






import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


if is_apex_available():
						from apex import amp


if version.parse(version.parse(torch.__version__).base_version) >= version.parse("""1.6"""):
						__UpperCamelCase      :      List[Any]     						=						True
						from torch.cuda.amp import autocast

__UpperCamelCase      :      Tuple     						=						logging.getLogger(__name__)





def 				snake_case  (     lowerCamelCase=None				,  lowerCamelCase=None					):




	'''simple docstring'''







	return field(default_factory=lambda: default				,  metadata=lowerCamelCase					)


@dataclass
class        __UpperCamelCase    :
	__snake_case	:str   =       field(
	    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}   )
	__snake_case	:Optional[str]   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}   ,			)
	__snake_case	:Optional[bool]   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Whether to freeze the feature extractor layers of the model.'}   )
	__snake_case	:Optional[float]   =       field(
	    default=0.1   ,			metadata={'help': 'The dropout ratio for the attention probabilities.'}   )
	__snake_case	:Optional[float]   =       field(
	    default=0.1   ,			metadata={'help': 'The dropout ratio for activations inside the fully connected layer.'}   )
	__snake_case	:Optional[float]   =       field(
	    default=0.1   ,			metadata={
	        'help': 'The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.'
	    }   ,			)
	__snake_case	:Optional[float]   =       field(
	    default=0.1   ,			metadata={'help': 'The dropout probabilitiy for all 1D convolutional layers in feature extractor.'}   ,			)
	__snake_case	:Optional[float]   =       field(
	    default=0.05   ,			metadata={
	        'help': (
	            'Propability of each feature vector along the time axis to be chosen as the start of the vector'
	            'span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature'
	            'vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``.'
	        )
	    }   ,			)
	__snake_case	:Optional[float]   =       field(default=0.0   ,			metadata={'help': 'The LayerDrop probability.'}   )


@dataclass
class        __UpperCamelCase    :
	__snake_case	:Optional[str]   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}   )
	__snake_case	:Optional[str]   =       field(
	    default='train+validation'   ,			metadata={
	        'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
	    }   ,			)
	__snake_case	:bool   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Overwrite the cached preprocessed datasets or not.'}   )
	__snake_case	:Optional[int]   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'The number of processes to use for the preprocessing.'}   ,			)
	__snake_case	:Optional[int]   =       field(
	    default=_lowerCAmelCase   ,			metadata={
	        'help': (
	            'For debugging purposes or quicker training, truncate the number of training examples to this '
	            'value if set.'
	        )
	    }   ,			)
	__snake_case	:Optional[int]   =       field(
	    default=_lowerCAmelCase   ,			metadata={
	        'help': (
	            'For debugging purposes or quicker training, truncate the number of validation examples to this '
	            'value if set.'
	        )
	    }   ,			)
	__snake_case	:List[str]   =       list_field(
	    default=[',', '?', '.', '!', '-', ';', ':', '""', '%', '\'', '"', '�']   ,			metadata={'help': 'A list of characters to remove from the transcripts.'}   ,			)


@dataclass
class        __UpperCamelCase    :
	__snake_case	:WavaVecaProcessor
	__snake_case	:Union[bool, str]   =       True
	__snake_case	:Optional[int]   =       None
	__snake_case	:Optional[int]   =       None
	__snake_case	:Optional[int]   =       None
	__snake_case	:Optional[int]   =       None







	def __call__(  self      :							Tuple       ,				_lowerCAmelCase      :							List[Dict[str, Union[List[int], torch.Tensor]]]			)		-> Dict[str, torch.Tensor]:
		"""simple docstring"""







		__lowercase             =  [{"""input_values""": feature["""input_values"""]} for feature in features]
		__lowercase             =  [{"""input_ids""": feature["""labels"""]} for feature in features]

		__lowercase             =  self.processor.pad(
		    _lowerCAmelCase       ,				padding=self.padding       ,				max_length=self.max_length       ,				pad_to_multiple_of=self.pad_to_multiple_of       ,				return_tensors="""pt"""       ,				)
		__lowercase             =  self.processor.pad(
		    labels=_lowerCAmelCase       ,				padding=self.padding       ,				max_length=self.max_length_labels       ,				pad_to_multiple_of=self.pad_to_multiple_of_labels       ,				return_tensors="""pt"""       ,				)

		# replace padding with -100 to ignore loss correctly
		__lowercase             =  labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1			)       ,				-100			)

		__lowercase             =  labels

		return batch


class        __UpperCamelCase    (     _lowerCAmelCase   ):







	def  _a   (  self      :							List[Any]       ,				_lowerCAmelCase      :							nn.Module       ,				_lowerCAmelCase      :							Dict[str, Union[torch.Tensor, Any]]			)		-> torch.Tensor:
		"""simple docstring"""







		model.train()
		__lowercase             =  self._prepare_inputs(_lowerCAmelCase			)

		if self.use_amp:
			with autocast():
				__lowercase             =  self.compute_loss(_lowerCAmelCase       ,				_lowerCAmelCase			)
		else:
			__lowercase             =  self.compute_loss(_lowerCAmelCase       ,				_lowerCAmelCase			)

		if self.args.n_gpu > 1:
			if model.module.config.ctc_loss_reduction == "mean":
				__lowercase             =  loss.mean()
			elif model.module.config.ctc_loss_reduction == "sum":
				__lowercase             =  loss.sum() / (inputs["""labels"""] >= 0).sum()
			else:
				raise ValueError(F'{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']'			)

		if self.args.gradient_accumulation_steps > 1:
			__lowercase             =  loss / self.args.gradient_accumulation_steps

		if self.use_amp:
			self.scaler.scale(_lowerCAmelCase			).backward()
		elif self.use_apex:
			with amp.scale_loss(_lowerCAmelCase       ,				self.optimizer			) as scaled_loss:
				scaled_loss.backward()
		elif self.deepspeed:
			self.deepspeed.backward(_lowerCAmelCase			)
		else:
			loss.backward()

		return loss.detach()





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
	if len(sys.argv					) == 2 and sys.argv[1].endswith(""".json"""					):
		# If we pass only one argument to the script and it's the path to a json file,
		# let's parse it to get our arguments.
		__lowercase       ,						__lowercase       ,						__lowercase             =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
	else:
		__lowercase       ,						__lowercase       ,						__lowercase             =  parser.parse_args_into_dataclasses()

	# Detecting last checkpoint.
	__lowercase             =  None
	if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
		__lowercase             =  get_last_checkpoint(training_args.output_dir					)
		if last_checkpoint is None and len(os.listdir(training_args.output_dir					)					) > 0:
			raise ValueError(
			    F'Output directory ({training_args.output_dir}) already exists and is not empty. '
			    """Use --overwrite_output_dir to overcome."""					)
		elif last_checkpoint is not None:
			logger.info(
			    F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
			    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""					)

    # Setup logging
	logging.basicConfig(
	    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""				,  datefmt="""%m/%d/%Y %H:%M:%S"""				,  handlers=[logging.StreamHandler(sys.stdout					)]				,  )
	logger.setLevel(logging.INFO if is_main_process(training_args.local_rank					) else logging.WARN					)

	# Log on each process the small summary:
	logger.warning(
	    F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
	    + F'distributed training: {bool(training_args.local_rank != -1					)}, 16-bits training: {training_args.fpaa}'					)
	# Set the verbosity to info of the Transformers logger (on main process only):
	if is_main_process(training_args.local_rank					):
		transformers.utils.logging.set_verbosity_info()
	logger.info("""Training/evaluation parameters %s"""				,  lowerCamelCase					)

	# Set seed before initializing model.
	set_seed(training_args.seed					)

	# Get the datasets:
	__lowercase             =  datasets.load_dataset(
	    """common_voice"""				,  data_args.dataset_config_name				,  split=data_args.train_split_name					)
	__lowercase             =  datasets.load_dataset("""common_voice"""				,  data_args.dataset_config_name				,  split="""test"""					)

	# Create and save tokenizer
	__lowercase             =  F'[{"".join(data_args.chars_to_ignore					)}]'

	def remove_special_characters(lowerCamelCase					):
		__lowercase             =  re.sub(lowerCamelCase				,  """"""				,  batch["""sentence"""]					).lower() + """ """
		return batch

	__lowercase             =  train_dataset.map(lowerCamelCase				,  remove_columns=["""sentence"""]					)
	__lowercase             =  eval_dataset.map(lowerCamelCase				,  remove_columns=["""sentence"""]					)

	def extract_all_chars(lowerCamelCase					):
		__lowercase             =  """ """.join(batch["""text"""]					)
		__lowercase             =  list(set(lowerCamelCase					)					)
		return {"vocab": [vocab], "all_text": [all_text]}

	__lowercase             =  train_dataset.map(
	    lowerCamelCase				,  batched=lowerCamelCase				,  batch_size=-1				,  keep_in_memory=lowerCamelCase				,  remove_columns=train_dataset.column_names				,  )
	__lowercase             =  train_dataset.map(
	    lowerCamelCase				,  batched=lowerCamelCase				,  batch_size=-1				,  keep_in_memory=lowerCamelCase				,  remove_columns=eval_dataset.column_names				,  )

	__lowercase             =  list(set(vocab_train["""vocab"""][0]					) | set(vocab_test["""vocab"""][0]					)					)
	__lowercase             =  {v: k for k, v in enumerate(lowerCamelCase					)}
	__lowercase             =  vocab_dict[""" """]
	del vocab_dict[" "]
	__lowercase             =  len(lowerCamelCase					)
	__lowercase             =  len(lowerCamelCase					)

	with open("""vocab.json"""				,  """w"""					) as vocab_file:
		json.dump(lowerCamelCase				,  lowerCamelCase					)

	# Load pretrained model and tokenizer
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.
	__lowercase             =  WavaVecaCTCTokenizer(
	    """vocab.json"""				,  unk_token="""[UNK]"""				,  pad_token="""[PAD]"""				,  word_delimiter_token="""|"""				,  )
	__lowercase             =  WavaVecaFeatureExtractor(
	    feature_size=1				,  sampling_rate=16_000				,  padding_value=0.0				,  do_normalize=lowerCamelCase				,  return_attention_mask=lowerCamelCase					)
	__lowercase             =  WavaVecaProcessor(feature_extractor=lowerCamelCase				,  tokenizer=lowerCamelCase					)
	__lowercase             =  WavaVecaForCTC.from_pretrained(
	    model_args.model_name_or_path				,  cache_dir=model_args.cache_dir				,  activation_dropout=model_args.activation_dropout				,  attention_dropout=model_args.attention_dropout				,  hidden_dropout=model_args.hidden_dropout				,  feat_proj_dropout=model_args.feat_proj_dropout				,  mask_time_prob=model_args.mask_time_prob				,  gradient_checkpointing=training_args.gradient_checkpointing				,  layerdrop=model_args.layerdrop				,  ctc_loss_reduction="""mean"""				,  pad_token_id=processor.tokenizer.pad_token_id				,  vocab_size=len(processor.tokenizer					)				,  )

	if data_args.max_train_samples is not None:
		__lowercase             =  min(len(lowerCamelCase					)				,  data_args.max_train_samples					)
		__lowercase             =  train_dataset.select(range(lowerCamelCase					)					)

	if data_args.max_val_samples is not None:
		__lowercase             =  eval_dataset.select(range(data_args.max_val_samples					)					)

	__lowercase             =  torchaudio.transforms.Resample(48_000				,  16_000					)

	# Preprocessing the datasets.
	# We need to read the aduio files as arrays and tokenize the targets.
	def speech_file_to_array_fn(lowerCamelCase					):
		__lowercase       ,						__lowercase             =  torchaudio.load(batch["""path"""]					)
		__lowercase             =  resampler(lowerCamelCase					).squeeze().numpy()
		__lowercase             =  16_000
		__lowercase             =  batch["""text"""]
		return batch

	__lowercase             =  train_dataset.map(
	    lowerCamelCase				,  remove_columns=train_dataset.column_names				,  num_proc=data_args.preprocessing_num_workers				,  )
	__lowercase             =  eval_dataset.map(
	    lowerCamelCase				,  remove_columns=eval_dataset.column_names				,  num_proc=data_args.preprocessing_num_workers				,  )

	def prepare_dataset(lowerCamelCase					):
		# check that all files have the correct sampling rate
		assert (
		    len(set(batch["""sampling_rate"""]					)					) == 1
		), F'Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.'

		__lowercase             =  processor(
		    audio=batch["""speech"""]				,  text=batch["""target_text"""]				,  sampling_rate=batch["""sampling_rate"""][0]					)
		batch.update(lowerCamelCase					)
		return batch

	__lowercase             =  train_dataset.map(
	    lowerCamelCase				,  remove_columns=train_dataset.column_names				,  batch_size=training_args.per_device_train_batch_size				,  batched=lowerCamelCase				,  num_proc=data_args.preprocessing_num_workers				,  )
	__lowercase             =  eval_dataset.map(
	    lowerCamelCase				,  remove_columns=eval_dataset.column_names				,  batch_size=training_args.per_device_train_batch_size				,  batched=lowerCamelCase				,  num_proc=data_args.preprocessing_num_workers				,  )

	# Metric
	__lowercase             =  datasets.load_metric("""wer"""					)

	def compute_metrics(lowerCamelCase					):
		__lowercase             =  pred.predictions
		__lowercase             =  np.argmax(lowerCamelCase				,  axis=-1					)

		__lowercase             =  processor.tokenizer.pad_token_id

		__lowercase             =  processor.batch_decode(lowerCamelCase					)
		# we do not want to group tokens when computing the metrics
		__lowercase             =  processor.batch_decode(pred.label_ids				,  group_tokens=lowerCamelCase					)

		__lowercase             =  wer_metric.compute(predictions=lowerCamelCase				,  references=lowerCamelCase					)

		return {"wer": wer}

	if model_args.freeze_feature_extractor:
		model.freeze_feature_extractor()

	# Data collator
	__lowercase             =  DataCollatorCTCWithPadding(processor=lowerCamelCase				,  padding=lowerCamelCase					)

	# Initialize our Trainer
	__lowercase             =  CTCTrainer(
	    model=lowerCamelCase				,  data_collator=lowerCamelCase				,  args=lowerCamelCase				,  compute_metrics=lowerCamelCase				,  train_dataset=train_dataset if training_args.do_train else None				,  eval_dataset=eval_dataset if training_args.do_eval else None				,  tokenizer=processor.feature_extractor				,  )

	# Training
	if training_args.do_train:
		if last_checkpoint is not None:
			__lowercase             =  last_checkpoint
		elif os.path.isdir(model_args.model_name_or_path					):
			__lowercase             =  model_args.model_name_or_path
		else:
			__lowercase             =  None

		# Save the feature_extractor and the tokenizer
		if is_main_process(training_args.local_rank					):
			processor.save_pretrained(training_args.output_dir					)

		__lowercase             =  trainer.train(resume_from_checkpoint=lowerCamelCase					)
		trainer.save_model()

		__lowercase             =  train_result.metrics
		__lowercase             =  (
		    data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCamelCase					)
		)
		__lowercase             =  min(lowerCamelCase				,  len(lowerCamelCase					)					)

		trainer.log_metrics("""train"""				,  lowerCamelCase					)
		trainer.save_metrics("""train"""				,  lowerCamelCase					)
		trainer.save_state()

	# Evaluation
	__lowercase             =  {}
	if training_args.do_eval:
		logger.info("""*** Evaluate ***"""					)
		__lowercase             =  trainer.evaluate()
		__lowercase             =  data_args.max_val_samples if data_args.max_val_samples is not None else len(lowerCamelCase					)
		__lowercase             =  min(lowerCamelCase				,  len(lowerCamelCase					)					)

		trainer.log_metrics("""eval"""				,  lowerCamelCase					)
		trainer.save_metrics("""eval"""				,  lowerCamelCase					)

	return results


if __name__ == "__main__":
						main()









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      :      Tuple     						=						2


class        __UpperCamelCase    :







	def __init__(  self      :							List[str]       ,				*,  # begin keyword-only arguments
	    _lowerCAmelCase      :							Optional[int]="<s>"       ,				_lowerCAmelCase      :							Optional[int]="<pad>"       ,				_lowerCAmelCase      :							int="</s>"       ,				_lowerCAmelCase      :							str="<unk>"       ,				_lowerCAmelCase      :							List[str]=None       ,				)		-> Tuple:
		"""simple docstring"""







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







	def __eq__(  self      :							Dict       ,				_lowerCAmelCase      :							List[str]			)		-> Any:
		"""simple docstring"""







		return self.indices == other.indices







	def __getitem__(  self      :							Any       ,				_lowerCAmelCase      :							str			)		-> Dict:
		"""simple docstring"""







		if idx < len(self.symbols			):
			return self.symbols[idx]
		return self.unk_word







	def __len__(  self      :							str			)		-> List[str]:
		"""simple docstring"""







		return len(self.symbols			)







	def __contains__(  self      :							Union[str, Any]       ,				_lowerCAmelCase      :							List[Any]			)		-> Optional[int]:
		"""simple docstring"""







		return sym in self.indices







	@classmethod
	def  _a   (  cls      :							Dict       ,				_lowerCAmelCase      :							Optional[int]			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  cls()
		d.add_from_file(_lowerCAmelCase			)
		return d







	def  _a   (  self      :							int       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							Union[str, Any]=1       ,				_lowerCAmelCase      :							Optional[int]=False			)		-> Union[str, Any]:
		"""simple docstring"""







		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(_lowerCAmelCase			)
			self.count.append(_lowerCAmelCase			)
			return idx







	def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							Optional[int]			)		-> Optional[Any]:
		"""simple docstring"""







		return 0







	def  _a   (  self      :							Optional[Any]       ,				_lowerCAmelCase      :							Dict			)		-> str:
		"""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

		__lowercase             =  f.readlines()
		__lowercase             =  self._load_meta(_lowerCAmelCase			)

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





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	__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 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	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""": 1_024,
	    """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__":
						__UpperCamelCase      :      int     						=						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      :      Optional[Any]     						=						parser.parse_args()
						convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)

