


'''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()

__lowercase	:						List[Any]       =	2





class 							__UpperCamelCase				:




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

						'''simple docstring'''





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




	def __eq__(   self						,				__a							):

						'''simple docstring'''





						return self.indices == other.indices




	def __getitem__(   self						,				__a							):

						'''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						,				__a							):

						'''simple docstring'''





						return sym in self.indices




	@classmethod
	def       __UpperCAmelCase							(   cls						,				__a							):

						'''simple docstring'''





						__a			:       Tuple					    =  cls()
						d.add_from_file(__a							)
						return d




	def       __UpperCAmelCase							(   self						,				__a						,				__a=1						,				__a=False							):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						return 0





	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						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			:       List[Any]					    =  f.readlines()
						__a			:       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			:       Optional[int]					    =  True
																					__a    ,				__a			:       Optional[Any]					    =  line.rsplit(' '						,				1							)
																else:
																					__a			:       List[str]					    =  False
																__a			:       Any					    =  int(__a							)
																__a			:       Optional[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(__a							)							)
																self.add_symbol(__a						,				n=__a						,				overwrite=__a							)
											except ValueError:
																raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\''							)






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					# (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			:       Tuple					    =  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()   )
					__a			:       str					    =  '<s> <pad> </s> <unk>'.split()
					# restore the special tokens
					for k in keep_keys:
										del da[F"""{k}</w>"""]
										__a			:       str					    =  d[k]  # restore
					return da






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   Dict   ):
					# 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

					__a			:       List[Any]					    =  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!"""   )
					__a			:       Union[str, Any]					    =  torch.load(_SCREAMING_SNAKE_CASE						,      map_location='cpu'   )

					__a			:       List[Any]					    =  chkpt['cfg']['model']

					# dicts
					__a			:       str					    =  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!"""   )
					__a			:       int					    =  Dictionary.load(_SCREAMING_SNAKE_CASE   )
					__a			:       List[Any]					    =  rewrite_dict_keys(src_dict.indices   )
					__a			:       Union[str, Any]					    =  len(_SCREAMING_SNAKE_CASE   )
					__a			:       List[Any]					    =  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)
					__a			:       List[Any]					    =  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!"""   )

					__a			:       Any					    =  os.path.join(_SCREAMING_SNAKE_CASE						,      VOCAB_FILES_NAMES['merges_file']   )
					shutil.copyfile(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					# model config
					__a			:       List[Any]					    =  os.path.join(_SCREAMING_SNAKE_CASE						,      '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': 0.0_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(_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
					__a			:       Optional[int]					    =  os.path.join(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					__a			:       List[Any]					    =  {
					    '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(_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
					__a			:       Union[str, Any]					    =  chkpt['model']

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

					__a			:       List[str]					    =  list(model_state_dict.keys()   )
					for layer_name in layer_names:
										if layer_name.endswith('output_projection.weight'   ):
															__a			:       Optional[int]					    =  model_state_dict.pop(_SCREAMING_SNAKE_CASE   )
										else:
															__a			:       int					    =  model_state_dict.pop(_SCREAMING_SNAKE_CASE   )

					__a			:       Union[str, Any]					    =  BioGptConfig.from_pretrained(_SCREAMING_SNAKE_CASE   )
					__a			:       Optional[Any]					    =  BioGptForCausalLM(_SCREAMING_SNAKE_CASE   )

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

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







'''simple docstring'''



from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__lowercase	:						Dict       =	logging.get_logger(__name__)

__lowercase	:						Optional[Any]       =	{
    'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}





class 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    "umt5"
	A_				=    ["past_key_values"]




	def __init__(   self						,				__a=25_0112						,				__a=512						,				__a=64						,				__a=1024						,				__a=8						,				__a=None						,				__a=6						,				__a=32						,				__a=128						,				__a=0.1						,				__a=1E-6						,				__a=1.0						,				__a="gated-gelu"						,				__a=True						,				__a=True						,				__a="T5Tokenizer"						,				__a=True						,				__a=0						,				__a=1						,				__a=0						,				**__a						,				):

						'''simple docstring'''





						super().__init__(
						    is_encoder_decoder=__a						,				tokenizer_class=__a						,				tie_word_embeddings=__a						,				pad_token_id=__a						,				eos_token_id=__a						,				decoder_start_token_id=__a						,				**__a						,				)
						__a			:       Any					    =  vocab_size
						__a			:       Any					    =  d_model
						__a			:       str					    =  d_kv
						__a			:       Dict					    =  d_ff
						__a			:       Union[str, Any]					    =  num_layers
						__a			:       int					    =  (
						    num_decoder_layers if num_decoder_layers is not None else self.num_layers
						)  # default = symmetry
						__a			:       Optional[int]					    =  num_heads
						__a			:       Tuple					    =  relative_attention_num_buckets
						__a			:       Optional[Any]					    =  relative_attention_max_distance
						__a			:       Optional[int]					    =  dropout_rate
						__a			:       List[Any]					    =  layer_norm_epsilon
						__a			:       int					    =  initializer_factor
						__a			:       Union[str, Any]					    =  feed_forward_proj
						__a			:       Any					    =  use_cache

						__a			:       List[Any]					    =  self.feed_forward_proj.split('-'							)
						__a			:       Dict					    =  act_info[-1]
						__a			:       Dict					    =  act_info[0] == 'gated'

						if len(__a							) > 1 and act_info[0] != "gated" or len(__a							) > 2:
											raise ValueError(
											    f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
											    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
											    '\'gated-gelu\' or \'relu\''							)

						if feed_forward_proj == "gated-gelu":
											__a			:       Optional[int]					    =  'gelu_new'




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.d_model




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.num_heads





	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.num_layers





class 							__UpperCamelCase				(		lowerCAmelCase_					):




	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       str					    =  {
						    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
						    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
						}
						if self.use_past:
											__a			:       Dict					    =  'past_encoder_sequence + sequence'
											__a			:       Tuple					    =  {0: 'batch'}
											__a			:       Tuple					    =  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
						else:
											__a			:       List[Any]					    =  {0: 'batch', 1: 'decoder_sequence'}
											__a			:       int					    =  {0: 'batch', 1: 'decoder_sequence'}

						if self.use_past:
											self.fill_with_past_key_values_(__a						,				direction='inputs'							)

						return common_inputs




	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return 13





	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return 5E-4



