


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      (      SCREAMING_SNAKE_CASE_ ):
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		tempfile.mkdtemp()
          lowerCamelCase__           =		8

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

          lowerCamelCase__           =		os.path.join(self.tmpdirname							,					'bart_tokenizer'				)
          os.makedirs(SCREAMING_SNAKE_CASE__							,					exist_ok=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		os.path.join(SCREAMING_SNAKE_CASE__							,					BART_VOCAB_FILES_NAMES['vocab_file']				)
          lowerCamelCase__           =		os.path.join(SCREAMING_SNAKE_CASE__							,					BART_VOCAB_FILES_NAMES['merges_file']				)
          with open(self.vocab_file							,					'w'							,					encoding='utf-8'				) as fp:
                fp.write(json.dumps(SCREAMING_SNAKE_CASE__				) + '\n'				)
          with open(self.merges_file							,					'w'							,					encoding='utf-8'				) as fp:
                fp.write('\n'.join(SCREAMING_SNAKE_CASE__				)				)
    def      _UpperCamelCase       ( self							:					Dict				):
          return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,					'dpr_tokenizer'				)				)
    def      _UpperCamelCase       ( self							:					str				):
          return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,					'dpr_tokenizer'				)				)
    def      _UpperCamelCase       ( self							:					Any				):
          return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname							,					'bart_tokenizer'				)				)
    def      _UpperCamelCase       ( self							:					int				):
          shutil.rmtree(self.tmpdirname				)
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		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							:					Tuple				):
          lowerCamelCase__           =		self.get_dummy_dataset()
          lowerCamelCase__           =		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:
                lowerCamelCase__           =		dataset
                lowerCamelCase__           =		RagRetriever(
                    SCREAMING_SNAKE_CASE__							,					question_encoder_tokenizer=self.get_dpr_tokenizer()							,					generator_tokenizer=self.get_bart_tokenizer()							,					)
          return retriever
    def      _UpperCamelCase       ( self							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					bool				):
          lowerCamelCase__           =		self.get_dummy_dataset()
          lowerCamelCase__           =		RagConfig(
              retrieval_vector_size=self.retrieval_vector_size							,					question_encoder=DPRConfig().to_dict()							,					generator=BartConfig().to_dict()							,					index_name='custom'							,					)
          if from_disk:
                lowerCamelCase__           =		os.path.join(self.tmpdirname							,					'dataset'				)
                lowerCamelCase__           =		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
                lowerCamelCase__           =		RagRetriever(
                    SCREAMING_SNAKE_CASE__							,					question_encoder_tokenizer=self.get_dpr_tokenizer()							,					generator_tokenizer=self.get_bart_tokenizer()							,					)
          else:
                lowerCamelCase__           =		RagRetriever(
                    SCREAMING_SNAKE_CASE__							,					question_encoder_tokenizer=self.get_dpr_tokenizer()							,					generator_tokenizer=self.get_bart_tokenizer()							,					index=CustomHFIndex(config.retrieval_vector_size							,					SCREAMING_SNAKE_CASE__				)							,					)
          return retriever
    def      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__           =		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				)

          lowerCamelCase__           =		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'				)				)

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

          lowerCamelCase__           =		RagConfig(
              retrieval_vector_size=self.retrieval_vector_size							,					question_encoder=DPRConfig().to_dict()							,					generator=BartConfig().to_dict()							,					index_name='legacy'							,					index_path=self.tmpdirname							,					)
          lowerCamelCase__           =		RagRetriever(
              SCREAMING_SNAKE_CASE__							,					question_encoder_tokenizer=self.get_dpr_tokenizer()							,					generator_tokenizer=self.get_bart_tokenizer()				)
          return retriever
    def      _UpperCamelCase       ( self							:					Optional[int]				):
          lowerCamelCase__           =		1
          lowerCamelCase__           =		self.get_dummy_canonical_hf_index_retriever()
          lowerCamelCase__           =		np.array(
              [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]							,					dtype=np.floataa				)
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		retriever.retrieve(SCREAMING_SNAKE_CASE__							,					n_docs=SCREAMING_SNAKE_CASE__				)
          self.assertEqual(retrieved_doc_embeds.shape							,					(2, n_docs, self.retrieval_vector_size)				)
          self.assertEqual(len(SCREAMING_SNAKE_CASE__				)							,					2				)
          self.assertEqual(sorted(doc_dicts[0]				)							,					['embeddings', 'id', 'text', 'title']				)
          self.assertEqual(len(doc_dicts[0]['id']				)							,					SCREAMING_SNAKE_CASE__				)
          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[int]				):
          lowerCamelCase__           =		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:
                      lowerCamelCase__           =		self.get_dummy_dataset()
                      retriever.save_pretrained(SCREAMING_SNAKE_CASE__				)
                      lowerCamelCase__           =		RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE__				)
                      self.assertIsInstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
                      lowerCamelCase__           =		np.array(
                          [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]							,					dtype=np.floataa				)
                      lowerCamelCase__           =		retriever.retrieve(SCREAMING_SNAKE_CASE__							,					n_docs=1				)
                      self.assertTrue(out is not None				)
    def      _UpperCamelCase       ( self							:					List[Any]				):
          lowerCamelCase__           =		1
          lowerCamelCase__           =		self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		np.array(
              [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]							,					dtype=np.floataa				)
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		retriever.retrieve(SCREAMING_SNAKE_CASE__							,					n_docs=SCREAMING_SNAKE_CASE__				)
          self.assertEqual(retrieved_doc_embeds.shape							,					(2, n_docs, self.retrieval_vector_size)				)
          self.assertEqual(len(SCREAMING_SNAKE_CASE__				)							,					2				)
          self.assertEqual(sorted(doc_dicts[0]				)							,					['embeddings', 'id', 'text', 'title']				)
          self.assertEqual(len(doc_dicts[0]['id']				)							,					SCREAMING_SNAKE_CASE__				)
          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							:					Union[str, Any]				):
          lowerCamelCase__           =		self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE__				)
          with tempfile.TemporaryDirectory() as tmp_dirname:
                retriever.save_pretrained(SCREAMING_SNAKE_CASE__				)
                lowerCamelCase__           =		RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE__				)
                self.assertIsInstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
                lowerCamelCase__           =		np.array(
                    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]							,					dtype=np.floataa				)
                lowerCamelCase__           =		retriever.retrieve(SCREAMING_SNAKE_CASE__							,					n_docs=1				)
                self.assertTrue(out is not None				)
    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		1
          lowerCamelCase__           =		self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		np.array(
              [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]							,					dtype=np.floataa				)
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		retriever.retrieve(SCREAMING_SNAKE_CASE__							,					n_docs=SCREAMING_SNAKE_CASE__				)
          self.assertEqual(retrieved_doc_embeds.shape							,					(2, n_docs, self.retrieval_vector_size)				)
          self.assertEqual(len(SCREAMING_SNAKE_CASE__				)							,					2				)
          self.assertEqual(sorted(doc_dicts[0]				)							,					['embeddings', 'id', 'text', 'title']				)
          self.assertEqual(len(doc_dicts[0]['id']				)							,					SCREAMING_SNAKE_CASE__				)
          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							:					Tuple				):
          lowerCamelCase__           =		self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE__				)
          with tempfile.TemporaryDirectory() as tmp_dirname:
                retriever.save_pretrained(SCREAMING_SNAKE_CASE__				)
                lowerCamelCase__           =		RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE__				)
                self.assertIsInstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
                lowerCamelCase__           =		np.array(
                    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]							,					dtype=np.floataa				)
                lowerCamelCase__           =		retriever.retrieve(SCREAMING_SNAKE_CASE__							,					n_docs=1				)
                self.assertTrue(out is not None				)
    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		1
          lowerCamelCase__           =		self.get_dummy_legacy_index_retriever()
          lowerCamelCase__           =		np.array(
              [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]							,					dtype=np.floataa				)
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		retriever.retrieve(SCREAMING_SNAKE_CASE__							,					n_docs=SCREAMING_SNAKE_CASE__				)
          self.assertEqual(retrieved_doc_embeds.shape							,					(2, n_docs, self.retrieval_vector_size)				)
          self.assertEqual(len(SCREAMING_SNAKE_CASE__				)							,					2				)
          self.assertEqual(sorted(doc_dicts[0]				)							,					['text', 'title']				)
          self.assertEqual(len(doc_dicts[0]['text']				)							,					SCREAMING_SNAKE_CASE__				)
          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							:					List[str]				):
          lowerCamelCase__           =		self.get_dummy_legacy_index_retriever()
          with tempfile.TemporaryDirectory() as tmp_dirname:
                retriever.save_pretrained(SCREAMING_SNAKE_CASE__				)
                lowerCamelCase__           =		RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE__				)
                self.assertIsInstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
                lowerCamelCase__           =		np.array(
                    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]							,					dtype=np.floataa				)
                lowerCamelCase__           =		retriever.retrieve(SCREAMING_SNAKE_CASE__							,					n_docs=1				)
                self.assertTrue(out is not None				)
    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def      _UpperCamelCase       ( self							:					Dict				):
          import torch

          lowerCamelCase__           =		1
          lowerCamelCase__           =		self.get_dummy_canonical_hf_index_retriever()
          lowerCamelCase__           =		[[5, 7], [10, 11]]
          lowerCamelCase__           =		np.array(
              [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]							,					dtype=np.floataa				)
          lowerCamelCase__           =		retriever(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					prefix=retriever.config.generator.prefix							,					n_docs=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		(
              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(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
          self.assertIsInstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
          self.assertIsInstance(SCREAMING_SNAKE_CASE__							,					np.ndarray				)

          lowerCamelCase__           =		retriever(
              SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					prefix=retriever.config.generator.prefix							,					n_docs=SCREAMING_SNAKE_CASE__							,					return_tensors='pt'							,					)
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		(  # 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(SCREAMING_SNAKE_CASE__							,					torch.Tensor				)
          self.assertIsInstance(SCREAMING_SNAKE_CASE__							,					torch.Tensor				)
          self.assertIsInstance(SCREAMING_SNAKE_CASE__							,					torch.Tensor				)


    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__           =		self.get_dpr_ctx_encoder_tokenizer()
          lowerCamelCase__           =		1
          lowerCamelCase__           =		self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE__				)
          retriever.set_ctx_encoder_tokenizer(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		[[5, 7], [10, 11]]
          lowerCamelCase__           =		np.array(
              [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]							,					dtype=np.floataa				)
          lowerCamelCase__           =		retriever(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					prefix=retriever.config.generator.prefix							,					n_docs=SCREAMING_SNAKE_CASE__				)

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







"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case          =							logging.get_logger(__name__)

_snake_case          =							{
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}



class   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				Union[str, Any]             =    'swinv2'

    a_    :				Optional[int]             =    {
        'num_attention_heads': 'num_heads',
        'num_hidden_layers': 'num_layers',
    }
    def __init__( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					int=2_24							,					SCREAMING_SNAKE_CASE__							:					str=4							,					SCREAMING_SNAKE_CASE__							:					List[Any]=3							,					SCREAMING_SNAKE_CASE__							:					str=96							,					SCREAMING_SNAKE_CASE__							:					Dict=[2, 2, 6, 2]							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=[3, 6, 12, 24]							,					SCREAMING_SNAKE_CASE__							:					Tuple=7							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=4.0							,					SCREAMING_SNAKE_CASE__							:					List[str]=True							,					SCREAMING_SNAKE_CASE__							:					List[Any]=0.0							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=0.0							,					SCREAMING_SNAKE_CASE__							:					Dict=0.1							,					SCREAMING_SNAKE_CASE__							:					Tuple="gelu"							,					SCREAMING_SNAKE_CASE__							:					Tuple=False							,					SCREAMING_SNAKE_CASE__							:					Any=0.02							,					SCREAMING_SNAKE_CASE__							:					List[Any]=1e-5							,					SCREAMING_SNAKE_CASE__							:					int=32							,					**SCREAMING_SNAKE_CASE__							:					List[str]							,					):
          super().__init__(**SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		image_size
          lowerCamelCase__           =		patch_size
          lowerCamelCase__           =		num_channels
          lowerCamelCase__           =		embed_dim
          lowerCamelCase__           =		depths
          lowerCamelCase__           =		len(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		num_heads
          lowerCamelCase__           =		window_size
          lowerCamelCase__           =		mlp_ratio
          lowerCamelCase__           =		qkv_bias
          lowerCamelCase__           =		hidden_dropout_prob
          lowerCamelCase__           =		attention_probs_dropout_prob
          lowerCamelCase__           =		drop_path_rate
          lowerCamelCase__           =		hidden_act
          lowerCamelCase__           =		use_absolute_embeddings
          lowerCamelCase__           =		layer_norm_eps
          lowerCamelCase__           =		initializer_range
          lowerCamelCase__           =		encoder_stride
          # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          lowerCamelCase__           =		int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE__				) - 1)				)
          lowerCamelCase__           =		(0, 0, 0, 0)



