



"""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()

_UpperCAmelCase     =			2
class 					a	:



		def __init__(						self :					List[str]       ,		*,  # begin keyword-only arguments
		    lowerCAmelCase :					Optional[Any]="<s>"       ,		lowerCAmelCase :					Any="<pad>"       ,		lowerCAmelCase :					Union[str, Any]="</s>"       ,		lowerCAmelCase :					List[Any]="<unk>"       ,		lowerCAmelCase :					Tuple=None       ,		)     ->					Dict:


					'''simple docstring'''




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



		def __eq__(						self :					Union[str, Any]       ,		lowerCAmelCase :					int		)     ->					Optional[int]:


					'''simple docstring'''




					return self.indices == other.indices



		def __getitem__(						self :					List[Any]       ,		lowerCAmelCase :					Optional[Any]		)     ->					Optional[int]:


					'''simple docstring'''




					if idx < len(self.symbols		):
								return self.symbols[idx]
					return self.unk_word



		def __len__(						self :					Optional[int]		)     ->					Optional[Any]:


					'''simple docstring'''




					return len(self.symbols		)



		def __contains__(						self :					Dict       ,		lowerCAmelCase :					Optional[int]		)     ->					int:


					'''simple docstring'''




					return sym in self.indices



		@classmethod
		def 	lowerCamelCase__       (						cls :					List[str]       ,		lowerCAmelCase :					Union[str, Any]		)     ->					str:


					'''simple docstring'''




					SCREAMING_SNAKE_CASE_: Union[str, Any]     			=cls()
					d.add_from_file(__UpperCamelCase		)
					return d



		def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					List[str]       ,		lowerCAmelCase :					int=1       ,		lowerCAmelCase :					Union[str, Any]=False		)     ->					int:


					'''simple docstring'''




					if word in self.indices and not overwrite:
								SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.indices[word]
								SCREAMING_SNAKE_CASE_: Any     			=self.count[idx] + n
								return idx
					else:
								SCREAMING_SNAKE_CASE_: Tuple     			=len(self.symbols		)
								SCREAMING_SNAKE_CASE_: Dict     			=idx
								self.symbols.append(__UpperCamelCase		)
								self.count.append(__UpperCamelCase		)
								return idx



		def 	lowerCamelCase__       (						self :					Optional[Any]       ,		lowerCAmelCase :					Any		)     ->					Optional[Any]:


					'''simple docstring'''




					return 0





		def 	lowerCamelCase__       (						self :					Tuple       ,		lowerCAmelCase :					int		)     ->					Union[str, Any]:


					'''simple docstring'''




					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_: str     			=f.readlines()
					SCREAMING_SNAKE_CASE_: List[Any]     			=self._load_meta(__UpperCamelCase		)

					for line in lines[indices_start_line:]:
								try:
											SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Any     			=line.rstrip().rsplit(""" """       ,		1		)
											if field == "#fairseq:overwrite":
														SCREAMING_SNAKE_CASE_: Optional[Any]     			=True
														SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Dict     			=line.rsplit(""" """       ,		1		)
											else:
														SCREAMING_SNAKE_CASE_: Dict     			=False
											SCREAMING_SNAKE_CASE_: List[Any]     			=int(__UpperCamelCase		)
											SCREAMING_SNAKE_CASE_: int     			=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      __magic_name__				(							lowercase ):
			# (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_: int     			=dict((re.sub(R"""@@$"""							,			""""""							,			UpperCAmelCase__ ), v) if k.endswith("""@@""" ) else (re.sub(R"""$"""							,			"""</w>"""							,			UpperCAmelCase__ ), v) for k, v in d.items() )
			SCREAMING_SNAKE_CASE_: Dict     			="""<s> <pad> </s> <unk>""".split()
			# restore the special tokens
			for k in keep_keys:
						del da[f'''{k}</w>''']
						SCREAMING_SNAKE_CASE_: List[Any]     			=d[k]  # restore
			return da





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

			# handle various types of models

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

			SCREAMING_SNAKE_CASE_: str     			=chkpt["""cfg"""]["""model"""]

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

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

			SCREAMING_SNAKE_CASE_: str     			=os.path.join(UpperCAmelCase__							,			VOCAB_FILES_NAMES["""merges_file"""] )
			shutil.copyfile(UpperCAmelCase__							,			UpperCAmelCase__ )

			# model config
			SCREAMING_SNAKE_CASE_: List[Any]     			=os.path.join(UpperCAmelCase__							,			"""config.json""" )

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

			# tokenizer config
			SCREAMING_SNAKE_CASE_: Tuple     			=os.path.join(UpperCAmelCase__							,			UpperCAmelCase__ )

			SCREAMING_SNAKE_CASE_: List[str]     			={
			    """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(UpperCAmelCase__							,			"""w"""							,			encoding="""utf-8""" ) as f:
						f.write(json.dumps(UpperCAmelCase__							,			ensure_ascii=UpperCAmelCase__							,			indent=UpperCAmelCase__ ) )

			# model
			SCREAMING_SNAKE_CASE_: List[Any]     			=chkpt["""model"""]

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

			SCREAMING_SNAKE_CASE_: Union[str, Any]     			=list(model_state_dict.keys() )
			for layer_name in layer_names:
						if layer_name.endswith("""output_projection.weight""" ):
									SCREAMING_SNAKE_CASE_: Tuple     			=model_state_dict.pop(UpperCAmelCase__ )
						else:
									SCREAMING_SNAKE_CASE_: Optional[int]     			=model_state_dict.pop(UpperCAmelCase__ )

			SCREAMING_SNAKE_CASE_: List[Any]     			=BioGptConfig.from_pretrained(UpperCAmelCase__ )
			SCREAMING_SNAKE_CASE_: Optional[int]     			=BioGptForCausalLM(UpperCAmelCase__ )

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

			# save
			SCREAMING_SNAKE_CASE_: Any     			=os.path.join(UpperCAmelCase__							,			UpperCAmelCase__ )
			print(f'''Generating {pytorch_weights_dump_path}''' )
			torch.save(UpperCAmelCase__							,			UpperCAmelCase__ )

			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)








"""simple docstring"""
def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: int     			=False
   while is_sorted is False:  # Until all the indices are traversed keep looping
      SCREAMING_SNAKE_CASE_: Tuple     			=True
      for i in range(0							,			len(lowercase ) - 1							,			2 ):  # iterating over all even indices
         if input_list[i] > input_list[i + 1]:
            SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[int]     			=input_list[i + 1], input_list[i]
            # swapping if elements not in order
            SCREAMING_SNAKE_CASE_: Tuple     			=False

      for i in range(1							,			len(lowercase ) - 1							,			2 ):  # iterating over all odd indices
         if input_list[i] > input_list[i + 1]:
            SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: str     			=input_list[i + 1], input_list[i]
            # swapping if elements not in order
            SCREAMING_SNAKE_CASE_: str     			=False
   return input_list


if __name__ == "__main__":
   print("""Enter list to be sorted""")
   _UpperCAmelCase     =			[int(x) for x in input().split()]
   # inputing elements of the list in one line
   _UpperCAmelCase     =			odd_even_sort(input_list)
   print("""The sorted list is""")
   print(sorted_list)



