




import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase      :						Optional[Any]							=  logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_=False		)  ->			Optional[int]:



       """simple docstring"""

       A__ 					=       []

       # fmt: off
       # stem:
       rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token''')		)
       rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings''')		)

       rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight''')		)
       rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias''')		)

       # backbone
       rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight''')		)
       rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight''')		)
       rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias''')		)

       for stage_idx in range(len(config.backbone_config.depths		)		):
              for layer_idx in range(config.backbone_config.depths[stage_idx]		):
                     rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""")		)
                     rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""")		)
                     rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""")		)
                     rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""")		)
                     rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""")		)
                     rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""")		)
                     rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""")		)
                     rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""")		)
                     rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""")		)

              rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""")		)
              rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""")		)
              rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""")		)

       # transformer encoder
       for i in range(config.num_hidden_layers		):
              # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
              rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")		)
              rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")		)
              rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")		)
              rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")		)
              rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")		)
              rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")		)
              rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")		)
              rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")		)
              rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")		)
              rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")		)

       if base_model:
              # layernorm + pooler
              rename_keys.extend(
                  [
                      ('''norm.weight''', '''layernorm.weight'''),
                      ('''norm.bias''', '''layernorm.bias'''),
                      ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
                      ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
                  ]		)

              # if just the base model, we should remove "vit" from all keys that start with "vit"
              A__ 					=       [(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''		) else pair for pair in rename_keys]
       else:
              # layernorm + classification head
              rename_keys.extend(
                  [
                      ('''norm.weight''', '''vit.layernorm.weight'''),
                      ('''norm.bias''', '''vit.layernorm.bias'''),
                      ('''head.weight''', '''classifier.weight'''),
                      ('''head.bias''', '''classifier.bias'''),
                  ]		)
       # fmt: on

       return rename_keys







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_=False		)  ->			Dict:



       """simple docstring"""

       for i in range(config.num_hidden_layers		):
              if base_model:
                     A__ 					=       ''''''
              else:
                     A__ 					=       '''vit.'''
              # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
              A__ 					=       state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""		)
              A__ 					=       state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""		)
              # next, add query, keys and values (in that order) to the state dict
              A__ 					=       in_proj_weight[
                  : config.hidden_size, :
              ]
              A__ 					=       in_proj_bias[: config.hidden_size]
              A__ 					=       in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              A__ 					=       in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              A__ 					=       in_proj_weight[
                  -config.hidden_size :, :
              ]
              A__ 					=       in_proj_bias[-config.hidden_size :]







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			Any:



       """simple docstring"""

       A__ 					=       ['''head.weight''', '''head.bias''']
       for k in ignore_keys:
              state_dict.pop(lowercase_					,							lowercase_		)







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_		)  ->			str:



       """simple docstring"""

       A__ 					=       dct.pop(lowercase_		)
       A__ 					=       val







def SCREAMING_SNAKE_CASE							(			)  ->			Dict:



       """simple docstring"""

       A__ 					=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
       A__ 					=       Image.open(requests.get(lowercase_					,							stream=lowercase_		).raw		)
       return im







@torch.no_grad()
def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_=False		)  ->			List[Any]:



       """simple docstring"""

       A__ 					=       BitConfig(
           global_padding='''same'''					,							layer_type='''bottleneck'''					,							depths=(3, 4, 9)					,							out_features=['''stage3''']					,							embedding_dynamic_padding=lowercase_					,							)
       A__ 					=       ViTHybridConfig(backbone_config=lowercase_					,							image_size=384					,							num_labels=1_000		)
       A__ 					=       False

       # load original model from timm
       A__ 					=       timm.create_model(lowercase_					,							pretrained=lowercase_		)
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       A__ 					=       timm_model.state_dict()
       if base_model:
              remove_classification_head_(lowercase_		)
       A__ 					=       create_rename_keys(lowercase_					,							lowercase_		)
       for src, dest in rename_keys:
              rename_key(lowercase_					,							lowercase_					,							lowercase_		)
       read_in_q_k_v(lowercase_					,							lowercase_					,							lowercase_		)

       A__ 					=       '''huggingface/label-files'''
       A__ 					=       '''imagenet-1k-id2label.json'''
       A__ 					=       json.load(open(hf_hub_download(lowercase_					,							lowercase_					,							repo_type='''dataset'''		)					,							'''r'''		)		)
       A__ 					=       {int(lowercase_		): v for k, v in idalabel.items()}
       A__ 					=       idalabel
       A__ 					=       {v: k for k, v in idalabel.items()}

       # load HuggingFace model
       if vit_name[-5:] == "in21k":
              A__ 					=       ViTHybridModel(lowercase_		).eval()
       else:
              A__ 					=       ViTHybridForImageClassification(lowercase_		).eval()
       model.load_state_dict(lowercase_		)

       # create image processor
       A__ 					=       create_transform(**resolve_data_config({}					,							model=lowercase_		)		)
       A__ 					=       transform.transforms

       A__ 					=       {
           '''bilinear''': PILImageResampling.BILINEAR,
           '''bicubic''': PILImageResampling.BICUBIC,
           '''nearest''': PILImageResampling.NEAREST,
       }

       A__ 					=       ViTHybridImageProcessor(
           do_resize=lowercase_					,							size={'''shortest_edge''': timm_transforms[0].size}					,							resample=pillow_resamplings[timm_transforms[0].interpolation.value]					,							do_center_crop=lowercase_					,							crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}					,							do_normalize=lowercase_					,							image_mean=timm_transforms[-1].mean.tolist()					,							image_std=timm_transforms[-1].std.tolist()					,							)

       A__ 					=       prepare_img()
       A__ 					=       transform(lowercase_		).unsqueeze(0		)
       A__ 					=       processor(lowercase_					,							return_tensors='''pt'''		).pixel_values

       # verify pixel values
       assert torch.allclose(lowercase_					,							lowercase_		)

       # verify logits
       with torch.no_grad():
              A__ 					=       model(lowercase_		)
              A__ 					=       outputs.logits

       print('''Predicted class:'''					,							logits.argmax(-1		).item()		)
       if base_model:
              A__ 					=       timm_model.forward_features(lowercase_		)
              assert timm_pooled_output.shape == outputs.pooler_output.shape
              assert torch.allclose(lowercase_					,							outputs.pooler_output					,							atol=1E-3		)
       else:
              A__ 					=       timm_model(lowercase_		)
              assert timm_logits.shape == outputs.logits.shape
              assert torch.allclose(lowercase_					,							outputs.logits					,							atol=1E-3		)
       print('''Looks ok!'''		)

       if pytorch_dump_folder_path is not None:
              Path(lowercase_		).mkdir(exist_ok=lowercase_		)
              print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""		)
              model.save_pretrained(lowercase_		)
              print(f"""Saving processor to {pytorch_dump_folder_path}"""		)
              processor.save_pretrained(lowercase_		)

       if push_to_hub:
              print(f"""Pushing model and processor to the hub {vit_name}"""		)
              model.push_to_hub(f"""ybelkada/{vit_name}"""		)
              processor.push_to_hub(f"""ybelkada/{vit_name}"""		)


if __name__ == "__main__":
  _lowerCamelCase      :						Dict							=  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--vit_name""",
      default="""vit_base_r50_s16_384""",
      type=str,
      help="""Name of the hybrid ViT timm model you'd like to convert.""",
  )
  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 to upload the model to the HuggingFace hub."""
  )

  _lowerCamelCase      :						Dict							=  parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)










"""simple docstring"""



import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

_lowerCAmelCase	:	Optional[int]				=      2




class 			lowerCAmelCase__			:
   def __init__(		self      :	Any							,			*,  # begin keyword-only arguments
       snake_case__      :	List[str]="<s>"							,			snake_case__      :	str="<pad>"							,			snake_case__      :	List[str]="</s>"							,			snake_case__      :	Any="<unk>"							,			snake_case__      :	List[Any]=None							,			):


        '''simple docstring'''
        UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    str							=							bos, unk, pad, eos
        UpperCAmelCase__						:    str							=							[]
        UpperCAmelCase__						:    Tuple							=							[]
        UpperCAmelCase__						:    Optional[int]							=							{}
        UpperCAmelCase__						:    Optional[int]							=							self.add_symbol(snake_case__							)
        UpperCAmelCase__						:    Tuple							=							self.add_symbol(snake_case__							)
        UpperCAmelCase__						:    str							=							self.add_symbol(snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							self.add_symbol(snake_case__							)
        if extra_special_symbols:
             for s in extra_special_symbols:
                  self.add_symbol(snake_case__							)
        UpperCAmelCase__						:    List[str]							=							len(self.symbols							)
   def __eq__(		self      :	List[Any]							,			snake_case__      :	str							):


        '''simple docstring'''
        return self.indices == other.indices
   def __getitem__(		self      :	Dict							,			snake_case__      :	Union[str, Any]							):


        '''simple docstring'''
        if idx < len(self.symbols							):
             return self.symbols[idx]
        return self.unk_word
   def __len__(		self      :	int							):


        '''simple docstring'''
        return len(self.symbols							)
   def __contains__(		self      :	Dict							,			snake_case__      :	Optional[Any]							):


        '''simple docstring'''
        return sym in self.indices
   @classmethod
   def       __a							(		cls      :	Optional[int]							,			snake_case__      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Dict							=							cls()
        d.add_from_file(snake_case__							)
        return d
   def       __a							(		self      :	Optional[Any]							,			snake_case__      :	str							,			snake_case__      :	Dict=1							,			snake_case__      :	Dict=False							):


        '''simple docstring'''
        if word in self.indices and not overwrite:
             UpperCAmelCase__						:    List[Any]							=							self.indices[word]
             UpperCAmelCase__						:    Dict							=							self.count[idx] + n
             return idx
        else:
             UpperCAmelCase__						:    Optional[Any]							=							len(self.symbols							)
             UpperCAmelCase__						:    List[str]							=							idx
             self.symbols.append(snake_case__							)
             self.count.append(snake_case__							)
             return idx
   def       __a							(		self      :	Union[str, Any]							,			snake_case__      :	List[Any]							):


        '''simple docstring'''
        return 0




   def       __a							(		self      :	Any							,			snake_case__      :	Dict							):


        '''simple docstring'''
        if isinstance(snake_case__							,			snake_case__							):
             try:
                  with open(snake_case__							,			"r"							,			encoding="utf-8"							) as fd:
                       self.add_from_file(snake_case__							)
             except FileNotFoundError as fnfe:
                  raise fnfe
             except UnicodeError:
                  raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(snake_case__							)							)
             return

        UpperCAmelCase__						:    Optional[int]							=							f.readlines()
        UpperCAmelCase__						:    Dict							=							self._load_meta(snake_case__							)

        for line in lines[indices_start_line:]:
             try:
                  UpperCAmelCase__							,				UpperCAmelCase__						:    str							=							line.rstrip().rsplit(" "							,			1							)
                  if field == "#fairseq:overwrite":
                       UpperCAmelCase__						:    Union[str, Any]							=							True
                       UpperCAmelCase__							,				UpperCAmelCase__						:    str							=							line.rsplit(" "							,			1							)
                  else:
                       UpperCAmelCase__						:    Tuple							=							False
                  UpperCAmelCase__						:    Any							=							int(snake_case__							)
                  UpperCAmelCase__						:    List[Any]							=							line
                  if word in self and not overwrite:
                       raise RuntimeError(
                           "Duplicate word found when loading Dictionary: '{}'. "
                           "Duplicate words can overwrite earlier ones by adding the "
                           "#fairseq:overwrite flag at the end of the corresponding row "
                           "in the dictionary file. If using the Camembert model, please "
                           "download an updated copy of the model file.".format(snake_case__							)							)
                  self.add_symbol(snake_case__							,			n=snake_case__							,			overwrite=snake_case__							)
             except ValueError:
                  raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'"							)




def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[Any]				)->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__						:    Optional[int]							=							dict((re.sub(r"@@$"			,					""			,					snake_case				), v) if k.endswith("@@"				) else (re.sub(r"$"			,					"</w>"			,					snake_case				), v) for k, v in d.items()				)
     UpperCAmelCase__						:    Optional[int]							=							"<s> <pad> </s> <unk>".split()
     # restore the special tokens
     for k in keep_keys:
          del da[f'{k}</w>']
          UpperCAmelCase__						:    List[str]							=							d[k]  # restore
     return da


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[int]			,					snake_case       : int				)->		Union[str, Any]:






     '''simple docstring'''



     if not os.path.exists(snake_case				):
          raise ValueError(f'path {biogpt_checkpoint_path} does not exist!'				)
     os.makedirs(snake_case			,					exist_ok=snake_case				)
     print(f'Writing results to {pytorch_dump_folder_path}'				)

     # handle various types of models

     UpperCAmelCase__						:    Dict							=							os.path.join(snake_case			,					"checkpoint.pt"				)
     if not os.path.isfile(snake_case				):
          raise ValueError(f'path to the file {checkpoint_file} does not exist!'				)
     UpperCAmelCase__						:    Optional[int]							=							torch.load(snake_case			,					map_location="cpu"				)

     UpperCAmelCase__						:    Optional[int]							=							chkpt["cfg"]["model"]

     # dicts
     UpperCAmelCase__						:    Optional[int]							=							os.path.join(snake_case			,					"dict.txt"				)
     if not os.path.isfile(snake_case				):
          raise ValueError(f'path to the file {dict_file} does not exist!'				)
     UpperCAmelCase__						:    Dict							=							Dictionary.load(snake_case				)
     UpperCAmelCase__						:    Optional[Any]							=							rewrite_dict_keys(src_dict.indices				)
     UpperCAmelCase__						:    List[str]							=							len(snake_case				)
     UpperCAmelCase__						:    Tuple							=							os.path.join(snake_case			,					VOCAB_FILES_NAMES["vocab_file"]				)
     print(f'Generating {src_vocab_file} of {src_vocab_size} records'				)
     with open(snake_case			,					"w"			,					encoding="utf-8"				) as f:
          f.write(json.dumps(snake_case			,					ensure_ascii=snake_case			,					indent=snake_case				)				)

     # merges_file (bpecodes)
     UpperCAmelCase__						:    List[Any]							=							os.path.join(snake_case			,					"bpecodes"				)
     if not os.path.isfile(snake_case				):
          raise ValueError(f'path to the file {bpecodes_file} does not exist!'				)

     UpperCAmelCase__						:    int							=							os.path.join(snake_case			,					VOCAB_FILES_NAMES["merges_file"]				)
     shutil.copyfile(snake_case			,					snake_case				)

     # model config
     UpperCAmelCase__						:    List[Any]							=							os.path.join(snake_case			,					"config.json"				)

     UpperCAmelCase__						:    Any							=							{
         "activation_dropout": args["activation_dropout"],
         "architectures": ["BioGptForCausalLM"],
         "attention_probs_dropout_prob": args["attention_dropout"],
         "bos_token_id": 0,
         "eos_token_id": 2,
         "hidden_act": args["activation_fn"],
         "hidden_dropout_prob": args["dropout"],
         "hidden_size": args["decoder_embed_dim"],
         "initializer_range": 0.02,
         "intermediate_size": args["decoder_ffn_embed_dim"],
         "layer_norm_eps": 1E-1_2,
         "layerdrop": args["decoder_layerdrop"],
         "max_position_embeddings": args["max_target_positions"],
         "model_type": "biogpt",
         "num_attention_heads": args["decoder_attention_heads"],
         "num_hidden_layers": args["decoder_layers"],
         "pad_token_id": 1,
         "scale_embedding": not args["no_scale_embedding"],
         "tie_word_embeddings": args["share_decoder_input_output_embed"],
         "vocab_size": src_vocab_size,
     }

     # good hparam defaults to start with

     print(f'Generating {biogpt_model_config_file}'				)
     with open(snake_case			,					"w"			,					encoding="utf-8"				) as f:
          f.write(json.dumps(snake_case			,					ensure_ascii=snake_case			,					indent=snake_case				)				)

     # tokenizer config
     UpperCAmelCase__						:    List[Any]							=							os.path.join(snake_case			,					snake_case				)

     UpperCAmelCase__						:    str							=							{
         "bos_token": "<s>",
         "eos_token": "</s>",
         "model_max_length": 1024,
         "pad_token": "<pad>",
         "special_tokens_map_file": None,
         "tokenizer_class": "BioGptTokenizer",
         "unk_token": "<unk>",
     }

     print(f'Generating {biogpt_tokenizer_config_file}'				)
     with open(snake_case			,					"w"			,					encoding="utf-8"				) as f:
          f.write(json.dumps(snake_case			,					ensure_ascii=snake_case			,					indent=snake_case				)				)

     # model
     UpperCAmelCase__						:    Union[str, Any]							=							chkpt["model"]

     # remove unneeded keys
     UpperCAmelCase__						:    Union[str, Any]							=							[
         "decoder.version",
     ]
     for k in ignore_keys:
          model_state_dict.pop(snake_case			,					snake_case				)

     UpperCAmelCase__						:    Union[str, Any]							=							list(model_state_dict.keys()				)
     for layer_name in layer_names:
          if layer_name.endswith("output_projection.weight"				):
               UpperCAmelCase__						:    int							=							model_state_dict.pop(snake_case				)
          else:
               UpperCAmelCase__						:    Tuple							=							model_state_dict.pop(snake_case				)

     UpperCAmelCase__						:    Any							=							BioGptConfig.from_pretrained(snake_case				)
     UpperCAmelCase__						:    List[Any]							=							BioGptForCausalLM(snake_case				)

     # check that it loads ok
     model_new.load_state_dict(snake_case				)

     # save
     UpperCAmelCase__						:    Union[str, Any]							=							os.path.join(snake_case			,					snake_case				)
     print(f'Generating {pytorch_weights_dump_path}'				)
     torch.save(snake_case			,					snake_case				)

     print("Conversion is done!"				)


if __name__ == "__main__":
 _lowerCAmelCase	:	List[Any]				=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--biogpt_checkpoint_path""",
     default=None,
     type=str,
     required=True,
     help=(
         """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
         """ bpecodes, etc."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 _lowerCAmelCase	:	List[str]				=      parser.parse_args()
 convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)


