





from typing import TYPE_CHECKING

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


_UpperCAmelCase				:       int	   =				{"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCAmelCase				:       Tuple	   =				["""SpeechEncoderDecoderModel"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCAmelCase				:       int	   =				["""FlaxSpeechEncoderDecoderModel"""]

if TYPE_CHECKING:
    from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
    import sys

    _UpperCAmelCase				:       Dict	   =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







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__		:str   =    2







class 				lowercase		:




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




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




							def  A__      (				self   ,A__):
												return 0





							def  A__      (				self   ,A__):
												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

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

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



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					# (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}
					lowercase							=						dict((re.sub(R'''@@$''' ,			'''''' ,			lowerCAmelCase__		), v) if k.endswith('''@@'''		) else (re.sub(R'''$''' ,			'''</w>''' ,			lowerCAmelCase__		), 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    (							lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					# prep
					if not os.path.exists(lowerCAmelCase__		):
										raise ValueError(f'path {biogpt_checkpoint_path} does not exist!'		)
					os.makedirs(lowerCAmelCase__ ,			exist_ok=lowerCAmelCase__		)
					print(f'Writing results to {pytorch_dump_folder_path}'		)

					# handle various types of models

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

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

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

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

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

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

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

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

					# model
					lowercase							=						chkpt['''model''']

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

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

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

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

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

					print('''Conversion is done!'''		)


if __name__ == "__main__":
							lowercase__		:Union[str, Any]   =    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__		:Any   =    parser.parse_args()
							convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)




