
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()

__a  			=					2
class  lowercase__:



	"""simple docstring"""




	def __init__(       self  :			Union[str, Any]       ,							*,  # begin keyword-only arguments
	    SCREAMING_SNAKE_CASE_  :			Optional[int]="<s>"       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]="<pad>"       ,							SCREAMING_SNAKE_CASE_  :			List[Any]="</s>"       ,							SCREAMING_SNAKE_CASE_  :			Tuple="<unk>"       ,							SCREAMING_SNAKE_CASE_  :			List[str]=None       ,							)       ->     Optional[int]:
					lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_       =					bos, unk, pad, eos
					lowercase_       =					[]
					lowercase_       =					[]
					lowercase_       =					{}
					lowercase_       =					self.add_symbol(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					self.add_symbol(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					self.add_symbol(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					self.add_symbol(SCREAMING_SNAKE_CASE_       )
					if extra_special_symbols:
									for s in extra_special_symbols:
													self.add_symbol(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					len(self.symbols       )
	def __eq__(       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     List[Any]:
					return self.indices == other.indices
	def __getitem__(       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       )       ->     Dict:
					if idx < len(self.symbols       ):
									return self.symbols[idx]
					return self.unk_word
	def __len__(       self  :			int       )       ->     int:
					return len(self.symbols       )
	def __contains__(       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			Any       )       ->     Optional[Any]:
					return sym in self.indices
	@classmethod
	def 						_lowercase  (       cls  :			str       ,							SCREAMING_SNAKE_CASE_  :			Any       )       ->     Optional[int]:
					lowercase_       =					cls()
					d.add_from_file(SCREAMING_SNAKE_CASE_       )
					return d
	def 						_lowercase  (       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			int=1       ,							SCREAMING_SNAKE_CASE_  :			Tuple=False       )       ->     Optional[Any]:
					if word in self.indices and not overwrite:
									lowercase_       =					self.indices[word]
									lowercase_       =					self.count[idx] + n
									return idx
					else:
									lowercase_       =					len(self.symbols       )
									lowercase_       =					idx
									self.symbols.append(SCREAMING_SNAKE_CASE_       )
									self.count.append(SCREAMING_SNAKE_CASE_       )
									return idx
	def 						_lowercase  (       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]       )       ->     Tuple:
					return 0




	def 						_lowercase  (       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       )       ->     Tuple:
					if isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ):
									try:
													with open(SCREAMING_SNAKE_CASE_       ,							'''r'''       ,							encoding='''utf-8'''       ) as fd:
																	self.add_from_file(SCREAMING_SNAKE_CASE_       )
									except FileNotFoundError as fnfe:
													raise fnfe
									except UnicodeError:
													raise Exception('''Incorrect encoding detected in {}, please rebuild the dataset'''.format(SCREAMING_SNAKE_CASE_       )       )
									return

					lowercase_       =					f.readlines()
					lowercase_       =					self._load_meta(SCREAMING_SNAKE_CASE_       )

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




def 				a						(							snake_case__:					Tuple					):


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




def 				a						(							snake_case__:					Union[str, Any]						,   snake_case__:					Tuple					):


				'''simple docstring'''
				# 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

				lowercase_       =					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!'''					)
				lowercase_       =					torch.load(snake_case__						,   map_location='''cpu'''					)

				lowercase_       =					chkpt['''cfg''']['''model''']

				# dicts
				lowercase_       =					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!'''					)
				lowercase_       =					Dictionary.load(snake_case__					)
				lowercase_       =					rewrite_dict_keys(src_dict.indices					)
				lowercase_       =					len(snake_case__					)
				lowercase_       =					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)
				lowercase_       =					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!'''					)

				lowercase_       =					os.path.join(snake_case__						,   VOCAB_FILES_NAMES['''merges_file''']					)
				shutil.copyfile(snake_case__						,   snake_case__					)

				# model config
				lowercase_       =					os.path.join(snake_case__						,   '''config.json'''					)

				lowercase_       =					{
				    '''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''': 0.0_2,
				    '''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(snake_case__						,   '''w'''						,   encoding='''utf-8'''					) as f:
								f.write(json.dumps(snake_case__						,   ensure_ascii=snake_case__						,   indent=snake_case__					)					)

				# tokenizer config
				lowercase_       =					os.path.join(snake_case__						,   snake_case__					)

				lowercase_       =					{
				    '''bos_token''': '''<s>''',
				    '''eos_token''': '''</s>''',
				    '''model_max_length''': 1_024,
				    '''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
				lowercase_       =					chkpt['''model''']

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

				lowercase_       =					list(model_state_dict.keys()					)
				for layer_name in layer_names:
								if layer_name.endswith('''output_projection.weight'''					):
												lowercase_       =					model_state_dict.pop(snake_case__					)
								else:
												lowercase_       =					model_state_dict.pop(snake_case__					)

				lowercase_       =					BioGptConfig.from_pretrained(snake_case__					)
				lowercase_       =					BioGptForCausalLM(snake_case__					)

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

				# save
				lowercase_       =					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__":
						__a  			=					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.'
						)
						__a  			=					parser.parse_args()
						convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)





import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__											=					logging.get_logger(__name__)


UpperCamelCase__											=					{
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

UpperCamelCase__											=					{
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

UpperCamelCase__											=					'</w>'
UpperCamelCase__											=					'@@ '




def    lowerCAmelCase_ (     __A							)     ->				str:







      '''simple docstring'''


      UpperCAmelCase__								=  set()
      UpperCAmelCase__								=  word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)							)
            UpperCAmelCase__								=  char
      return pairs


# Speech2Text2 has no max input length
UpperCamelCase__											=					{'facebook/s2t-wav2vec2-large-en-de': 1_0_2_4}







class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : str      =			VOCAB_FILES_NAMES
   __UpperCAmelCase : str      =			PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase : Union[str, Any]      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase : Dict      =			['input_ids', 'attention_mask']


   def __init__(self     :						Tuple       ,  __UpperCAmelCase     :						List[Any]       ,  __UpperCAmelCase     :						Dict="<s>"       ,  __UpperCAmelCase     :						Tuple="<pad>"       ,  __UpperCAmelCase     :						str="</s>"       ,  __UpperCAmelCase     :						int="<unk>"       ,  __UpperCAmelCase     :						List[str]=False       ,  __UpperCAmelCase     :						str=None       ,  **__UpperCAmelCase     :						Optional[Any]       ,  )						->				Tuple:
         """simple docstring"""



         super().__init__(
             unk_token=__UpperCAmelCase       ,  bos_token=__UpperCAmelCase       ,  eos_token=__UpperCAmelCase       ,  pad_token=__UpperCAmelCase       ,  do_lower_case=__UpperCAmelCase       ,  **__UpperCAmelCase       ,  )

         UpperCAmelCase__								=  do_lower_case

         with open(__UpperCAmelCase       ,  encoding="utf-8"		) as vocab_handle:
               UpperCAmelCase__								=  json.load(__UpperCAmelCase		)
         UpperCAmelCase__								=  {v: k for k, v in self.encoder.items()}

         if merges_file is None:
               logger.info(f"""No merges files provided. {self.__class__.__name__} can only be used for decoding."""		)

               UpperCAmelCase__								=  None
               UpperCAmelCase__								=  None
         else:
               with open(__UpperCAmelCase       ,  encoding="utf-8"		) as merges_handle:
                     UpperCAmelCase__								=  merges_handle.read().split("\n"		)[:-1]

               UpperCAmelCase__								=  [tuple(merge.split()[:2]		) for merge in merges]
               UpperCAmelCase__								=  dict(zip(__UpperCAmelCase       ,  range(len(__UpperCAmelCase		)		)		)		)
               UpperCAmelCase__								=  {}


   @property
   def 			lowercase_      (self     :						List[str]		)						->				int:
         """simple docstring"""



         return len(self.decoder		)


   def 			lowercase_      (self     :						Union[str, Any]		)						->				Dict:
         """simple docstring"""



         return dict(self.encoder       ,  **self.added_tokens_encoder		)


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						Union[str, Any]		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  tuple(token[:-1]		) + (token[-1] + BPE_TOKEN_MERGES,)
         if token in self.cache:
               return self.cache[token]
         UpperCAmelCase__								=  get_pairs(__UpperCAmelCase		)

         if not pairs:
               return token

         while True:
               UpperCAmelCase__								=  min(__UpperCAmelCase       ,  key=lambda __UpperCAmelCase		: self.bpe_ranks.get(__UpperCAmelCase       ,  float("inf"		)		)		)
               if bigram not in self.bpe_ranks:
                     break
               UpperCAmelCase__		,      UpperCAmelCase__								=  bigram
               UpperCAmelCase__								=  []
               UpperCAmelCase__								=  0
               while i < len(__UpperCAmelCase		):
                     try:
                           UpperCAmelCase__								=  word.index(__UpperCAmelCase       ,  __UpperCAmelCase		)
                     except ValueError:
                           new_word.extend(word[i:]		)
                           break
                     else:
                           new_word.extend(word[i:j]		)
                           UpperCAmelCase__								=  j

                     if word[i] == first and i < len(__UpperCAmelCase		) - 1 and word[i + 1] == second:
                           new_word.append(first + second		)
                           i += 2
                     else:
                           new_word.append(word[i]		)
                           i += 1
               UpperCAmelCase__								=  tuple(__UpperCAmelCase		)
               UpperCAmelCase__								=  new_word
               if len(__UpperCAmelCase		) == 1:
                     break
               else:
                     UpperCAmelCase__								=  get_pairs(__UpperCAmelCase		)
         UpperCAmelCase__								=  " ".join(__UpperCAmelCase		)
         if word == "\n  " + BPE_TOKEN_MERGES:
               UpperCAmelCase__								=  "\n" + BPE_TOKEN_MERGES

         if word.endswith(__UpperCAmelCase		):
               UpperCAmelCase__								=  word.replace(__UpperCAmelCase       ,  ""		)

         UpperCAmelCase__								=  word.replace(" "       ,  __UpperCAmelCase		)
         UpperCAmelCase__								=  word
         return word


   def 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						int		)						->				Optional[int]:
         """simple docstring"""



         if self.bpe_ranks is None:
               raise ValueError(
                   "This tokenizer was instantiated without a `merges.txt` file, so"
                   " that it can only be used for decoding, not for encoding."
                   "Make sure to provide `merges.txt` file at instantiation to enable "
                   "encoding."		)

         if self.do_lower_case:
               UpperCAmelCase__								=  text.lower()

         UpperCAmelCase__								=  text.split()

         UpperCAmelCase__								=  []
         for token in text:
               if token:
                     split_tokens.extend(list(self.bpe(__UpperCAmelCase		).split(" "		)		)		)

         return split_tokens


   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						str		)						->				int:
         """simple docstring"""



         return self.encoder.get(__UpperCAmelCase       ,  self.encoder.get(self.unk_token		)		)


   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						int		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  self.decoder.get(__UpperCAmelCase       ,  self.unk_token		)
         return result


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						List[str]		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  " ".join(__UpperCAmelCase		)

         # make sure @@ tokens are concatenated
         UpperCAmelCase__								=  "".join(string.split(__UpperCAmelCase		)		)

         return string





   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						Optional[str] = None		)						->				Tuple[str]:
         """simple docstring"""



         if not os.path.isdir(__UpperCAmelCase		):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
               return
         UpperCAmelCase__								=  os.path.join(
             __UpperCAmelCase       ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
         UpperCAmelCase__								=  os.path.join(
             __UpperCAmelCase       ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]		)

         with open(__UpperCAmelCase       ,  "w"       ,  encoding="utf-8"		) as f:
               f.write(json.dumps(self.encoder       ,  indent=2       ,  sort_keys=__UpperCAmelCase       ,  ensure_ascii=__UpperCAmelCase		) + "\n"		)

         UpperCAmelCase__								=  0
         if self.bpe_ranks is None:
               return (vocab_file,)

         with open(__UpperCAmelCase       ,  "w"       ,  encoding="utf-8"		) as writer:
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,  key=lambda __UpperCAmelCase		: kv[1]		):
                     if index != token_index:
                           logger.warning(
                               f"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
                               " Please check that the tokenizer is not corrupted!"		)
                           UpperCAmelCase__								=  token_index
                     writer.write(" ".join(__UpperCAmelCase		) + "\n"		)
                     index += 1

         return (vocab_file, merges_file)



