






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 				__UpperCAmelCase		:







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







									'''simple docstring'''




									_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:  Union[str, Any]				,     UpperCAmelCase_:  List[Any]					):







									'''simple docstring'''




									return self.indices == other.indices







			def __getitem__(     self:  List[str]				,     UpperCAmelCase_:  int					):







									'''simple docstring'''




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







			def __len__(     self:  List[Any]					):







									'''simple docstring'''




									return len(self.symbols					)







			def __contains__(     self:  List[Any]				,     UpperCAmelCase_:  Dict					):







									'''simple docstring'''




									return sym in self.indices







			@classmethod
			def 							UpperCamelCase				(     cls:  List[str]				,     UpperCAmelCase_:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  cls()
									d.add_from_file(UpperCAmelCase_					)
									return d







			def 							UpperCamelCase				(     self:  Any				,     UpperCAmelCase_:  str				,     UpperCAmelCase_:  str=1				,     UpperCAmelCase_:  int=False					):







									'''simple docstring'''




									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 							UpperCamelCase				(     self:  Optional[int]				,     UpperCAmelCase_:  Optional[int]					):







									'''simple docstring'''




									return 0







			def 							UpperCamelCase				(     self:  Optional[Any]				,     UpperCAmelCase_:  Dict					):







									'''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     				=  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 	__lowerCamelCase      (		snake_case__		)							->  Any:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  dict((re.sub(r"""@@$"""	,""""""	,snake_case__		), v) if k.endswith("""@@"""		) else (re.sub(r"""$"""	,"""</w>"""	,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 	__lowerCamelCase      (		snake_case__	,snake_case__		)							->  Optional[int]:






						"""simple docstring"""





						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

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

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

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

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

						# model config
						_SCREAMING_SNAKE_CASE     				=  os.path.join(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-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
						_SCREAMING_SNAKE_CASE     				=  os.path.join(snake_case__	,snake_case__		)

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

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

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

						# save
						_SCREAMING_SNAKE_CASE     				=  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__":
		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)









import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 				__UpperCAmelCase		(_UpperCAmelCase	,unittest.TestCase   ):
			__snake_case    :				Optional[Any]									=							OpenAIGPTTokenizer
			__snake_case    :				Dict									=							OpenAIGPTTokenizerFast
			__snake_case    :				Optional[Any]									=							True
			__snake_case    :				Dict									=							False







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									_SCREAMING_SNAKE_CASE     				=  [
									    """l""",
									    """o""",
									    """w""",
									    """e""",
									    """r""",
									    """s""",
									    """t""",
									    """i""",
									    """d""",
									    """n""",
									    """w</w>""",
									    """r</w>""",
									    """t</w>""",
									    """lo""",
									    """low""",
									    """er</w>""",
									    """low</w>""",
									    """lowest</w>""",
									    """newer</w>""",
									    """wider</w>""",
									    """<unk>""",
									]
									_SCREAMING_SNAKE_CASE     				=  dict(zip(UpperCAmelCase_				,     range(len(UpperCAmelCase_					)					)					)					)
									_SCREAMING_SNAKE_CASE     				=  ["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]

									_SCREAMING_SNAKE_CASE     				=  os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES["""vocab_file"""]					)
									_SCREAMING_SNAKE_CASE     				=  os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES["""merges_file"""]					)
									with open(self.vocab_file				,     """w"""					) as fp:
															fp.write(json.dumps(UpperCAmelCase_					)					)
									with open(self.merges_file				,     """w"""					) as fp:
															fp.write("""\n""".join(UpperCAmelCase_					)					)







			def 							UpperCamelCase				(     self:  List[Any]				,     UpperCAmelCase_:  Dict					):







									'''simple docstring'''




									return "lower newer", "lower newer"







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  OpenAIGPTTokenizer(self.vocab_file				,     self.merges_file					)

									_SCREAMING_SNAKE_CASE     				=  """lower"""
									_SCREAMING_SNAKE_CASE     				=  ["""low""", """er</w>"""]
									_SCREAMING_SNAKE_CASE     				=  tokenizer.tokenize(UpperCAmelCase_					)
									self.assertListEqual(UpperCAmelCase_				,     UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  tokens + ["""<unk>"""]
									_SCREAMING_SNAKE_CASE     				=  [14, 15, 20]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_					)				,     UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[Any]				,     UpperCAmelCase_:  Union[str, Any]=15					):







									'''simple docstring'''




									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'					):
																					_SCREAMING_SNAKE_CASE     				=  self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_				,     **UpperCAmelCase_					)

																					# Simple input
																					_SCREAMING_SNAKE_CASE     				=  """This is a simple input"""
																					_SCREAMING_SNAKE_CASE     				=  ["""This is a simple input 1""", """This is a simple input 2"""]
																					_SCREAMING_SNAKE_CASE     				=  ("""This is a simple input""", """This is a pair""")
																					_SCREAMING_SNAKE_CASE     				=  [
																					    ("""This is a simple input 1""", """This is a simple input 2"""),
																					    ("""This is a simple pair 1""", """This is a simple pair 2"""),
																					]

																					# Simple input tests
																					self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""					)

																					# Simple input
																					self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""					)

																					# Simple input
																					self.assertRaises(
																					    UpperCAmelCase_				,     tokenizer_r.batch_encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""				,     )

																					# Pair input
																					self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""					)

																					# Pair input
																					self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""					)

																					# Pair input
																					self.assertRaises(
																					    UpperCAmelCase_				,     tokenizer_r.batch_encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""				,     )







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									pass



@require_ftfy
@require_spacy
@require_tokenizers
class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			pass

