




"""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							:    List[Any]  							=							2






class 					__magic_name__							:






				'''simple docstring'''





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





							"""simple docstring"""
							lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase       =      bos, unk, pad, eos
							lowerCamelCase       =      []
							lowerCamelCase       =      []
							lowerCamelCase       =      {}
							lowerCamelCase       =      self.add_symbol(_a				)
							lowerCamelCase       =      self.add_symbol(_a				)
							lowerCamelCase       =      self.add_symbol(_a				)
							lowerCamelCase       =      self.add_symbol(_a				)
							if extra_special_symbols:
										for s in extra_special_symbols:
													self.add_symbol(_a				)
							lowerCamelCase       =      len(self.symbols				)





				def __eq__( self	,				_a				):





							"""simple docstring"""
							return self.indices == other.indices





				def __getitem__( self	,				_a				):





							"""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	,				_a				):





							"""simple docstring"""
							return sym in self.indices





				@classmethod
				def      _lowerCAmelCase       ( cls	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      cls()
							d.add_from_file(_a				)
							return d





				def      _lowerCAmelCase       ( self	,				_a	,				_a=1	,				_a=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(_a				)
										self.count.append(_a				)
										return idx





				def      _lowerCAmelCase       ( self	,				_a				):





							"""simple docstring"""
							return 0





				def      _lowerCAmelCase       ( self	,				_a				):





							"""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

							lowerCamelCase       =      f.readlines()
							lowerCamelCase       =      self._load_meta(_a				)

							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(_a				)
													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(_a				)				)
													self.add_symbol(_a	,				n=_a	,				overwrite=_a				)
										except ValueError:
													raise ValueError("""Incorrect dictionary format, expected '<token> <cnt> [flags]'"""				)




def 					a__				(       snake_case__       )		->      Tuple:
			# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
			# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
			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 					a__				(       snake_case__ ,				snake_case__       )		->      str:
			# 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""": 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
			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__":
					lowerCAmelCase							:    List[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							:    Union[str, Any]  							=							parser.parse_args()
					convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)









"""simple docstring"""




from typing import TYPE_CHECKING

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


lowerCAmelCase							:    List[str]  							=							{
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase							:    Tuple  							=							[
					    """AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """AutoformerForPrediction""",
					    """AutoformerModel""",
					    """AutoformerPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_autoformer import (
					    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    AutoformerConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_autoformer import (
										    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    AutoformerForPrediction,
										    AutoformerModel,
										    AutoformerPreTrainedModel,
										)

else:
					import sys

					lowerCAmelCase							:    str  							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



