






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 :			int              =	2


class     lowercase :



		def __init__(			self    ,				*,  # begin keyword-only arguments
		    A_="<s>"    ,				A_="<pad>"    ,				A_="</s>"    ,				A_="<unk>"    ,				A_=None    ,				)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					bos, unk, pad, eos
				UpperCamelCase          =					[]
				UpperCamelCase          =					[]
				UpperCamelCase          =					{}
				UpperCamelCase          =					self.add_symbol(UpperCamelCase__	)
				UpperCamelCase          =					self.add_symbol(UpperCamelCase__	)
				UpperCamelCase          =					self.add_symbol(UpperCamelCase__	)
				UpperCamelCase          =					self.add_symbol(UpperCamelCase__	)
				if extra_special_symbols:
						for s in extra_special_symbols:
								self.add_symbol(UpperCamelCase__	)
				UpperCamelCase          =					len(self.symbols	)



		def __eq__(			self    ,				A_	)  -> str:
				"""simple docstring"""
				return self.indices == other.indices



		def __getitem__(			self    ,				A_	)  -> List[Any]:
				"""simple docstring"""
				if idx < len(self.symbols	):
						return self.symbols[idx]
				return self.unk_word



		def __len__(			self	)  -> List[str]:
				"""simple docstring"""
				return len(self.symbols	)



		def __contains__(			self    ,				A_	)  -> List[Any]:
				"""simple docstring"""
				return sym in self.indices



		@classmethod
		def __UpperCamelCase			(			cls    ,				A_	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					cls()
				d.add_from_file(UpperCamelCase__	)
				return d



		def __UpperCamelCase			(			self    ,				A_    ,				A_=1    ,				A_=False	)  -> Tuple:
				"""simple docstring"""
				if word in self.indices and not overwrite:
						UpperCamelCase          =					self.indices[word]
						UpperCamelCase          =					self.count[idx] + n
						return idx
				else:
						UpperCamelCase          =					len(self.symbols	)
						UpperCamelCase          =					idx
						self.symbols.append(UpperCamelCase__	)
						self.count.append(UpperCamelCase__	)
						return idx



		def __UpperCamelCase			(			self    ,				A_	)  -> int:
				"""simple docstring"""
				return 0



		def __UpperCamelCase			(			self    ,				A_	)  -> str:
				"""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

				UpperCamelCase          =					f.readlines()
				UpperCamelCase          =					self._load_meta(UpperCamelCase__	)

				for line in lines[indices_start_line:]:
						try:
								UpperCamelCase     ,  UpperCamelCase          =					line.rstrip().rsplit(' '    ,				1	)
								if field == "#fairseq:overwrite":
										UpperCamelCase          =					True
										UpperCamelCase     ,  UpperCamelCase          =					line.rsplit(' '    ,				1	)
								else:
										UpperCamelCase          =					False
								UpperCamelCase          =					int(UpperCamelCase__	)
								UpperCamelCase          =					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 				A	( lowercase			)       ->  Union[str, Any]:
		'''simple docstring'''

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


def 				A	( lowercase   ,	lowercase			)       ->  Any:
		'''simple docstring'''

		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

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

		UpperCamelCase          =					chkpt['cfg']['model']

		# dicts
		UpperCamelCase          =					os.path.join(UpperCAmelCase__   ,	'dict.txt'			)
		if not os.path.isfile(UpperCAmelCase__			):
				raise ValueError(f'''path to the file {dict_file} does not exist!'''			)
		UpperCamelCase          =					Dictionary.load(UpperCAmelCase__			)
		UpperCamelCase          =					rewrite_dict_keys(src_dict.indices			)
		UpperCamelCase          =					len(UpperCAmelCase__			)
		UpperCamelCase          =					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)
		UpperCamelCase          =					os.path.join(UpperCAmelCase__   ,	'bpecodes'			)
		if not os.path.isfile(UpperCAmelCase__			):
				raise ValueError(f'''path to the file {bpecodes_file} does not exist!'''			)

		UpperCamelCase          =					os.path.join(UpperCAmelCase__   ,	VOCAB_FILES_NAMES['merges_file']			)
		shutil.copyfile(UpperCAmelCase__   ,	UpperCAmelCase__			)

		# model config
		UpperCamelCase          =					os.path.join(UpperCAmelCase__   ,	'config.json'			)

		UpperCamelCase          =					{
		    '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-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
		UpperCamelCase          =					os.path.join(UpperCAmelCase__   ,	UpperCAmelCase__			)

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

		# model
		UpperCamelCase          =					chkpt['model']

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

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

		UpperCamelCase          =					BioGptConfig.from_pretrained(UpperCAmelCase__			)
		UpperCamelCase          =					BioGptForCausalLM(UpperCAmelCase__			)

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

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

		print('Conversion is done!'			)


if __name__ == "__main__":
					_UpperCAmelCase :			Dict              =	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 :			Any              =	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.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class     lowercase (		_SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	Any     					=    CTRLTokenizer
		__lowercase	:	Any     					=    False
		__lowercase	:	Union[str, Any]     					=    False



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				super().setUp()

				# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
				UpperCamelCase          =					['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
				UpperCamelCase          =					dict(zip(A_    ,				range(len(A_	)	)	)	)
				UpperCamelCase          =					['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
				UpperCamelCase          =					{'unk_token': '<unk>'}

				UpperCamelCase          =					os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['vocab_file']	)
				UpperCamelCase          =					os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['merges_file']	)
				with open(self.vocab_file    ,				'w'    ,				encoding='utf-8'	) as fp:
						fp.write(json.dumps(A_	) + '\n'	)
				with open(self.merges_file    ,				'w'    ,				encoding='utf-8'	) as fp:
						fp.write('\n'.join(A_	)	)



		def __UpperCamelCase			(			self    ,				**A_	)  -> List[Any]:
				"""simple docstring"""
				kwargs.update(self.special_tokens_map	)
				return CTRLTokenizer.from_pretrained(self.tmpdirname    ,				**A_	)



		def __UpperCamelCase			(			self    ,				A_	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					'adapt react readapt apt'
				UpperCamelCase          =					'adapt react readapt apt'
				return input_text, output_text



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					CTRLTokenizer(self.vocab_file    ,				self.merges_file    ,				**self.special_tokens_map	)
				UpperCamelCase          =					'adapt react readapt apt'
				UpperCamelCase          =					'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
				UpperCamelCase          =					tokenizer.tokenize(A_	)
				self.assertListEqual(A_    ,				A_	)

				UpperCamelCase          =					tokens + [tokenizer.unk_token]

				UpperCamelCase          =					[0, 1, 2, 4, 5, 1, 0, 3, 6]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(A_	)    ,				A_	)
