



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 					__lowerCAmelCase :




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



									"""simple docstring"""





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




				def __eq__(							self						,				_snake_case		):



									"""simple docstring"""





									return self.indices == other.indices




				def __getitem__(							self						,				_snake_case		):



									"""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						,				_snake_case		):



									"""simple docstring"""





									return sym in self.indices




				@classmethod
				def 							snake_case   (							cls						,				_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    cls()
									d.add_from_file(_snake_case		)
									return d




				def 							snake_case   (							self						,				_snake_case						,				_snake_case=1						,				_snake_case=False		):



									"""simple docstring"""





									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(_snake_case		)
														self.count.append(_snake_case		)
														return idx




				def 							snake_case   (							self						,				_snake_case		):



									"""simple docstring"""





									return 0




				def 							snake_case   (							self						,				_snake_case		):



									"""simple docstring"""





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

									_lowerCAmelCase						=    f.readlines()
									_lowerCAmelCase						=    self._load_meta(_snake_case		)

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


def 		_UpperCAmelCase				(      snake_case			):





					"""simple docstring"""

					_lowerCAmelCase						=    dict((re.sub(R"""@@$"""	,						""""""	,						_a			), v) if k.endswith("""@@"""			) else (re.sub(R"""$"""	,						"""</w>"""	,						_a			), 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 		_UpperCAmelCase				(      snake_case	,						snake_case			):





					"""simple docstring"""

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

					# handle various types of models

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

					_lowerCAmelCase						=    chkpt["""cfg"""]["""model"""]

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

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

					_lowerCAmelCase						=    os.path.join(_a	,						VOCAB_FILES_NAMES["""merges_file"""]			)
					shutil.copyfile(_a	,						_a			)

					# model config
					_lowerCAmelCase						=    os.path.join(_a	,						"""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(_a	,						"""w"""	,						encoding="""utf-8"""			) as f:
										f.write(json.dumps(_a	,						ensure_ascii=_a	,						indent=_a			)			)

					# tokenizer config
					_lowerCAmelCase						=    os.path.join(_a	,						_a			)

					_lowerCAmelCase						=    {
					    """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(_a	,						"""w"""	,						encoding="""utf-8"""			) as f:
										f.write(json.dumps(_a	,						ensure_ascii=_a	,						indent=_a			)			)

					# model
					_lowerCAmelCase						=    chkpt["""model"""]

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

					_lowerCAmelCase						=    list(model_state_dict.keys()			)
					for layer_name in layer_names:
										if layer_name.endswith("""output_projection.weight"""			):
															_lowerCAmelCase						=    model_state_dict.pop(_a			)
										else:
															_lowerCAmelCase						=    model_state_dict.pop(_a			)

					_lowerCAmelCase						=    BioGptConfig.from_pretrained(_a			)
					_lowerCAmelCase						=    BioGptForCausalLM(_a			)

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

					# save
					_lowerCAmelCase						=    os.path.join(_a	,						_a			)
					print(F'Generating {pytorch_weights_dump_path}'			)
					torch.save(_a	,						_a			)

					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'''







from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info





def 						snake_case_							(_a	:							Tuple     ):
    return {key.lstrip('''-'''     ): value for key, value in zip(unknown_args[::2]    ,   unknown_args[1::2]     )}





def 						snake_case_							():
    UpperCAmelCase			       =		ArgumentParser(
        '''HuggingFace Datasets CLI tool'''    ,   usage='''datasets-cli <command> [<args>]'''    ,   allow_abbrev=_a     )
    UpperCAmelCase			       =		parser.add_subparsers(help='''datasets-cli command helpers'''     )
    set_verbosity_info()

    # Register commands
    ConvertCommand.register_subcommand(_a     )
    EnvironmentCommand.register_subcommand(_a     )
    TestCommand.register_subcommand(_a     )
    RunBeamCommand.register_subcommand(_a     )
    DummyDataCommand.register_subcommand(_a     )

    # Parse args
    UpperCAmelCase       ,     UpperCAmelCase			       =		parser.parse_known_args()
    if not hasattr(_a    ,   '''func'''     ):
        parser.print_help()
        exit(1     )
    UpperCAmelCase			       =		parse_unknown_args(_a     )

    # Run
    UpperCAmelCase			       =		args.func(_a    ,   **_a     )
    service.run()


if __name__ == "__main__":
   main()




