



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()

lowerCAmelCase__						:	Union[str, Any]					=2

class   UpperCAmelCase_    :

						'''simple docstring'''




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







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   bos, unk, pad, eos
													__SCREAMING_SNAKE_CASE										=   []
													__SCREAMING_SNAKE_CASE										=   []
													__SCREAMING_SNAKE_CASE										=   {}
													__SCREAMING_SNAKE_CASE										=   self.add_symbol(A__						)
													__SCREAMING_SNAKE_CASE										=   self.add_symbol(A__						)
													__SCREAMING_SNAKE_CASE										=   self.add_symbol(A__						)
													__SCREAMING_SNAKE_CASE										=   self.add_symbol(A__						)
													if extra_special_symbols:
																				for s in extra_special_symbols:
																											self.add_symbol(A__						)
													__SCREAMING_SNAKE_CASE										=   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 			_A      (       cls ,				_A						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   cls()
													d.add_from_file(A__						)
													return d






						def 			_A      (       self ,				_A ,				_A=1 ,				_A=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(A__						)
																				self.count.append(A__						)
																				return idx






						def 			_A      (       self ,				_A						):







													'''simple docstring'''

													return 0






						def 			_A      (       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

													__SCREAMING_SNAKE_CASE										=   f.readlines()
													__SCREAMING_SNAKE_CASE										=   self._load_meta(A__						)

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







def       __lowercase  (					a__  )							->					str:
							# (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}
							__SCREAMING_SNAKE_CASE										=   dict((re.sub(R'@@$'				,  ''				,  __lowercase  ), v) if k.endswith('@@'  ) else (re.sub(R'$'				,  '</w>'				,  __lowercase  ), 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       __lowercase  (					a__				,  a__  )							->					Optional[Any]:
							# prep
							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

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

							__SCREAMING_SNAKE_CASE										=   chkpt['''cfg''']['''model''']

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

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

							# model config
							__SCREAMING_SNAKE_CASE										=   os.path.join(__lowercase				,  '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-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
							__SCREAMING_SNAKE_CASE										=   os.path.join(__lowercase				,  __lowercase  )

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

							# model
							__SCREAMING_SNAKE_CASE										=   chkpt['''model''']

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

							__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(__lowercase  )
														else:
																					__SCREAMING_SNAKE_CASE										=   model_state_dict.pop(__lowercase  )

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

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

							# save
							__SCREAMING_SNAKE_CASE										=   os.path.join(__lowercase				,  __lowercase  )
							print(f"""Generating {pytorch_weights_dump_path}"""  )
							torch.save(__lowercase				,  __lowercase  )

							print('Conversion is done!'  )


if __name__ == "__main__":
			lowerCAmelCase__						:	Optional[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.'''
			)
			lowerCAmelCase__						:	Optional[int]					=parser.parse_args()
			convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)














from PIL import Image



def  a__ (__lowercase							:Image					,  __lowercase							:float   )		-> Image:

				def brightness(__lowercase							:int   ) -> float:
								return 128 + level + (c - 128)

				if not -255.0 <= level <= 255.0:
								raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''   )
				return img.point(__lowercase   )


if __name__ == "__main__":
	# Load image
	with Image.open('image_data/lena.jpg') as img:
		# Change brightness to 100
		_UpperCamelCase			:	Any								=change_brightness(img, 100)
		brigt_img.save('image_data/lena_brightness.png', format='png')



