'''simple docstring'''





import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


SCREAMING_SNAKE_CASE_:     Optional[int]  =logging.getLogger(__name__)

SCREAMING_SNAKE_CASE_:     str  =tf.data.AUTOTUNE





def 	lowerCAmelCase_				(					)    ->      Any:


     '''simple docstring'''




     UpperCAmelCase_           =   argparse.ArgumentParser(description="Train a masked language model on TPU."			)
     parser.add_argument(
         "--pretrained_model_config"					,    type=__lowerCamelCase					,    default="roberta-base"					,    help="The model config to use. Note that we don't copy the model's weights, only the config!"					,    )
     parser.add_argument(
         "--tokenizer"					,    type=__lowerCamelCase					,    default="unigram-tokenizer-wikitext"					,    help="The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model's vocab size."					,    )

     parser.add_argument(
         "--per_replica_batch_size"					,    type=__lowerCamelCase					,    default=8					,    help="Batch size per TPU core."					,    )

     parser.add_argument(
         "--no_tpu"					,    action="store_true"					,    help="If set, run on CPU and don't try to initialize a TPU. Useful for debugging on non-TPU instances."					,    )

     parser.add_argument(
         "--tpu_name"					,    type=__lowerCamelCase					,    help="Name of TPU resource to initialize. Should be blank on Colab, and 'local' on TPU VMs."					,    default="local"					,    )

     parser.add_argument(
         "--tpu_zone"					,    type=__lowerCamelCase					,    help="Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes."					,    )

     parser.add_argument(
         "--gcp_project"					,    type=__lowerCamelCase					,    help="Google cloud project name. Only used for non-Colab TPU nodes."			)

     parser.add_argument(
         "--bfloat16"					,    action="store_true"					,    help="Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU."					,    )

     parser.add_argument(
         "--train_dataset"					,    type=__lowerCamelCase					,    help="Path to training dataset to load. If the path begins with `gs://`"
         " then the dataset will be loaded from a Google Cloud Storage bucket."					,    )

     parser.add_argument(
         "--shuffle_buffer_size"					,    type=__lowerCamelCase					,    default=2**18					,    help="Size of the shuffle buffer (in samples)"					,    )

     parser.add_argument(
         "--eval_dataset"					,    type=__lowerCamelCase					,    help="Path to evaluation dataset to load. If the path begins with `gs://`"
         " then the dataset will be loaded from a Google Cloud Storage bucket."					,    )

     parser.add_argument(
         "--num_epochs"					,    type=__lowerCamelCase					,    default=1					,    help="Number of epochs to train for."					,    )

     parser.add_argument(
         "--learning_rate"					,    type=__lowerCamelCase					,    default=1E-4					,    help="Learning rate to use for training."					,    )

     parser.add_argument(
         "--weight_decay_rate"					,    type=__lowerCamelCase					,    default=1E-3					,    help="Weight decay rate to use for training."					,    )

     parser.add_argument(
         "--max_length"					,    type=__lowerCamelCase					,    default=5_12					,    help="Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py"					,    )

     parser.add_argument(
         "--mlm_probability"					,    type=__lowerCamelCase					,    default=0.15					,    help="Fraction of tokens to mask during training."					,    )

     parser.add_argument("--output_dir"					,    type=__lowerCamelCase					,    required=__lowerCamelCase					,    help="Path to save model checkpoints to."			)
     parser.add_argument("--hub_model_id"					,    type=__lowerCamelCase					,    help="Model ID to upload to on the Hugging Face Hub."			)

     UpperCAmelCase_           =   parser.parse_args()
     return args





def 	lowerCAmelCase_				(					snake_case_       : List[str]			)    ->      Optional[int]:


     '''simple docstring'''




     try:
          if args.tpu_name:
               UpperCAmelCase_           =   tf.distribute.cluster_resolver.TPUClusterResolver(
                   args.tpu_name					,    zone=args.tpu_zone					,    project=args.gcp_project			)
          else:
               UpperCAmelCase_           =   tf.distribute.cluster_resolver.TPUClusterResolver()
     except ValueError:
          raise RuntimeError(
              "Couldn't connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or "
              "--gcp_project. When running on a TPU VM, use --tpu_name local."			)

     tf.config.experimental_connect_to_cluster(__lowerCamelCase			)
     tf.tpu.experimental.initialize_tpu_system(__lowerCamelCase			)

     return tpu





def 	lowerCAmelCase_				(					snake_case_       : int			)    ->      List[Any]:


     '''simple docstring'''




     UpperCAmelCase_           =   0
     for file in file_list:
          UpperCAmelCase_           =   file.split("/"			)[-1]
          UpperCAmelCase_           =   re.search(R"-\d+-(\d+)\.tfrecord"					,    __lowerCamelCase			).group(1			)
          UpperCAmelCase_           =   int(__lowerCamelCase			)
          num_samples += sample_count

     return num_samples





def 	lowerCAmelCase_				(					snake_case_       : str					,    snake_case_       : List[Any]					,    snake_case_       : Tuple					,    snake_case_       : Tuple					,    snake_case_       : Optional[Any]					,    snake_case_       : int=None			)    ->      str:


     '''simple docstring'''




     UpperCAmelCase_           =   count_samples(__lowerCamelCase			)
     UpperCAmelCase_           =   tf.data.Dataset.from_tensor_slices(__lowerCamelCase			)
     if shuffle:
          UpperCAmelCase_           =   dataset.shuffle(len(__lowerCamelCase			)			)
     UpperCAmelCase_           =   tf.data.TFRecordDataset(__lowerCamelCase					,    num_parallel_reads=__lowerCamelCase			)
     # TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
     UpperCAmelCase_           =   dataset.apply(tf.data.experimental.assert_cardinality(__lowerCamelCase			)			)
     UpperCAmelCase_           =   dataset.map(__lowerCamelCase					,    num_parallel_calls=__lowerCamelCase			)
     if shuffle:
          assert shuffle_buffer_size is not None
          UpperCAmelCase_           =   dataset.shuffle(args.shuffle_buffer_size			)
     UpperCAmelCase_           =   dataset.batch(__lowerCamelCase					,    drop_remainder=__lowerCamelCase			)
     UpperCAmelCase_           =   dataset.map(__lowerCamelCase					,    num_parallel_calls=__lowerCamelCase			)
     UpperCAmelCase_           =   dataset.prefetch(__lowerCamelCase			)
     return dataset





def 	lowerCAmelCase_				(					snake_case_       : List[Any]			)    ->      Dict:


     '''simple docstring'''




     if not args.no_tpu:
          UpperCAmelCase_           =   initialize_tpu(__lowerCamelCase			)
          UpperCAmelCase_           =   tf.distribute.TPUStrategy(__lowerCamelCase			)
     else:
          UpperCAmelCase_           =   tf.distribute.OneDeviceStrategy(device="/gpu:0"			)

     if args.bfloataa:
          tf.keras.mixed_precision.set_global_policy("mixed_bfloat16"			)

     UpperCAmelCase_           =   AutoTokenizer.from_pretrained(args.tokenizer			)
     UpperCAmelCase_           =   AutoConfig.from_pretrained(args.pretrained_model_config			)
     UpperCAmelCase_           =   tokenizer.vocab_size

     UpperCAmelCase_           =   tf.io.gfile.glob(os.path.join(args.train_dataset					,    "*.tfrecord"			)			)
     if not training_records:
          raise ValueError(f"""No .tfrecord files found in {args.train_dataset}."""			)
     UpperCAmelCase_           =   tf.io.gfile.glob(os.path.join(args.eval_dataset					,    "*.tfrecord"			)			)
     if not eval_records:
          raise ValueError(f"""No .tfrecord files found in {args.eval_dataset}."""			)

     UpperCAmelCase_           =   count_samples(__lowerCamelCase			)

     UpperCAmelCase_           =   num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
     UpperCAmelCase_           =   steps_per_epoch * args.num_epochs

     with strategy.scope():
          UpperCAmelCase_           =   TFAutoModelForMaskedLM.from_config(__lowerCamelCase			)
          model(model.dummy_inputs			)  # Pass some dummy inputs through the model to ensure all the weights are built
          UpperCAmelCase_           =   create_optimizer(
              num_train_steps=__lowerCamelCase					,    num_warmup_steps=total_train_steps // 20					,    init_lr=args.learning_rate					,    weight_decay_rate=args.weight_decay_rate					,    )

          # Transformers models compute the right loss for their task by default when labels are passed, and will
          # use this for training unless you specify your own loss function in compile().
          model.compile(optimizer=__lowerCamelCase					,    metrics=["accuracy"]			)

     def decode_fn(snake_case_       : List[Any]			):
          UpperCAmelCase_           =   {
              "input_ids": tf.io.FixedLenFeature(dtype=tf.intaa					,    shape=(args.max_length,)			),
              "attention_mask": tf.io.FixedLenFeature(dtype=tf.intaa					,    shape=(args.max_length,)			),
          }
          return tf.io.parse_single_example(__lowerCamelCase					,    __lowerCamelCase			)

     # Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
     # use their methods in our data pipeline.
     UpperCAmelCase_           =   DataCollatorForLanguageModeling(
         tokenizer=__lowerCamelCase					,    mlm_probability=args.mlm_probability					,    mlm=__lowerCamelCase					,    return_tensors="tf"			)

     def mask_with_collator(snake_case_       : str			):
          # TF really needs an isin() function
          UpperCAmelCase_           =   (
              ~tf.cast(batch["attention_mask"]					,    tf.bool			)
              | (batch["input_ids"] == tokenizer.cls_token_id)
              | (batch["input_ids"] == tokenizer.sep_token_id)
          )
          UpperCAmelCase_           =   data_collator.tf_mask_tokens(
              batch["input_ids"]					,    vocab_size=len(__lowerCamelCase			)					,    mask_token_id=tokenizer.mask_token_id					,    special_tokens_mask=__lowerCamelCase					,    )
          return batch

     UpperCAmelCase_           =   args.per_replica_batch_size * strategy.num_replicas_in_sync

     UpperCAmelCase_           =   prepare_dataset(
         __lowerCamelCase					,    decode_fn=__lowerCamelCase					,    mask_fn=__lowerCamelCase					,    batch_size=__lowerCamelCase					,    shuffle=__lowerCamelCase					,    shuffle_buffer_size=args.shuffle_buffer_size					,    )

     UpperCAmelCase_           =   prepare_dataset(
         __lowerCamelCase					,    decode_fn=__lowerCamelCase					,    mask_fn=__lowerCamelCase					,    batch_size=__lowerCamelCase					,    shuffle=__lowerCamelCase					,    )

     UpperCAmelCase_           =   []
     if args.hub_model_id:
          callbacks.append(
              PushToHubCallback(output_dir=args.output_dir					,    hub_model_id=args.hub_model_id					,    tokenizer=__lowerCamelCase			)			)

     model.fit(
         __lowerCamelCase					,    validation_data=__lowerCamelCase					,    epochs=args.num_epochs					,    callbacks=__lowerCamelCase					,    )

     model.save_pretrained(args.output_dir			)


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:     Union[str, Any]  =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)





