



"""simple docstring"""






import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
		import torch

if is_datasets_available():
		from datasets import load_dataset

_lowercase			:			List[str]										=						random.Random()






def 					snake_case__       (		__lowerCamelCase    :   int							,		__lowerCamelCase    :   Union[str, Any]=1.0							,		__lowerCamelCase    :   int=None							,		__lowerCamelCase    :   str=None     ):
			"""simple docstring"""
			if rng is None:
						lowerCamelCase__		:		Tuple		     =global_rng

			lowerCamelCase__		:		Optional[int]		     =[]
			for batch_idx in range(shape[0]     ):
						values.append([]     )
						for _ in range(shape[1]     ):
									values[-1].append(rng.random() * scale     )

			return values

class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					def __init__(	self							:  List[str],  lowerCamelCase							:  Dict,  lowerCamelCase							:  Union[str, Any]=7,  lowerCamelCase							:  Optional[int]=400,  lowerCamelCase							:  int=2000,  lowerCamelCase							:  Any=2048,  lowerCamelCase							:  Optional[int]=128,  lowerCamelCase							:  Dict=1,  lowerCamelCase							:  Any=512,  lowerCamelCase							:  List[str]=30,  lowerCamelCase							:  List[str]=4_4100,  )-> List[str]:
								lowerCamelCase__		:		Tuple		     =parent
								lowerCamelCase__		:		Tuple		     =batch_size
								lowerCamelCase__		:		str		     =min_seq_length
								lowerCamelCase__		:		List[Any]		     =max_seq_length
								lowerCamelCase__		:		Dict		     =(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								lowerCamelCase__		:		List[str]		     =spectrogram_length
								lowerCamelCase__		:		Optional[int]		     =feature_size
								lowerCamelCase__		:		Optional[Any]		     =num_audio_channels
								lowerCamelCase__		:		Union[str, Any]		     =hop_length
								lowerCamelCase__		:		Tuple		     =chunk_length
								lowerCamelCase__		:		List[str]		     =sampling_rate



					def 		snake_case	(	self							:  List[Any]					)-> Tuple:
								return {
								    "spectrogram_length": self.spectrogram_length,
								    "feature_size": self.feature_size,
								    "num_audio_channels": self.num_audio_channels,
								    "hop_length": self.hop_length,
								    "chunk_length": self.chunk_length,
								    "sampling_rate": self.sampling_rate,
								}




					def 		snake_case	(	self							:  int,  lowerCamelCase							:  str=False,  lowerCamelCase							:  List[Any]=False					)-> List[Any]:
								def _flatten(lowerCamelCase							:  str					):
											return list(itertools.chain(*lowerCamelCase					)					)

								if equal_length:
											lowerCamelCase__		:		int		     =[floats_list((self.max_seq_length, self.feature_size)					) for _ in range(self.batch_size					)]
								else:
											# make sure that inputs increase in size
											lowerCamelCase__		:		List[str]		     =[
											    floats_list((x, self.feature_size)					)
											    for x in range(self.min_seq_length,  self.max_seq_length,  self.seq_length_diff					)
											]
								if numpify:
											lowerCamelCase__		:		Optional[int]		     =[np.asarray(lowerCamelCase					) for x in speech_inputs]
								return speech_inputs





@require_torch
@require_torchaudio
class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_		,       unittest.TestCase       ):


					'''simple docstring'''





					_a             =							TvltFeatureExtractor



					def 		snake_case	(	self							:  int					)-> List[str]:
								lowerCamelCase__		:		List[Any]		     =TvltFeatureExtractionTester(self					)



					def 		snake_case	(	self							:  int					)-> int:
								lowerCamelCase__		:		Optional[int]		     =self.feature_extraction_class(**self.feat_extract_dict					)
								self.assertTrue(hasattr(lowerCamelCase,  '''spectrogram_length'''					)					)
								self.assertTrue(hasattr(lowerCamelCase,  '''feature_size'''					)					)
								self.assertTrue(hasattr(lowerCamelCase,  '''num_audio_channels'''					)					)
								self.assertTrue(hasattr(lowerCamelCase,  '''hop_length'''					)					)
								self.assertTrue(hasattr(lowerCamelCase,  '''chunk_length'''					)					)
								self.assertTrue(hasattr(lowerCamelCase,  '''sampling_rate'''					)					)



					def 		snake_case	(	self							:  Dict					)-> Any:
								lowerCamelCase__		:		Tuple		     =self.feature_extraction_class(**self.feat_extract_dict					)

								with tempfile.TemporaryDirectory() as tmpdirname:
											lowerCamelCase__		:		List[str]		     =feat_extract_first.save_pretrained(lowerCamelCase					)[0]
											check_json_file_has_correct_format(lowerCamelCase					)
											lowerCamelCase__		:		str		     =self.feature_extraction_class.from_pretrained(lowerCamelCase					)

								lowerCamelCase__		:		Optional[int]		     =feat_extract_first.to_dict()
								lowerCamelCase__		:		Optional[int]		     =feat_extract_second.to_dict()
								lowerCamelCase__		:		Optional[Any]		     =dict_first.pop('''mel_filters'''					)
								lowerCamelCase__		:		List[str]		     =dict_second.pop('''mel_filters'''					)
								self.assertTrue(np.allclose(lowerCamelCase,  lowerCamelCase					)					)
								self.assertEqual(lowerCamelCase,  lowerCamelCase					)



					def 		snake_case	(	self							:  Optional[Any]					)-> List[str]:
								lowerCamelCase__		:		Union[str, Any]		     =self.feature_extraction_class(**self.feat_extract_dict					)

								with tempfile.TemporaryDirectory() as tmpdirname:
											lowerCamelCase__		:		List[Any]		     =os.path.join(lowerCamelCase,  '''feat_extract.json'''					)
											feat_extract_first.to_json_file(lowerCamelCase					)
											lowerCamelCase__		:		List[str]		     =self.feature_extraction_class.from_json_file(lowerCamelCase					)

								lowerCamelCase__		:		int		     =feat_extract_first.to_dict()
								lowerCamelCase__		:		Tuple		     =feat_extract_second.to_dict()
								lowerCamelCase__		:		List[Any]		     =dict_first.pop('''mel_filters'''					)
								lowerCamelCase__		:		int		     =dict_second.pop('''mel_filters'''					)
								self.assertTrue(np.allclose(lowerCamelCase,  lowerCamelCase					)					)
								self.assertEqual(lowerCamelCase,  lowerCamelCase					)



					def 		snake_case	(	self							:  List[Any]					)-> Tuple:
								# Initialize feature_extractor
								lowerCamelCase__		:		Union[str, Any]		     =self.feature_extraction_class(**self.feat_extract_dict					)

								# create three inputs of length 800, 1000, and 1200
								lowerCamelCase__		:		Union[str, Any]		     =[floats_list((1, x)					)[0] for x in range(800,  1400,  200					)]
								lowerCamelCase__		:		str		     =[np.asarray(lowerCamelCase					) for speech_input in speech_inputs]

								# Test not batched input
								lowerCamelCase__		:		int		     =feature_extractor(np_speech_inputs[0],  return_tensors='''np''',  sampling_rate=4_4100					).audio_values

								self.assertTrue(encoded_audios.ndim == 4					)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size					)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length					)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels					)

								# Test batched
								lowerCamelCase__		:		Optional[Any]		     =feature_extractor(lowerCamelCase,  return_tensors='''np''',  sampling_rate=4_4100					).audio_values

								self.assertTrue(encoded_audios.ndim == 4					)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size					)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length					)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels					)

								# Test audio masking
								lowerCamelCase__		:		Any		     =feature_extractor(
								    lowerCamelCase,  return_tensors='''np''',  sampling_rate=4_4100,  mask_audio=lowerCamelCase					).audio_values

								self.assertTrue(encoded_audios.ndim == 4					)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size					)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length					)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels					)

								# Test 2-D numpy arrays are batched.
								lowerCamelCase__		:		List[str]		     =[floats_list((1, x)					)[0] for x in (800, 800, 800)]
								lowerCamelCase__		:		List[Any]		     =np.asarray(lowerCamelCase					)
								lowerCamelCase__		:		Optional[Any]		     =feature_extractor(lowerCamelCase,  return_tensors='''np''',  sampling_rate=4_4100					).audio_values
								self.assertTrue(encoded_audios.ndim == 4					)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size					)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length					)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels					)



					def 		snake_case	(	self							:  List[Any],  lowerCamelCase							:  Optional[Any]					)-> int:
								lowerCamelCase__		:		Optional[Any]		     =load_dataset('''hf-internal-testing/librispeech_asr_dummy''',  '''clean''',  split='''validation'''					)
								# automatic decoding with librispeech
								lowerCamelCase__		:		Dict		     =ds.sort('''id'''					).select(range(lowerCamelCase					)					)[:num_samples]['''audio''']

								return [x["array"] for x in speech_samples]




					def 		snake_case	(	self							:  List[str]					)-> Optional[int]:
								lowerCamelCase__		:		Tuple		     =self._load_datasamples(1					)
								lowerCamelCase__		:		Any		     =TvltFeatureExtractor()
								lowerCamelCase__		:		Union[str, Any]		     =feature_extractor(lowerCamelCase,  return_tensors='''pt'''					).audio_values

								self.assertEquals(audio_values.shape,  (1, 1, 192, 128)					)

								lowerCamelCase__		:		Optional[Any]		     =torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]]					)
								self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2],  lowerCamelCase,  atol=1E-4					)					)









"""simple docstring"""






# 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()

_lowercase			:			Dict										=						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
_lowercase			:			str										=						{
    # 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
_lowercase			:			str										=						{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
		_lowercase			:			Any										=						"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",
]:
		_lowercase			:			List[Any]										=						"allenai"






def 					snake_case__       (		__lowerCamelCase    :   Optional[Any]     ):
			"""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}
			lowerCamelCase__		:		Optional[Any]		     =dict((re.sub(R'''@@$'''							,		''''''							,		__lowerCamelCase     ), v) if k.endswith('''@@'''     ) else (re.sub(R'''$'''							,		'''</w>'''							,		__lowerCamelCase     ), v) for k, v in d.items()     )
			lowerCamelCase__		:		Tuple		     ='''<s> <pad> </s> <unk>'''.split()
			# restore the special tokens
			for k in keep_keys:
						del da[f'''{k}</w>''']
						lowerCamelCase__		:		str		     =d[k]  # restore
			return da






def 					snake_case__       (		__lowerCamelCase    :   Union[str, Any]							,		__lowerCamelCase    :   Dict     ):
			"""simple docstring"""
			# prep
			assert os.path.exists(__lowerCamelCase     )
			os.makedirs(__lowerCamelCase							,		exist_ok=__lowerCamelCase     )
			print(f'''Writing results to {pytorch_dump_folder_path}'''     )

			# handle various types of models

			lowerCamelCase__		:		Union[str, Any]		     =basename(__lowerCamelCase     )
			lowerCamelCase__		:		str		     =dirname(__lowerCamelCase     )

			lowerCamelCase__		:		Dict		     =fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
			lowerCamelCase__		:		Union[str, Any]		     =cls.hub_models()
			lowerCamelCase__		:		Optional[Any]		     ={'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
			lowerCamelCase__		:		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}'''     )
			lowerCamelCase__		:		Optional[int]		     =hub_utils.from_pretrained(
			    __lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		archive_map=__lowerCamelCase							,		**__lowerCamelCase     )

			lowerCamelCase__		:		Any		     =vars(chkpt['''args''']['''model''']     )

			lowerCamelCase__		:		int		     =args['''source_lang''']
			lowerCamelCase__		:		Optional[Any]		     =args['''target_lang''']

			lowerCamelCase__		:		Dict		     =dirname(__lowerCamelCase     )
			lowerCamelCase__		:		str		     =basename(__lowerCamelCase     )

			# dicts
			lowerCamelCase__		:		Optional[Any]		     =os.path.join(__lowerCamelCase							,		f'''dict.{src_lang}.txt'''     )
			lowerCamelCase__		:		int		     =os.path.join(__lowerCamelCase							,		f'''dict.{tgt_lang}.txt'''     )

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

			# 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
			lowerCamelCase__		:		Optional[int]		     =True
			for k in src_vocab.keys():
						if not k.islower():
									lowerCamelCase__		:		int		     =False
									break

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

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

			# model config
			lowerCamelCase__		:		List[Any]		     =os.path.join(__lowerCamelCase							,		'''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"]}'''

			lowerCamelCase__		:		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
			lowerCamelCase__		:		Optional[int]		     =5
			lowerCamelCase__		:		List[str]		     =False
			if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
						lowerCamelCase__		:		Optional[int]		     =best_score_hparams[model_dir]['''length_penalty''']
			else:
						lowerCamelCase__		:		Union[str, Any]		     =1.0

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

			# tokenizer config
			lowerCamelCase__		:		Any		     =os.path.join(__lowerCamelCase							,		__lowerCamelCase     )

			lowerCamelCase__		:		int		     ={
			    '''langs''': [src_lang, tgt_lang],
			    '''model_max_length''': 1024,
			    '''do_lower_case''': do_lower_case,
			}

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

			# model
			lowerCamelCase__		:		int		     =chkpt['''models'''][0]
			lowerCamelCase__		:		Union[str, Any]		     =model.state_dict()

			# rename keys to start with 'model.'
			lowerCamelCase__		:		Union[str, Any]		     =OrderedDict(('''model.''' + k, v) for k, v in model_state_dict.items()     )

			# remove unneeded keys
			lowerCamelCase__		:		str		     =[
			    '''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(__lowerCamelCase							,		__lowerCamelCase     )

			lowerCamelCase__		:		Dict		     =FSMTConfig.from_pretrained(__lowerCamelCase     )
			lowerCamelCase__		:		int		     =FSMTForConditionalGeneration(__lowerCamelCase     )

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

			# save
			lowerCamelCase__		:		Optional[int]		     =os.path.join(__lowerCamelCase							,		__lowerCamelCase     )
			print(f'''Generating {pytorch_weights_dump_path}'''     )
			torch.save(__lowerCamelCase							,		__lowerCamelCase     )

			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__":
		_lowercase			:			List[Any]										=						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."
		)
		_lowercase			:			str										=						parser.parse_args()
		convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)




