






"""simple docstring"""


import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation

_UpperCamelCase :    Tuple      =			logging.get_logger(__name__)

_UpperCamelCase :    Optional[int]      =			{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

_UpperCamelCase :    List[Any]      =			{
    """vocab_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/vocab.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/vocab.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/vocab.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/vocab.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/merges.txt""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/merges.txt""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/merges.txt""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/merges.txt""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/merges.txt""",
    },
}

_UpperCamelCase :    Optional[Any]      =			{
    """gpt2""": 10_24,
    """gpt2-medium""": 10_24,
    """gpt2-large""": 10_24,
    """gpt2-xl""": 10_24,
    """distilgpt2""": 10_24,
}




@lru_cache()
def 					a			(  )	->		Union[str, Any]:
 __A						:							Dict      =						(
     list(range(ord('!'		)  ,						ord('~'		) + 1		)		) + list(range(ord('¡'		)  ,						ord('¬'		) + 1		)		) + list(range(ord('®'		)  ,						ord('ÿ'		) + 1		)		)
 )
 __A						:							Tuple      =						bs[:]
 __A						:							str      =						0
 for b in range(2**8		):
  if b not in bs:
   bs.append(UpperCamelCase_		)
   cs.append(2**8 + n		)
   n += 1
 __A						:							Optional[Any]      =						[chr(UpperCamelCase_		) for n in cs]
 return dict(zip(UpperCamelCase_  ,						UpperCamelCase_		)		)




def 					a			(  UpperCamelCase_				:					Dict		)	->		Optional[Any]:
 __A						:							Dict      =						set()
 __A						:							Dict      =						word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)		)
  __A						:							Any      =						char
 return pairs






class __a ( snake_case_					):
  __lowercase     :     Dict       						=       VOCAB_FILES_NAMES
  __lowercase     :     str       						=       PRETRAINED_VOCAB_FILES_MAP
  __lowercase     :     int       						=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowercase     :     Optional[Any]       						=       ['input_ids', 'attention_mask']



  def __init__(self	:		Tuple					,      lowercase_	:		int					,      lowercase_	:		str					,      lowercase_	:		Any="replace"					,      lowercase_	:		Dict="<|endoftext|>"					,      lowercase_	:		Optional[int]="<|endoftext|>"					,      lowercase_	:		List[str]="<|endoftext|>"					,      lowercase_	:		Tuple=None					,      lowercase_	:		Union[str, Any]=False					,      lowercase_	:		Tuple=False					,      **lowercase_	:		List[str]					,      )		->				Any:
   __A						:							Union[str, Any]      =						AddedToken(lowercase_					,      lstrip=lowercase_					,      rstrip=lowercase_ ) if isinstance(lowercase_					,      lowercase_ ) else bos_token
   __A						:							Union[str, Any]      =						AddedToken(lowercase_					,      lstrip=lowercase_					,      rstrip=lowercase_ ) if isinstance(lowercase_					,      lowercase_ ) else eos_token
   __A						:							int      =						AddedToken(lowercase_					,      lstrip=lowercase_					,      rstrip=lowercase_ ) if isinstance(lowercase_					,      lowercase_ ) else unk_token
   __A						:							Optional[Any]      =						AddedToken(lowercase_					,      lstrip=lowercase_					,      rstrip=lowercase_ ) if isinstance(lowercase_					,      lowercase_ ) else pad_token
   super().__init__(
       errors=lowercase_					,      unk_token=lowercase_					,      bos_token=lowercase_					,      eos_token=lowercase_					,      pad_token=lowercase_					,      add_prefix_space=lowercase_					,      add_bos_token=lowercase_					,      **lowercase_					,      )
   __A						:							int      =						add_bos_token

   with open(lowercase_					,      encoding='utf-8' ) as vocab_handle:
    __A						:							str      =						json.load(lowercase_ )
   __A						:							Optional[int]      =						{v: k for k, v in self.encoder.items()}
   __A						:							List[Any]      =						errors  # how to handle errors in decoding
   __A						:							int      =						bytes_to_unicode()
   __A						:							Optional[int]      =						{v: k for k, v in self.byte_encoder.items()}
   with open(lowercase_					,      encoding='utf-8' ) as merges_handle:
    __A						:							Dict      =						merges_handle.read().split('\n' )[1:-1]
   __A						:							Optional[Any]      =						[tuple(merge.split() ) for merge in bpe_merges]
   __A						:							List[Any]      =						dict(zip(lowercase_					,      range(len(lowercase_ ) ) ) )
   __A						:							List[Any]      =						{}
   __A						:							Dict      =						add_prefix_space

   # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
   __A						:							Optional[int]      =						re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )



  @property
  def     A_					(self	:		Dict )		->				int:
   return len(self.encoder )



  def     A_					(self	:		Any )		->				str:
   return dict(self.encoder					,      **self.added_tokens_encoder )



  def     A_					(self	:		Union[str, Any]					,      lowercase_	:		str )		->				List[Any]:
   if token in self.cache:
    return self.cache[token]
   __A						:							Optional[int]      =						tuple(lowercase_ )
   __A						:							int      =						get_pairs(lowercase_ )

   if not pairs:
    return token

   while True:
    __A						:							Optional[int]      =						min(lowercase_					,      key=lambda lowercase_ : self.bpe_ranks.get(lowercase_					,      float('inf' ) ) )
    if bigram not in self.bpe_ranks:
     break
    __A     ,       __A						:							Union[str, Any]      =						bigram
    __A						:							Any      =						[]
    __A						:							Dict      =						0
    while i < len(lowercase_ ):
     try:
      __A						:							Optional[int]      =						word.index(lowercase_					,      lowercase_ )
     except ValueError:
      new_word.extend(word[i:] )
      break
     else:
      new_word.extend(word[i:j] )
      __A						:							Union[str, Any]      =						j

     if word[i] == first and i < len(lowercase_ ) - 1 and word[i + 1] == second:
      new_word.append(first + second )
      i += 2
     else:
      new_word.append(word[i] )
      i += 1
    __A						:							Any      =						tuple(lowercase_ )
    __A						:							Union[str, Any]      =						new_word
    if len(lowercase_ ) == 1:
     break
    else:
     __A						:							Optional[int]      =						get_pairs(lowercase_ )
   __A						:							Optional[int]      =						' '.join(lowercase_ )
   __A						:							Dict      =						word
   return word



  def     A_					(self	:		Tuple					,      lowercase_	:		List[Any]					,      lowercase_	:		List[Any]=None )		->				List[Any]:
   if self.add_bos_token:
    __A						:							Optional[Any]      =						[self.bos_token_id]
   else:
    __A						:							Union[str, Any]      =						[]

   __A						:							Optional[int]      =						bos_token_ids + token_ids_a

   if token_ids_a is None:
    return output

   return output + bos_token_ids + token_ids_a



  def     A_					(self	:		str					,      lowercase_	:		List[int]					,      lowercase_	:		Optional[List[int]] = None					,      lowercase_	:		bool = False )		->				List[int]:
   if already_has_special_tokens:
    return super().get_special_tokens_mask(
        token_ids_a=lowercase_					,      token_ids_a=lowercase_					,      already_has_special_tokens=lowercase_ )

   if not self.add_bos_token:
    return super().get_special_tokens_mask(
        token_ids_a=lowercase_					,      token_ids_a=lowercase_					,      already_has_special_tokens=lowercase_ )

   if token_ids_a is None:
    return [1] + ([0] * len(lowercase_ ))
   return [1] + ([0] * len(lowercase_ )) + [1] + ([0] * len(lowercase_ ))



  def     A_					(self	:		int					,      lowercase_	:		Tuple )		->				List[str]:
   __A						:							Optional[Any]      =						[]
   for token in re.findall(self.pat					,      lowercase_ ):
    __A						:							Optional[Any]      =						''.join(
        self.byte_encoder[b] for b in token.encode('utf-8' ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
    bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase_ ).split(' ' ) )
   return bpe_tokens



  def     A_					(self	:		str					,      lowercase_	:		List[Any] )		->				Optional[int]:
   return self.encoder.get(lowercase_					,      self.encoder.get(self.unk_token ) )



  def     A_					(self	:		Any					,      lowercase_	:		Dict )		->				Union[str, Any]:
   return self.decoder.get(lowercase_ )



  def     A_					(self	:		Tuple					,      lowercase_	:		List[str] )		->				Optional[int]:
   __A						:							Optional[int]      =						''.join(lowercase_ )
   __A						:							List[Any]      =						bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8'					,      errors=self.errors )
   return text



  def     A_					(self	:		List[str]					,      lowercase_	:		str					,      lowercase_	:		Optional[str] = None )		->				Tuple[str]:
   if not os.path.isdir(lowercase_ ):
    logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
    return
   __A						:							Tuple      =						os.path.join(
       lowercase_					,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
   __A						:							List[Any]      =						os.path.join(
       lowercase_					,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )

   with open(lowercase_					,      'w'					,      encoding='utf-8' ) as f:
    f.write(json.dumps(self.encoder					,      indent=2					,      sort_keys=lowercase_					,      ensure_ascii=lowercase_ ) + '\n' )

   __A						:							List[Any]      =						0
   with open(lowercase_					,      'w'					,      encoding='utf-8' ) as writer:
    writer.write('#version: 0.2\n' )
    for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,      key=lambda lowercase_ : kv[1] ):
     if index != token_index:
      logger.warning(
          f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
          ' Please check that the tokenizer is not corrupted!' )
      __A						:							Optional[Any]      =						token_index
     writer.write(' '.join(lowercase_ ) + '\n' )
     index += 1

   return vocab_file, merge_file



  def     A_					(self	:		Optional[Any]					,      lowercase_	:		Union[str, Any]					,      lowercase_	:		List[str]=False					,      **lowercase_	:		Optional[int] )		->				Union[str, Any]:
   __A						:							Dict      =						kwargs.pop('add_prefix_space'					,      self.add_prefix_space )
   if is_split_into_words or add_prefix_space:
    __A						:							Optional[Any]      =						' ' + text
   return (text, kwargs)



  def     A_					(self	:		Tuple					,      lowercase_	:		"Conversation" )		->				List[int]:
   __A						:							List[str]      =						[]
   for is_user, text in conversation.iter_texts():
    input_ids.extend(self.encode(lowercase_					,      add_special_tokens=lowercase_ ) + [self.eos_token_id] )
   if len(lowercase_ ) > self.model_max_length:
    __A						:							Optional[int]      =						input_ids[-self.model_max_length :]
   return input_ids













"""simple docstring"""


import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)




def 					a			(  UpperCamelCase_				:					Optional[Any]		)	->		str:
 __A						:							Union[str, Any]      =						CLIPSegTextConfig()
 __A						:							Dict      =						CLIPSegVisionConfig(patch_size=1_6		)

 __A						:							Dict      =						True if 'refined' in model_name else False
 __A						:							Optional[int]      =						1_6 if 'rd16' in model_name else 6_4

 __A						:							Tuple      =						CLIPSegConfig.from_text_vision_configs(
     UpperCamelCase_  ,						UpperCamelCase_  ,						use_complex_transposed_convolution=UpperCamelCase_  ,						reduce_dim=UpperCamelCase_  ,						)
 return config




def 					a			(  UpperCamelCase_				:					List[str]		)	->		Dict:
 # update prefixes
 if "clip_model" in name:
  __A						:							str      =						name.replace('clip_model'  ,						'clip'		)
 if "transformer" in name:
  if "visual" in name:
   __A						:							List[str]      =						name.replace('visual.transformer'  ,						'vision_model'		)
  else:
   __A						:							Dict      =						name.replace('transformer'  ,						'text_model'		)
 if "resblocks" in name:
  __A						:							List[str]      =						name.replace('resblocks'  ,						'encoder.layers'		)
 if "ln_1" in name:
  __A						:							Dict      =						name.replace('ln_1'  ,						'layer_norm1'		)
 if "ln_2" in name:
  __A						:							Any      =						name.replace('ln_2'  ,						'layer_norm2'		)
 if "c_fc" in name:
  __A						:							List[Any]      =						name.replace('c_fc'  ,						'fc1'		)
 if "c_proj" in name:
  __A						:							int      =						name.replace('c_proj'  ,						'fc2'		)
 if "attn" in name and "self" not in name:
  __A						:							Dict      =						name.replace('attn'  ,						'self_attn'		)
 # text encoder
 if "token_embedding" in name:
  __A						:							Dict      =						name.replace('token_embedding'  ,						'text_model.embeddings.token_embedding'		)
 if "positional_embedding" in name and "visual" not in name:
  __A						:							int      =						name.replace('positional_embedding'  ,						'text_model.embeddings.position_embedding.weight'		)
 if "ln_final" in name:
  __A						:							Optional[Any]      =						name.replace('ln_final'  ,						'text_model.final_layer_norm'		)
 # vision encoder
 if "visual.class_embedding" in name:
  __A						:							Tuple      =						name.replace('visual.class_embedding'  ,						'vision_model.embeddings.class_embedding'		)
 if "visual.conv1" in name:
  __A						:							List[Any]      =						name.replace('visual.conv1'  ,						'vision_model.embeddings.patch_embedding'		)
 if "visual.positional_embedding" in name:
  __A						:							str      =						name.replace('visual.positional_embedding'  ,						'vision_model.embeddings.position_embedding.weight'		)
 if "visual.ln_pre" in name:
  __A						:							Any      =						name.replace('visual.ln_pre'  ,						'vision_model.pre_layrnorm'		)
 if "visual.ln_post" in name:
  __A						:							str      =						name.replace('visual.ln_post'  ,						'vision_model.post_layernorm'		)
 # projection layers
 if "visual.proj" in name:
  __A						:							Any      =						name.replace('visual.proj'  ,						'visual_projection.weight'		)
 if "text_projection" in name:
  __A						:							int      =						name.replace('text_projection'  ,						'text_projection.weight'		)
 # decoder
 if "trans_conv" in name:
  __A						:							int      =						name.replace('trans_conv'  ,						'transposed_convolution'		)
 if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
  __A						:							List[Any]      =						'decoder.' + name
 if "blocks" in name:
  __A						:							Optional[Any]      =						name.replace('blocks'  ,						'decoder.layers'		)
 if "linear1" in name:
  __A						:							int      =						name.replace('linear1'  ,						'mlp.fc1'		)
 if "linear2" in name:
  __A						:							Tuple      =						name.replace('linear2'  ,						'mlp.fc2'		)
 if "norm1" in name and "layer_" not in name:
  __A						:							List[Any]      =						name.replace('norm1'  ,						'layer_norm1'		)
 if "norm2" in name and "layer_" not in name:
  __A						:							Optional[Any]      =						name.replace('norm2'  ,						'layer_norm2'		)

 return name




def 					a			(  UpperCamelCase_				:					List[str]  ,						UpperCamelCase_				:					Dict		)	->		Any:
 for key in orig_state_dict.copy().keys():
  __A						:							str      =						orig_state_dict.pop(UpperCamelCase_		)

  if key.startswith('clip_model'		) and "attn.in_proj" in key:
   __A						:							Any      =						key.split('.'		)
   if "visual" in key:
    __A						:							Optional[int]      =						int(key_split[4]		)
    __A						:							Dict      =						config.vision_config.hidden_size
    __A						:							str      =						'vision_model'
   else:
    __A						:							Union[str, Any]      =						int(key_split[3]		)
    __A						:							List[str]      =						config.text_config.hidden_size
    __A						:							Union[str, Any]      =						'text_model'

   if "weight" in key:
    __A						:							List[Any]      =						val[:dim, :]
    __A						:							Optional[Any]      =						val[
        dim : dim * 2, :
    ]
    __A						:							List[str]      =						val[-dim:, :]
   else:
    __A						:							Any      =						val[:dim]
    __A						:							Tuple      =						val[dim : dim * 2]
    __A						:							Dict      =						val[-dim:]
  elif "self_attn" in key and "out_proj" not in key:
   __A						:							Any      =						key.split('.'		)
   __A						:							Optional[int]      =						int(key_split[1]		)
   __A						:							int      =						config.reduce_dim
   if "weight" in key:
    __A						:							Tuple      =						val[:dim, :]
    __A						:							Optional[int]      =						val[dim : dim * 2, :]
    __A						:							List[Any]      =						val[-dim:, :]
   else:
    __A						:							List[Any]      =						val[:dim]
    __A						:							Union[str, Any]      =						val[dim : dim * 2]
    __A						:							Union[str, Any]      =						val[-dim:]
  else:
   __A						:							List[Any]      =						rename_key(UpperCamelCase_		)
   if "visual_projection" in new_name or "text_projection" in new_name:
    __A						:							Dict      =						val.T
   __A						:							int      =						val

 return orig_state_dict




def 					a			(  )	->		Dict:
 __A						:							List[Any]      =						'http://images.cocodataset.org/val2017/000000039769.jpg'
 __A						:							Any      =						Image.open(requests.get(UpperCamelCase_  ,						stream=UpperCamelCase_		).raw		)
 return image




def 					a			(  UpperCamelCase_				:					List[str]  ,						UpperCamelCase_				:					List[str]  ,						UpperCamelCase_				:					Dict  ,						UpperCamelCase_				:					int		)	->		Optional[int]:
 __A						:							str      =						get_clipseg_config(UpperCamelCase_		)
 __A						:							int      =						CLIPSegForImageSegmentation(UpperCamelCase_		)
 model.eval()

 __A						:							Optional[int]      =						torch.load(UpperCamelCase_  ,						map_location='cpu'		)

 # remove some keys
 for key in state_dict.copy().keys():
  if key.startswith('model'		):
   state_dict.pop(UpperCamelCase_  ,						UpperCamelCase_		)

    # rename some keys
 __A						:							int      =						convert_state_dict(UpperCamelCase_  ,						UpperCamelCase_		)
 __A     ,       __A						:							Tuple      =						model.load_state_dict(UpperCamelCase_  ,						strict=UpperCamelCase_		)

 if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
  raise ValueError('Missing keys that are not expected: {}'.format(UpperCamelCase_		)		)
 if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
  raise ValueError(F"Unexpected keys: {unexpected_keys}"		)

 __A						:							Optional[Any]      =						ViTImageProcessor(size=3_5_2		)
 __A						:							List[Any]      =						CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'		)
 __A						:							Optional[int]      =						CLIPSegProcessor(image_processor=UpperCamelCase_  ,						tokenizer=UpperCamelCase_		)

 __A						:							List[str]      =						prepare_img()
 __A						:							Optional[Any]      =						['a glass', 'something to fill', 'wood', 'a jar']

 __A						:							Optional[Any]      =						processor(text=UpperCamelCase_  ,						images=[image] * len(UpperCamelCase_		)  ,						padding='max_length'  ,						return_tensors='pt'		)

 with torch.no_grad():
  __A						:							int      =						model(**UpperCamelCase_		)

 # verify values
 __A						:							Union[str, Any]      =						torch.tensor([0.1110, -0.1882, 0.1645]		)
 __A						:							List[Any]      =						torch.tensor([0.2692, -0.7197, -0.1328]		)
 if model_name == "clipseg-rd64-refined":
  __A						:							int      =						torch.tensor(
      [[-10.0407, -9.9431, -10.2646], [-9.9751, -9.7064, -9.9586], [-9.6891, -9.5645, -9.9618]]		)
 elif model_name == "clipseg-rd64":
  __A						:							Optional[Any]      =						torch.tensor(
      [[-7.2877, -7.2711, -7.2463], [-7.2652, -7.2780, -7.2520], [-7.2239, -7.2204, -7.2001]]		)
 elif model_name == "clipseg-rd16":
  __A						:							Optional[Any]      =						torch.tensor(
      [[-6.3955, -6.4055, -6.4151], [-6.3911, -6.4033, -6.4100], [-6.3474, -6.3702, -6.3762]]		)
 else:
  raise ValueError(F"Model name {model_name} not supported."		)

 assert torch.allclose(outputs.logits[0, :3, :3]  ,						UpperCamelCase_  ,						atol=1E-3		)
 assert torch.allclose(outputs.conditional_embeddings[0, :3]  ,						UpperCamelCase_  ,						atol=1E-3		)
 assert torch.allclose(outputs.pooled_output[0, :3]  ,						UpperCamelCase_  ,						atol=1E-3		)
 print('Looks ok!'		)

 if pytorch_dump_folder_path is not None:
  print(F"Saving model and processor to {pytorch_dump_folder_path}"		)
  model.save_pretrained(UpperCamelCase_		)
  processor.save_pretrained(UpperCamelCase_		)

 if push_to_hub:
  print(F"Pushing model and processor for {model_name} to the hub"		)
  model.push_to_hub(F"CIDAS/{model_name}"		)
  processor.push_to_hub(F"CIDAS/{model_name}"		)


if __name__ == "__main__":
   _UpperCamelCase :    str      =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--model_name""",
       default="""clipseg-rd64""",
       type=str,
       choices=["""clipseg-rd16""", """clipseg-rd64""", """clipseg-rd64-refined"""],
       help=(
           """Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning"""
           """ reduce dimension)"""
       ),
   )
   parser.add_argument(
       """--checkpoint_path""",
       default="""/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth""",
       type=str,
       help=(
           """Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and"""
           """ the decoder weights."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
   )
   parser.add_argument(
       """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
   )

   _UpperCamelCase :    Tuple      =			parser.parse_args()
   convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





