






import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation

_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE   =  {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

_SCREAMING_SNAKE_CASE   =  {
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

_SCREAMING_SNAKE_CASE   =  {"facebook/blenderbot-3B": 1_28}






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Any					=VOCAB_FILES_NAMES
       __lowerCAmelCase    :		int					=PRETRAINED_VOCAB_FILES_MAP
       __lowerCAmelCase    :		Optional[Any]					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __lowerCAmelCase    :		Union[str, Any]					=['''input_ids''', '''attention_mask''']
       __lowerCAmelCase    :		str					=BlenderbotTokenizer





       def __init__(				self	:Dict,      snake_case	:Optional[Any]=None,      snake_case	:int=None,      snake_case	:Dict=None,      snake_case	:Optional[Any]="replace",      snake_case	:List[Any]="<s>",      snake_case	:Dict="</s>",      snake_case	:List[Any]="</s>",      snake_case	:Optional[int]="<s>",      snake_case	:int="<unk>",      snake_case	:Any="<pad>",      snake_case	:str="<mask>",      snake_case	:Union[str, Any]=False,      snake_case	:Dict=True,      **snake_case	:List[Any],      ):



          """simple docstring"""

          super().__init__(
              snake_case,      snake_case,      tokenizer_file=snake_case,      errors=snake_case,      bos_token=snake_case,      eos_token=snake_case,      sep_token=snake_case,      cls_token=snake_case,      unk_token=snake_case,      pad_token=snake_case,      mask_token=snake_case,      add_prefix_space=snake_case,      trim_offsets=snake_case,      **snake_case,      )

          _lowercase              =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
          if pre_tok_state.get('add_prefix_space',      snake_case) != add_prefix_space:
             _lowercase              =getattr(snake_case,      pre_tok_state.pop('type'))
             _lowercase              =add_prefix_space
             _lowercase              =pre_tok_class(**snake_case)

          _lowercase              =add_prefix_space

          _lowercase              ='post_processor'
          _lowercase              =getattr(self.backend_tokenizer,      snake_case,      snake_case)
          if tokenizer_component_instance:
             _lowercase              =json.loads(tokenizer_component_instance.__getstate__())

             # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
             if "sep" in state:
                _lowercase              =tuple(state['sep'])
             if "cls" in state:
                _lowercase              =tuple(state['cls'])

             _lowercase              =False

             if state.get('add_prefix_space',      snake_case) != add_prefix_space:
                _lowercase              =add_prefix_space
                _lowercase              =True

             if state.get('trim_offsets',      snake_case) != trim_offsets:
                _lowercase              =trim_offsets
                _lowercase              =True

             if changes_to_apply:
                _lowercase              =getattr(snake_case,      state.pop('type'))
                _lowercase              =component_class(**snake_case)
                setattr(self.backend_tokenizer,      snake_case,      snake_case)





       @property
       # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
       def 				UpperCamelCase__						(				self	:List[Any]):



          """simple docstring"""

          if self._mask_token is None:
             if self.verbose:
                logger.error('Using mask_token, but it is not set yet.')
             return None
          return str(self._mask_token)





       @mask_token.setter
       def 				UpperCamelCase__						(				self	:Any,      snake_case	:Optional[int]):



          """simple docstring"""

          _lowercase              =AddedToken(snake_case,      lstrip=snake_case,      rstrip=snake_case) if isinstance(snake_case,      snake_case) else value
          _lowercase              =value





       def 				UpperCamelCase__						(				self	:Any,      *snake_case	:Tuple,      **snake_case	:Tuple):



          """simple docstring"""

          _lowercase              =kwargs.get('is_split_into_words',      snake_case)
          assert self.add_prefix_space or not is_split_into_words, (
              f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
              "to use it with pretokenized inputs."
          )

          return super()._batch_encode_plus(*snake_case,      **snake_case)





       def 				UpperCamelCase__						(				self	:Optional[int],      *snake_case	:Optional[Any],      **snake_case	:Optional[Any]):



          """simple docstring"""

          _lowercase              =kwargs.get('is_split_into_words',      snake_case)

          assert self.add_prefix_space or not is_split_into_words, (
              f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
              "to use it with pretokenized inputs."
          )

          return super()._encode_plus(*snake_case,      **snake_case)





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:str,      snake_case	:Optional[str] = None):



          """simple docstring"""

          _lowercase              =self._tokenizer.model.save(snake_case,      name=snake_case)
          return tuple(snake_case)





       def 				UpperCamelCase__						(				self	:Dict,      snake_case	:List[int],      snake_case	:Optional[List[int]] = None):



          """simple docstring"""

          _lowercase              =[self.sep_token_id]
          _lowercase              =[self.cls_token_id]

          if token_ids_a is None:
             return len(cls + token_ids_a + sep) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]





       def 				UpperCamelCase__						(				self	:Optional[Any],      snake_case	:List[int],      snake_case	:Optional[List[int]] = None):



          """simple docstring"""

          return token_ids_a + [self.eos_token_id]





       def 				UpperCamelCase__						(				self	:str,      snake_case	:"Conversation"):



          """simple docstring"""

          _lowercase              =[]
          for is_user, text in conversation.iter_texts():
             if is_user:
                # We need to space prefix as it's being done within blenderbot
                inputs.append(' ' + text)
             else:
                # Generated responses should contain them already.
                inputs.append(snake_case)

          _lowercase              ='  '.join(snake_case)
          _lowercase              =self.encode(snake_case)
          if len(snake_case) > self.model_max_length:
             _lowercase              =input_ids[-self.model_max_length :]
             logger.warning(f'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.''')
          return input_ids










import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_SCREAMING_SNAKE_CASE   =  []
for i in range(6):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append(
        (f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', f'''encoder.layers.{i}.self_attn.out_proj.weight''')
    )
    rename_keys.append(
        (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')
    )
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
    rename_keys.append(
        (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')
    )
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
    # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''decoder.layers.{i}.self_attn.out_proj.weight''')
    )
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')
    )
    rename_keys.append(
        (
            f'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
            f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
        )
    )
    rename_keys.append(
        (
            f'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
            f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
        )
    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')
    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
    )
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))

    # q, k, v projections in self/cross-attention in decoder for conditional DETR
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', f'''decoder.layers.{i}.sa_qcontent_proj.weight''')
    )
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', f'''decoder.layers.{i}.sa_kcontent_proj.weight''')
    )
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', f'''decoder.layers.{i}.sa_qpos_proj.weight''')
    )
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', f'''decoder.layers.{i}.sa_kpos_proj.weight''')
    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.weight''', f'''decoder.layers.{i}.sa_v_proj.weight'''))
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', f'''decoder.layers.{i}.ca_qcontent_proj.weight''')
    )
    # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', f'''decoder.layers.{i}.ca_kcontent_proj.weight''')
    )
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', f'''decoder.layers.{i}.ca_kpos_proj.weight''')
    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.weight''', f'''decoder.layers.{i}.ca_v_proj.weight'''))
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', f'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
    )

    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', f'''decoder.layers.{i}.sa_qcontent_proj.bias''')
    )
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', f'''decoder.layers.{i}.sa_kcontent_proj.bias''')
    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', f'''decoder.layers.{i}.sa_qpos_proj.bias'''))
    rename_keys.append((f'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', f'''decoder.layers.{i}.sa_kpos_proj.bias'''))
    rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.bias''', f'''decoder.layers.{i}.sa_v_proj.bias'''))
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', f'''decoder.layers.{i}.ca_qcontent_proj.bias''')
    )
    # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', f'''decoder.layers.{i}.ca_kcontent_proj.bias''')
    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', f'''decoder.layers.{i}.ca_kpos_proj.bias'''))
    rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.bias''', f'''decoder.layers.{i}.ca_v_proj.bias'''))
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', f'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
    )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("input_proj.weight", "input_projection.weight"),
        ("input_proj.bias", "input_projection.bias"),
        ("query_embed.weight", "query_position_embeddings.weight"),
        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
        ("class_embed.weight", "class_labels_classifier.weight"),
        ("class_embed.bias", "class_labels_classifier.bias"),
        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
        ("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"),
        ("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"),
        ("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"),
        ("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"),
        ("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"),
        ("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"),
        ("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"),
        ("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"),
        ("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"),
        ("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"),
    ]
)





def        _snake_case   (_snake_case				:       Optional[int]      ,    _snake_case				:       Dict      ,    _snake_case				:       Dict)					->	str:
   _lowercase              =state_dict.pop(_snake_case)
   _lowercase              =val





def        _snake_case   (_snake_case				:       Any)					->	Union[str, Any]:
   _lowercase              =OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         _lowercase              =key.replace('backbone.0.body'      ,    'backbone.conv_encoder.model')
         _lowercase              =value
      else:
         _lowercase              =value

   return new_state_dict





def        _snake_case   (_snake_case				:       Tuple      ,    _snake_case				:       Tuple=False)					->	str:
   _lowercase              =''
   if is_panoptic:
      _lowercase              ='conditional_detr.'

   # first: transformer encoder
   for i in range(6):
      # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
      _lowercase              =state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''')
      _lowercase              =state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''')
      # next, add query, keys and values (in that order) to the state dict
      _lowercase              =in_proj_weight[:256, :]
      _lowercase              =in_proj_bias[:256]
      _lowercase              =in_proj_weight[256:512, :]
      _lowercase              =in_proj_bias[256:512]
      _lowercase              =in_proj_weight[-256:, :]
      _lowercase              =in_proj_bias[-256:]





def        _snake_case   ()					->	List[Any]:
   _lowercase              ='http://images.cocodataset.org/val2017/000000039769.jpg'
   _lowercase              =Image.open(requests.get(_snake_case      ,    stream=_snake_case).raw)

   return im





@torch.no_grad()
def        _snake_case   (_snake_case				:       Any      ,    _snake_case				:       int)					->	Union[str, Any]:
   _lowercase              =ConditionalDetrConfig()
   # set backbone and dilation attributes
   if "resnet101" in model_name:
      _lowercase              ='resnet101'
   if "dc5" in model_name:
      _lowercase              =True
   _lowercase              ='panoptic' in model_name
   if is_panoptic:
      _lowercase              =250
   else:
      _lowercase              =91
      _lowercase              ='huggingface/label-files'
      _lowercase              ='coco-detection-id2label.json'
      _lowercase              =json.load(open(hf_hub_download(_snake_case      ,    _snake_case      ,    repo_type='dataset')      ,    'r'))
      _lowercase              ={int(_snake_case): v for k, v in idalabel.items()}
      _lowercase              =idalabel
      _lowercase              ={v: k for k, v in idalabel.items()}

   # load image processor
   _lowercase              ='coco_panoptic' if is_panoptic else 'coco_detection'
   _lowercase              =ConditionalDetrImageProcessor(format=_snake_case)

   # prepare image
   _lowercase              =prepare_img()
   _lowercase              =image_processor(images=_snake_case      ,    return_tensors='pt')
   _lowercase              =encoding['pixel_values']

   logger.info(f'''Converting model {model_name}...''')

   # load original model from torch hub
   _lowercase              =torch.hub.load('DeppMeng/ConditionalDETR'      ,    _snake_case      ,    pretrained=_snake_case).eval()
   _lowercase              =conditional_detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         _lowercase              ='conditional_detr.' + src
      rename_key(_snake_case      ,    _snake_case      ,    _snake_case)
   _lowercase              =rename_backbone_keys(_snake_case)
   # query, key and value matrices need special treatment
   read_in_q_k_v(_snake_case      ,    is_panoptic=_snake_case)
   # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
   _lowercase              ='conditional_detr.model.' if is_panoptic else 'model.'
   for key in state_dict.copy().keys():
      if is_panoptic:
         if (
             key.startswith('conditional_detr')
             and not key.startswith('class_labels_classifier')
             and not key.startswith('bbox_predictor')
         ):
            _lowercase              =state_dict.pop(_snake_case)
            _lowercase              =val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            _lowercase              =state_dict.pop(_snake_case)
            _lowercase              =val
         elif key.startswith('bbox_attention') or key.startswith('mask_head'):
            continue
         else:
            _lowercase              =state_dict.pop(_snake_case)
            _lowercase              =val
      else:
         if not key.startswith('class_labels_classifier') and not key.startswith('bbox_predictor'):
            _lowercase              =state_dict.pop(_snake_case)
            _lowercase              =val
    # finally, create HuggingFace model and load state dict
   _lowercase              =ConditionalDetrForSegmentation(_snake_case) if is_panoptic else ConditionalDetrForObjectDetection(_snake_case)
   model.load_state_dict(_snake_case)
   model.eval()
   model.push_to_hub(repo_id=_snake_case      ,    organization='DepuMeng'      ,    commit_message='Add model')
   # verify our conversion
   _lowercase              =conditional_detr(_snake_case)
   _lowercase              =model(_snake_case)
   assert torch.allclose(outputs.logits      ,    original_outputs['pred_logits']      ,    atol=1E-4)
   assert torch.allclose(outputs.pred_boxes      ,    original_outputs['pred_boxes']      ,    atol=1E-4)
   if is_panoptic:
      assert torch.allclose(outputs.pred_masks      ,    original_outputs['pred_masks']      ,    atol=1E-4)

   # Save model and image processor
   logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''')
   Path(_snake_case).mkdir(exist_ok=_snake_case)
   model.save_pretrained(_snake_case)
   image_processor.save_pretrained(_snake_case)


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE   =  argparse.ArgumentParser()

    parser.add_argument(
        "--model_name",
        default="conditional_detr_resnet50",
        type=str,
        help="Name of the CONDITIONAL_DETR model you'd like to convert.",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
    )
    _SCREAMING_SNAKE_CASE   =  parser.parse_args()
    convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


