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 UpperCAmelCase						:


				'''simple docstring'''






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



						"""simple docstring"""





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



						"""simple docstring"""





						return self.indices == other.indices
				def __getitem__(						self		,     lowercase ):



						"""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		,     lowercase ):



						"""simple docstring"""





						return sym in self.indices
				@classmethod
				def   lowerCAmelCase_       (						cls		,     lowercase ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				cls()
						d.add_from_file(lowercase )
						return d
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase=1		,     lowercase=False ):



						"""simple docstring"""





						if word in self.indices and not overwrite:
								A_		:    int         =				self.indices[word]
								A_		:    List[str]         =				self.count[idx] + n
								return idx
						else:
								A_		:    Optional[Any]         =				len(self.symbols )
								A_		:    str         =				idx
								self.symbols.append(lowercase )
								self.count.append(lowercase )
								return idx
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						return 0







				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





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

						A_		:    Optional[int]         =				f.readlines()
						A_		:    Any         =				self._load_meta(lowercase )

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




def     UpperCamelCase						(	__lowercase						:			Tuple     ):
		'''simple docstring'''




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




def     UpperCamelCase						(	__lowercase						:			Tuple						,__lowercase						:			Optional[int]     ):
		'''simple docstring'''




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

		# handle various types of models

		A_		:    Optional[Any]         =				os.path.join(__lowercase						,'checkpoint.pt'     )
		if not os.path.isfile(__lowercase     ):
				raise ValueError(f'''path to the file {checkpoint_file} does not exist!'''     )
		A_		:    Optional[int]         =				torch.load(__lowercase						,map_location='cpu'     )

		A_		:    str         =				chkpt['cfg']['model']

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

		# merges_file (bpecodes)
		A_		:    Union[str, Any]         =				os.path.join(__lowercase						,'bpecodes'     )
		if not os.path.isfile(__lowercase     ):
				raise ValueError(f'''path to the file {bpecodes_file} does not exist!'''     )

		A_		:    int         =				os.path.join(__lowercase						,VOCAB_FILES_NAMES['merges_file']     )
		shutil.copyfile(__lowercase						,__lowercase     )

		# model config
		A_		:    int         =				os.path.join(__lowercase						,'config.json'     )

		A_		:    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-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(__lowercase						,'w'						,encoding='utf-8'     ) as f:
				f.write(json.dumps(__lowercase						,ensure_ascii=__lowercase						,indent=__lowercase     )     )

		# tokenizer config
		A_		:    str         =				os.path.join(__lowercase						,__lowercase     )

		A_		:    Dict         =				{
		    '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(__lowercase						,'w'						,encoding='utf-8'     ) as f:
				f.write(json.dumps(__lowercase						,ensure_ascii=__lowercase						,indent=__lowercase     )     )

		# model
		A_		:    List[str]         =				chkpt['model']

		# remove unneeded keys
		A_		:    Tuple         =				[
		    'decoder.version',
		]
		for k in ignore_keys:
				model_state_dict.pop(__lowercase						,__lowercase     )

		A_		:    str         =				list(model_state_dict.keys()     )
		for layer_name in layer_names:
				if layer_name.endswith('output_projection.weight'     ):
						A_		:    Optional[Any]         =				model_state_dict.pop(__lowercase     )
				else:
						A_		:    Any         =				model_state_dict.pop(__lowercase     )

		A_		:    Union[str, Any]         =				BioGptConfig.from_pretrained(__lowercase     )
		A_		:    List[str]         =				BioGptForCausalLM(__lowercase     )

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

		# save
		A_		:    Dict         =				os.path.join(__lowercase						,__lowercase     )
		print(f'''Generating {pytorch_weights_dump_path}'''     )
		torch.save(__lowercase						,__lowercase     )

		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)







from ..utils import DummyObject, requires_backends





class UpperCAmelCase						( metaclass=__A  ):


				'''simple docstring'''






				lowerCamelCase_    = ['''onnx''']
				def __init__(						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(self		,     ['onnx'] )
				@classmethod
				def   lowerCAmelCase_       (						cls		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(cls		,     ['onnx'] )







				@classmethod
				def   lowerCAmelCase_       (						cls		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(cls		,     ['onnx'] )






