





'''simple docstring'''


import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE									=				get_tests_dir("fixtures/test_sentencepiece.model")

if is_torch_available():
       from transformers.models.mbart.modeling_mbart import shift_tokens_right

_SCREAMING_SNAKE_CASE									=				25_00_04
_SCREAMING_SNAKE_CASE									=				25_00_20

@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase_		(		__magic_name__    ,unittest.TestCase		):
 __lowerCamelCase    :				Any								=       MBartaaTokenizer
 __lowerCamelCase    :				List[Any]								=       MBartaaTokenizerFast
 __lowerCamelCase    :				List[Any]								=       True
 __lowerCamelCase    :				str								=       True




 def 				_snake_case (						self		)     ->				Optional[int]:
     super().setUp()

     # We have a SentencePiece fixture for testing
     _lowerCAmelCase						      =   MBartaaTokenizer(_lowerCAmelCase      ,  src_lang="en_XX"      ,  tgt_lang="ro_RO"      ,  keep_accents=_lowerCAmelCase		)
     tokenizer.save_pretrained(self.tmpdirname		)




 def 				_snake_case (						self		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   "<s>"
     _lowerCAmelCase						      =   0

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCAmelCase		)      ,  _lowerCAmelCase		)
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCAmelCase		)      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				str:
     _lowerCAmelCase						      =   list(self.get_tokenizer().get_vocab().keys()		)

     self.assertEqual(vocab_keys[0]      ,  "<s>"		)
     self.assertEqual(vocab_keys[1]      ,  "<pad>"		)
     self.assertEqual(vocab_keys[-1]      ,  "<mask>"		)
     self.assertEqual(len(_lowerCAmelCase		)      ,  1054		)




 def 				_snake_case (						self		)     ->				int:
     self.assertEqual(self.get_tokenizer().vocab_size      ,  1054		)




 def 				_snake_case (						self		)     ->				str:
     _lowerCAmelCase						      =   MBartaaTokenizer(_lowerCAmelCase      ,  src_lang="en_XX"      ,  tgt_lang="ro_RO"      ,  keep_accents=_lowerCAmelCase		)

     _lowerCAmelCase						      =   tokenizer.tokenize("This is a test"		)
     self.assertListEqual(_lowerCAmelCase      ,  ["▁This", "▁is", "▁a", "▁t", "est"]		)

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(_lowerCAmelCase		)      ,  [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]      ,  )

     _lowerCAmelCase						      =   tokenizer.tokenize("I was born in 92000, and this is falsé."		)
     self.assertListEqual(
         _lowerCAmelCase      ,  [SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", "."]      ,  )
     _lowerCAmelCase						      =   tokenizer.convert_tokens_to_ids(_lowerCAmelCase		)
     self.assertListEqual(
         _lowerCAmelCase      ,  [
             value + tokenizer.fairseq_offset
             for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
         ]      ,  )

     _lowerCAmelCase						      =   tokenizer.convert_ids_to_tokens(_lowerCAmelCase		)
     self.assertListEqual(
         _lowerCAmelCase      ,  [SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "<unk>", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "<unk>", "."]      ,  )




 @slow
 def 				_snake_case (						self		)     ->				Tuple:
     # fmt: off
     _lowerCAmelCase						      =   {"input_ids": [[250004, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [250004, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [250004, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=_lowerCAmelCase      ,  model_name="facebook/mbart-large-50"      ,  revision="d3913889c59cd5c9e456b269c376325eabad57e2"      ,  )





 def 				_snake_case (						self		)     ->				Dict:
     if not self.test_slow_tokenizer:
         # as we don't have a slow version, we can't compare the outputs between slow and fast versions
         return

     _lowerCAmelCase						      =   (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart50", {})
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
             _lowerCAmelCase						      =   self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase      ,  **_lowerCAmelCase		)
             _lowerCAmelCase						      =   self.tokenizer_class.from_pretrained(_lowerCAmelCase      ,  **_lowerCAmelCase		)

             _lowerCAmelCase						      =   tempfile.mkdtemp()

             _lowerCAmelCase						      =   tokenizer_r.save_pretrained(_lowerCAmelCase		)
             _lowerCAmelCase						      =   tokenizer_p.save_pretrained(_lowerCAmelCase		)

             # Checks it save with the same files + the tokenizer.json file for the fast one
             self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files		)		)
             _lowerCAmelCase						      =   tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f		)
             self.assertSequenceEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)

             # Checks everything loads correctly in the same way
             _lowerCAmelCase						      =   tokenizer_r.from_pretrained(_lowerCAmelCase		)
             _lowerCAmelCase						      =   tokenizer_p.from_pretrained(_lowerCAmelCase		)

             # Check special tokens are set accordingly on Rust and Python
             for key in tokenizer_pp.special_tokens_map:
                 self.assertTrue(hasattr(_lowerCAmelCase      ,  _lowerCAmelCase		)		)
                 # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                 # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

             shutil.rmtree(_lowerCAmelCase		)

             # Save tokenizer rust, legacy_format=True
             _lowerCAmelCase						      =   tempfile.mkdtemp()

             _lowerCAmelCase						      =   tokenizer_r.save_pretrained(_lowerCAmelCase      ,  legacy_format=_lowerCAmelCase		)
             _lowerCAmelCase						      =   tokenizer_p.save_pretrained(_lowerCAmelCase		)

             # Checks it save with the same files
             self.assertSequenceEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)

             # Checks everything loads correctly in the same way
             _lowerCAmelCase						      =   tokenizer_r.from_pretrained(_lowerCAmelCase		)
             _lowerCAmelCase						      =   tokenizer_p.from_pretrained(_lowerCAmelCase		)

             # Check special tokens are set accordingly on Rust and Python
             for key in tokenizer_pp.special_tokens_map:
                 self.assertTrue(hasattr(_lowerCAmelCase      ,  _lowerCAmelCase		)		)

             shutil.rmtree(_lowerCAmelCase		)

             # Save tokenizer rust, legacy_format=False
             _lowerCAmelCase						      =   tempfile.mkdtemp()

             _lowerCAmelCase						      =   tokenizer_r.save_pretrained(_lowerCAmelCase      ,  legacy_format=_lowerCAmelCase		)
             _lowerCAmelCase						      =   tokenizer_p.save_pretrained(_lowerCAmelCase		)

             # Checks it saved the tokenizer.json file
             self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files		)		)

             # Checks everything loads correctly in the same way
             _lowerCAmelCase						      =   tokenizer_r.from_pretrained(_lowerCAmelCase		)
             _lowerCAmelCase						      =   tokenizer_p.from_pretrained(_lowerCAmelCase		)

             # Check special tokens are set accordingly on Rust and Python
             for key in tokenizer_pp.special_tokens_map:
                 self.assertTrue(hasattr(_lowerCAmelCase      ,  _lowerCAmelCase		)		)

             shutil.rmtree(_lowerCAmelCase		)







@require_torch
@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase_		(		unittest.TestCase		):
 __lowerCamelCase    :				List[str]								=       "facebook/mbart-large-50-one-to-many-mmt"
 __lowerCamelCase    :				int								=       [
     " UN Chief Says There Is No Military Solution in Syria",
     " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
 ]
 __lowerCamelCase    :				Dict								=       [
     "Şeful ONU declară că nu există o soluţie militară în Siria",
     "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
     " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
     " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
 ]
 __lowerCamelCase    :				Optional[Any]								=       [EN_CODE, 8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2]




 @classmethod
 def 				_snake_case (						cls		)     ->				List[str]:
     _lowerCAmelCase						      =   MBartaaTokenizer.from_pretrained(
         cls.checkpoint_name      ,  src_lang="en_XX"      ,  tgt_lang="ro_RO"		)
     _lowerCAmelCase						      =   1
     return cls




 def 				_snake_case (						self		)     ->				Any:
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]      ,  250001		)
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]      ,  250004		)
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]      ,  250020		)
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["mr_IN"]      ,  250038		)




 def 				_snake_case (						self		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   self.tokenizer.batch_encode_plus(self.src_text		).input_ids[0]
     self.assertListEqual(self.expected_src_tokens      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				int:
     self.assertIn(_lowerCAmelCase      ,  self.tokenizer.all_special_ids		)
     _lowerCAmelCase						      =   [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
     _lowerCAmelCase						      =   self.tokenizer.decode(_lowerCAmelCase      ,  skip_special_tokens=_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.tokenizer.decode(generated_ids[1:]      ,  skip_special_tokens=_lowerCAmelCase		)
     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)
     self.assertNotIn(self.tokenizer.eos_token      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase						      =   ["this is gunna be a long sentence " * 20]
     assert isinstance(src_text[0]      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   10
     _lowerCAmelCase						      =   self.tokenizer(_lowerCAmelCase      ,  max_length=_lowerCAmelCase      ,  truncation=_lowerCAmelCase		).input_ids[0]
     self.assertEqual(ids[0]      ,  _lowerCAmelCase		)
     self.assertEqual(ids[-1]      ,  2		)
     self.assertEqual(len(_lowerCAmelCase		)      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[str]:
     self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]		)      ,  [250053, 250001]		)




 def 				_snake_case (						self		)     ->				str:
     _lowerCAmelCase						      =   tempfile.mkdtemp()
     _lowerCAmelCase						      =   self.tokenizer.fairseq_tokens_to_ids
     self.tokenizer.save_pretrained(_lowerCAmelCase		)
     _lowerCAmelCase						      =   MBartaaTokenizer.from_pretrained(_lowerCAmelCase		)
     self.assertDictEqual(new_tok.fairseq_tokens_to_ids      ,  _lowerCAmelCase		)




 @require_torch
 def 				_snake_case (						self		)     ->				str:
     _lowerCAmelCase						      =   self.tokenizer(self.src_text      ,  text_target=self.tgt_text      ,  padding=_lowerCAmelCase      ,  return_tensors="pt"		)
     _lowerCAmelCase						      =   shift_tokens_right(batch["labels"]      ,  self.tokenizer.pad_token_id		)

     # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
     assert batch.input_ids[1][0] == EN_CODE
     assert batch.input_ids[1][-1] == 2
     assert batch.labels[1][0] == RO_CODE
     assert batch.labels[1][-1] == 2
     assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]




 @require_torch
 def 				_snake_case (						self		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   self.tokenizer(
         self.src_text      ,  text_target=self.tgt_text      ,  padding=_lowerCAmelCase      ,  truncation=_lowerCAmelCase      ,  max_length=len(self.expected_src_tokens		)      ,  return_tensors="pt"      ,  )
     _lowerCAmelCase						      =   shift_tokens_right(batch["labels"]      ,  self.tokenizer.pad_token_id		)

     self.assertIsInstance(_lowerCAmelCase      ,  _lowerCAmelCase		)

     self.assertEqual((2, 14)      ,  batch.input_ids.shape		)
     self.assertEqual((2, 14)      ,  batch.attention_mask.shape		)
     _lowerCAmelCase						      =   batch.input_ids.tolist()[0]
     self.assertListEqual(self.expected_src_tokens      ,  _lowerCAmelCase		)
     self.assertEqual(2      ,  batch.decoder_input_ids[0, 0]		)  # decoder_start_token_id
     # Test that special tokens are reset
     self.assertEqual(self.tokenizer.prefix_tokens      ,  [EN_CODE]		)
     self.assertEqual(self.tokenizer.suffix_tokens      ,  [self.tokenizer.eos_token_id]		)




 def 				_snake_case (						self		)     ->				str:
     _lowerCAmelCase						      =   self.tokenizer(self.src_text      ,  padding=_lowerCAmelCase      ,  truncation=_lowerCAmelCase      ,  max_length=3      ,  return_tensors="pt"		)
     _lowerCAmelCase						      =   self.tokenizer(
         text_target=self.tgt_text      ,  padding=_lowerCAmelCase      ,  truncation=_lowerCAmelCase      ,  max_length=10      ,  return_tensors="pt"		)
     _lowerCAmelCase						      =   targets["input_ids"]
     _lowerCAmelCase						      =   shift_tokens_right(_lowerCAmelCase      ,  self.tokenizer.pad_token_id		)

     self.assertEqual(batch.input_ids.shape[1]      ,  3		)
     self.assertEqual(batch.decoder_input_ids.shape[1]      ,  10		)





 @require_torch
 def 				_snake_case (						self		)     ->				str:
     _lowerCAmelCase						      =   self.tokenizer._build_translation_inputs(
         "A test"      ,  return_tensors="pt"      ,  src_lang="en_XX"      ,  tgt_lang="ar_AR"		)

     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)      ,  {
             # en_XX, A, test, EOS
             "input_ids": [[250004, 62, 3034, 2]],
             "attention_mask": [[1, 1, 1, 1]],
             # ar_AR
             "forced_bos_token_id": 250001,
         }      ,  )









'''simple docstring'''


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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

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

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Union[str, Any]								=       "swinv2"

 __lowerCamelCase    :				int								=       {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }




 def __init__(						self      ,  _lowerCAmelCase=224      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=96      ,  _lowerCAmelCase=[2, 2, 6, 2]      ,  _lowerCAmelCase=[3, 6, 12, 24]      ,  _lowerCAmelCase=7      ,  _lowerCAmelCase=4.0      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1E-5      ,  _lowerCAmelCase=32      ,  **_lowerCAmelCase      ,  )     ->				Tuple:
     super().__init__(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   image_size
     _lowerCAmelCase						      =   patch_size
     _lowerCAmelCase						      =   num_channels
     _lowerCAmelCase						      =   embed_dim
     _lowerCAmelCase						      =   depths
     _lowerCAmelCase						      =   len(_lowerCAmelCase		)
     _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(_lowerCAmelCase		) - 1)		)
     _lowerCAmelCase						      =   (0, 0, 0, 0)


