


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 		a__    :





							'''simple docstring'''






							def __init__(		self ,   *,  # begin keyword-only arguments
							    lowerCamelCase_="<s>" ,   lowerCamelCase_="<pad>" ,   lowerCamelCase_="</s>" ,   lowerCamelCase_="<unk>" ,   lowerCamelCase_=None ,   )	->							List[str]:
														lowerCAmelCase__	       =     bos, unk, pad, eos
														lowerCAmelCase__	       =     []
														lowerCAmelCase__	       =     []
														lowerCAmelCase__	       =     {}
														lowerCAmelCase__	       =     self.add_symbol(SCREAMING_SNAKE_CASE_       )
														lowerCAmelCase__	       =     self.add_symbol(SCREAMING_SNAKE_CASE_       )
														lowerCAmelCase__	       =     self.add_symbol(SCREAMING_SNAKE_CASE_       )
														lowerCAmelCase__	       =     self.add_symbol(SCREAMING_SNAKE_CASE_       )
														if extra_special_symbols:
																					for s in extra_special_symbols:
																												self.add_symbol(SCREAMING_SNAKE_CASE_       )
														lowerCAmelCase__	       =     len(self.symbols       )







							def __eq__(		self ,   lowerCamelCase_       )	->							int:
														return self.indices == other.indices







							def __getitem__(		self ,   lowerCamelCase_       )	->							Optional[Any]:
														if idx < len(self.symbols       ):
																					return self.symbols[idx]
														return self.unk_word







							def __len__(		self       )	->							List[str]:
														return len(self.symbols       )







							def __contains__(		self ,   lowerCamelCase_       )	->							Optional[Any]:
														return sym in self.indices







							@classmethod
							def   __SCREAMING_SNAKE_CASE							(		cls ,   lowerCamelCase_       )	->							str:
														lowerCAmelCase__	       =     cls()
														d.add_from_file(SCREAMING_SNAKE_CASE_       )
														return d







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_=1 ,   lowerCamelCase_=False       )	->							Tuple:
														if word in self.indices and not overwrite:
																					lowerCAmelCase__	       =     self.indices[word]
																					lowerCAmelCase__	       =     self.count[idx] + n
																					return idx
														else:
																					lowerCAmelCase__	       =     len(self.symbols       )
																					lowerCAmelCase__	       =     idx
																					self.symbols.append(SCREAMING_SNAKE_CASE_       )
																					self.count.append(SCREAMING_SNAKE_CASE_       )
																					return idx







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							Union[str, Any]:
														return 0







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							Optional[int]:
														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

														lowerCAmelCase__	       =     f.readlines()
														lowerCAmelCase__	       =     self._load_meta(SCREAMING_SNAKE_CASE_       )

														for line in lines[indices_start_line:]:
																					try:
																												lowerCAmelCase__	       =     line.rstrip().rsplit(''' ''' ,   1       )
																												if field == "#fairseq:overwrite":
																																			lowerCAmelCase__	       =     True
																																			lowerCAmelCase__	       =     line.rsplit(''' ''' ,   1       )
																												else:
																																			lowerCAmelCase__	       =     False
																												lowerCAmelCase__	       =     int(SCREAMING_SNAKE_CASE_       )
																												lowerCAmelCase__	       =     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  _snake_case       (					A	)						->   List[Any]:
							lowerCAmelCase__	       =     dict((re.sub(R'''@@$'''			,   ''''''			,   snake_case_	), v) if k.endswith('''@@'''	) else (re.sub(R'''$'''			,   '''</w>'''			,   snake_case_	), v) for k, v in d.items()	)
							lowerCAmelCase__	       =     """<s> <pad> </s> <unk>""".split()
							# restore the special tokens
							for k in keep_keys:
														del da[F"""{k}</w>"""]
														lowerCAmelCase__	       =     d[k]  # restore
							return da


def  _snake_case       (					A			,   A	)						->   Union[str, Any]:
							# prep
							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

							lowerCAmelCase__	       =     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!"""	)
							lowerCAmelCase__	       =     torch.load(snake_case_			,   map_location='''cpu'''	)

							lowerCAmelCase__	       =     chkpt["""cfg"""]["""model"""]

							# dicts
							lowerCAmelCase__	       =     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!"""	)
							lowerCAmelCase__	       =     Dictionary.load(snake_case_	)
							lowerCAmelCase__	       =     rewrite_dict_keys(src_dict.indices	)
							lowerCAmelCase__	       =     len(snake_case_	)
							lowerCAmelCase__	       =     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)
							lowerCAmelCase__	       =     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!"""	)

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

							# model config
							lowerCAmelCase__	       =     os.path.join(snake_case_			,   '''config.json'''	)

							lowerCAmelCase__	       =     {
							    """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
							lowerCAmelCase__	       =     os.path.join(snake_case_			,   snake_case_	)

							lowerCAmelCase__	       =     {
							    """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
							lowerCAmelCase__	       =     chkpt["""model"""]

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

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

							lowerCAmelCase__	       =     BioGptConfig.from_pretrained(snake_case_	)
							lowerCAmelCase__	       =     BioGptForCausalLM(snake_case_	)

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

							# save
							lowerCAmelCase__	       =     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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCAmelCase       =							logging.get_logger(__name__)

__UpperCAmelCase       =							{
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       Optional[Any]     						=				"distilbert"
							lowercase__      :       Tuple     						=				{
							    "hidden_size": "dim",
							    "num_attention_heads": "n_heads",
							    "num_hidden_layers": "n_layers",
							}







							def __init__(		self ,   lowerCamelCase_=3_05_22 ,   lowerCamelCase_=5_12 ,   lowerCamelCase_=False ,   lowerCamelCase_=6 ,   lowerCamelCase_=12 ,   lowerCamelCase_=7_68 ,   lowerCamelCase_=4 * 7_68 ,   lowerCamelCase_=0.1 ,   lowerCamelCase_=0.1 ,   lowerCamelCase_="gelu" ,   lowerCamelCase_=0.02 ,   lowerCamelCase_=0.1 ,   lowerCamelCase_=0.2 ,   lowerCamelCase_=0 ,   **lowerCamelCase_ ,   )	->							Any:
														lowerCAmelCase__	       =     vocab_size
														lowerCAmelCase__	       =     max_position_embeddings
														lowerCAmelCase__	       =     sinusoidal_pos_embds
														lowerCAmelCase__	       =     n_layers
														lowerCAmelCase__	       =     n_heads
														lowerCAmelCase__	       =     dim
														lowerCAmelCase__	       =     hidden_dim
														lowerCAmelCase__	       =     dropout
														lowerCAmelCase__	       =     attention_dropout
														lowerCAmelCase__	       =     activation
														lowerCAmelCase__	       =     initializer_range
														lowerCAmelCase__	       =     qa_dropout
														lowerCAmelCase__	       =     seq_classif_dropout
														super().__init__(**lowerCamelCase_ ,   pad_token_id=lowerCamelCase_       )




class 		a__    (							a__  ):





							'''simple docstring'''






							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Mapping[str, Mapping[int, str]]:
														if self.task == "multiple-choice":
																					lowerCAmelCase__	       =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
														else:
																					lowerCAmelCase__	       =     {0: '''batch''', 1: '''sequence'''}
														return OrderedDict(
														    [
														        ('''input_ids''', dynamic_axis),
														        ('''attention_mask''', dynamic_axis),
														    ]       )