
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

lowerCamelCase__     :		List[Any]        = 2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
lowerCamelCase__     :		Dict        = {
    # fairseq:
    """wmt19-ru-en""": {"""length_penalty""": 1.1},
    """wmt19-en-ru""": {"""length_penalty""": 1.15},
    """wmt19-en-de""": {"""length_penalty""": 1.0},
    """wmt19-de-en""": {"""length_penalty""": 1.1},
    # allenai:
    """wmt16-en-de-dist-12-1""": {"""length_penalty""": 0.6},
    """wmt16-en-de-dist-6-1""": {"""length_penalty""": 0.6},
    """wmt16-en-de-12-1""": {"""length_penalty""": 0.8},
    """wmt19-de-en-6-6-base""": {"""length_penalty""": 0.6},
    """wmt19-de-en-6-6-big""": {"""length_penalty""": 0.6},
}

# this remaps the different models to their organization names
lowerCamelCase__     :		str        = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
     lowerCamelCase__     :		List[str]        = """facebook"""
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
     lowerCamelCase__     :		Optional[Any]        = """allenai"""

def 		UpperCamelCase						(   lowercase_ )		->						Union[str, Any]:


  '''simple docstring'''






  lowercase__	: Any     		=   dict((re.sub(R"""@@$"""			,			""""""			,			lowercase_ ), v) if k.endswith("""@@""" ) else (re.sub(R"""$"""			,			"""</w>"""			,			lowercase_ ), v) for k, v in d.items() )
  lowercase__	: List[str]     		=   """<s> <pad> </s> <unk>""".split()
  # restore the special tokens
  for k in keep_keys:
    del da[F'{k}</w>']
    lowercase__	: int     		=   d[k]  # restore
  return da

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Dict:


  '''simple docstring'''






  assert os.path.exists(lowercase_ )
  os.makedirs(lowercase_			,			exist_ok=lowercase_ )
  print(F'Writing results to {pytorch_dump_folder_path}' )

  # handle various types of models

  lowercase__	: int     		=   basename(lowercase_ )
  lowercase__	: List[Any]     		=   dirname(lowercase_ )

  lowercase__	: List[str]     		=   fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
  lowercase__	: int     		=   cls.hub_models()
  lowercase__	: Tuple     		=   {"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
  lowercase__	: Optional[Any]     		=   """."""
  # note: since the model dump is old, fairseq has upgraded its model some
  # time later, and it does a whole lot of rewrites and splits on the saved
  # weights, therefore we can't use torch.load() directly on the model file.
  # see: upgrade_state_dict(state_dict) in fairseq_model.py
  print(F'using checkpoint {checkpoint_file}' )
  lowercase__	: Optional[int]     		=   hub_utils.from_pretrained(
      lowercase_			,			lowercase_			,			lowercase_			,			archive_map=lowercase_			,			**lowercase_ )

  lowercase__	: Tuple     		=   vars(chkpt["""args"""]["""model"""] )

  lowercase__	: List[Any]     		=   args["""source_lang"""]
  lowercase__	: Dict     		=   args["""target_lang"""]

  lowercase__	: Optional[Any]     		=   dirname(lowercase_ )
  lowercase__	: int     		=   basename(lowercase_ )

  # dicts
  lowercase__	: Union[str, Any]     		=   os.path.join(lowercase_			,			F'dict.{src_lang}.txt' )
  lowercase__	: Optional[int]     		=   os.path.join(lowercase_			,			F'dict.{tgt_lang}.txt' )

  lowercase__	: Optional[int]     		=   Dictionary.load(lowercase_ )
  lowercase__	: Union[str, Any]     		=   rewrite_dict_keys(src_dict.indices )
  lowercase__	: str     		=   len(lowercase_ )
  lowercase__	: Any     		=   os.path.join(lowercase_			,			"""vocab-src.json""" )
  print(F'Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records' )
  with open(lowercase_			,			"""w"""			,			encoding="""utf-8""" ) as f:
    f.write(json.dumps(lowercase_			,			ensure_ascii=lowercase_			,			indent=lowercase_ ) )

  # detect whether this is a do_lower_case situation, which can be derived by checking whether we
  # have at least one uppercase letter in the source vocab
  lowercase__	: Any     		=   True
  for k in src_vocab.keys():
    if not k.islower():
      lowercase__	: List[str]     		=   False
      break

  lowercase__	: Tuple     		=   Dictionary.load(lowercase_ )
  lowercase__	: Any     		=   rewrite_dict_keys(tgt_dict.indices )
  lowercase__	: Any     		=   len(lowercase_ )
  lowercase__	: Any     		=   os.path.join(lowercase_			,			"""vocab-tgt.json""" )
  print(F'Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records' )
  with open(lowercase_			,			"""w"""			,			encoding="""utf-8""" ) as f:
    f.write(json.dumps(lowercase_			,			ensure_ascii=lowercase_			,			indent=lowercase_ ) )

  # merges_file (bpecodes)
  lowercase__	: Union[str, Any]     		=   os.path.join(lowercase_			,			VOCAB_FILES_NAMES["""merges_file"""] )
  for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
    lowercase__	: Optional[int]     		=   os.path.join(lowercase_			,			lowercase_ )
    if os.path.exists(lowercase_ ):
      break
  with open(lowercase_			,			encoding="""utf-8""" ) as fin:
    lowercase__	: List[Any]     		=   fin.read()
  lowercase__	: Optional[Any]     		=   re.sub(R""" \d+$"""			,			""""""			,			lowercase_			,			0			,			re.M )  # remove frequency number
  print(F'Generating {merges_file}' )
  with open(lowercase_			,			"""w"""			,			encoding="""utf-8""" ) as fout:
    fout.write(lowercase_ )

  # model config
  lowercase__	: Tuple     		=   os.path.join(lowercase_			,			"""config.json""" )

  # validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
  # may have to modify the tokenizer if a different type is used by a future model
  assert args["bpe"] == "fastbpe", F'need to extend tokenizer to support bpe={args["bpe"]}'
  assert args["tokenizer"] == "moses", F'need to extend tokenizer to support bpe={args["tokenizer"]}'

  lowercase__	: List[str]     		=   {
      """architectures""": ["""FSMTForConditionalGeneration"""],
      """model_type""": """fsmt""",
      """activation_dropout""": args["""activation_dropout"""],
      """activation_function""": """relu""",
      """attention_dropout""": args["""attention_dropout"""],
      """d_model""": args["""decoder_embed_dim"""],
      """dropout""": args["""dropout"""],
      """init_std""": 0.02,
      """max_position_embeddings""": args["""max_source_positions"""],
      """num_hidden_layers""": args["""encoder_layers"""],
      """src_vocab_size""": src_vocab_size,
      """tgt_vocab_size""": tgt_vocab_size,
      """langs""": [src_lang, tgt_lang],
      """encoder_attention_heads""": args["""encoder_attention_heads"""],
      """encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""],
      """encoder_layerdrop""": args["""encoder_layerdrop"""],
      """encoder_layers""": args["""encoder_layers"""],
      """decoder_attention_heads""": args["""decoder_attention_heads"""],
      """decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""],
      """decoder_layerdrop""": args["""decoder_layerdrop"""],
      """decoder_layers""": args["""decoder_layers"""],
      """bos_token_id""": 0,
      """pad_token_id""": 1,
      """eos_token_id""": 2,
      """is_encoder_decoder""": True,
      """scale_embedding""": not args["""no_scale_embedding"""],
      """tie_word_embeddings""": args["""share_all_embeddings"""],
  }

  # good hparam defaults to start with
  lowercase__	: Optional[int]     		=   5
  lowercase__	: List[str]     		=   False
  if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
    lowercase__	: Optional[int]     		=   best_score_hparams[model_dir]["""length_penalty"""]
  else:
    lowercase__	: Union[str, Any]     		=   1.0

  print(F'Generating {fsmt_model_config_file}' )
  with open(lowercase_			,			"""w"""			,			encoding="""utf-8""" ) as f:
    f.write(json.dumps(lowercase_			,			ensure_ascii=lowercase_			,			indent=lowercase_ ) )

  # tokenizer config
  lowercase__	: Optional[int]     		=   os.path.join(lowercase_			,			lowercase_ )

  lowercase__	: int     		=   {
      """langs""": [src_lang, tgt_lang],
      """model_max_length""": 10_24,
      """do_lower_case""": do_lower_case,
  }

  print(F'Generating {fsmt_tokenizer_config_file}' )
  with open(lowercase_			,			"""w"""			,			encoding="""utf-8""" ) as f:
    f.write(json.dumps(lowercase_			,			ensure_ascii=lowercase_			,			indent=lowercase_ ) )

  # model
  lowercase__	: Dict     		=   chkpt["""models"""][0]
  lowercase__	: Optional[Any]     		=   model.state_dict()

  # rename keys to start with 'model.'
  lowercase__	: Any     		=   OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items() )

  # remove unneeded keys
  lowercase__	: List[Any]     		=   [
      """model.model""",
      """model.encoder.version""",
      """model.decoder.version""",
      """model.encoder_embed_tokens.weight""",
      """model.decoder_embed_tokens.weight""",
      """model.encoder.embed_positions._float_tensor""",
      """model.decoder.embed_positions._float_tensor""",
  ]
  for k in ignore_keys:
    model_state_dict.pop(lowercase_			,			lowercase_ )

  lowercase__	: str     		=   FSMTConfig.from_pretrained(lowercase_ )
  lowercase__	: List[str]     		=   FSMTForConditionalGeneration(lowercase_ )

  # check that it loads ok
  model_new.load_state_dict(lowercase_			,			strict=lowercase_ )

  # save
  lowercase__	: str     		=   os.path.join(lowercase_			,			lowercase_ )
  print(F'Generating {pytorch_weights_dump_path}' )
  torch.save(lowercase_			,			lowercase_ )

  print("""Conversion is done!""" )
  print("""\nLast step is to upload the files to s3""" )
  print(F'cd {data_root}' )
  print(F'transformers-cli upload {model_dir}' )


if __name__ == "__main__":
     lowerCamelCase__     :		List[str]        = argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--fsmt_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."""
     )
     lowerCamelCase__     :		List[str]        = parser.parse_args()
     convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)











'''simple docstring'''

import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> Dict:
		return params[f"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_="attention"    )     -> int:
		_a	: int								=				np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]    )
		_a	: Dict								=				k_tmp.reshape(k_tmp.shape[0]		,     k_tmp.shape[1] * k_tmp.shape[2]    )
		_a	: Any								=				np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]    )
		_a	: int								=				o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]		,     o_tmp.shape[2]    )
		_a	: List[str]								=				np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]    )
		_a	: Dict								=				q_tmp.reshape(q_tmp.shape[0]		,     q_tmp.shape[1] * q_tmp.shape[2]    )
		_a	: List[str]								=				np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]    )
		_a	: int								=				v_tmp.reshape(v_tmp.shape[0]		,     v_tmp.shape[1] * v_tmp.shape[2]    )
		return k, o, q, v



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_=False    )     -> Optional[Any]:
		if split_mlp_wi:
				_a	: Dict								=				params[f"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
				_a	: Dict								=				params[f"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
				_a	: Optional[int]								=				(wi_a, wi_a)
		else:
				_a	: Tuple								=				params[f"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

		_a	: Tuple								=				params[f"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
		return wi, wo



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> Any:
		return params[f"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]



def 	__lowerCamelCase	( lowerCAmelCase_		,     *, lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_ = False    )     -> Any:
		_a	: Dict								=				traverse_util.flatten_dict(variables['target']    )
		_a	: Tuple								=				{'/'.join(lowerCAmelCase_    ): v for k, v in old.items()}

		# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
		_a	: int								=				'encoder/encoder/mlp/wi_0/kernel' in old
		print('Split MLP:'		,     lowerCAmelCase_    )

		_a	: str								=				collections.OrderedDict()

		# Shared embeddings.
		_a	: List[str]								=				old['token_embedder/embedding']

		# Encoder.
		for i in range(lowerCAmelCase_    ):
				# Block i, layer 0 (Self Attention).
				_a	: Any								=				tax_layer_norm_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'encoder'		,     'pre_attention_layer_norm'    )
				_a			,   _a			,   _a			,   _a	: List[str]								=				tax_attention_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'encoder'		,     'attention'    )
				_a	: str								=				layer_norm
				_a	: Union[str, Any]								=				k.T
				_a	: Dict								=				o.T
				_a	: int								=				q.T
				_a	: List[str]								=				v.T

				# Block i, layer 1 (MLP).
				_a	: Optional[int]								=				tax_layer_norm_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'encoder'		,     'pre_mlp_layer_norm'    )
				_a			,   _a	: Any								=				tax_mlp_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'encoder'		,     lowerCAmelCase_    )
				_a	: Optional[Any]								=				layer_norm
				if split_mlp_wi:
						_a	: Tuple								=				wi[0].T
						_a	: List[str]								=				wi[1].T
				else:
						_a	: Union[str, Any]								=				wi.T
				_a	: Any								=				wo.T
				if scalable_attention:
						# convert the rel_embedding of each layer
						_a	: Any								=				tax_relpos_bias_lookup(
						    lowerCAmelCase_		,     lowerCAmelCase_		,     'encoder'    ).T

		_a	: Optional[Any]								=				old['encoder/encoder_norm/scale']

		if not scalable_attention:
				_a	: Union[str, Any]								=				tax_relpos_bias_lookup(
				    lowerCAmelCase_		,     0		,     'encoder'    ).T
				_a	: Any								=				tax_relpos_bias_lookup(
				    lowerCAmelCase_		,     0		,     'decoder'    ).T

		if not is_encoder_only:
				# Decoder.
				for i in range(lowerCAmelCase_    ):
						# Block i, layer 0 (Self Attention).
						_a	: Dict								=				tax_layer_norm_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     'pre_self_attention_layer_norm'    )
						_a			,   _a			,   _a			,   _a	: Union[str, Any]								=				tax_attention_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     'self_attention'    )
						_a	: str								=				layer_norm
						_a	: List[Any]								=				k.T
						_a	: Union[str, Any]								=				o.T
						_a	: int								=				q.T
						_a	: List[Any]								=				v.T

						# Block i, layer 1 (Cross Attention).
						_a	: List[Any]								=				tax_layer_norm_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     'pre_cross_attention_layer_norm'    )
						_a			,   _a			,   _a			,   _a	: Optional[int]								=				tax_attention_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     'encoder_decoder_attention'    )
						_a	: str								=				layer_norm
						_a	: Union[str, Any]								=				k.T
						_a	: Union[str, Any]								=				o.T
						_a	: Any								=				q.T
						_a	: str								=				v.T

						# Block i, layer 2 (MLP).
						_a	: str								=				tax_layer_norm_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     'pre_mlp_layer_norm'    )
						_a			,   _a	: int								=				tax_mlp_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     lowerCAmelCase_    )
						_a	: List[str]								=				layer_norm
						if split_mlp_wi:
								_a	: List[str]								=				wi[0].T
								_a	: Union[str, Any]								=				wi[1].T
						else:
								_a	: Optional[Any]								=				wi.T
						_a	: List[str]								=				wo.T

						if scalable_attention:
								# convert the rel_embedding of each layer
								_a	: int								=				tax_relpos_bias_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'    ).T

				_a	: List[Any]								=				old['decoder/decoder_norm/scale']

				# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
				if "decoder/logits_dense/kernel" in old:
						_a	: List[Any]								=				old['decoder/logits_dense/kernel'].T

		return new



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_    )     -> str:
		_a	: List[str]								=				collections.OrderedDict([(k, torch.from_numpy(v.copy()    )) for (k, v) in converted_params.items()]    )

		# Add what is missing.
		if "encoder.embed_tokens.weight" not in state_dict:
				_a	: Tuple								=				state_dict['shared.weight']

		if not is_encoder_only:
				if "decoder.embed_tokens.weight" not in state_dict:
						_a	: Union[str, Any]								=				state_dict['shared.weight']

				if "lm_head.weight" not in state_dict:  # For old 1.0 models.
						print('Using shared word embeddings as lm_head.'    )
						_a	: Optional[Any]								=				state_dict['shared.weight']

		return state_dict



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> int:
		_a	: Any								=				checkpoints.load_tax_checkpoint(lowerCAmelCase_    )
		_a	: str								=				convert_tax_to_pytorch(
		    lowerCAmelCase_		,     num_layers=config.num_layers		,     is_encoder_only=lowerCAmelCase_		,     scalable_attention=lowerCAmelCase_    )
		_a	: Dict								=				make_state_dict(lowerCAmelCase_		,     lowerCAmelCase_    )
		model.load_state_dict(lowerCAmelCase_		,     strict=lowerCAmelCase_    )



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_ = False		,     lowerCAmelCase_ = False		,     )     -> str:
		_a	: int								=				MTaConfig.from_json_file(lowerCAmelCase_    )
		print(f"""Building PyTorch model from configuration: {config}"""    )
		# Non-v1.1 checkpoints could also use T5Model, but this works for all.
		# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
		if is_encoder_only:
				_a	: Any								=				UMTaEncoderModel(lowerCAmelCase_    )
		else:
				_a	: Optional[int]								=				UMTaForConditionalGeneration(lowerCAmelCase_    )

		# Load weights from tf checkpoint
		load_tax_weights_in_ta(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )

		# Save pytorch-model
		print(f"""Save PyTorch model to {pytorch_dump_path}"""    )
		model.save_pretrained(lowerCAmelCase_    )

		# Verify that we can load the checkpoint.
		model.from_pretrained(lowerCAmelCase_    )
		print('Done'    )


if __name__ == "__main__":
							__lowerCAmelCase		     =  argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
							# Required parameters
							parser.add_argument(
							    '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
							)
							parser.add_argument(
							    '''--config_file''',
							    default=None,
							    type=str,
							    required=True,
							    help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
							)
							parser.add_argument(
							    '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
							)
							parser.add_argument(
							    '''--scalable_attention''',
							    action='''store_true''',
							    help='''Whether the model uses scaled attention (umt5 model)''',
							    default=False,
							)
							__lowerCAmelCase		     =  parser.parse_args()
							convert_tax_checkpoint_to_pytorch(
							    args.tax_checkpoint_path,
							    args.config_file,
							    args.pytorch_dump_path,
							    args.is_encoder_only,
							    args.scalable_attention,
							)




