



'''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__      (				lowercase  ):





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							_UpperCamelCase   :						Tuple												=  tempfile.mkdtemp()
							_UpperCamelCase   :						Any												=  8

							# DPR tok
							_UpperCamelCase   :						Union[str, Any]												=  [
							    '[UNK]',
							    '[CLS]',
							    '[SEP]',
							    '[PAD]',
							    '[MASK]',
							    'want',
							    '##want',
							    '##ed',
							    'wa',
							    'un',
							    'runn',
							    '##ing',
							    ',',
							    'low',
							    'lowest',
							]
							_UpperCamelCase   :						Any												=  os.path.join(self.tmpdirname					,'dpr_tokenizer'					)
							os.makedirs(lowerCamelCase__					,exist_ok=lowerCamelCase__					)
							_UpperCamelCase   :						Tuple												=  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
							_UpperCamelCase   :						Any												=  [
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    '\u0120',
							    '\u0120l',
							    '\u0120n',
							    '\u0120lo',
							    '\u0120low',
							    'er',
							    '\u0120lowest',
							    '\u0120newer',
							    '\u0120wider',
							    '<unk>',
							]
							_UpperCamelCase   :						int												=  dict(zip(lowerCamelCase__					,range(len(lowerCamelCase__					)					)					)					)
							_UpperCamelCase   :						Any												=  ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
							_UpperCamelCase   :						Tuple												=  {'unk_token': '<unk>'}

							_UpperCamelCase   :						List[Any]												=  os.path.join(self.tmpdirname					,'bart_tokenizer'					)
							os.makedirs(lowerCamelCase__					,exist_ok=lowerCamelCase__					)
							_UpperCamelCase   :						Optional[int]												=  os.path.join(lowerCamelCase__					,BART_VOCAB_FILES_NAMES['vocab_file']					)
							_UpperCamelCase   :						List[Any]												=  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       UpperCamelCase_    (					self   :							Optional[Any]					):







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





	def       UpperCamelCase_    (					self   :							Any					):







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





	def       UpperCamelCase_    (					self   :							Optional[int]					):







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





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							shutil.rmtree(self.tmpdirname					)





	def       UpperCamelCase_    (					self   :							Optional[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						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       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  self.get_dummy_dataset()
							_UpperCamelCase   :						Optional[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:
													_UpperCamelCase   :						List[Any]												=  dataset
													_UpperCamelCase   :						List[str]												=  RagRetriever(
													    lowerCamelCase__					,question_encoder_tokenizer=self.get_dpr_tokenizer()					,generator_tokenizer=self.get_bart_tokenizer()					,)
							return retriever





	def       UpperCamelCase_    (					self   :							str					,lowerCamelCase__   :							bool					):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  self.get_dummy_dataset()
							_UpperCamelCase   :						List[Any]												=  RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size					,question_encoder=DPRConfig().to_dict()					,generator=BartConfig().to_dict()					,index_name='custom'					,)
							if from_disk:
													_UpperCamelCase   :						Optional[int]												=  os.path.join(self.tmpdirname					,'dataset'					)
													_UpperCamelCase   :						List[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
													_UpperCamelCase   :						Optional[int]												=  RagRetriever(
													    lowerCamelCase__					,question_encoder_tokenizer=self.get_dpr_tokenizer()					,generator_tokenizer=self.get_bart_tokenizer()					,)
							else:
													_UpperCamelCase   :						List[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       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  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					)

							_UpperCamelCase   :						Optional[Any]												=  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'					)					)

							_UpperCamelCase   :						List[str]												=  os.path.join(self.tmpdirname					,'psgs_w100.tsv.pkl'					)
							_UpperCamelCase   :						List[str]												=  {sample['id']: [sample['text'], sample['title']] for sample in dataset}
							pickle.dump(lowerCamelCase__					,open(lowerCamelCase__					,'wb'					)					)

							_UpperCamelCase   :						Any												=  RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size					,question_encoder=DPRConfig().to_dict()					,generator=BartConfig().to_dict()					,index_name='legacy'					,index_path=self.tmpdirname					,)
							_UpperCamelCase   :						Dict												=  RagRetriever(
							    lowerCamelCase__					,question_encoder_tokenizer=self.get_dpr_tokenizer()					,generator_tokenizer=self.get_bart_tokenizer()					)
							return retriever





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







							'''simple docstring'''
							_UpperCamelCase   :						Tuple												=  1
							_UpperCamelCase   :						Optional[Any]												=  self.get_dummy_canonical_hf_index_retriever()
							_UpperCamelCase   :						Union[str, Any]												=  np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]					,dtype=np.floataa					)
							_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						Tuple												=  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       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[int]												=  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:
																			_UpperCamelCase   :						List[str]												=  self.get_dummy_dataset()
																			retriever.save_pretrained(lowerCamelCase__					)
																			_UpperCamelCase   :						str												=  RagRetriever.from_pretrained(lowerCamelCase__					)
																			self.assertIsInstance(lowerCamelCase__					,lowerCamelCase__					)
																			_UpperCamelCase   :						int												=  np.array(
																			    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]					,dtype=np.floataa					)
																			_UpperCamelCase   :						Optional[int]												=  retriever.retrieve(lowerCamelCase__					,n_docs=1					)
																			self.assertTrue(out is not None					)





	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  1
							_UpperCamelCase   :						Dict												=  self.get_dummy_custom_hf_index_retriever(from_disk=lowerCamelCase__					)
							_UpperCamelCase   :						int												=  np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]					,dtype=np.floataa					)
							_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						Optional[Any]												=  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       UpperCamelCase_    (					self   :							Optional[Any]					):







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





	def       UpperCamelCase_    (					self   :							Any					):







							'''simple docstring'''
							_UpperCamelCase   :						Tuple												=  1
							_UpperCamelCase   :						Tuple												=  self.get_dummy_custom_hf_index_retriever(from_disk=lowerCamelCase__					)
							_UpperCamelCase   :						Optional[Any]												=  np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]					,dtype=np.floataa					)
							_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						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       UpperCamelCase_    (					self   :							List[Any]					):







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





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  1
							_UpperCamelCase   :						Dict												=  self.get_dummy_legacy_index_retriever()
							_UpperCamelCase   :						Optional[int]												=  np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]					,dtype=np.floataa					)
							_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						Dict												=  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       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  self.get_dummy_legacy_index_retriever()
							with tempfile.TemporaryDirectory() as tmp_dirname:
													retriever.save_pretrained(lowerCamelCase__					)
													_UpperCamelCase   :						Dict												=  RagRetriever.from_pretrained(lowerCamelCase__					)
													self.assertIsInstance(lowerCamelCase__					,lowerCamelCase__					)
													_UpperCamelCase   :						Dict												=  np.array(
													    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]					,dtype=np.floataa					)
													_UpperCamelCase   :						Dict												=  retriever.retrieve(lowerCamelCase__					,n_docs=1					)
													self.assertTrue(out is not None					)





	@require_torch
	@require_tokenizers
	@require_sentencepiece
	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							import torch

							_UpperCamelCase   :						int												=  1
							_UpperCamelCase   :						Optional[Any]												=  self.get_dummy_canonical_hf_index_retriever()
							_UpperCamelCase   :						Union[str, Any]												=  [[5, 7], [10, 11]]
							_UpperCamelCase   :						Dict												=  np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]					,dtype=np.floataa					)
							_UpperCamelCase   :						List[str]												=  retriever(lowerCamelCase__					,lowerCamelCase__					,prefix=retriever.config.generator.prefix					,n_docs=lowerCamelCase__					)
							_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						str												=  (
							    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					)

							_UpperCamelCase   :						Optional[Any]												=  retriever(
							    lowerCamelCase__					,lowerCamelCase__					,prefix=retriever.config.generator.prefix					,n_docs=lowerCamelCase__					,return_tensors='pt'					,)
							_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						Dict												=  (  # 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       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  self.get_dpr_ctx_encoder_tokenizer()
							_UpperCamelCase   :						Optional[Any]												=  1
							_UpperCamelCase   :						str												=  self.get_dummy_custom_hf_index_retriever(from_disk=lowerCamelCase__					)
							retriever.set_ctx_encoder_tokenizer(lowerCamelCase__					)

							_UpperCamelCase   :						Optional[Any]												=  [[5, 7], [10, 11]]
							_UpperCamelCase   :						Union[str, Any]												=  np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]					,dtype=np.floataa					)
							_UpperCamelCase   :						Optional[Any]												=  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

import torch

from transformers import YosoConfig, YosoForMaskedLM






def A__       (	UpperCAmelCase_				):
						if "model" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('model.'    ,    ''				)
						if "norm1" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('norm1'    ,    'attention.output.LayerNorm'				)
						if "norm2" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.replace('norm2'    ,    'output.LayerNorm'				)
						if "norm" in orig_key:
												_UpperCamelCase   :						str												=  orig_key.replace('norm'    ,    'LayerNorm'				)
						if "transformer" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.split('.'				)[0].split('_'				)[-1]
												_UpperCamelCase   :						Tuple												=  orig_key.replace(f'transformer_{layer_num}'    ,    f'encoder.layer.{layer_num}'				)
						if "mha.attn" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('mha.attn'    ,    'attention.self'				)
						if "mha" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('mha'    ,    'attention'				)
						if "W_q" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('W_q'    ,    'self.query'				)
						if "W_k" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('W_k'    ,    'self.key'				)
						if "W_v" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('W_v'    ,    'self.value'				)
						if "ff1" in orig_key:
												_UpperCamelCase   :						Optional[Any]												=  orig_key.replace('ff1'    ,    'intermediate.dense'				)
						if "ff2" in orig_key:
												_UpperCamelCase   :						List[str]												=  orig_key.replace('ff2'    ,    'output.dense'				)
						if "ff" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('ff'    ,    'output.dense'				)
						if "mlm_class" in orig_key:
												_UpperCamelCase   :						Tuple												=  orig_key.replace('mlm.mlm_class'    ,    'cls.predictions.decoder'				)
						if "mlm" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.replace('mlm'    ,    'cls.predictions.transform'				)
						if "cls" not in orig_key:
												_UpperCamelCase   :						Optional[int]												=  'yoso.' + orig_key

						return orig_key






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						for key in orig_state_dict.copy().keys():
												_UpperCamelCase   :						Any												=  orig_state_dict.pop(UpperCAmelCase_				)

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		_UpperCamelCase   :						Union[str, Any]												=  val

						_UpperCamelCase   :						Any												=  orig_state_dict['cls.predictions.decoder.bias']
						_UpperCamelCase   :						Union[str, Any]												=  torch.arange(UpperCAmelCase_				).expand((1, -1)				) + 2

						return orig_state_dict






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						List[Any]												=  torch.load(UpperCAmelCase_    ,    map_location='cpu'				)['model_state_dict']
						_UpperCamelCase   :						List[str]												=  YosoConfig.from_json_file(UpperCAmelCase_				)
						_UpperCamelCase   :						Union[str, Any]												=  YosoForMaskedLM(UpperCAmelCase_				)

						_UpperCamelCase   :						Tuple												=  convert_checkpoint_helper(config.max_position_embeddings    ,    UpperCAmelCase_				)

						print(model.load_state_dict(UpperCAmelCase_				)				)
						model.eval()
						model.save_pretrained(UpperCAmelCase_				)

						print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'				)


if __name__ == "__main__":
					snake_case_					:      int    			=    argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for YOSO model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					snake_case_					:      List[Any]    			=    parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


