


'''simple docstring'''







import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD






def 				lowerCAmelCase__	(    ):
       _A					:     Optional[int]     = 'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'
       _A					:     Any     = Image.open(requests.get(lowerCamelCase  ,stream=lowerCamelCase       ).raw       ).convert('RGB'       )

       return image






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[Any]       ):
       _A					:     Tuple     = []
       # fmt: off

       # vision encoder
       rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding')       )
       rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding')       )
       rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight')       )
       rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias')       )
       rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight')       )
       rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias')       )

       for i in range(config.vision_config.num_hidden_layers       ):
              rename_keys.append((F'visual_encoder.blocks.{i}.norm1.weight', F'vision_model.encoder.layers.{i}.layer_norm1.weight')       )
              rename_keys.append((F'visual_encoder.blocks.{i}.norm1.bias', F'vision_model.encoder.layers.{i}.layer_norm1.bias')       )
              rename_keys.append((F'visual_encoder.blocks.{i}.norm2.weight', F'vision_model.encoder.layers.{i}.layer_norm2.weight')       )
              rename_keys.append((F'visual_encoder.blocks.{i}.norm2.bias', F'vision_model.encoder.layers.{i}.layer_norm2.bias')       )
              rename_keys.append((F'visual_encoder.blocks.{i}.attn.qkv.weight', F'vision_model.encoder.layers.{i}.self_attn.qkv.weight')       )
              rename_keys.append((F'visual_encoder.blocks.{i}.attn.proj.weight', F'vision_model.encoder.layers.{i}.self_attn.projection.weight',)       )
              rename_keys.append((F'visual_encoder.blocks.{i}.attn.proj.bias', F'vision_model.encoder.layers.{i}.self_attn.projection.bias')       )
              rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc1.weight', F'vision_model.encoder.layers.{i}.mlp.fc1.weight')       )
              rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc1.bias', F'vision_model.encoder.layers.{i}.mlp.fc1.bias')       )
              rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc2.weight', F'vision_model.encoder.layers.{i}.mlp.fc2.weight')       )
              rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc2.bias', F'vision_model.encoder.layers.{i}.mlp.fc2.bias')       )

       # QFormer
       rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.layernorm.weight')       )
       rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.layernorm.bias')       )

       # fmt: on
       return rename_keys






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      Any       ):
       _A					:     int     = dct.pop(lowerCamelCase       )
       _A					:     Any     = val






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      str       ):
       for i in range(config.vision_config.num_hidden_layers       ):
              # read in original q and v biases
              _A					:     List[str]     = state_dict.pop(F'visual_encoder.blocks.{i}.attn.q_bias'       )
              _A					:     List[Any]     = state_dict.pop(F'visual_encoder.blocks.{i}.attn.v_bias'       )

              # next, set bias in the state dict
              _A					:     Dict     = torch.cat((q_bias, torch.zeros_like(lowerCamelCase  ,requires_grad=lowerCamelCase       ), v_bias)       )
              _A					:     int     = qkv_bias






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      Dict       ):
       _A					:     Any     = 364 if 'coco' in model_name else 224
       _A					:     Dict     = BlipaVisionConfig(image_size=lowerCamelCase       ).to_dict()

       # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
       # seems like flan-T5 models don't have bos_token_id properly set?
       if "opt-2.7b" in model_name:
              _A					:     Tuple     = OPTConfig.from_pretrained('facebook/opt-2.7b'  ,eos_token_id=lowerCamelCase       ).to_dict()
       elif "opt-6.7b" in model_name:
              _A					:     List[str]     = OPTConfig.from_pretrained('facebook/opt-6.7b'  ,eos_token_id=lowerCamelCase       ).to_dict()
       elif "t5-xl" in model_name:
              _A					:     List[Any]     = TaConfig.from_pretrained('google/flan-t5-xl'  ,dense_act_fn='gelu'  ,bos_token_id=1       ).to_dict()
       elif "t5-xxl" in model_name:
              _A					:     Optional[Any]     = TaConfig.from_pretrained('google/flan-t5-xxl'  ,dense_act_fn='gelu'  ,bos_token_id=1       ).to_dict()

       _A					:     List[Any]     = BlipaConfig(vision_config=lowerCamelCase  ,text_config=lowerCamelCase       )

       return config, image_size






@torch.no_grad()
def 				lowerCAmelCase__	(    lowerCamelCase					:      Any  ,lowerCamelCase					:      Optional[Any]=None  ,lowerCamelCase					:      int=False       ):
       _A					:     str     = (
           AutoTokenizer.from_pretrained('facebook/opt-2.7b'       )
           if 'opt' in model_name
           else AutoTokenizer.from_pretrained('google/flan-t5-xl'       )
       )
       _A					:     Union[str, Any]     = tokenizer('\n'  ,add_special_tokens=lowerCamelCase       ).input_ids[0]
       _A  ,			_A					:     str     = get_blipa_config(lowerCamelCase  ,eos_token_id=lowerCamelCase       )

       _A					:     List[str]     = BlipaForConditionalGeneration(lowerCamelCase       ).eval()

       _A					:     Optional[Any]     = {
           'blip2-opt-2.7b': ('blip2_opt', 'pretrain_opt2.7b'),
           'blip2-opt-6.7b': ('blip2_opt', 'pretrain_opt6.7b'),
           'blip2-opt-2.7b-coco': ('blip2_opt', 'caption_coco_opt2.7b'),
           'blip2-opt-6.7b-coco': ('blip2_opt', 'caption_coco_opt6.7b'),
           'blip2-flan-t5-xl': ('blip2_t5', 'pretrain_flant5xl'),
           'blip2-flan-t5-xl-coco': ('blip2_t5', 'caption_coco_flant5xl'),
           'blip2-flan-t5-xxl': ('blip2_t5', 'pretrain_flant5xxl'),
       }

       _A  ,			_A					:     Optional[Any]     = model_name_to_original[model_name]

       # load original model
       print('Loading original model...'       )
       _A					:     Dict     = 'cuda' if torch.cuda.is_available() else 'cpu'
       _A  ,			_A  ,			_A					:     Optional[int]     = load_model_and_preprocess(
           name=lowerCamelCase  ,model_type=lowerCamelCase  ,is_eval=lowerCamelCase  ,device=lowerCamelCase       )
       original_model.eval()
       print('Done!'       )

       # update state dict keys
       _A					:     List[str]     = original_model.state_dict()
       _A					:     Optional[int]     = create_rename_keys(lowerCamelCase       )
       for src, dest in rename_keys:
              rename_key(lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase       )

       # some keys can be renamed efficiently
       for key, val in state_dict.copy().items():
              _A					:     str     = state_dict.pop(lowerCamelCase       )
              if key.startswith('Qformer.bert'       ):
                     _A					:     Optional[Any]     = key.replace('Qformer.bert'  ,'qformer'       )
              if "attention.self" in key:
                     _A					:     Tuple     = key.replace('self'  ,'attention'       )
              if "opt_proj" in key:
                     _A					:     str     = key.replace('opt_proj'  ,'language_projection'       )
              if "t5_proj" in key:
                     _A					:     Any     = key.replace('t5_proj'  ,'language_projection'       )
              if key.startswith('opt'       ):
                     _A					:     List[str]     = key.replace('opt'  ,'language'       )
              if key.startswith('t5'       ):
                     _A					:     List[str]     = key.replace('t5'  ,'language'       )
              _A					:     Dict     = val

       # read in qv biases
       read_in_q_v_bias(lowerCamelCase  ,lowerCamelCase       )

       _A  ,			_A					:     Dict     = hf_model.load_state_dict(lowerCamelCase  ,strict=lowerCamelCase       )
       assert len(lowerCamelCase       ) == 0
       assert unexpected_keys == ["qformer.embeddings.position_ids"]

       _A					:     List[Any]     = load_demo_image()
       _A					:     str     = vis_processors['eval'](lowerCamelCase       ).unsqueeze(0       ).to(lowerCamelCase       )
       _A					:     Dict     = tokenizer(['\n']  ,return_tensors='pt'       ).input_ids.to(lowerCamelCase       )

       # create processor
       _A					:     List[Any]     = BlipImageProcessor(
           size={'height': image_size, 'width': image_size}  ,image_mean=lowerCamelCase  ,image_std=lowerCamelCase       )
       _A					:     Dict     = BlipaProcessor(image_processor=lowerCamelCase  ,tokenizer=lowerCamelCase       )
       _A					:     int     = processor(images=lowerCamelCase  ,return_tensors='pt'       ).pixel_values.to(lowerCamelCase       )

       # make sure processor creates exact same pixel values
       assert torch.allclose(lowerCamelCase  ,lowerCamelCase       )

       original_model.to(lowerCamelCase       )
       hf_model.to(lowerCamelCase       )
       with torch.no_grad():
              if "opt" in model_name:
                     _A					:     Optional[int]     = original_model({'image': original_pixel_values, 'text_input': ['']}       ).logits
                     _A					:     Any     = hf_model(lowerCamelCase  ,lowerCamelCase       ).logits
              else:
                     _A					:     Optional[int]     = original_model(
                         {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']}       ).logits
                     _A					:     str     = input_ids.masked_fill(input_ids == tokenizer.pad_token_id  ,-100       )
                     _A					:     List[str]     = hf_model(lowerCamelCase  ,lowerCamelCase  ,labels=lowerCamelCase       ).logits

       assert original_logits.shape == logits.shape
       print('First values of original logits:'  ,original_logits[0, :3, :3]       )
       print('First values of HF logits:'  ,logits[0, :3, :3]       )

       # assert values
       if model_name == "blip2-flan-t5-xl":
              _A					:     Optional[int]     = torch.tensor(
                  [[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]]  ,device=lowerCamelCase       )
              assert torch.allclose(logits[0, :3, :3]  ,lowerCamelCase  ,atol=1E-4       )
       elif model_name == "blip2-flan-t5-xl-coco":
              _A					:     Any     = torch.tensor(
                  [[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]]  ,device=lowerCamelCase       )
       else:
              # cast to same type
              _A					:     List[str]     = logits.dtype
              assert torch.allclose(original_logits.to(lowerCamelCase       )  ,lowerCamelCase  ,atol=1E-2       )
       print('Looks ok!'       )

       print('Generating a caption...'       )
       _A					:     Optional[Any]     = ''
       _A					:     Any     = tokenizer(lowerCamelCase  ,return_tensors='pt'       ).input_ids.to(lowerCamelCase       )

       _A					:     List[Any]     = original_model.generate({'image': original_pixel_values}       )
       _A					:     Union[str, Any]     = hf_model.generate(
           lowerCamelCase  ,lowerCamelCase  ,do_sample=lowerCamelCase  ,num_beams=5  ,max_length=30  ,min_length=1  ,top_p=0.9  ,repetition_penalty=1.0  ,length_penalty=1.0  ,temperature=1  ,)
       print('Original generation:'  ,lowerCamelCase       )
       _A					:     Tuple     = input_ids.shape[1]
       _A					:     List[Any]     = processor.batch_decode(outputs[:, prompt_length:]  ,skip_special_tokens=lowerCamelCase       )
       _A					:     Any     = [text.strip() for text in output_text]
       print('HF generation:'  ,lowerCamelCase       )

       if pytorch_dump_folder_path is not None:
              processor.save_pretrained(lowerCamelCase       )
              hf_model.save_pretrained(lowerCamelCase       )

       if push_to_hub:
              processor.push_to_hub(F'nielsr/{model_name}'       )
              hf_model.push_to_hub(F'nielsr/{model_name}'       )


if __name__ == "__main__":
   A				:					Any    						=			argparse.ArgumentParser()
   A				:					Tuple    						=			[
       '''blip2-opt-2.7b''',
       '''blip2-opt-6.7b''',
       '''blip2-opt-2.7b-coco''',
       '''blip2-opt-6.7b-coco''',
       '''blip2-flan-t5-xl''',
       '''blip2-flan-t5-xl-coco''',
       '''blip2-flan-t5-xxl''',
   ]
   parser.add_argument(
       '''--model_name''',
       default='''blip2-opt-2.7b''',
       choices=choices,
       type=str,
       help='''Path to hf config.json of model to convert''',
   )
   parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
   parser.add_argument(
       '''--push_to_hub''',
       action='''store_true''',
       help='''Whether to push the model and processor to the hub after converting''',
   )

   A				:					Any    						=			parser.parse_args()

   convert_blipa_checkpoint(args.model_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()

A				:					int    						=			2


class 			__lowerCamelCase			:
    """simple docstring"""


    def __init__(  self      :							List[str]				,							*,  # begin keyword-only arguments
        SCREAMING_SNAKE_CASE      :							Optional[Any]="<s>"				,							SCREAMING_SNAKE_CASE      :							int="<pad>"				,							SCREAMING_SNAKE_CASE      :							Optional[Any]="</s>"				,							SCREAMING_SNAKE_CASE      :							Tuple="<unk>"				,							SCREAMING_SNAKE_CASE      :							List[Any]=None				,							):
           _A  ,			_A  ,			_A  ,			_A					:     Any     = bos, unk, pad, eos
           _A					:     Optional[Any]     = []
           _A					:     Optional[Any]     = []
           _A					:     Optional[int]     = {}
           _A					:     Dict     = self.add_symbol(SCREAMING_SNAKE_CASE)
           _A					:     List[str]     = self.add_symbol(SCREAMING_SNAKE_CASE)
           _A					:     str     = self.add_symbol(SCREAMING_SNAKE_CASE)
           _A					:     Any     = self.add_symbol(SCREAMING_SNAKE_CASE)
           if extra_special_symbols:
                  for s in extra_special_symbols:
                         self.add_symbol(SCREAMING_SNAKE_CASE)
           _A					:     List[str]     = len(self.symbols)


    def __eq__(  self      :							int				,							SCREAMING_SNAKE_CASE      :							Optional[Any]):
           return self.indices == other.indices


    def __getitem__(  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Tuple):
           if idx < len(self.symbols):
                  return self.symbols[idx]
           return self.unk_word


    def __len__(  self      :							Union[str, Any]):
           return len(self.symbols)


    def __contains__(  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[str]):
           return sym in self.indices


    @classmethod
    def        A   (  cls      :							Dict				,							SCREAMING_SNAKE_CASE      :							Optional[Any]):
           _A					:     Any     = cls()
           d.add_from_file(SCREAMING_SNAKE_CASE)
           return d


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Optional[int]=1				,							SCREAMING_SNAKE_CASE      :							int=False):
           if word in self.indices and not overwrite:
                  _A					:     str     = self.indices[word]
                  _A					:     List[str]     = self.count[idx] + n
                  return idx
           else:
                  _A					:     Optional[Any]     = len(self.symbols)
                  _A					:     Union[str, Any]     = idx
                  self.symbols.append(SCREAMING_SNAKE_CASE)
                  self.count.append(SCREAMING_SNAKE_CASE)
                  return idx


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							Optional[int]):
           return 0


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[Any]):
           if isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  try:
                         with open(SCREAMING_SNAKE_CASE				,							'r'				,							encoding='utf-8') as fd:
                                self.add_from_file(SCREAMING_SNAKE_CASE)
                  except FileNotFoundError as fnfe:
                         raise fnfe
                  except UnicodeError:
                         raise Exception('Incorrect encoding detected in {}, please rebuild the dataset'.format(SCREAMING_SNAKE_CASE))
                  return

           _A					:     Union[str, Any]     = f.readlines()
           _A					:     Any     = self._load_meta(SCREAMING_SNAKE_CASE)

           for line in lines[indices_start_line:]:
                  try:
                         _A  ,			_A					:     List[str]     = line.rstrip().rsplit(' '				,							1)
                         if field == "#fairseq:overwrite":
                                _A					:     int     = True
                                _A  ,			_A					:     List[str]     = line.rsplit(' '				,							1)
                         else:
                                _A					:     Union[str, Any]     = False
                         _A					:     List[str]     = int(SCREAMING_SNAKE_CASE)
                         _A					:     Optional[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(SCREAMING_SNAKE_CASE))
                         self.add_symbol(SCREAMING_SNAKE_CASE				,							n=SCREAMING_SNAKE_CASE				,							overwrite=SCREAMING_SNAKE_CASE)
                  except ValueError:
                         raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\'')






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]       ):
       # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
       # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
       _A					:     Union[str, Any]     = dict((re.sub(R'@@$'  ,''  ,lowerCamelCase       ), v) if k.endswith('@@'       ) else (re.sub(R'$'  ,'</w>'  ,lowerCamelCase       ), v) for k, v in d.items()       )
       _A					:     Optional[Any]     = '<s> <pad> </s> <unk>'.split()
       # restore the special tokens
       for k in keep_keys:
              del da[F'{k}</w>']
              _A					:     str     = d[k]  # restore
       return da






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[str]  ,lowerCamelCase					:      List[str]       ):
       # prep
       if not os.path.exists(lowerCamelCase       ):
              raise ValueError(F'path {biogpt_checkpoint_path} does not exist!'       )
       os.makedirs(lowerCamelCase  ,exist_ok=lowerCamelCase       )
       print(F'Writing results to {pytorch_dump_folder_path}'       )

       # handle various types of models

       _A					:     Dict     = os.path.join(lowerCamelCase  ,'checkpoint.pt'       )
       if not os.path.isfile(lowerCamelCase       ):
              raise ValueError(F'path to the file {checkpoint_file} does not exist!'       )
       _A					:     int     = torch.load(lowerCamelCase  ,map_location='cpu'       )

       _A					:     Dict     = chkpt['cfg']['model']

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

       # merges_file (bpecodes)
       _A					:     Optional[int]     = os.path.join(lowerCamelCase  ,'bpecodes'       )
       if not os.path.isfile(lowerCamelCase       ):
              raise ValueError(F'path to the file {bpecodes_file} does not exist!'       )

       _A					:     Dict     = os.path.join(lowerCamelCase  ,VOCAB_FILES_NAMES['merges_file']       )
       shutil.copyfile(lowerCamelCase  ,lowerCamelCase       )

       # model config
       _A					:     str     = os.path.join(lowerCamelCase  ,'config.json'       )

       _A					:     int     = {
           '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-12,
           '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(lowerCamelCase  ,'w'  ,encoding='utf-8'       ) as f:
              f.write(json.dumps(lowerCamelCase  ,ensure_ascii=lowerCamelCase  ,indent=lowerCamelCase       )       )

       # tokenizer config
       _A					:     Union[str, Any]     = os.path.join(lowerCamelCase  ,lowerCamelCase       )

       _A					:     Any     = {
           '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(lowerCamelCase  ,'w'  ,encoding='utf-8'       ) as f:
              f.write(json.dumps(lowerCamelCase  ,ensure_ascii=lowerCamelCase  ,indent=lowerCamelCase       )       )

       # model
       _A					:     List[Any]     = chkpt['model']

       # remove unneeded keys
       _A					:     int     = [
           'decoder.version',
       ]
       for k in ignore_keys:
              model_state_dict.pop(lowerCamelCase  ,lowerCamelCase       )

       _A					:     Any     = list(model_state_dict.keys()       )
       for layer_name in layer_names:
              if layer_name.endswith('output_projection.weight'       ):
                     _A					:     str     = model_state_dict.pop(lowerCamelCase       )
              else:
                     _A					:     Dict     = model_state_dict.pop(lowerCamelCase       )

       _A					:     Any     = BioGptConfig.from_pretrained(lowerCamelCase       )
       _A					:     Union[str, Any]     = BioGptForCausalLM(lowerCamelCase       )

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

       # save
       _A					:     Union[str, Any]     = os.path.join(lowerCamelCase  ,lowerCamelCase       )
       print(F'Generating {pytorch_weights_dump_path}'       )
       torch.save(lowerCamelCase  ,lowerCamelCase       )

       print('Conversion is done!'       )


if __name__ == "__main__":
   A				:					Optional[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.'''
   )
   A				:					int    						=			parser.parse_args()
   convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)






