedConfig(
               depths=[2, 5, 11, 1]				,					hidden_sizes=[224, 448, 896, 2240]				,					groups_width=112				,					layer_type="x"    ),
           "regnet-x-160": ImageNetPreTrainedConfig(
               depths=[2, 6, 13, 1]				,					hidden_sizes=[256, 512, 896, 2048]				,					groups_width=128				,					layer_type="x"    ),
           "regnet-x-320": ImageNetPreTrainedConfig(
               depths=[2, 7, 13, 1]				,					hidden_sizes=[336, 672, 1344, 2520]				,					groups_width=168				,					layer_type="x"    ),
           # y variant
           "regnet-y-002": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]				,					hidden_sizes=[24, 56, 152, 368]				,					groups_width=8    ),
           "regnet-y-004": ImageNetPreTrainedConfig(
               depths=[1, 3, 6, 6]				,					hidden_sizes=[48, 104, 208, 440]				,					groups_width=8    ),
           "regnet-y-006": ImageNetPreTrainedConfig(
               depths=[1, 3, 7, 4]				,					hidden_sizes=[48, 112, 256, 608]				,					groups_width=16    ),
           "regnet-y-008": ImageNetPreTrainedConfig(
               depths=[1, 3, 8, 2]				,					hidden_sizes=[64, 128, 320, 768]				,					groups_width=16    ),
           "regnet-y-016": ImageNetPreTrainedConfig(
               depths=[2, 6, 17, 2]				,					hidden_sizes=[48, 120, 336, 888]				,					groups_width=24    ),
           "regnet-y-032": ImageNetPreTrainedConfig(
               depths=[2, 5, 13, 1]				,					hidden_sizes=[72, 216, 576, 1512]				,					groups_width=24    ),
           "regnet-y-040": ImageNetPreTrainedConfig(
               depths=[2, 6, 12, 2]				,					hidden_sizes=[128, 192, 512, 1088]				,					groups_width=64    ),
           "regnet-y-064": ImageNetPreTrainedConfig(
               depths=[2, 7, 14, 2]				,					hidden_sizes=[144, 288, 576, 1296]				,					groups_width=72    ),
           "regnet-y-080": ImageNetPreTrainedConfig(
               depths=[2, 4, 10, 1]				,					hidden_sizes=[168, 448, 896, 2016]				,					groups_width=56    ),
           "regnet-y-120": ImageNetPreTrainedConfig(
               depths=[2, 5, 11, 1]				,					hidden_sizes=[224, 448, 896, 2240]				,					groups_width=112    ),
           "regnet-y-160": ImageNetPreTrainedConfig(
               depths=[2, 4, 11, 1]				,					hidden_sizes=[224, 448, 1232, 3024]				,					groups_width=112    ),
           "regnet-y-320": ImageNetPreTrainedConfig(
               depths=[2, 5, 12, 1]				,					hidden_sizes=[232, 696, 1392, 3712]				,					groups_width=232    ),
           # models created by SEER -> https://arxiv.org/abs/2202.08360
           "regnet-y-320-seer": RegNetConfig(depths=[2, 5, 12, 1]				,					hidden_sizes=[232, 696, 1392, 3712]				,					groups_width=232    ),
           "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1]				,					hidden_sizes=[328, 984, 1968, 4920]				,					groups_width=328    ),
           "regnet-y-1280-seer": RegNetConfig(
               depths=[2, 7, 17, 1]				,					hidden_sizes=[528, 1056, 2904, 7392]				,					groups_width=264    ),
           "regnet-y-2560-seer": RegNetConfig(
               depths=[3, 7, 16, 1]				,					hidden_sizes=[640, 1696, 2544, 5088]				,					groups_width=640    ),
           "regnet-y-10b-seer": ImageNetPreTrainedConfig(
               depths=[2, 7, 17, 1]				,					hidden_sizes=[2020, 4040, 11110, 28280]				,					groups_width=1010    ),
           # finetuned on imagenet
           "regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
               depths=[2, 5, 12, 1]				,					hidden_sizes=[232, 696, 1392, 3712]				,					groups_width=232    ),
           "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
               depths=[2, 5, 12, 1]				,					hidden_sizes=[328, 984, 1968, 4920]				,					groups_width=328    ),
           "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
               depths=[2, 7, 17, 1]				,					hidden_sizes=[528, 1056, 2904, 7392]				,					groups_width=264    ),
           "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
               depths=[3, 7, 16, 1]				,					hidden_sizes=[640, 1696, 2544, 5088]				,					groups_width=640    ),
           "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
               depths=[2, 7, 17, 1]				,					hidden_sizes=[2020, 4040, 11110, 28280]				,					groups_width=1010    ),
       }

       snake_case       :   Union[str, Any]							=      NameToOurModelFuncMap()
       snake_case       :   str							=      NameToFromModelFuncMap()
       # add seer weights logic

       def load_using_classy_vision(__lowerCamelCase     : str				,					__lowerCamelCase     : Callable[[], nn.Module]    ) -> Tuple[nn.Module, Dict]:
              snake_case       :   List[Any]							=      torch.hub.load_state_dict_from_url(__lowerCamelCase				,					model_dir=str(__lowerCamelCase    )				,					map_location="cpu"    )
              snake_case       :   Dict							=      model_func()
              # check if we have a head, if yes add it
              snake_case       :   str							=      files["classy_state_dict"]["base_model"]["model"]
              snake_case       :   Dict							=      model_state_dict["trunk"]
              model.load_state_dict(__lowerCamelCase    )
              return model.eval(), model_state_dict["heads"]

       # pretrained
       snake_case       :   List[Any]							=      partial(
           __lowerCamelCase				,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"				,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )				,					)

       snake_case       :   Optional[int]							=      partial(
           __lowerCamelCase				,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"				,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )				,					)

       snake_case       :   List[str]							=      partial(
           __lowerCamelCase				,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"				,					lambda: FakeRegNetVisslWrapper(RegNetYaaagf()    )				,					)

       snake_case       :   Tuple							=      partial(
           __lowerCamelCase				,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"				,					lambda: FakeRegNetVisslWrapper(
               RegNet(RegNetParams(depth=27				,					group_width=1010				,					w_a=1744				,					w_a=620.83				,					w_m=2.52    )    )    )				,					)

       # IN1K finetuned
       snake_case       :   List[Any]							=      partial(
           __lowerCamelCase				,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"				,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )				,					)

       snake_case       :   Tuple							=      partial(
           __lowerCamelCase				,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"				,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )				,					)

       snake_case       :   str							=      partial(
           __lowerCamelCase				,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"				,					lambda: FakeRegNetVisslWrapper(RegNetYaaagf()    )				,					)

       snake_case       :   Dict							=      partial(
           __lowerCamelCase				,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"				,					lambda: FakeRegNetVisslWrapper(
               RegNet(RegNetParams(depth=27				,					group_width=1010				,					w_a=1744				,					w_a=620.83				,					w_m=2.52    )    )    )				,					)

       if model_name:
              convert_weight_and_push(
                  __lowerCamelCase				,					names_to_from_model_map[model_name]				,					names_to_ours_model_map[model_name]				,					names_to_config[model_name]				,					__lowerCamelCase				,					__lowerCamelCase				,					)
       else:
              for model_name, config in names_to_config.items():
                     convert_weight_and_push(
                         __lowerCamelCase				,					names_to_from_model_map[model_name]				,					names_to_ours_model_map[model_name]				,					__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase				,					)
       return config, expected_shape


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)





