

'''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()

UpperCAmelCase       =     2


class      lowerCAmelCase   :







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







            """simple docstring"""







            __lowercase							=bos, unk, pad, eos
            __lowercase							=[]
            __lowercase							=[]
            __lowercase							={}
            __lowercase							=self.add_symbol(__lowercase					)
            __lowercase							=self.add_symbol(__lowercase					)
            __lowercase							=self.add_symbol(__lowercase					)
            __lowercase							=self.add_symbol(__lowercase					)
            if extra_special_symbols:
                 for s in extra_special_symbols:
                      self.add_symbol(__lowercase					)
            __lowercase							=len(self.symbols					)







       def __eq__(							self	:       str  ,					__lowercase	:       Tuple					):







            """simple docstring"""







            return self.indices == other.indices







       def __getitem__(							self	:       int  ,					__lowercase	:       Tuple					):







            """simple docstring"""







            if idx < len(self.symbols					):
                 return self.symbols[idx]
            return self.unk_word







       def __len__(							self	:       Any					):







            """simple docstring"""







            return len(self.symbols					)







       def __contains__(							self	:       Optional[Any]  ,					__lowercase	:       Optional[int]					):







            """simple docstring"""







            return sym in self.indices







       @classmethod
       def 	snake_case      (							cls	:       Optional[int]  ,					__lowercase	:       Dict					):







            """simple docstring"""







            __lowercase							=cls()
            d.add_from_file(__lowercase					)
            return d







       def 	snake_case      (							self	:       List[Any]  ,					__lowercase	:       int  ,					__lowercase	:       List[Any]=1  ,					__lowercase	:       List[str]=False					):







            """simple docstring"""







            if word in self.indices and not overwrite:
                 __lowercase							=self.indices[word]
                 __lowercase							=self.count[idx] + n
                 return idx
            else:
                 __lowercase							=len(self.symbols					)
                 __lowercase							=idx
                 self.symbols.append(__lowercase					)
                 self.count.append(__lowercase					)
                 return idx







       def 	snake_case      (							self	:       int  ,					__lowercase	:       Tuple					):







            """simple docstring"""







            return 0







       def 	snake_case      (							self	:       str  ,					__lowercase	:       str					):







            """simple docstring"""







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

            __lowercase							=f.readlines()
            __lowercase							=self._load_meta(__lowercase					)

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






def  __UpperCamelCase							(		lowercase__  :     Dict       ):






     '''simple docstring'''





     __lowercase							=dict((re.sub(R'@@$',    '',    __snake_case       ), v) if k.endswith('@@'       ) else (re.sub(R'$',    '</w>',    __snake_case       ), v) for k, v in d.items()       )
     __lowercase							='''<s> <pad> </s> <unk>'''.split()
     # restore the special tokens
     for k in keep_keys:
          del da[F'''{k}</w>''']
          __lowercase							=d[k]  # restore
     return da

def  __UpperCamelCase							(		lowercase__  :     Tuple,    lowercase__  :     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

     __lowercase							=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!'''       )
     __lowercase							=torch.load(__snake_case,    map_location='cpu'       )

     __lowercase							=chkpt['''cfg''']['''model''']

     # dicts
     __lowercase							=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!'''       )
     __lowercase							=Dictionary.load(__snake_case       )
     __lowercase							=rewrite_dict_keys(src_dict.indices       )
     __lowercase							=len(__snake_case       )
     __lowercase							=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)
     __lowercase							=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!'''       )

     __lowercase							=os.path.join(__snake_case,    VOCAB_FILES_NAMES['merges_file']       )
     shutil.copyfile(__snake_case,    __snake_case       )

     # model config
     __lowercase							=os.path.join(__snake_case,    'config.json'       )

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

     # tokenizer config
     __lowercase							=os.path.join(__snake_case,    __snake_case       )

     __lowercase							={
         '''bos_token''': '''<s>''',
         '''eos_token''': '''</s>''',
         '''model_max_length''': 10_24,
         '''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
     __lowercase							=chkpt['''model''']

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

     __lowercase							=list(model_state_dict.keys()       )
     for layer_name in layer_names:
          if layer_name.endswith('output_projection.weight'       ):
               __lowercase							=model_state_dict.pop(__snake_case       )
          else:
               __lowercase							=model_state_dict.pop(__snake_case       )

     __lowercase							=BioGptConfig.from_pretrained(__snake_case       )
     __lowercase							=BioGptForCausalLM(__snake_case       )

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

     # save
     __lowercase							=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__":
       UpperCAmelCase       =     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.'''
       )
       UpperCAmelCase       =     parser.parse_args()
       convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)










"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__A		:       List[str]  				=		{
    '''configuration_mobilenet_v2''': [
        '''MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileNetV2Config''',
        '''MobileNetV2OnnxConfig''',
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       Dict  				=		['''MobileNetV2FeatureExtractor''']
						__A		:       Optional[int]  				=		['''MobileNetV2ImageProcessor''']


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       Optional[Any]  				=		[
						    '''MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MobileNetV2ForImageClassification''',
						    '''MobileNetV2ForSemanticSegmentation''',
						    '''MobileNetV2Model''',
						    '''MobileNetV2PreTrainedModel''',
						    '''load_tf_weights_in_mobilenet_v2''',
						]


if TYPE_CHECKING:
						from .configuration_mobilenet_va import (
						    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    MobileNetVaConfig,
						    MobileNetVaOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
												from .image_processing_mobilenet_va import MobileNetVaImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilenet_va import (
												    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileNetVaForImageClassification,
												    MobileNetVaForSemanticSegmentation,
												    MobileNetVaModel,
												    MobileNetVaPreTrainedModel,
												    load_tf_weights_in_mobilenet_va,
												)


else:
						import sys

						__A		:       List[Any]  				=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



