





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						:   Tuple			  =     2





class 						_A:


				"""simple docstring"""


				def __init__(			self     ,					*,  # begin keyword-only arguments
				    _A="<s>"     ,					_A="<pad>"     ,					_A="</s>"     ,					_A="<unk>"     ,					_A=None     ,					):
									__A    ,				__A    ,				__A    ,				__A	:						Tuple       =				bos, unk, pad, eos
									__A	:						Optional[int]       =				[]
									__A	:						Dict       =				[]
									__A	:						Optional[Any]       =				{}
									__A	:						List[str]       =				self.add_symbol(_A     )
									__A	:						Tuple       =				self.add_symbol(_A     )
									__A	:						List[Any]       =				self.add_symbol(_A     )
									__A	:						Dict       =				self.add_symbol(_A     )
									if extra_special_symbols:
														for s in extra_special_symbols:
																			self.add_symbol(_A     )
									__A	:						int       =				len(self.symbols     )



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



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



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



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



				@classmethod
				def 					UpperCAmelCase_		(			cls     ,					_A     ):
									__A	:						Dict       =				cls()
									d.add_from_file(_A     )
									return d



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A=1     ,					_A=False     ):
									if word in self.indices and not overwrite:
														__A	:						Union[str, Any]       =				self.indices[word]
														__A	:						Optional[int]       =				self.count[idx] + n
														return idx
									else:
														__A	:						Tuple       =				len(self.symbols     )
														__A	:						Optional[Any]       =				idx
														self.symbols.append(_A     )
														self.count.append(_A     )
														return idx



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									return 0





				def 					UpperCAmelCase_		(			self     ,					_A     ):
									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

									__A	:						Dict       =				f.readlines()
									__A	:						List[str]       =				self._load_meta(_A     )

									for line in lines[indices_start_line:]:
														try:
																			__A    ,				__A	:						Optional[int]       =				line.rstrip().rsplit(' '     ,					1     )
																			if field == "#fairseq:overwrite":
																								__A	:						Dict       =				True
																								__A    ,				__A	:						Tuple       =				line.rsplit(' '     ,					1     )
																			else:
																								__A	:						List[str]       =				False
																			__A	:						Union[str, Any]       =				int(_A     )
																			__A	:						Dict       =				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  _SCREAMING_SNAKE_CASE				(   a  )      ->      Dict:
					# (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}
					__A	:						List[Any]       =				dict((re.sub(r'@@$'					,						''					,						a  ), v) if k.endswith('@@'  ) else (re.sub(r'$'					,						'</w>'					,						a  ), v) for k, v in d.items()  )
					__A	:						List[Any]       =				'<s> <pad> </s> <unk>'.split()
					# restore the special tokens
					for k in keep_keys:
										del da[F"""{k}</w>"""]
										__A	:						Union[str, Any]       =				d[k]  # restore
					return da







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      Union[str, Any]:
					# prep
					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

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

					__A	:						Optional[int]       =				chkpt['cfg']['model']

					# dicts
					__A	:						List[str]       =				os.path.join(a					,						'dict.txt'  )
					if not os.path.isfile(a  ):
										raise ValueError(F"""path to the file {dict_file} does not exist!"""  )
					__A	:						Optional[int]       =				Dictionary.load(a  )
					__A	:						int       =				rewrite_dict_keys(src_dict.indices  )
					__A	:						List[str]       =				len(a  )
					__A	:						int       =				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)
					__A	:						int       =				os.path.join(a					,						'bpecodes'  )
					if not os.path.isfile(a  ):
										raise ValueError(F"""path to the file {bpecodes_file} does not exist!"""  )

					__A	:						List[str]       =				os.path.join(a					,						VOCAB_FILES_NAMES['merges_file']  )
					shutil.copyfile(a					,						a  )

					# model config
					__A	:						Tuple       =				os.path.join(a					,						'config.json'  )

					__A	:						Any       =				{
					    '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(a					,						'w'					,						encoding='utf-8'  ) as f:
										f.write(json.dumps(a					,						ensure_ascii=a					,						indent=a  )  )

					# tokenizer config
					__A	:						Optional[int]       =				os.path.join(a					,						a  )

					__A	:						Optional[int]       =				{
					    '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
					__A	:						Tuple       =				chkpt['model']

					# remove unneeded keys
					__A	:						Dict       =				[
					    'decoder.version',
					]
					for k in ignore_keys:
										model_state_dict.pop(a					,						a  )

					__A	:						Dict       =				list(model_state_dict.keys()  )
					for layer_name in layer_names:
										if layer_name.endswith('output_projection.weight'  ):
															__A	:						Tuple       =				model_state_dict.pop(a  )
										else:
															__A	:						int       =				model_state_dict.pop(a  )

					__A	:						Any       =				BioGptConfig.from_pretrained(a  )
					__A	:						Optional[Any]       =				BioGptForCausalLM(a  )

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

					# save
					__A	:						Tuple       =				os.path.join(a					,						a  )
					print(F"""Generating {pytorch_weights_dump_path}"""  )
					torch.save(a					,						a  )

					print('Conversion is done!'  )


if __name__ == "__main__":
				UpperCAmelCase						:   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.'''
				)
				UpperCAmelCase						:   Dict			  =     parser.parse_args()
				convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)











def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      list[int]:
					__A	:						Optional[int]       =				int(a  )

					# Initialize Result
					__A	:						Optional[int]       =				[]

					# Traverse through all denomination
					for denomination in reversed(a  ):
										# Find denominations
										while int(a  ) >= int(a  ):
															total_value -= int(a  )
															answer.append(a  )  # Append the "answers" array

					return answer


# Driver Code
if __name__ == "__main__":
				UpperCAmelCase						:   List[str]			  =     []
				UpperCAmelCase						:   Optional[int]			  =     '''0'''

				if (
				    input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
				    == "y"
				):
								UpperCAmelCase						:   List[Any]			  =     int(input('''Enter the number of denominations you want to add: ''').strip())

								for i in range(0, n):
												denominations.append(int(input(F"""Denomination {i}: """).strip()))
								UpperCAmelCase						:   int			  =     input('''Enter the change you want to make in Indian Currency: ''').strip()
				else:
								# All denominations of Indian Currency if user does not enter
								UpperCAmelCase						:   Optional[int]			  =     [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
								UpperCAmelCase						:   Tuple			  =     input('''Enter the change you want to make: ''').strip()

				if int(value) == 0 or int(value) < 0:
								print('''The total value cannot be zero or negative.''')

				else:
								print(F"""Following is minimal change for {value}: """)
								UpperCAmelCase						:   Optional[int]			  =     find_minimum_change(denominations, value)
								# Print result
								for i in range(len(answer)):
												print(answer[i], end=''' ''')




