




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			:





				'''simple docstring'''



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



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



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



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



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



				@classmethod
				def       __snake_case		( cls       , UpperCAmelCase_						):
											lowerCAmelCase   =		cls()
											d.add_from_file(_A						)
											return d



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_=1       , UpperCAmelCase_=False						):
											if word in self.indices and not overwrite:
																		lowerCAmelCase   =		self.indices[word]
																		lowerCAmelCase   =		self.count[idx] + n
																		return idx
											else:
																		lowerCAmelCase   =		len(self.symbols						)
																		lowerCAmelCase   =		idx
																		self.symbols.append(_A						)
																		self.count.append(_A						)
																		return idx



				def       __snake_case		( self       , UpperCAmelCase_						):
											return 0






				def       __snake_case		( self       , UpperCAmelCase_						):
											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

											lowerCAmelCase   =		f.readlines()
											lowerCAmelCase   =		self._load_meta(_A						)

											for line in lines[indices_start_line:]:
																		try:
																									lowerCAmelCase   ,     lowerCAmelCase   =		line.rstrip().rsplit(''' '''       , 1						)
																									if field == "#fairseq:overwrite":
																																lowerCAmelCase   =		True
																																lowerCAmelCase   ,     lowerCAmelCase   =		line.rsplit(''' '''       , 1						)
																									else:
																																lowerCAmelCase   =		False
																									lowerCAmelCase   =		int(_A						)
																									lowerCAmelCase   =		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       UpperCAmelCase       (     _snake_case							):
							# (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}
							lowerCAmelCase   =		dict((re.sub(R'''@@$'''						, ''''''						, __snake_case							), v) if k.endswith('''@@'''							) else (re.sub(R'''$'''						, '''</w>'''						, __snake_case							), v) for k, v in d.items()							)
							lowerCAmelCase   =		'''<s> <pad> </s> <unk>'''.split()
							# restore the special tokens
							for k in keep_keys:
														del da[F"""{k}</w>"""]
														lowerCAmelCase   =		d[k]  # restore
							return da



def       UpperCAmelCase       (     _snake_case						, _snake_case							):
							# prep
							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

							lowerCAmelCase   =		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!"""							)
							lowerCAmelCase   =		torch.load(__snake_case						, map_location='''cpu'''							)

							lowerCAmelCase   =		chkpt['''cfg''']['''model''']

							# dicts
							lowerCAmelCase   =		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!"""							)
							lowerCAmelCase   =		Dictionary.load(__snake_case							)
							lowerCAmelCase   =		rewrite_dict_keys(src_dict.indices							)
							lowerCAmelCase   =		len(__snake_case							)
							lowerCAmelCase   =		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)
							lowerCAmelCase   =		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!"""							)

							lowerCAmelCase   =		os.path.join(__snake_case						, VOCAB_FILES_NAMES['''merges_file''']							)
							shutil.copyfile(__snake_case						, __snake_case							)

							# model config
							lowerCAmelCase   =		os.path.join(__snake_case						, '''config.json'''							)

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

							# tokenizer config
							lowerCAmelCase   =		os.path.join(__snake_case						, __snake_case							)

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

							# model
							lowerCAmelCase   =		chkpt['''model''']

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

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

							lowerCAmelCase   =		BioGptConfig.from_pretrained(__snake_case							)
							lowerCAmelCase   =		BioGptForCausalLM(__snake_case							)

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

							# save
							lowerCAmelCase   =		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 torch

from diffusers import StableDiffusionPipeline


UpperCAmelCase_						="""path-to-your-trained-model"""
UpperCAmelCase_						=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

UpperCAmelCase_						="""A photo of sks dog in a bucket"""
UpperCAmelCase_						=pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("""dog-bucket.png""")



