

"""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__ :     str							  =     2






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






				'''simple docstring'''







				_UpperCAmelCase			:							Dict	  =    bos, unk, pad, eos
				_UpperCAmelCase			:							int	  =    []
				_UpperCAmelCase			:							Dict	  =    []
				_UpperCAmelCase			:							Dict	  =    {}
				_UpperCAmelCase			:							Optional[Any]	  =    self.add_symbol(_A   )
				_UpperCAmelCase			:							Dict	  =    self.add_symbol(_A   )
				_UpperCAmelCase			:							int	  =    self.add_symbol(_A   )
				_UpperCAmelCase			:							List[Any]	  =    self.add_symbol(_A   )
				if extra_special_symbols:
					for s in extra_special_symbols:
						self.add_symbol(_A   )
				_UpperCAmelCase			:							Tuple	  =    len(self.symbols   )
			def __eq__(						self					,						_A   )			->			Optional[Any]:






				'''simple docstring'''







				return self.indices == other.indices
			def __getitem__(						self					,						_A   )			->			int:






				'''simple docstring'''







				if idx < len(self.symbols   ):
					return self.symbols[idx]
				return self.unk_word
			def __len__(						self   )			->			Tuple:






				'''simple docstring'''







				return len(self.symbols   )
			def __contains__(						self					,						_A   )			->			Union[str, Any]:






				'''simple docstring'''







				return sym in self.indices
			@classmethod
			def 							__snake_case       (						cls					,						_A   )			->			Any:






				'''simple docstring'''







				_UpperCAmelCase			:							List[Any]	  =    cls()
				d.add_from_file(_A   )
				return d
			def 							__snake_case       (						self					,						_A					,						_A=1					,						_A=False   )			->			Tuple:






				'''simple docstring'''







				if word in self.indices and not overwrite:
					_UpperCAmelCase			:							Union[str, Any]	  =    self.indices[word]
					_UpperCAmelCase			:							Tuple	  =    self.count[idx] + n
					return idx
				else:
					_UpperCAmelCase			:							List[Any]	  =    len(self.symbols   )
					_UpperCAmelCase			:							int	  =    idx
					self.symbols.append(_A   )
					self.count.append(_A   )
					return idx
			def 							__snake_case       (						self					,						_A   )			->			Optional[int]:






				'''simple docstring'''







				return 0
			def 							__snake_case       (						self					,						_A   )			->			Any:






				'''simple docstring'''







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

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

				for line in lines[indices_start_line:]:
					try:
						_UpperCAmelCase			:							Any	  =    line.rstrip().rsplit(""" """					,						1   )
						if field == "#fairseq:overwrite":
							_UpperCAmelCase			:							Tuple	  =    True
							_UpperCAmelCase			:							List[Any]	  =    line.rsplit(""" """					,						1   )
						else:
							_UpperCAmelCase			:							Optional[Any]	  =    False
						_UpperCAmelCase			:							str	  =    int(_A   )
						_UpperCAmelCase			:							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(_A   )   )
						self.add_symbol(_A					,						n=_A					,						overwrite=_A   )
					except ValueError:
						raise ValueError("""Incorrect dictionary format, expected '<token> <cnt> [flags]'"""   )
def 					UpperCamelCase    (	_lowerCAmelCase   :  Tuple       ) ->	Any:
	_UpperCAmelCase			:							str	  =    dict((re.sub(R"""@@$""",     """""",     _UpperCAmelCase       ), v) if k.endswith("""@@"""       ) else (re.sub(R"""$""",     """</w>""",     _UpperCAmelCase       ), v) for k, v in d.items()       )
	_UpperCAmelCase			:							str	  =    "<s> <pad> </s> <unk>".split()
	# restore the special tokens
	for k in keep_keys:
		del da[f'''{k}</w>''']
		_UpperCAmelCase			:							Any	  =    d[k]  # restore
	return da
def 					UpperCamelCase    (	_lowerCAmelCase   :  Union[str, Any],     _lowerCAmelCase   :  Optional[Any]       ) ->	Optional[int]:







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

	# handle various types of models

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

	_UpperCAmelCase			:							Any	  =    chkpt["cfg"]["model"]

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

	# merges_file (bpecodes)
	_UpperCAmelCase			:							Tuple	  =    os.path.join(_UpperCAmelCase,     """bpecodes"""       )
	if not os.path.isfile(_UpperCAmelCase       ):
		raise ValueError(f'''path to the file {bpecodes_file} does not exist!'''       )

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

	# model config
	_UpperCAmelCase			:							Any	  =    os.path.join(_UpperCAmelCase,     """config.json"""       )

	_UpperCAmelCase			:							Optional[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-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(_UpperCAmelCase,     """w""",     encoding="""utf-8"""       ) as f:
		f.write(json.dumps(_UpperCAmelCase,     ensure_ascii=_UpperCAmelCase,     indent=_UpperCAmelCase       )       )

	# tokenizer config
	_UpperCAmelCase			:							Tuple	  =    os.path.join(_UpperCAmelCase,     _UpperCAmelCase       )

	_UpperCAmelCase			:							List[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(_UpperCAmelCase,     """w""",     encoding="""utf-8"""       ) as f:
		f.write(json.dumps(_UpperCAmelCase,     ensure_ascii=_UpperCAmelCase,     indent=_UpperCAmelCase       )       )

	# model
	_UpperCAmelCase			:							str	  =    chkpt["model"]

	# remove unneeded keys
	_UpperCAmelCase			:							Optional[Any]	  =    [
	    "decoder.version",
	]
	for k in ignore_keys:
		model_state_dict.pop(_UpperCAmelCase,     _UpperCAmelCase       )

	_UpperCAmelCase			:							Any	  =    list(model_state_dict.keys()       )
	for layer_name in layer_names:
		if layer_name.endswith("""output_projection.weight"""       ):
			_UpperCAmelCase			:							Optional[Any]	  =    model_state_dict.pop(_UpperCAmelCase       )
		else:
			_UpperCAmelCase			:							Optional[Any]	  =    model_state_dict.pop(_UpperCAmelCase       )

	_UpperCAmelCase			:							Any	  =    BioGptConfig.from_pretrained(_UpperCAmelCase       )
	_UpperCAmelCase			:							str	  =    BioGptForCausalLM(_UpperCAmelCase       )

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

	# save
	_UpperCAmelCase			:							Tuple	  =    os.path.join(_UpperCAmelCase,     _UpperCAmelCase       )
	print(f'''Generating {pytorch_weights_dump_path}'''       )
	torch.save(_UpperCAmelCase,     _UpperCAmelCase       )

	print("""Conversion is done!"""       )


if __name__ == "__main__":
						lowerCamelCase__ :     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)






'''simple docstring'''




from typing import Any

def 	UpperCamelCase_							(					_UpperCAmelCase  :      list			,	_UpperCAmelCase  :      list			,	_UpperCAmelCase  :      dict			,	_UpperCAmelCase  :      dict			,	_UpperCAmelCase  :      dict			,	)    ->    list:


       """simple docstring"""







       _validation(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	)
       # Creates data structures and fill initial step
       _UpperCAmelCase     :  dict							      =    {}
       _UpperCAmelCase     :  dict							      =    {}
       for state in states_space:
              _UpperCAmelCase     :  Union[str, Any]							      =    observations_space[0]
              _UpperCAmelCase     :  Tuple							      =    (
                  initial_probabilities[state] * emission_probabilities[state][observation]
              )
              _UpperCAmelCase     :  List[str]							      =    None

       # Fills the data structure with the probabilities of
       # different transitions and pointers to previous states
       for o in range(1			,	len(_UpperCAmelCase    )    ):
              _UpperCAmelCase     :  Optional[Any]							      =    observations_space[o]
              _UpperCAmelCase     :  int							      =    observations_space[o - 1]
              for state in states_space:
                     # Calculates the argmax for probability function
                     _UpperCAmelCase     :  str							      =    ""
                     _UpperCAmelCase     :  Tuple							      =    -1
                     for k_state in states_space:
                            _UpperCAmelCase     :  Any							      =    (
                                probabilities[(k_state, prior_observation)]
                                * transition_probabilities[k_state][state]
                                * emission_probabilities[state][observation]
                            )
                            if probability > max_probability:
                                   _UpperCAmelCase     :  Union[str, Any]							      =    probability
                                   _UpperCAmelCase     :  str							      =    k_state

            # Update probabilities and pointers dicts
                     _UpperCAmelCase     :  Optional[int]							      =    (
                         probabilities[(arg_max, prior_observation)]
                         * transition_probabilities[arg_max][state]
                         * emission_probabilities[state][observation]
                     )

                     _UpperCAmelCase     :  Tuple							      =    arg_max

    # The final observation
       _UpperCAmelCase     :  Optional[Any]							      =    observations_space[len(_UpperCAmelCase    ) - 1]

       # argmax for given final observation
       _UpperCAmelCase     :  List[str]							      =    ""
       _UpperCAmelCase     :  Any							      =    -1
       for k_state in states_space:
              _UpperCAmelCase     :  Optional[int]							      =    probabilities[(k_state, final_observation)]
              if probability > max_probability:
                     _UpperCAmelCase     :  int							      =    probability
                     _UpperCAmelCase     :  Dict							      =    k_state
       _UpperCAmelCase     :  Dict							      =    arg_max

       # Process pointers backwards
       _UpperCAmelCase     :  List[Any]							      =    last_state
       _UpperCAmelCase     :  str							      =    []
       for o in range(len(_UpperCAmelCase    ) - 1			,	-1			,	-1    ):
              result.append(_UpperCAmelCase    )
              _UpperCAmelCase     :  List[Any]							      =    pointers[previous, observations_space[o]]
       result.reverse()

       return result

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	)    ->    None:


       """simple docstring"""







       _validate_not_empty(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	)
       _validate_lists(_UpperCAmelCase			,	_UpperCAmelCase    )
       _validate_dicts(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	)    ->    None:


       """simple docstring"""







       if not all(
           [
               observations_space,
               states_space,
               initial_probabilities,
               transition_probabilities,
               emission_probabilities,
           ]    ):
              raise ValueError("There's an empty parameter"    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any    )    ->    None:


       """simple docstring"""







       _validate_list(_UpperCAmelCase			,	"observations_space"    )
       _validate_list(_UpperCAmelCase			,	"states_space"    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      str    )    ->    None:


       """simple docstring"""







       if not isinstance(_object			,	_UpperCAmelCase    ):
              _UpperCAmelCase     :  Optional[int]							      =    F"""{var_name} must be a list"""
              raise ValueError(_UpperCAmelCase    )
       else:
              for x in _object:
                     if not isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ):
                            _UpperCAmelCase     :  Optional[int]							      =    F"""{var_name} must be a list of strings"""
                            raise ValueError(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	)    ->    None:


       """simple docstring"""







       _validate_dict(_UpperCAmelCase			,	"initial_probabilities"			,	_UpperCAmelCase    )
       _validate_nested_dict(_UpperCAmelCase			,	"transition_probabilities"    )
       _validate_nested_dict(_UpperCAmelCase			,	"emission_probabilities"    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      str    )    ->    None:


       """simple docstring"""







       _validate_dict(_object			,	_UpperCAmelCase			,	_UpperCAmelCase    )
       for x in _object.values():
              _validate_dict(_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      type			,	_UpperCAmelCase  :      bool = False    )    ->    None:


       """simple docstring"""







       if not isinstance(_object			,	_UpperCAmelCase    ):
              _UpperCAmelCase     :  Any							      =    F"""{var_name} must be a dict"""
              raise ValueError(_UpperCAmelCase    )
       if not all(isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ) for x in _object    ):
              _UpperCAmelCase     :  Tuple							      =    F"""{var_name} all keys must be strings"""
              raise ValueError(_UpperCAmelCase    )
       if not all(isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ) for x in _object.values()    ):
              _UpperCAmelCase     :  List[str]							      =    "nested dictionary " if nested else ""
              _UpperCAmelCase     :  List[str]							      =    F"""{var_name} {nested_text}all values must be {value_type.__name__}"""
              raise ValueError(_UpperCAmelCase    )


if __name__ == "__main__":
  from doctest import testmod

  testmod()

