
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       __A:

					def __init__(	self					:     List[str]   ,					*,  # begin keyword-only arguments
					    __UpperCamelCase					:     Optional[Any]="<s>"   ,					__UpperCamelCase					:     Any="<pad>"   ,					__UpperCamelCase					:     Union[str, Any]="</s>"   ,					__UpperCamelCase					:     List[Any]="<unk>"   ,					__UpperCamelCase					:     Tuple=None   ,					):
												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					bos, unk, pad, eos
												lowerCamelCase_							  =					[]
												lowerCamelCase_							  =					[]
												lowerCamelCase_							  =					{}
												lowerCamelCase_							  =					self.add_symbol(__UpperCamelCase   )
												lowerCamelCase_							  =					self.add_symbol(__UpperCamelCase   )
												lowerCamelCase_							  =					self.add_symbol(__UpperCamelCase   )
												lowerCamelCase_							  =					self.add_symbol(__UpperCamelCase   )
												if extra_special_symbols:
																			for s in extra_special_symbols:
																										self.add_symbol(__UpperCamelCase   )
												lowerCamelCase_							  =					len(self.symbols   )

					def __eq__(	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ):
												return self.indices == other.indices

					def __getitem__(	self					:     List[Any]   ,					__UpperCamelCase					:     Optional[Any]   ):
												if idx < len(self.symbols   ):
																			return self.symbols[idx]
												return self.unk_word

					def __len__(	self					:     Optional[int]   ):
												return len(self.symbols   )

					def __contains__(	self					:     Dict   ,					__UpperCamelCase					:     Optional[int]   ):
												return sym in self.indices

					@classmethod
					def    lowercase__      (	cls					:     List[str]   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					cls()
												d.add_from_file(__UpperCamelCase   )
												return d

					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     int=1   ,					__UpperCamelCase					:     Union[str, Any]=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(__UpperCamelCase   )
																			self.count.append(__UpperCamelCase   )
																			return idx

					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     Any   ):
												return 0



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

												lowerCamelCase_							  =					f.readlines()
												lowerCamelCase_							  =					self._load_meta(__UpperCamelCase   )

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



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[Any]       )  ->    Dict:
							# (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"""@@$"""			,				""""""			,				UpperCAmelCase__       ), v) if k.endswith("""@@"""       ) else (re.sub(R"""$"""			,				"""</w>"""			,				UpperCAmelCase__       ), 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      __lowerCAmelCase						(							UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      List[str]       )  ->    str:
							# prep
							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

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

							lowerCamelCase_							  =					chkpt["""cfg"""]["""model"""]

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

							lowerCamelCase_							  =					os.path.join(UpperCAmelCase__			,				VOCAB_FILES_NAMES["""merges_file"""]       )
							shutil.copyfile(UpperCAmelCase__			,				UpperCAmelCase__       )

							# model config
							lowerCamelCase_							  =					os.path.join(UpperCAmelCase__			,				"""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""": 0.0_2,
							    """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(UpperCAmelCase__			,				"""w"""			,				encoding="""utf-8"""       ) as f:
														f.write(json.dumps(UpperCAmelCase__			,				ensure_ascii=UpperCAmelCase__			,				indent=UpperCAmelCase__       )       )

							# tokenizer config
							lowerCamelCase_							  =					os.path.join(UpperCAmelCase__			,				UpperCAmelCase__       )

							lowerCamelCase_							  =					{
							    """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
							lowerCamelCase_							  =					chkpt["""model"""]

							# remove unneeded keys
							lowerCamelCase_							  =					[
							    """decoder.version""",
							]
							for k in ignore_keys:
														model_state_dict.pop(UpperCAmelCase__			,				UpperCAmelCase__       )

							lowerCamelCase_							  =					list(model_state_dict.keys()       )
							for layer_name in layer_names:
														if layer_name.endswith("""output_projection.weight"""       ):
																					lowerCamelCase_							  =					model_state_dict.pop(UpperCAmelCase__       )
														else:
																					lowerCamelCase_							  =					model_state_dict.pop(UpperCAmelCase__       )

							lowerCamelCase_							  =					BioGptConfig.from_pretrained(UpperCAmelCase__       )
							lowerCamelCase_							  =					BioGptForCausalLM(UpperCAmelCase__       )

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

							# save
							lowerCamelCase_							  =					os.path.join(UpperCAmelCase__			,				UpperCAmelCase__       )
							print(F'''Generating {pytorch_weights_dump_path}'''       )
							torch.save(UpperCAmelCase__			,				UpperCAmelCase__       )

							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,
)


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

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

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

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  [
		    '''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

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





