






"""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      UpperCamelCase_			(__A	):





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     int:
				UpperCAmelCase_	:					str     	=   tempfile.mkdtemp()
				UpperCAmelCase_	:					Dict     	=   8

				# DPR tok
				UpperCAmelCase_	:					Dict     	=   [
				    "[UNK]",
				    "[CLS]",
				    "[SEP]",
				    "[PAD]",
				    "[MASK]",
				    "want",
				    "##want",
				    "##ed",
				    "wa",
				    "un",
				    "runn",
				    "##ing",
				    ",",
				    "low",
				    "lowest",
				]
				UpperCAmelCase_	:					str     	=   os.path.join(self.tmpdirname      ,      "dpr_tokenizer"		)
				os.makedirs(lowerCAmelCase_      ,      exist_ok=lowerCAmelCase_		)
				UpperCAmelCase_	:					int     	=   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_	:					List[str]     	=   [
				    "l",
				    "o",
				    "w",
				    "e",
				    "r",
				    "s",
				    "t",
				    "i",
				    "d",
				    "n",
				    "\u0120",
				    "\u0120l",
				    "\u0120n",
				    "\u0120lo",
				    "\u0120low",
				    "er",
				    "\u0120lowest",
				    "\u0120newer",
				    "\u0120wider",
				    "<unk>",
				]
				UpperCAmelCase_	:					Union[str, Any]     	=   dict(zip(lowerCAmelCase_      ,      range(len(lowerCAmelCase_		)		)		)		)
				UpperCAmelCase_	:					Dict     	=   ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
				UpperCAmelCase_	:					Tuple     	=   {"unk_token": "<unk>"}

				UpperCAmelCase_	:					int     	=   os.path.join(self.tmpdirname      ,      "bart_tokenizer"		)
				os.makedirs(lowerCAmelCase_      ,      exist_ok=lowerCAmelCase_		)
				UpperCAmelCase_	:					Tuple     	=   os.path.join(lowerCAmelCase_      ,      BART_VOCAB_FILES_NAMES["vocab_file"]		)
				UpperCAmelCase_	:					Tuple     	=   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 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     DPRQuestionEncoderTokenizer:
				return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname      ,      "dpr_tokenizer"		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     DPRContextEncoderTokenizer:
				return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname      ,      "dpr_tokenizer"		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     BartTokenizer:
				return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname      ,      "bart_tokenizer"		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Tuple:
				shutil.rmtree(self.tmpdirname		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Tuple:
				UpperCAmelCase_	:					Union[str, Any]     	=   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 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     str:
				UpperCAmelCase_	:					int     	=   self.get_dummy_dataset()
				UpperCAmelCase_	:					Dict     	=   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[str]     	=   dataset
							UpperCAmelCase_	:					Dict     	=   RagRetriever(
							    lowerCAmelCase_      ,      question_encoder_tokenizer=self.get_dpr_tokenizer()      ,      generator_tokenizer=self.get_bart_tokenizer()      ,      )
				return retriever





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					bool		)		->     List[Any]:
				UpperCAmelCase_	:					Optional[Any]     	=   self.get_dummy_dataset()
				UpperCAmelCase_	:					str     	=   RagConfig(
				    retrieval_vector_size=self.retrieval_vector_size      ,      question_encoder=DPRConfig().to_dict()      ,      generator=BartConfig().to_dict()      ,      index_name="custom"      ,      )
				if from_disk:
							UpperCAmelCase_	:					List[str]     	=   os.path.join(self.tmpdirname      ,      "dataset"		)
							UpperCAmelCase_	:					List[Any]     	=   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[Any]     	=   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 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     int:
				UpperCAmelCase_	:					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_	:					Union[str, 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_	:					Tuple     	=   os.path.join(self.tmpdirname      ,      "psgs_w100.tsv.pkl"		)
				UpperCAmelCase_	:					Optional[Any]     	=   {sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
				pickle.dump(lowerCAmelCase_      ,      open(lowerCAmelCase_      ,      "wb"		)		)

				UpperCAmelCase_	:					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      ,      )
				UpperCAmelCase_	:					Optional[int]     	=   RagRetriever(
				    lowerCAmelCase_      ,      question_encoder_tokenizer=self.get_dpr_tokenizer()      ,      generator_tokenizer=self.get_bart_tokenizer()		)
				return retriever





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Tuple:
				UpperCAmelCase_	:					Dict     	=   1
				UpperCAmelCase_	:					Dict     	=   self.get_dummy_canonical_hf_index_retriever()
				UpperCAmelCase_	:					Dict     	=   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]		)      ,      ["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 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     Any:
				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_	:					Dict     	=   self.get_dummy_dataset()
										retriever.save_pretrained(lowerCAmelCase_		)
										UpperCAmelCase_	:					Dict     	=   RagRetriever.from_pretrained(lowerCAmelCase_		)
										self.assertIsInstance(lowerCAmelCase_      ,      lowerCAmelCase_		)
										UpperCAmelCase_	:					Tuple     	=   np.array(
										    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]      ,      dtype=np.floataa		)
										UpperCAmelCase_	:					Tuple     	=   retriever.retrieve(lowerCAmelCase_      ,      n_docs=1		)
										self.assertTrue(out is not None		)





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





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





	@require_torch
	@require_tokenizers
	@require_sentencepiece
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     List[Any]:
				import torch

				UpperCAmelCase_	:					Any     	=   1
				UpperCAmelCase_	:					List[Any]     	=   self.get_dummy_canonical_hf_index_retriever()
				UpperCAmelCase_	:					str     	=   [[5, 7], [10, 11]]
				UpperCAmelCase_	:					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_		)
				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_	:					List[str]     	=   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 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Tuple:
				UpperCAmelCase_	:					Optional[Any]     	=   self.get_dpr_ctx_encoder_tokenizer()
				UpperCAmelCase_	:					List[Any]     	=   1
				UpperCAmelCase_	:					Union[str, Any]     	=   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_	:					List[str]     	=   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"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase    : str				 =						{
    '''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : List[Any]				 =						[
							    '''SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''Swinv2ForImageClassification''',
							    '''Swinv2ForMaskedImageModeling''',
							    '''Swinv2Model''',
							    '''Swinv2PreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_swinva import (
														    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SwinvaForImageClassification,
														    SwinvaForMaskedImageModeling,
														    SwinvaModel,
														    SwinvaPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase    : List[Any]				 =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




