






"""simple docstring"""







import tempfile
import unittest

from transformers import DPRConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import DPRContextEncoder, DPRQuestionEncoder, DPRReader, DPRReaderTokenizer
 from transformers.models.dpr.modeling_dpr import (
     DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
     DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
     DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
 )





class      UpperCamelCase_       :



  def __init__(					self       ,						snake_case__       ,						snake_case__=13       ,						snake_case__=7       ,						snake_case__=False       ,						snake_case__=True       ,						snake_case__=True       ,						snake_case__=True       ,						snake_case__=99       ,						snake_case__=32       ,						snake_case__=5       ,						snake_case__=4       ,						snake_case__=37       ,						snake_case__="gelu"       ,						snake_case__=0.1       ,						snake_case__=0.1       ,						snake_case__=5_12       ,						snake_case__=16       ,						snake_case__=2       ,						snake_case__=0.02       ,						snake_case__=3       ,						snake_case__=4       ,						snake_case__=None       ,						snake_case__=0       ,						)	->	Optional[int]:



         """simple docstring"""





         UpperCAmelCase					=	parent
         UpperCAmelCase					=	batch_size
         UpperCAmelCase					=	seq_length
         UpperCAmelCase					=	is_training
         UpperCAmelCase					=	use_input_mask
         UpperCAmelCase					=	use_token_type_ids
         UpperCAmelCase					=	use_labels
         UpperCAmelCase					=	vocab_size
         UpperCAmelCase					=	hidden_size
         UpperCAmelCase					=	num_hidden_layers
         UpperCAmelCase					=	num_attention_heads
         UpperCAmelCase					=	intermediate_size
         UpperCAmelCase					=	hidden_act
         UpperCAmelCase					=	hidden_dropout_prob
         UpperCAmelCase					=	attention_probs_dropout_prob
         UpperCAmelCase					=	max_position_embeddings
         UpperCAmelCase					=	type_vocab_size
         UpperCAmelCase					=	type_sequence_label_size
         UpperCAmelCase					=	initializer_range
         UpperCAmelCase					=	num_labels
         UpperCAmelCase					=	num_choices
         UpperCAmelCase					=	scope
         UpperCAmelCase					=	projection_dim



  def 						UpperCamelCase_ (					self )	->	Tuple:



         """simple docstring"""





         UpperCAmelCase					=	ids_tensor([self.batch_size, self.seq_length]       ,						self.vocab_size )

         UpperCAmelCase					=	None
         if self.use_input_mask:
                UpperCAmelCase					=	random_attention_mask([self.batch_size, self.seq_length] )

         UpperCAmelCase					=	None
         if self.use_token_type_ids:
                UpperCAmelCase					=	ids_tensor([self.batch_size, self.seq_length]       ,						self.type_vocab_size )

         UpperCAmelCase					=	None
         UpperCAmelCase					=	None
         UpperCAmelCase					=	None
         if self.use_labels:
                UpperCAmelCase					=	ids_tensor([self.batch_size]       ,						self.type_sequence_label_size )
                UpperCAmelCase					=	ids_tensor([self.batch_size, self.seq_length]       ,						self.num_labels )
                UpperCAmelCase					=	ids_tensor([self.batch_size]       ,						self.num_choices )

         UpperCAmelCase					=	self.get_config()

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



  def 						UpperCamelCase_ (					self )	->	List[str]:



         """simple docstring"""





         return DPRConfig(
             projection_dim=self.projection_dim       ,						vocab_size=self.vocab_size       ,						hidden_size=self.hidden_size       ,						num_hidden_layers=self.num_hidden_layers       ,						num_attention_heads=self.num_attention_heads       ,						intermediate_size=self.intermediate_size       ,						hidden_act=self.hidden_act       ,						hidden_dropout_prob=self.hidden_dropout_prob       ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,						max_position_embeddings=self.max_position_embeddings       ,						type_vocab_size=self.type_vocab_size       ,						initializer_range=self.initializer_range       ,						)



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ )	->	Any:



         """simple docstring"""





         UpperCAmelCase					=	DPRContextEncoder(config=snake_case__ )
         model.to(snake_case__ )
         model.eval()
         UpperCAmelCase					=	model(snake_case__       ,						attention_mask=snake_case__       ,						token_type_ids=snake_case__ )
         UpperCAmelCase					=	model(snake_case__       ,						token_type_ids=snake_case__ )
         UpperCAmelCase					=	model(snake_case__ )
         self.parent.assertEqual(result.pooler_output.shape       ,						(self.batch_size, self.projection_dim or self.hidden_size) )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ )	->	Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase					=	DPRQuestionEncoder(config=snake_case__ )
         model.to(snake_case__ )
         model.eval()
         UpperCAmelCase					=	model(snake_case__       ,						attention_mask=snake_case__       ,						token_type_ids=snake_case__ )
         UpperCAmelCase					=	model(snake_case__       ,						token_type_ids=snake_case__ )
         UpperCAmelCase					=	model(snake_case__ )
         self.parent.assertEqual(result.pooler_output.shape       ,						(self.batch_size, self.projection_dim or self.hidden_size) )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ )	->	Any:



         """simple docstring"""





         UpperCAmelCase					=	DPRReader(config=snake_case__ )
         model.to(snake_case__ )
         model.eval()
         UpperCAmelCase					=	model(
             snake_case__       ,						attention_mask=snake_case__       ,						)

         self.parent.assertEqual(result.start_logits.shape       ,						(self.batch_size, self.seq_length) )
         self.parent.assertEqual(result.end_logits.shape       ,						(self.batch_size, self.seq_length) )
         self.parent.assertEqual(result.relevance_logits.shape       ,						(self.batch_size,) )



  def 						UpperCamelCase_ (					self )	->	int:



         """simple docstring"""





         UpperCAmelCase					=	self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase					
         )			,				(
             UpperCAmelCase					
         )			,				(
             UpperCAmelCase					
         )			,				(
             UpperCAmelCase					
         )			,				(
             UpperCAmelCase					
         )			,				(
             UpperCAmelCase					
         )			,				(
             UpperCAmelCase					
         )			,									
         )					=	config_and_inputs
         UpperCAmelCase					=	{"""input_ids""": input_ids}
         return config, inputs_dict





@require_torch
class      UpperCamelCase_       (				a_      ,    a_      ,    unittest.TestCase     ):
  _A				:    str =		(
      (
          DPRContextEncoder,
          DPRQuestionEncoder,
          DPRReader,
      )
      if is_torch_available()
      else ()
  )
  _A				:    Optional[Any] =		{'feature-extraction': DPRQuestionEncoder} if is_torch_available() else {}

  _A				:    Optional[int] =		False
  _A				:    Any =		False  # why?
  _A				:    List[str] =		False
  _A				:    List[str] =		False



  def 						UpperCamelCase_ (					self )	->	Dict:



         """simple docstring"""





         UpperCAmelCase					=	DPRModelTester(self )
         UpperCAmelCase					=	ConfigTester(self       ,						config_class=snake_case__       ,						hidden_size=37 )



  def 						UpperCamelCase_ (					self )	->	Union[str, Any]:



         """simple docstring"""





         self.config_tester.run_common_tests()



  def 						UpperCamelCase_ (					self )	->	Optional[Any]:



         """simple docstring"""





         UpperCAmelCase					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_context_encoder(*snake_case__ )



  def 						UpperCamelCase_ (					self )	->	List[Any]:



         """simple docstring"""





         UpperCAmelCase					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_question_encoder(*snake_case__ )



  def 						UpperCamelCase_ (					self )	->	Any:



         """simple docstring"""





         UpperCAmelCase					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_reader(*snake_case__ )



  def 						UpperCamelCase_ (					self )	->	Dict:



         """simple docstring"""





         UpperCAmelCase					=	self.model_tester.prepare_config_and_inputs()[0]

         UpperCAmelCase					=	DPRQuestionEncoder(config=snake_case__ )
         model.to(snake_case__ )
         model.eval()

         with tempfile.TemporaryDirectory() as tmp_dirname:
                model.save_pretrained(snake_case__ )
                UpperCAmelCase					=	DPRQuestionEncoder.from_pretrained(snake_case__       ,						projection_dim=5_12 )

         self.assertIsNotNone(snake_case__ )



  @slow
  def 						UpperCamelCase_ (					self )	->	List[str]:



         """simple docstring"""





         for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase					=	DPRContextEncoder.from_pretrained(snake_case__ )
                self.assertIsNotNone(snake_case__ )

         for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase					=	DPRContextEncoder.from_pretrained(snake_case__ )
                self.assertIsNotNone(snake_case__ )

         for model_name in DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase					=	DPRQuestionEncoder.from_pretrained(snake_case__ )
                self.assertIsNotNone(snake_case__ )

         for model_name in DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase					=	DPRReader.from_pretrained(snake_case__ )
                self.assertIsNotNone(snake_case__ )





@require_torch
class      UpperCamelCase_       (				unittest.TestCase     ):



  @slow
  def 						UpperCamelCase_ (					self )	->	Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase					=	DPRQuestionEncoder.from_pretrained("""facebook/dpr-question_encoder-single-nq-base"""       ,						return_dict=snake_case__ )
         model.to(snake_case__ )

         UpperCAmelCase					=	torch.tensor(
             [[1_01, 75_92, 10_10, 20_03, 20_26, 38_99, 1_01_40, 10_29, 1_02]]       ,						dtype=torch.long       ,						device=snake_case__ )  # [CLS] hello, is my dog cute? [SEP]
         UpperCAmelCase					=	model(snake_case__ )[0]  # embedding shape = (1, 768)
         # compare the actual values for a slice.
         UpperCAmelCase					=	torch.tensor(
             [
                 [
                     0.03_236_253,
                     0.12_753_335,
                     0.16_818_509,
                     0.00_279_786,
                     0.3_896_933,
                     0.24_264_945,
                     0.2_178_971,
                     -0.02_335_227,
                     -0.08_481_959,
                     -0.14_324_117,
                 ]
             ]       ,						dtype=torch.float       ,						device=snake_case__       ,						)
         self.assertTrue(torch.allclose(output[:, :10]       ,						snake_case__       ,						atol=1e-4 ) )



  @slow
  def 						UpperCamelCase_ (					self )	->	Any:



         """simple docstring"""





         UpperCAmelCase					=	DPRReaderTokenizer.from_pretrained("""facebook/dpr-reader-single-nq-base""" )
         UpperCAmelCase					=	DPRReader.from_pretrained("""facebook/dpr-reader-single-nq-base""" )
         model.to(snake_case__ )

         UpperCAmelCase					=	tokenizer(
             questions="""What is love ?"""       ,						titles="""Haddaway"""       ,						texts="""What Is Love is a song recorded by the artist Haddaway"""       ,						padding=snake_case__       ,						return_tensors="""pt"""       ,						)
         encoded_inputs.to(snake_case__ )

         UpperCAmelCase					=	model(**snake_case__ )

         # compare the actual values for a slice.
         UpperCAmelCase					=	torch.tensor(
             [[-10.3_005, -10.7_765, -11.4_872, -11.6_841, -11.9_312, -10.3_002, -9.8_544, -11.7_378, -12.0_821, -10.2_975]]       ,						dtype=torch.float       ,						device=snake_case__       ,						)

         UpperCAmelCase					=	torch.tensor(
             [[-11.0_684, -11.7_041, -11.5_397, -10.3_465, -10.8_791, -6.8_443, -11.9_959, -11.0_364, -10.0_096, -6.8_405]]       ,						dtype=torch.float       ,						device=snake_case__       ,						)
         self.assertTrue(torch.allclose(outputs.start_logits[:, :10]       ,						snake_case__       ,						atol=1e-4 ) )
         self.assertTrue(torch.allclose(outputs.end_logits[:, :10]       ,						snake_case__       ,						atol=1e-4 ) )








"""simple docstring"""







import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCamelCase     			=  logging.get_logger()







@dataclass
class 					lowerCamelCase__					:
    SCREAMING_SNAKE_CASE		=			42
    SCREAMING_SNAKE_CASE		=			field(default_factory=snake_case )
    SCREAMING_SNAKE_CASE		=			field(default_factory=snake_case )
    SCREAMING_SNAKE_CASE		=			field(default_factory=snake_case )






    def 				_UpperCamelCase							( self	,A	,A	,A	,A					):
          UpperCAmelCase							=       len(list(m.modules()					)					) == 1 or isinstance(A	,nn.Convad					) or isinstance(A	,nn.BatchNormad					)
          if has_not_submodules:
                self.traced.append(A					)
                UpperCAmelCase							=       m






    def __call__( self	,A					):
          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 				_UpperCamelCase							( self					):
          # 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	,A					):
          super().__init__()

          UpperCAmelCase							=       []
          # - get the stem
          feature_blocks.append(("""conv1""", model.stem)					)
          # - get all the feature blocks
          for k, v in model.trunk_output.named_children():
                assert k.startswith("""block"""					), F'''Unexpected layer name {k}'''
                UpperCAmelCase							=       len(A					) + 1
                feature_blocks.append((F'''res{block_index}''', v)					)

          UpperCAmelCase							=       nn.ModuleDict(A					)






    def 				_UpperCamelCase							( self	,A					):
          return get_trunk_forward_outputs(
              A	,out_feat_keys=A	,feature_blocks=self._feature_blocks	,)







class 					lowerCamelCase__					(       snake_case ):






    def 				_UpperCamelCase							( self					):
          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 _a (		_snake_case							):







      """simple docstring"""






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

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

            UpperCAmelCase							=       torch.randn((1, 3, 32, 32)							)
            # trace both
            UpperCAmelCase							=       Tracker(_snake_case							)
            UpperCAmelCase							=       dest_tracker(_snake_case							).parametrized

            pprint(dest_tracker.nameamodule							)
            UpperCAmelCase							=       Tracker(_snake_case							)
            UpperCAmelCase							=       src_tracker(_snake_case							).parametrized

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

      UpperCAmelCase							=       {}

      UpperCAmelCase							=       to_params_dict(_snake_case							)
      UpperCAmelCase							=       to_params_dict(_snake_case							)

      for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()      ,       dst_state_dict.items()							):
            UpperCAmelCase							=       dest_key
            logger.info(F'''{src_key} -> {dest_key}'''							)
      # if "in1k" was in the model_name it means it must have a classification head (was finetuned)
      if "in1k" in model_name:
            UpperCAmelCase							=       """classifier.1.weight"""
            UpperCAmelCase							=       """classifier.1.bias"""

      return from_to_ours_keys

def _a (		_snake_case      ,       _snake_case = None      ,       _snake_case = True							):







      """simple docstring"""






      UpperCAmelCase							=       """imagenet-1k-id2label.json"""
      UpperCAmelCase							=       1000

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

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

      UpperCAmelCase							=       partial(_snake_case      ,       num_labels=_snake_case      ,       idalabel=_snake_case      ,       labelaid=_snake_case							)

      UpperCAmelCase							=       {
          """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]      ,       hidden_sizes=[2020, 4040, 1_1110, 2_8280]      ,       groups_width=1010							),
          # finetuned on imagenet
          """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]      ,       hidden_sizes=[2020, 4040, 1_1110, 2_8280]      ,       groups_width=1010							),
      }

      # add seer weights logic
      def load_using_classy_vision(_snake_case							) -> Tuple[Dict, Dict]:
            UpperCAmelCase							=       torch.hub.load_state_dict_from_url(_snake_case      ,       model_dir=str(_snake_case							)      ,       map_location="""cpu"""							)
            # check if we have a head, if yes add it
            UpperCAmelCase							=       files["""classy_state_dict"""]["""base_model"""]["""model"""]
            return model_state_dict["trunk"], model_state_dict["heads"]

      UpperCAmelCase							=       {
          """regnet-y-10b-seer""": partial(
              _snake_case      ,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""      ,       ),
          """regnet-y-10b-seer-in1k""": partial(
              _snake_case      ,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""      ,       ),
      }

      UpperCAmelCase							=       get_from_to_our_keys(_snake_case							)

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

            UpperCAmelCase							=       {}

            UpperCAmelCase							=       list(from_state_dict.keys()							)
            UpperCAmelCase							=       R"""\.block.-part."""
            # this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
            for key in from_state_dict.keys():
                  # remove the weird "block[0,1]-part" from the key
                  UpperCAmelCase							=       re.sub(_snake_case      ,       """"""      ,       _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
                  UpperCAmelCase							=       from_to_ours_keys[src_key]
                  # store the parameter with our key
                  UpperCAmelCase							=       from_state_dict[key]
                  not_used_keys.remove(_snake_case							)
            # check that all keys have been updated
            assert len(_snake_case							) == 0, F'''Some keys where not used {','.join(_snake_case							)}'''

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

            # save our state dict to disk
            torch.save(_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
            UpperCAmelCase							=       names_to_config[model_name]
            UpperCAmelCase							=       RegNetModel
            if "in1k" in model_name:
                  UpperCAmelCase							=       RegNetForImageClassification
            UpperCAmelCase							=       our_model_func(_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
            UpperCAmelCase							=       our_model.state_dict().keys()
            PreTrainedModel._load_pretrained_model_low_mem(
                _snake_case      ,       _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      ,       )
            UpperCAmelCase							=       384
            # we can use the convnext one
            UpperCAmelCase							=       AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""      ,       size=_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__":
  _UpperCamelCase     			=  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--model_name""",
      default=None,
      type=str,
      help=(
          """The name of the model you wish to convert, it must be one of the supported regnet* architecture,"""
          """ currently: regnetx-*, regnety-*. If `None`, all of them will the converted."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default=None,
      type=Path,
      required=True,
      help="""Path to the output PyTorch model directory.""",
  )
  parser.add_argument(
      """--push_to_hub""",
      default=True,
      type=bool,
      required=False,
      help="""If True, push model and image processor to the hub.""",
  )

  _UpperCamelCase     			=  parser.parse_args()

  _UpperCamelCase     			=  args.pytorch_dump_folder_path
  pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)



