"""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()

SCREAMING_SNAKE_CASE__   =					2



class 					lowerCAmelCase_      :



				"""simple docstring"""

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






										"""simple docstring"""

										snake_case   ,snake_case   ,snake_case   ,snake_case        =     bos, unk, pad, eos
										snake_case        =     []
										snake_case        =     []
										snake_case        =     {}
										snake_case        =     self.add_symbol(lowerCAmelCase      )
										snake_case        =     self.add_symbol(lowerCAmelCase      )
										snake_case        =     self.add_symbol(lowerCAmelCase      )
										snake_case        =     self.add_symbol(lowerCAmelCase      )
										if extra_special_symbols:
																for s in extra_special_symbols:
																						self.add_symbol(lowerCAmelCase      )
										snake_case        =     len(self.symbols      )

				def __eq__(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										return self.indices == other.indices

				def __getitem__(			self						,      lowerCAmelCase      ):






										"""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						,      lowerCAmelCase      ):






										"""simple docstring"""

										return sym in self.indices

				@classmethod
				def 						snake_case	(			cls						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     cls()
										d.add_from_file(lowerCAmelCase      )
										return d

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase=1						,      lowerCAmelCase=False      ):






										"""simple docstring"""

										if word in self.indices and not overwrite:
																snake_case        =     self.indices[word]
																snake_case        =     self.count[idx] + n
																return idx
										else:
																snake_case        =     len(self.symbols      )
																snake_case        =     idx
																self.symbols.append(lowerCAmelCase      )
																self.count.append(lowerCAmelCase      )
																return idx

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										return 0

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

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

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

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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[Any]   )   ->			List[Any]:





						"""simple docstring"""






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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[Any]	,     _UpperCamelCase    :			Tuple   )   ->			int:





						"""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

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

						snake_case        =     chkpt['cfg']['model']

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

						snake_case        =     os.path.join(_UpperCamelCase	,     VOCAB_FILES_NAMES['merges_file']   )
						shutil.copyfile(_UpperCamelCase	,     _UpperCamelCase   )

						# model config
						snake_case        =     os.path.join(_UpperCamelCase	,     'config.json'   )

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

						# tokenizer config
						snake_case        =     os.path.join(_UpperCamelCase	,     _UpperCamelCase   )

						snake_case        =     {
						    'bos_token': '<s>',
						    'eos_token': '</s>',
						    'model_max_length': 1_0_2_4,
						    '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
						snake_case        =     chkpt['model']

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

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

						snake_case        =     BioGptConfig.from_pretrained(_UpperCamelCase   )
						snake_case        =     BioGptForCausalLM(_UpperCamelCase   )

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

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

						print('Conversion is done!'   )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   =					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."
					)
					SCREAMING_SNAKE_CASE__   =					parser.parse_args()
					convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE__   =					{
    "configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
    "configuration_data2vec_text": [
        "DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecTextConfig",
        "Data2VecTextOnnxConfig",
    ],
    "configuration_data2vec_vision": [
        "DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecVisionConfig",
        "Data2VecVisionOnnxConfig",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Data2VecAudioForAudioFrameClassification",
					    "Data2VecAudioForCTC",
					    "Data2VecAudioForSequenceClassification",
					    "Data2VecAudioForXVector",
					    "Data2VecAudioModel",
					    "Data2VecAudioPreTrainedModel",
					]
					SCREAMING_SNAKE_CASE__   =					[
					    "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Data2VecTextForCausalLM",
					    "Data2VecTextForMaskedLM",
					    "Data2VecTextForMultipleChoice",
					    "Data2VecTextForQuestionAnswering",
					    "Data2VecTextForSequenceClassification",
					    "Data2VecTextForTokenClassification",
					    "Data2VecTextModel",
					    "Data2VecTextPreTrainedModel",
					]
					SCREAMING_SNAKE_CASE__   =					[
					    "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Data2VecVisionForImageClassification",
					    "Data2VecVisionForMaskedImageModeling",
					    "Data2VecVisionForSemanticSegmentation",
					    "Data2VecVisionModel",
					    "Data2VecVisionPreTrainedModel",
					]

if is_tf_available():
					SCREAMING_SNAKE_CASE__   =					[
					    "TFData2VecVisionForImageClassification",
					    "TFData2VecVisionForSemanticSegmentation",
					    "TFData2VecVisionModel",
					    "TFData2VecVisionPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
					from .configuration_dataavec_text import (
					    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DataaVecTextConfig,
					    DataaVecTextOnnxConfig,
					)
					from .configuration_dataavec_vision import (
					    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DataaVecVisionConfig,
					    DataaVecVisionOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_dataavec_audio import (
										    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DataaVecAudioForAudioFrameClassification,
										    DataaVecAudioForCTC,
										    DataaVecAudioForSequenceClassification,
										    DataaVecAudioForXVector,
										    DataaVecAudioModel,
										    DataaVecAudioPreTrainedModel,
										)
										from .modeling_dataavec_text import (
										    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DataaVecTextForCausalLM,
										    DataaVecTextForMaskedLM,
										    DataaVecTextForMultipleChoice,
										    DataaVecTextForQuestionAnswering,
										    DataaVecTextForSequenceClassification,
										    DataaVecTextForTokenClassification,
										    DataaVecTextModel,
										    DataaVecTextPreTrainedModel,
										)
										from .modeling_dataavec_vision import (
										    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DataaVecVisionForImageClassification,
										    DataaVecVisionForMaskedImageModeling,
										    DataaVecVisionForSemanticSegmentation,
										    DataaVecVisionModel,
										    DataaVecVisionPreTrainedModel,
										)
					if is_tf_available():
										from .modeling_tf_dataavec_vision import (
										    TFDataaVecVisionForImageClassification,
										    TFDataaVecVisionForSemanticSegmentation,
										    TFDataaVecVisionModel,
										    TFDataaVecVisionPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


