
'''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()

_snake_case				:   int     =  2


class 			lowerCAmelCase :




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




		def __eq__(				self					,						UpperCamelCase      ):
								return self.indices == other.indices




		def __getitem__(				self					,						UpperCamelCase      ):
								if idx < len(self.symbols      ):
														return self.symbols[idx]
								return self.unk_word




		def __len__(				self      ):
								return len(self.symbols      )




		def __contains__(				self					,						UpperCamelCase      ):
								return sym in self.indices




		@classmethod
		def     lowercase       (				cls					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= cls()
								d.add_from_file(UpperCamelCase      )
								return d




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase=1					,						UpperCamelCase=False      ):
								if word in self.indices and not overwrite:
														_SCREAMING_SNAKE_CASE				= self.indices[word]
														_SCREAMING_SNAKE_CASE				= self.count[idx] + n
														return idx
								else:
														_SCREAMING_SNAKE_CASE				= len(self.symbols      )
														_SCREAMING_SNAKE_CASE				= idx
														self.symbols.append(UpperCamelCase      )
														self.count.append(UpperCamelCase      )
														return idx




		def     lowercase       (				self					,						UpperCamelCase      ):
								return 0




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

								_SCREAMING_SNAKE_CASE				= f.readlines()
								_SCREAMING_SNAKE_CASE				= self._load_meta(UpperCamelCase      )

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



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



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

						# handle various types of models

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

						_SCREAMING_SNAKE_CASE				= chkpt["cfg"]["model"]

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

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

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

						# model config
						_SCREAMING_SNAKE_CASE				= os.path.join(_SCREAMING_SNAKE_CASE	,			"config.json"  )

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

						# tokenizer config
						_SCREAMING_SNAKE_CASE				= os.path.join(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )

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

						# model
						_SCREAMING_SNAKE_CASE				= chkpt["model"]

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

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

						_SCREAMING_SNAKE_CASE				= BioGptConfig.from_pretrained(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= BioGptForCausalLM(_SCREAMING_SNAKE_CASE  )

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

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

						print("Conversion is done!"  )


if __name__ == "__main__":
						_snake_case				:   Optional[int]     =  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."""
						)
						_snake_case				:   List[Any]     =  parser.parse_args()
						convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)

'''simple docstring'''







def 			_a		(	_SCREAMING_SNAKE_CASE		:   list[int]	,			_SCREAMING_SNAKE_CASE		:   list[int]  ):

						# Check if the input is valid
						if not len(_SCREAMING_SNAKE_CASE  ) == len(_SCREAMING_SNAKE_CASE  ) == 3:
												raise ValueError("Please enter a valid equation."  )
						if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
												raise ValueError("Both a & b of two equations can't be zero."  )

						# Extract the coefficients
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= equationa
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= equationa

						# Calculate the determinants of the matrices
						_SCREAMING_SNAKE_CASE				= aa * ba - aa * ba
						_SCREAMING_SNAKE_CASE				= ca * ba - ca * ba
						_SCREAMING_SNAKE_CASE				= aa * ca - aa * ca

						# Check if the system of linear equations has a solution (using Cramer's rule)
						if determinant == 0:
												if determinant_x == determinant_y == 0:
																		raise ValueError("Infinite solutions. (Consistent system)"  )
												else:
																		raise ValueError("No solution. (Inconsistent system)"  )
						else:
												if determinant_x == determinant_y == 0:
																		# Trivial solution (Inconsistent system)
																		return (0.0, 0.0)
												else:
																		_SCREAMING_SNAKE_CASE				= determinant_x / determinant
																		_SCREAMING_SNAKE_CASE				= determinant_y / determinant
																		# Non-Trivial Solution (Consistent system)
																		return (x, y)