


"""simple docstring"""




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()

lowercase_			     =  2
class 					__lowerCAmelCase					:


		'''simple docstring'''



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

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

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

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

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

		@classmethod
		def 			__UpperCAmelCase       (  cls							, _a      ):
				__a           =			cls()
				d.add_from_file(SCREAMING_SNAKE_CASE_      )
				return d

		def 			__UpperCAmelCase       (  self							, _a							, _a=1							, _a=False      ):
				if word in self.indices and not overwrite:
						__a           =			self.indices[word]
						__a           =			self.count[idx] + n
						return idx
				else:
						__a           =			len(self.symbols      )
						__a           =			idx
						self.symbols.append(SCREAMING_SNAKE_CASE_      )
						self.count.append(SCREAMING_SNAKE_CASE_      )
						return idx

		def 			__UpperCAmelCase       (  self							, _a      ):
				return 0





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

				__a           =			f.readlines()
				__a           =			self._load_meta(SCREAMING_SNAKE_CASE_      )

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







def        lowercase							(		lowerCAmelCase__ :     Tuple			) ->      Union[str, Any]:
		__a           =			dict((re.sub(r'''@@$'''    ,					''''''    ,					snake_case__			), v) if k.endswith('''@@'''			) else (re.sub(r'''$'''    ,					'''</w>'''    ,					snake_case__			), v) for k, v in d.items()			)
		__a           =			'''<s> <pad> </s> <unk>'''.split()
		# restore the special tokens
		for k in keep_keys:
				del da[f'''{k}</w>''']
				__a           =			d[k]  # restore
		return da

def        lowercase							(		lowerCAmelCase__ :     Union[str, Any]    ,					lowerCAmelCase__ :     Tuple			) ->      Optional[int]:
		# 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

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

		__a           =			chkpt['''cfg''']['''model''']

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

		__a           =			os.path.join(snake_case__    ,					VOCAB_FILES_NAMES['''merges_file''']			)
		shutil.copyfile(snake_case__    ,					snake_case__			)

		# model config
		__a           =			os.path.join(snake_case__    ,					'''config.json'''			)

		__a           =			{
		    '''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
		__a           =			os.path.join(snake_case__    ,					snake_case__			)

		__a           =			{
		    '''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
		__a           =			chkpt['''model''']

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

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

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

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

		# save
		__a           =			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__":
				lowercase_			     =  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."
				)
				lowercase_			     =  parser.parse_args()
				convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__a  			=					{'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['DeiTFeatureExtractor']
						__a  			=					['DeiTImageProcessor']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'DeiTForImageClassification',
						    'DeiTForImageClassificationWithTeacher',
						    'DeiTForMaskedImageModeling',
						    'DeiTModel',
						    'DeiTPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFDeiTForImageClassification',
						    'TFDeiTForImageClassificationWithTeacher',
						    'TFDeiTForMaskedImageModeling',
						    'TFDeiTModel',
						    'TFDeiTPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_deit import DeiTFeatureExtractor
												from .image_processing_deit import DeiTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deit import (
												    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DeiTForImageClassification,
												    DeiTForImageClassificationWithTeacher,
												    DeiTForMaskedImageModeling,
												    DeiTModel,
												    DeiTPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_deit import (
												    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDeiTForImageClassification,
												    TFDeiTForImageClassificationWithTeacher,
												    TFDeiTForMaskedImageModeling,
												    TFDeiTModel,
												    TFDeiTPreTrainedModel,
												)


else:
						import sys

						__a  			=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




