




"""simple docstring"""

import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
		import faiss

@require_faiss
class        lowercase      (    __UpperCAmelCase):







				def 					a_  (							self   :					Optional[Any]       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		tempfile.mkdtemp()
					A_  :				List[str]	   =		8

					# DPR tok
					A_  :				Optional[int]	   =		[
					    '''[UNK]''',
					    '''[CLS]''',
					    '''[SEP]''',
					    '''[PAD]''',
					    '''[MASK]''',
					    '''want''',
					    '''##want''',
					    '''##ed''',
					    '''wa''',
					    '''un''',
					    '''runn''',
					    '''##ing''',
					    ''',''',
					    '''low''',
					    '''lowest''',
					]
					A_  :				Optional[Any]	   =		os.path.join(self.tmpdirname      ,     '''dpr_tokenizer'''       )
					os.makedirs(_lowerCamelCase      ,     exist_ok=_lowerCamelCase       )
					A_  :				Optional[Any]	   =		os.path.join(_lowerCamelCase      ,     DPR_VOCAB_FILES_NAMES['''vocab_file''']       )
					with open(self.vocab_file      ,     '''w'''      ,     encoding='''utf-8'''       ) as vocab_writer:
						vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]       )       )

					# BART tok
					A_  :				Optional[int]	   =		[
					    '''l''',
					    '''o''',
					    '''w''',
					    '''e''',
					    '''r''',
					    '''s''',
					    '''t''',
					    '''i''',
					    '''d''',
					    '''n''',
					    '''\u0120''',
					    '''\u0120l''',
					    '''\u0120n''',
					    '''\u0120lo''',
					    '''\u0120low''',
					    '''er''',
					    '''\u0120lowest''',
					    '''\u0120newer''',
					    '''\u0120wider''',
					    '''<unk>''',
					]
					A_  :				Dict	   =		dict(zip(_lowerCamelCase      ,     range(len(_lowerCamelCase       )       )       )       )
					A_  :				int	   =		['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
					A_  :				Optional[Any]	   =		{'''unk_token''': '''<unk>'''}

					A_  :				Union[str, Any]	   =		os.path.join(self.tmpdirname      ,     '''bart_tokenizer'''       )
					os.makedirs(_lowerCamelCase      ,     exist_ok=_lowerCamelCase       )
					A_  :				Union[str, Any]	   =		os.path.join(_lowerCamelCase      ,     BART_VOCAB_FILES_NAMES['''vocab_file''']       )
					A_  :				List[str]	   =		os.path.join(_lowerCamelCase      ,     BART_VOCAB_FILES_NAMES['''merges_file''']       )
					with open(self.vocab_file      ,     '''w'''      ,     encoding='''utf-8'''       ) as fp:
						fp.write(json.dumps(_lowerCamelCase       ) + '''\n'''       )
					with open(self.merges_file      ,     '''w'''      ,     encoding='''utf-8'''       ) as fp:
						fp.write('''\n'''.join(_lowerCamelCase       )       )







				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname      ,     '''dpr_tokenizer'''       )       )







				def 					a_  (							self   :					Optional[Any]       ):



					"""simple docstring"""

					return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname      ,     '''dpr_tokenizer'''       )       )







				def 					a_  (							self   :					int       ):



					"""simple docstring"""

					return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname      ,     '''bart_tokenizer'''       )       )







				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					shutil.rmtree(self.tmpdirname       )







				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					A_  :				List[str]	   =		Dataset.from_dict(
					    {
					        '''id''': ['''0''', '''1'''],
					        '''text''': ['''foo''', '''bar'''],
					        '''title''': ['''Foo''', '''Bar'''],
					        '''embeddings''': [np.ones(self.retrieval_vector_size       ), 2 * np.ones(self.retrieval_vector_size       )],
					    }       )
					dataset.add_faiss_index('''embeddings'''      ,     string_factory='''Flat'''      ,     metric_type=faiss.METRIC_INNER_PRODUCT       )
					return dataset







				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					A_  :				Optional[int]	   =		self.get_dummy_dataset()
					A_  :				int	   =		RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size      ,     question_encoder=DPRConfig().to_dict()      ,     generator=BartConfig().to_dict()      ,     )
					with patch('''transformers.models.rag.retrieval_rag.load_dataset'''       ) as mock_load_dataset:
						A_  :				Any	   =		dataset
						A_  :				List[Any]	   =		RagRetriever(
						    _lowerCamelCase      ,     question_encoder_tokenizer=self.get_dpr_tokenizer()      ,     generator_tokenizer=self.get_bart_tokenizer()      ,     )
					return retriever







				def 					a_  (							self   :					List[str]      ,     _lowerCamelCase   :					bool       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		self.get_dummy_dataset()
					A_  :				List[str]	   =		RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size      ,     question_encoder=DPRConfig().to_dict()      ,     generator=BartConfig().to_dict()      ,     index_name='''custom'''      ,     )
					if from_disk:
						A_  :				List[str]	   =		os.path.join(self.tmpdirname      ,     '''dataset'''       )
						A_  :				str	   =		os.path.join(self.tmpdirname      ,     '''index.faiss'''       )
						dataset.get_index('''embeddings'''       ).save(os.path.join(self.tmpdirname      ,     '''index.faiss'''       )       )
						dataset.drop_index('''embeddings'''       )
						dataset.save_to_disk(os.path.join(self.tmpdirname      ,     '''dataset'''       )       )
						del dataset
						A_  :				Dict	   =		RagRetriever(
						    _lowerCamelCase      ,     question_encoder_tokenizer=self.get_dpr_tokenizer()      ,     generator_tokenizer=self.get_bart_tokenizer()      ,     )
					else:
						A_  :				Any	   =		RagRetriever(
						    _lowerCamelCase      ,     question_encoder_tokenizer=self.get_dpr_tokenizer()      ,     generator_tokenizer=self.get_bart_tokenizer()      ,     index=CustomHFIndex(config.retrieval_vector_size      ,     _lowerCamelCase       )      ,     )
					return retriever







				def 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					A_  :				str	   =		Dataset.from_dict(
					    {
					        '''id''': ['''0''', '''1'''],
					        '''text''': ['''foo''', '''bar'''],
					        '''title''': ['''Foo''', '''Bar'''],
					        '''embeddings''': [np.ones(self.retrieval_vector_size + 1       ), 2 * np.ones(self.retrieval_vector_size + 1       )],
					    }       )
					dataset.add_faiss_index('''embeddings'''      ,     string_factory='''Flat'''      ,     metric_type=faiss.METRIC_INNER_PRODUCT       )

					A_  :				Optional[int]	   =		os.path.join(self.tmpdirname      ,     '''hf_bert_base.hnswSQ8_correct_phi_128.c_index'''       )
					dataset.save_faiss_index('''embeddings'''      ,     index_file_name + '''.index.dpr'''       )
					pickle.dump(dataset['''id''']      ,     open(index_file_name + '''.index_meta.dpr'''      ,     '''wb'''       )       )

					A_  :				int	   =		os.path.join(self.tmpdirname      ,     '''psgs_w100.tsv.pkl'''       )
					A_  :				Union[str, Any]	   =		{sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
					pickle.dump(_lowerCamelCase      ,     open(_lowerCamelCase      ,     '''wb'''       )       )

					A_  :				int	   =		RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size      ,     question_encoder=DPRConfig().to_dict()      ,     generator=BartConfig().to_dict()      ,     index_name='''legacy'''      ,     index_path=self.tmpdirname      ,     )
					A_  :				List[str]	   =		RagRetriever(
					    _lowerCamelCase      ,     question_encoder_tokenizer=self.get_dpr_tokenizer()      ,     generator_tokenizer=self.get_bart_tokenizer()       )
					return retriever







				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					A_  :				Tuple	   =		1
					A_  :				Optional[Any]	   =		self.get_dummy_canonical_hf_index_retriever()
					A_  :				List[str]	   =		np.array(
					    [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]      ,     dtype=np.floataa       )
					A_  :				List[str]	   =		retriever.retrieve(_lowerCamelCase      ,     n_docs=_lowerCamelCase       )
					self.assertEqual(retrieved_doc_embeds.shape      ,     (2, n_docs, self.retrieval_vector_size)       )
					self.assertEqual(len(_lowerCamelCase       )      ,     2       )
					self.assertEqual(sorted(doc_dicts[0]       )      ,     ['''embeddings''', '''id''', '''text''', '''title''']       )
					self.assertEqual(len(doc_dicts[0]['''id''']       )      ,     _lowerCamelCase       )
					self.assertEqual(doc_dicts[0]['''id'''][0]      ,     '''1'''       )  # max inner product is reached with second doc
					self.assertEqual(doc_dicts[1]['''id'''][0]      ,     '''0'''       )  # max inner product is reached with first doc
					self.assertListEqual(doc_ids.tolist()      ,     [[1], [0]]       )







				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					A_  :				Dict	   =		self.get_dummy_canonical_hf_index_retriever()
					with tempfile.TemporaryDirectory() as tmp_dirname:
						with patch('''transformers.models.rag.retrieval_rag.load_dataset'''       ) as mock_load_dataset:
							A_  :				Dict	   =		self.get_dummy_dataset()
							retriever.save_pretrained(_lowerCamelCase       )
							A_  :				Any	   =		RagRetriever.from_pretrained(_lowerCamelCase       )
							self.assertIsInstance(_lowerCamelCase      ,     _lowerCamelCase       )
							A_  :				Union[str, Any]	   =		np.array(
							    [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]      ,     dtype=np.floataa       )
							A_  :				Dict	   =		retriever.retrieve(_lowerCamelCase      ,     n_docs=1       )
							self.assertTrue(out is not None       )







				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					A_  :				Any	   =		1
					A_  :				Union[str, Any]	   =		self.get_dummy_custom_hf_index_retriever(from_disk=_lowerCamelCase       )
					A_  :				Optional[int]	   =		np.array(
					    [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]      ,     dtype=np.floataa       )
					A_  :				List[str]	   =		retriever.retrieve(_lowerCamelCase      ,     n_docs=_lowerCamelCase       )
					self.assertEqual(retrieved_doc_embeds.shape      ,     (2, n_docs, self.retrieval_vector_size)       )
					self.assertEqual(len(_lowerCamelCase       )      ,     2       )
					self.assertEqual(sorted(doc_dicts[0]       )      ,     ['''embeddings''', '''id''', '''text''', '''title''']       )
					self.assertEqual(len(doc_dicts[0]['''id''']       )      ,     _lowerCamelCase       )
					self.assertEqual(doc_dicts[0]['''id'''][0]      ,     '''1'''       )  # max inner product is reached with second doc
					self.assertEqual(doc_dicts[1]['''id'''][0]      ,     '''0'''       )  # max inner product is reached with first doc
					self.assertListEqual(doc_ids.tolist()      ,     [[1], [0]]       )







				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					A_  :				Dict	   =		self.get_dummy_custom_hf_index_retriever(from_disk=_lowerCamelCase       )
					with tempfile.TemporaryDirectory() as tmp_dirname:
						retriever.save_pretrained(_lowerCamelCase       )
						A_  :				str	   =		RagRetriever.from_pretrained(_lowerCamelCase       )
						self.assertIsInstance(_lowerCamelCase      ,     _lowerCamelCase       )
						A_  :				int	   =		np.array(
						    [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]      ,     dtype=np.floataa       )
						A_  :				Optional[Any]	   =		retriever.retrieve(_lowerCamelCase      ,     n_docs=1       )
						self.assertTrue(out is not None       )







				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					A_  :				List[str]	   =		1
					A_  :				Dict	   =		self.get_dummy_custom_hf_index_retriever(from_disk=_lowerCamelCase       )
					A_  :				Optional[Any]	   =		np.array(
					    [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]      ,     dtype=np.floataa       )
					A_  :				Optional[int]	   =		retriever.retrieve(_lowerCamelCase      ,     n_docs=_lowerCamelCase       )
					self.assertEqual(retrieved_doc_embeds.shape      ,     (2, n_docs, self.retrieval_vector_size)       )
					self.assertEqual(len(_lowerCamelCase       )      ,     2       )
					self.assertEqual(sorted(doc_dicts[0]       )      ,     ['''embeddings''', '''id''', '''text''', '''title''']       )
					self.assertEqual(len(doc_dicts[0]['''id''']       )      ,     _lowerCamelCase       )
					self.assertEqual(doc_dicts[0]['''id'''][0]      ,     '''1'''       )  # max inner product is reached with second doc
					self.assertEqual(doc_dicts[1]['''id'''][0]      ,     '''0'''       )  # max inner product is reached with first doc
					self.assertListEqual(doc_ids.tolist()      ,     [[1], [0]]       )







				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					A_  :				Optional[int]	   =		self.get_dummy_custom_hf_index_retriever(from_disk=_lowerCamelCase       )
					with tempfile.TemporaryDirectory() as tmp_dirname:
						retriever.save_pretrained(_lowerCamelCase       )
						A_  :				str	   =		RagRetriever.from_pretrained(_lowerCamelCase       )
						self.assertIsInstance(_lowerCamelCase      ,     _lowerCamelCase       )
						A_  :				List[Any]	   =		np.array(
						    [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]      ,     dtype=np.floataa       )
						A_  :				Tuple	   =		retriever.retrieve(_lowerCamelCase      ,     n_docs=1       )
						self.assertTrue(out is not None       )







				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					A_  :				Tuple	   =		1
					A_  :				int	   =		self.get_dummy_legacy_index_retriever()
					A_  :				Optional[int]	   =		np.array(
					    [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]      ,     dtype=np.floataa       )
					A_  :				Optional[int]	   =		retriever.retrieve(_lowerCamelCase      ,     n_docs=_lowerCamelCase       )
					self.assertEqual(retrieved_doc_embeds.shape      ,     (2, n_docs, self.retrieval_vector_size)       )
					self.assertEqual(len(_lowerCamelCase       )      ,     2       )
					self.assertEqual(sorted(doc_dicts[0]       )      ,     ['''text''', '''title''']       )
					self.assertEqual(len(doc_dicts[0]['''text''']       )      ,     _lowerCamelCase       )
					self.assertEqual(doc_dicts[0]['''text'''][0]      ,     '''bar'''       )  # max inner product is reached with second doc
					self.assertEqual(doc_dicts[1]['''text'''][0]      ,     '''foo'''       )  # max inner product is reached with first doc
					self.assertListEqual(doc_ids.tolist()      ,     [[1], [0]]       )







				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		self.get_dummy_legacy_index_retriever()
					with tempfile.TemporaryDirectory() as tmp_dirname:
						retriever.save_pretrained(_lowerCamelCase       )
						A_  :				str	   =		RagRetriever.from_pretrained(_lowerCamelCase       )
						self.assertIsInstance(_lowerCamelCase      ,     _lowerCamelCase       )
						A_  :				str	   =		np.array(
						    [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]      ,     dtype=np.floataa       )
						A_  :				Any	   =		retriever.retrieve(_lowerCamelCase      ,     n_docs=1       )
						self.assertTrue(out is not None       )







				@require_torch
				@require_tokenizers
				@require_sentencepiece
				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					import torch

					A_  :				List[str]	   =		1
					A_  :				int	   =		self.get_dummy_canonical_hf_index_retriever()
					A_  :				int	   =		[[5, 7], [10, 11]]
					A_  :				Union[str, Any]	   =		np.array(
					    [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]      ,     dtype=np.floataa       )
					A_  :				Optional[Any]	   =		retriever(_lowerCamelCase      ,     _lowerCamelCase      ,     prefix=retriever.config.generator.prefix      ,     n_docs=_lowerCamelCase       )
					A_  :				Optional[int]	   =		(
					    out['''context_input_ids'''],
					    out['''context_attention_mask'''],
					    out['''retrieved_doc_embeds'''],
					)
					self.assertEqual(retrieved_doc_embeds.shape      ,     (2, n_docs, self.retrieval_vector_size)       )
					self.assertIsInstance(_lowerCamelCase      ,     _lowerCamelCase       )
					self.assertIsInstance(_lowerCamelCase      ,     _lowerCamelCase       )
					self.assertIsInstance(_lowerCamelCase      ,     np.ndarray       )

					A_  :				Optional[int]	   =		retriever(
					    _lowerCamelCase      ,     _lowerCamelCase      ,     prefix=retriever.config.generator.prefix      ,     n_docs=_lowerCamelCase      ,     return_tensors='''pt'''      ,     )
					A_  :				Union[str, Any]	   =		(  # noqa: F841
					    out['''context_input_ids'''],
					    out['''context_attention_mask'''],
					    out['''retrieved_doc_embeds'''],
					    out['''doc_ids'''],
					)
					self.assertEqual(retrieved_doc_embeds.shape      ,     (2, n_docs, self.retrieval_vector_size)       )
					self.assertIsInstance(_lowerCamelCase      ,     torch.Tensor       )
					self.assertIsInstance(_lowerCamelCase      ,     torch.Tensor       )
					self.assertIsInstance(_lowerCamelCase      ,     torch.Tensor       )







				@require_torch
				@require_tokenizers
				@require_sentencepiece
				def 					a_  (							self   :					Optional[Any]       ):



					"""simple docstring"""

					A_  :				Dict	   =		self.get_dpr_ctx_encoder_tokenizer()
					A_  :				List[str]	   =		1
					A_  :				str	   =		self.get_dummy_custom_hf_index_retriever(from_disk=_lowerCamelCase       )
					retriever.set_ctx_encoder_tokenizer(_lowerCamelCase       )

					A_  :				Optional[Any]	   =		[[5, 7], [10, 11]]
					A_  :				Union[str, Any]	   =		np.array(
					    [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]      ,     dtype=np.floataa       )
					A_  :				int	   =		retriever(_lowerCamelCase      ,     _lowerCamelCase      ,     prefix=retriever.config.generator.prefix      ,     n_docs=_lowerCamelCase       )

					self.assertEqual(
					    len(_lowerCamelCase       )      ,     6       )  # check whether the retriever output consist of 6 attributes including tokenized docs
					self.assertEqual(
					    all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''')       )      ,     _lowerCamelCase       )  # check for doc token related keys in dictionary.












"""simple docstring"""

import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Optional[Any]	   =		[
	    '''decoder.version''',
	    '''decoder.output_projection.weight''',
	    '''_float_tensor''',
	    '''decoder.embed_positions._float_tensor''',
	]
	for k in ignore_keys:
		state_dict.pop(_UpperCAmelCase  ,      _UpperCAmelCase	)

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_   , A_  :				List[Any]	   =		emb.weight.shape
	A_  :				List[Any]	   =		nn.Linear(_UpperCAmelCase  ,      _UpperCAmelCase  ,      bias=_UpperCAmelCase	)
	A_  :				Any	   =		emb.weight.data
	return lin_layer

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				int	   =		torch.load(_UpperCAmelCase  ,      map_location='''cpu'''	)
	A_  :				Any	   =		Namespace(**checkpoint['''cfg''']['''model''']	)
	A_  :				List[str]	   =		checkpoint['''model''']
	remove_ignore_keys_(_UpperCAmelCase	)
	A_  :				Union[str, Any]	   =		state_dict['''decoder.embed_tokens.weight'''].shape[0]

	A_  :				List[str]	   =		{key.replace('''decoder'''  ,      '''model'''	): val for key, val in state_dict.items()}

	A_  :				int	   =		XGLMConfig(
	    vocab_size=_UpperCAmelCase  ,      max_position_embeddings=args.max_target_positions  ,      num_layers=args.decoder_layers  ,      attention_heads=args.decoder_attention_heads  ,      ffn_dim=args.decoder_ffn_embed_dim  ,      d_model=args.decoder_embed_dim  ,      layerdrop=args.decoder_layerdrop  ,      dropout=args.dropout  ,      attention_dropout=args.attention_dropout  ,      activation_dropout=args.activation_dropout  ,      activation_function='''gelu'''  ,      scale_embedding=not args.no_scale_embedding  ,      tie_word_embeddings=args.share_decoder_input_output_embed  ,      )

	A_  :				Any	   =		XGLMForCausalLM(_UpperCAmelCase	)
	A_  :				int	   =		model.load_state_dict(_UpperCAmelCase  ,      strict=_UpperCAmelCase	)
	print(_UpperCAmelCase	)
	A_  :				int	   =		make_linear_from_emb(model.model.embed_tokens	)

	return model


if __name__ == "__main__":
		_lowerCamelCase  :			Any        =       argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
		parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		_lowerCamelCase  :			int        =       parser.parse_args()
		_lowerCamelCase  :			Optional[Any]        =       convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)





