
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_   :




	'''simple docstring'''


	def __init__(       self		,    *,  # begin keyword-only arguments
	    snake_case="<s>"		,    snake_case="<pad>"		,    snake_case="</s>"		,    snake_case="<unk>"		,    snake_case=None		,    ):
		lowercase  ,					lowercase  ,					lowercase  ,					lowercase       =    bos, unk, pad, eos
		lowercase       =    []
		lowercase       =    []
		lowercase       =    {}
		lowercase       =    self.add_symbol(snake_case_       )
		lowercase       =    self.add_symbol(snake_case_       )
		lowercase       =    self.add_symbol(snake_case_       )
		lowercase       =    self.add_symbol(snake_case_       )
		if extra_special_symbols:
			for s in extra_special_symbols:
				self.add_symbol(snake_case_       )
		lowercase       =    len(self.symbols       )





	def __eq__(       self		,    snake_case       ):
		return self.indices == other.indices





	def __getitem__(       self		,    snake_case       ):
		if idx < len(self.symbols       ):
			return self.symbols[idx]
		return self.unk_word





	def __len__(       self       ):
		return len(self.symbols       )





	def __contains__(       self		,    snake_case       ):
		return sym in self.indices





	@classmethod
	def 	SCREAMING_SNAKE_CASE__						(       cls		,    snake_case       ):
		lowercase       =    cls()
		d.add_from_file(snake_case_       )
		return d





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case=1		,    snake_case=False       ):
		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(snake_case_       )
			self.count.append(snake_case_       )
			return idx





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		return 0





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		if isinstance(snake_case_		,    snake_case_       ):
			try:
				with open(snake_case_		,    'r'		,    encoding='utf-8'       ) as fd:
					self.add_from_file(snake_case_       )
			except FileNotFoundError as fnfe:
				raise fnfe
			except UnicodeError:
				raise Exception('Incorrect encoding detected in {}, please rebuild the dataset'.format(snake_case_       )       )
			return

		lowercase       =    f.readlines()
		lowercase       =    self._load_meta(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(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(snake_case_       )       )
				self.add_symbol(snake_case_		,    n=snake_case_		,    overwrite=snake_case_       )
			except ValueError:
				raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\''       )






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase       =    dict((re.sub(r'@@$' ,							'' ,							__lowercase							), v) if k.endswith('@@'							) else (re.sub(r'$' ,							'</w>' ,							__lowercase							), 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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):



	if not os.path.exists(__lowercase							):
		raise ValueError(F'''path {biogpt_checkpoint_path} does not exist!'''							)
	os.makedirs(__lowercase ,							exist_ok=__lowercase							)
	print(F'''Writing results to {pytorch_dump_folder_path}'''							)

	# handle various types of models

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

	lowercase       =    chkpt['cfg']['model']

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

	# merges_file (bpecodes)
	lowercase       =    os.path.join(__lowercase ,							'bpecodes'							)
	if not os.path.isfile(__lowercase							):
		raise ValueError(F'''path to the file {bpecodes_file} does not exist!'''							)

	lowercase       =    os.path.join(__lowercase ,							VOCAB_FILES_NAMES['merges_file']							)
	shutil.copyfile(__lowercase ,							__lowercase							)

	# model config
	lowercase       =    os.path.join(__lowercase ,							'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': 0.02,
	    '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(__lowercase ,							'w' ,							encoding='utf-8'							) as f:
		f.write(json.dumps(__lowercase ,							ensure_ascii=__lowercase ,							indent=__lowercase							)							)

	# tokenizer config
	lowercase       =    os.path.join(__lowercase ,							__lowercase							)

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

	# model
	lowercase       =    chkpt['model']

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

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

	lowercase       =    BioGptConfig.from_pretrained(__lowercase							)
	lowercase       =    BioGptForCausalLM(__lowercase							)

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

	# save
	lowercase       =    os.path.join(__lowercase ,							__lowercase							)
	print(F'''Generating {pytorch_weights_dump_path}'''							)
	torch.save(__lowercase ,							__lowercase							)

	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'''




import math
def 	UpperCAmelCase_							(					__lowercase							:      int       )		->       bool:


     '''simple docstring'''



     return math.sqrt(__lowercase       ) * math.sqrt(__lowercase       ) == num
def 	UpperCAmelCase_							(					__lowercase							:      int       )		->       bool:


     '''simple docstring'''



     _UpperCAmelCase     							=   0
     _UpperCAmelCase     							=   n
     while left <= right:
          _UpperCAmelCase     							=   (left + right) // 2
          if mid**2 == n:
               return True
          elif mid**2 > n:
               _UpperCAmelCase     							=   mid - 1
          else:
               _UpperCAmelCase     							=   mid + 1
     return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()




