





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 				a			(      __lowercase			):






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Dict           =   tempfile.mkdtemp()
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   8

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

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






				def 			snake_case_    (  self					):

										"""simple docstring"""




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






				def 			snake_case_    (  self					):

										"""simple docstring"""




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






				def 			snake_case_    (  self					):

										"""simple docstring"""




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






				def 			snake_case_    (  self					):

										"""simple docstring"""




										shutil.rmtree(self.tmpdirname					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				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 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   self.get_dummy_dataset()
										__SCREAMING_SNAKE_CASE:				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:
																__SCREAMING_SNAKE_CASE:				List[Any]           =   dataset
																__SCREAMING_SNAKE_CASE:				Any           =   RagRetriever(
																    _lowercase			, question_encoder_tokenizer=self.get_dpr_tokenizer()			, generator_tokenizer=self.get_bart_tokenizer()			, )
										return retriever






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Any           =   self.get_dummy_dataset()
										__SCREAMING_SNAKE_CASE:				Tuple           =   RagConfig(
										    retrieval_vector_size=self.retrieval_vector_size			, question_encoder=DPRConfig().to_dict()			, generator=BartConfig().to_dict()			, index_name='''custom'''			, )
										if from_disk:
																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   os.path.join(self.tmpdirname			, '''dataset'''					)
																__SCREAMING_SNAKE_CASE:				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
																__SCREAMING_SNAKE_CASE:				int           =   RagRetriever(
																    _lowercase			, question_encoder_tokenizer=self.get_dpr_tokenizer()			, generator_tokenizer=self.get_bart_tokenizer()			, )
										else:
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   RagRetriever(
																    _lowercase			, question_encoder_tokenizer=self.get_dpr_tokenizer()			, generator_tokenizer=self.get_bart_tokenizer()			, index=CustomHFIndex(config.retrieval_vector_size			, _lowercase					)			, )
										return retriever






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   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					)

										__SCREAMING_SNAKE_CASE:				Tuple           =   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'''					)					)

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

										__SCREAMING_SNAKE_CASE:				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			, )
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   RagRetriever(
										    _lowercase			, question_encoder_tokenizer=self.get_dpr_tokenizer()			, generator_tokenizer=self.get_bart_tokenizer()					)
										return retriever






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   1
										__SCREAMING_SNAKE_CASE:				Tuple           =   self.get_dummy_canonical_hf_index_retriever()
										__SCREAMING_SNAKE_CASE:				List[str]           =   np.array(
										    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]			, dtype=np.floataa					)
										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Any           =   retriever.retrieve(_lowercase			, n_docs=_lowercase					)
										self.assertEqual(retrieved_doc_embeds.shape			, (2, n_docs, self.retrieval_vector_size)					)
										self.assertEqual(len(_lowercase					)			, 2					)
										self.assertEqual(sorted(doc_dicts[0]					)			, ['''embeddings''', '''id''', '''text''', '''title''']					)
										self.assertEqual(len(doc_dicts[0]['''id''']					)			, _lowercase					)
										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 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Tuple           =   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:
																						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   self.get_dummy_dataset()
																						retriever.save_pretrained(_lowercase					)
																						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   RagRetriever.from_pretrained(_lowercase					)
																						self.assertIsInstance(_lowercase			, _lowercase					)
																						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   np.array(
																						    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]			, dtype=np.floataa					)
																						__SCREAMING_SNAKE_CASE:				int           =   retriever.retrieve(_lowercase			, n_docs=1					)
																						self.assertTrue(out is not None					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Dict           =   1
										__SCREAMING_SNAKE_CASE:				Dict           =   self.get_dummy_custom_hf_index_retriever(from_disk=_lowercase					)
										__SCREAMING_SNAKE_CASE:				int           =   np.array(
										    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]			, dtype=np.floataa					)
										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Tuple           =   retriever.retrieve(_lowercase			, n_docs=_lowercase					)
										self.assertEqual(retrieved_doc_embeds.shape			, (2, n_docs, self.retrieval_vector_size)					)
										self.assertEqual(len(_lowercase					)			, 2					)
										self.assertEqual(sorted(doc_dicts[0]					)			, ['''embeddings''', '''id''', '''text''', '''title''']					)
										self.assertEqual(len(doc_dicts[0]['''id''']					)			, _lowercase					)
										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 			snake_case_    (  self					):

										"""simple docstring"""




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






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   1
										__SCREAMING_SNAKE_CASE:				Any           =   self.get_dummy_custom_hf_index_retriever(from_disk=_lowercase					)
										__SCREAMING_SNAKE_CASE:				int           =   np.array(
										    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]			, dtype=np.floataa					)
										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Optional[int]           =   retriever.retrieve(_lowercase			, n_docs=_lowercase					)
										self.assertEqual(retrieved_doc_embeds.shape			, (2, n_docs, self.retrieval_vector_size)					)
										self.assertEqual(len(_lowercase					)			, 2					)
										self.assertEqual(sorted(doc_dicts[0]					)			, ['''embeddings''', '''id''', '''text''', '''title''']					)
										self.assertEqual(len(doc_dicts[0]['''id''']					)			, _lowercase					)
										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 			snake_case_    (  self					):

										"""simple docstring"""




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






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   1
										__SCREAMING_SNAKE_CASE:				str           =   self.get_dummy_legacy_index_retriever()
										__SCREAMING_SNAKE_CASE:				str           =   np.array(
										    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]			, dtype=np.floataa					)
										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				List[Any]           =   retriever.retrieve(_lowercase			, n_docs=_lowercase					)
										self.assertEqual(retrieved_doc_embeds.shape			, (2, n_docs, self.retrieval_vector_size)					)
										self.assertEqual(len(_lowercase					)			, 2					)
										self.assertEqual(sorted(doc_dicts[0]					)			, ['''text''', '''title''']					)
										self.assertEqual(len(doc_dicts[0]['''text''']					)			, _lowercase					)
										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 			snake_case_    (  self					):

										"""simple docstring"""




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






				@require_torch
				@require_tokenizers
				@require_sentencepiece
				def 			snake_case_    (  self					):

										"""simple docstring"""




										import torch

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

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   retriever(
										    _lowercase			, _lowercase			, prefix=retriever.config.generator.prefix			, n_docs=_lowercase			, return_tensors='''pt'''			, )
										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Optional[int]           =   (  # 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(_lowercase			, torch.Tensor					)
										self.assertIsInstance(_lowercase			, torch.Tensor					)
										self.assertIsInstance(_lowercase			, torch.Tensor					)






				@require_torch
				@require_tokenizers
				@require_sentencepiece
				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.get_dpr_ctx_encoder_tokenizer()
										__SCREAMING_SNAKE_CASE:				List[str]           =   1
										__SCREAMING_SNAKE_CASE:				Any           =   self.get_dummy_custom_hf_index_retriever(from_disk=_lowercase					)
										retriever.set_ctx_encoder_tokenizer(_lowercase					)

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

										self.assertEqual(
										    len(_lowercase					)			, 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''')					)			, _lowercase					)  # check for doc token related keys in dictionary.






import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Optional[int]:



    """simple docstring"""






    UpperCamelCase_			  =						SwinvaConfig()
    UpperCamelCase_			  =						swinva_name.split("_"			)

    UpperCamelCase_			  =						name_split[1]
    if "to" in name_split[3]:
        UpperCamelCase_			  =						int(name_split[3][-3:]			)
    else:
        UpperCamelCase_			  =						int(name_split[3]			)
    if "to" in name_split[2]:
        UpperCamelCase_			  =						int(name_split[2][-2:]			)
    else:
        UpperCamelCase_			  =						int(name_split[2][6:]			)

    if model_size == "tiny":
        UpperCamelCase_			  =						9_6
        UpperCamelCase_			  =						(2, 2, 6, 2)
        UpperCamelCase_			  =						(3, 6, 1_2, 2_4)
    elif model_size == "small":
        UpperCamelCase_			  =						9_6
        UpperCamelCase_			  =						(2, 2, 1_8, 2)
        UpperCamelCase_			  =						(3, 6, 1_2, 2_4)
    elif model_size == "base":
        UpperCamelCase_			  =						1_2_8
        UpperCamelCase_			  =						(2, 2, 1_8, 2)
        UpperCamelCase_			  =						(4, 8, 1_6, 3_2)
    else:
        UpperCamelCase_			  =						1_9_2
        UpperCamelCase_			  =						(2, 2, 1_8, 2)
        UpperCamelCase_			  =						(6, 1_2, 2_4, 4_8)

    if "to" in swinva_name:
        UpperCamelCase_			  =						(1_2, 1_2, 1_2, 6)

    if ("22k" in swinva_name) and ("to" not in swinva_name):
        UpperCamelCase_			  =						2_1_8_4_1
        UpperCamelCase_			  =						"huggingface/label-files"
        UpperCamelCase_			  =						"imagenet-22k-id2label.json"
        UpperCamelCase_			  =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						repo_type="dataset"			)			,						"r"			)			)
        UpperCamelCase_			  =						{int(SCREAMING_SNAKE_CASE_			): v for k, v in idalabel.items()}
        UpperCamelCase_			  =						idalabel
        UpperCamelCase_			  =						{v: k for k, v in idalabel.items()}

    else:
        UpperCamelCase_			  =						1_0_0_0
        UpperCamelCase_			  =						"huggingface/label-files"
        UpperCamelCase_			  =						"imagenet-1k-id2label.json"
        UpperCamelCase_			  =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						repo_type="dataset"			)			,						"r"			)			)
        UpperCamelCase_			  =						{int(SCREAMING_SNAKE_CASE_			): v for k, v in idalabel.items()}
        UpperCamelCase_			  =						idalabel
        UpperCamelCase_			  =						{v: k for k, v in idalabel.items()}

    UpperCamelCase_			  =						img_size
    UpperCamelCase_			  =						num_classes
    UpperCamelCase_			  =						embed_dim
    UpperCamelCase_			  =						depths
    UpperCamelCase_			  =						num_heads
    UpperCamelCase_			  =						window_size

    return config




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Optional[Any]:



    """simple docstring"""






    if "patch_embed.proj" in name:
        UpperCamelCase_			  =						name.replace("patch_embed.proj"			,						"embeddings.patch_embeddings.projection"			)
    if "patch_embed.norm" in name:
        UpperCamelCase_			  =						name.replace("patch_embed.norm"			,						"embeddings.norm"			)
    if "layers" in name:
        UpperCamelCase_			  =						"encoder." + name
    if "attn.proj" in name:
        UpperCamelCase_			  =						name.replace("attn.proj"			,						"attention.output.dense"			)
    if "attn" in name:
        UpperCamelCase_			  =						name.replace("attn"			,						"attention.self"			)
    if "norm1" in name:
        UpperCamelCase_			  =						name.replace("norm1"			,						"layernorm_before"			)
    if "norm2" in name:
        UpperCamelCase_			  =						name.replace("norm2"			,						"layernorm_after"			)
    if "mlp.fc1" in name:
        UpperCamelCase_			  =						name.replace("mlp.fc1"			,						"intermediate.dense"			)
    if "mlp.fc2" in name:
        UpperCamelCase_			  =						name.replace("mlp.fc2"			,						"output.dense"			)
    if "q_bias" in name:
        UpperCamelCase_			  =						name.replace("q_bias"			,						"query.bias"			)
    if "k_bias" in name:
        UpperCamelCase_			  =						name.replace("k_bias"			,						"key.bias"			)
    if "v_bias" in name:
        UpperCamelCase_			  =						name.replace("v_bias"			,						"value.bias"			)
    if "cpb_mlp" in name:
        UpperCamelCase_			  =						name.replace("cpb_mlp"			,						"continuous_position_bias_mlp"			)
    if name == "norm.weight":
        UpperCamelCase_			  =						"layernorm.weight"
    if name == "norm.bias":
        UpperCamelCase_			  =						"layernorm.bias"

    if "head" in name:
        UpperCamelCase_			  =						name.replace("head"			,						"classifier"			)
    else:
        UpperCamelCase_			  =						"swinv2." + name

    return name




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Optional[int]:



    """simple docstring"""






    for key in orig_state_dict.copy().keys():
        UpperCamelCase_			  =						orig_state_dict.pop(SCREAMING_SNAKE_CASE_			)

        if "mask" in key:
            continue
        elif "qkv" in key:
            UpperCamelCase_			  =						key.split("."			)
            UpperCamelCase_			  =						int(key_split[1]			)
            UpperCamelCase_			  =						int(key_split[3]			)
            UpperCamelCase_			  =						model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                UpperCamelCase_			  =						val[:dim, :]
                UpperCamelCase_			  =						val[dim : dim * 2, :]
                UpperCamelCase_			  =						val[-dim:, :]
            else:
                UpperCamelCase_			  =						val[:dim]
                UpperCamelCase_			  =						val[
                    dim : dim * 2
                ]
                UpperCamelCase_			  =						val[-dim:]
        else:
            UpperCamelCase_			  =						val

    return orig_state_dict




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Any:



    """simple docstring"""






    UpperCamelCase_			  =						timm.create_model(SCREAMING_SNAKE_CASE_			,						pretrained=SCREAMING_SNAKE_CASE_			)
    timm_model.eval()

    UpperCamelCase_			  =						get_swinva_config(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						SwinvaForImageClassification(SCREAMING_SNAKE_CASE_			)
    model.eval()

    UpperCamelCase_			  =						convert_state_dict(timm_model.state_dict()			,						SCREAMING_SNAKE_CASE_			)
    model.load_state_dict(SCREAMING_SNAKE_CASE_			)

    UpperCamelCase_			  =						"http://images.cocodataset.org/val2017/000000039769.jpg"

    UpperCamelCase_			  =						AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"			,						"-"			)			)			)
    UpperCamelCase_			  =						Image.open(requests.get(SCREAMING_SNAKE_CASE_			,						stream=SCREAMING_SNAKE_CASE_			).raw			)
    UpperCamelCase_			  =						image_processor(images=SCREAMING_SNAKE_CASE_			,						return_tensors="pt"			)

    UpperCamelCase_			  =						timm_model(inputs["pixel_values"]			)
    UpperCamelCase_			  =						model(**SCREAMING_SNAKE_CASE_			).logits

    assert torch.allclose(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						atol=1E-3			)

    print(f"Saving model {swinva_name} to {pytorch_dump_folder_path}"			)
    model.save_pretrained(SCREAMING_SNAKE_CASE_			)

    print(f"Saving image processor to {pytorch_dump_folder_path}"			)
    image_processor.save_pretrained(SCREAMING_SNAKE_CASE_			)

    model.push_to_hub(
        repo_path_or_name=Path(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)			,						organization="nandwalritik"			,						commit_message="Add model"			,						)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE		:Tuple						=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--swinv2_name""",
     default="""swinv2_tiny_patch4_window8_256""",
     type=str,
     help="""Name of the Swinv2 timm model you'd like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
 )

 SCREAMING_SNAKE_CASE		:int						=     parser.parse_args()
 convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)
