





"""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_			=      2






class 		__snake_case :





 """simple docstring"""


 def __init__(   self ,						*,  # begin keyword-only arguments
     __lowerCamelCase="<s>" ,						__lowerCamelCase="<pad>" ,						__lowerCamelCase="</s>" ,						__lowerCamelCase="<unk>" ,						__lowerCamelCase=None ,						):



     '''simple docstring'''




     __A			,	__A			,	__A			,	__A   :						Dict									=					bos, unk, pad, eos
     __A   :						List[Any]									=					[]
     __A   :						str									=					[]
     __A   :						Optional[Any]									=					{}
     __A   :						List[Any]									=					self.add_symbol(__lowerCamelCase  )
     __A   :						Optional[int]									=					self.add_symbol(__lowerCamelCase  )
     __A   :						str									=					self.add_symbol(__lowerCamelCase  )
     __A   :						List[Any]									=					self.add_symbol(__lowerCamelCase  )
     if extra_special_symbols:
         for s in extra_special_symbols:
             self.add_symbol(__lowerCamelCase  )
     __A   :						int									=					len(self.symbols  )

 def __eq__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     return self.indices == other.indices

 def __getitem__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




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

 def __len__(   self  ):



     '''simple docstring'''




     return len(self.symbols  )

 def __contains__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     return sym in self.indices

 @classmethod
 def  UpperCamelCase__(   cls ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						str									=					cls()
     d.add_from_file(__lowerCamelCase  )
     return d

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase=1 ,						__lowerCamelCase=False  ):



     '''simple docstring'''




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

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     return 0







 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




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

     __A   :						List[str]									=					f.readlines()
     __A   :						Optional[Any]									=					self._load_meta(__lowerCamelCase  )

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







def 		__lowercase					(       snake_case_  :  Any						)       ->Any:



    '''simple docstring'''







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







def 		__lowercase					(       snake_case_  :  Dict    ,snake_case_  :  List[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

    __A   :						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!"""						)
    __A   :						List[str]									=					torch.load(snake_case_    ,map_location='''cpu'''						)

    __A   :						Optional[Any]									=					chkpt['''cfg''']['''model''']

    # dicts
    __A   :						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!"""						)
    __A   :						Optional[Any]									=					Dictionary.load(snake_case_						)
    __A   :						Any									=					rewrite_dict_keys(src_dict.indices						)
    __A   :						Union[str, Any]									=					len(snake_case_						)
    __A   :						str									=					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)
    __A   :						Dict									=					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!"""						)

    __A   :						Tuple									=					os.path.join(snake_case_    ,VOCAB_FILES_NAMES['''merges_file''']						)
    shutil.copyfile(snake_case_    ,snake_case_						)

    # model config
    __A   :						Dict									=					os.path.join(snake_case_    ,'''config.json'''						)

    __A   :						List[str]									=					{
        '''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
    __A   :						List[Any]									=					os.path.join(snake_case_    ,snake_case_						)

    __A   :						List[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
    __A   :						Dict									=					chkpt['''model''']

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

    __A   :						Union[str, Any]									=					list(model_state_dict.keys()						)
    for layer_name in layer_names:
        if layer_name.endswith('''output_projection.weight'''						):
            __A   :						List[Any]									=					model_state_dict.pop(snake_case_						)
        else:
            __A   :						Optional[int]									=					model_state_dict.pop(snake_case_						)

    __A   :						Dict									=					BioGptConfig.from_pretrained(snake_case_						)
    __A   :						int									=					BioGptForCausalLM(snake_case_						)

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

    # save
    __A   :						Optional[int]									=					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__":
       a_			=      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_			=      parser.parse_args()
       convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)











"""simple docstring"""



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a_			=      {
    """configuration_vivit""": ["""VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VivitConfig"""],
}
try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      ["""VivitImageProcessor"""]


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """VivitModel""",
           """VivitPreTrainedModel""",
           """VivitForVideoClassification""",
       ]


if TYPE_CHECKING:
       from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_vivit import VivitImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vivit import (
                  VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VivitForVideoClassification,
                  VivitModel,
                  VivitPreTrainedModel,
              )


else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




