"""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


_UpperCamelCase   :						List[str]      =    get_tests_dir("fixtures/test_sentencepiece.model")

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

_UpperCamelCase   :						Any      =    25_00_04
_UpperCamelCase   :						Optional[int]      =    25_00_20


@require_sentencepiece
@require_tokenizers
class      UpperCAmelCase_	(    __UpperCAmelCase ,							unittest.TestCase):
      lowerCamelCase__		:    Dict   =  MBartaaTokenizer
      lowerCamelCase__		:    Any   =  MBartaaTokenizerFast
      lowerCamelCase__		:    Any   =  True
      lowerCamelCase__		:    Tuple   =  True


      def 		_UpperCAmelCase       (   self							)       ->					Optional[Any]:
             super().setUp()

             # We have a SentencePiece fixture for testing
             lowercase__			:		str       =	MBartaaTokenizer(a		,			src_lang='en_XX'		,			tgt_lang='ro_RO'		,			keep_accents=a							)
             tokenizer.save_pretrained(self.tmpdirname							)


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             lowercase__			:		Dict       =	'<s>'
             lowercase__			:		Union[str, Any]       =	0

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


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             lowercase__			:		Tuple       =	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(a							)		,			1_0_5_4							)


      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             self.assertEqual(self.get_tokenizer().vocab_size		,			1_0_5_4							)


      def 		_UpperCAmelCase       (   self							)       ->					str:
             lowercase__			:		Any       =	MBartaaTokenizer(a		,			src_lang='en_XX'		,			tgt_lang='ro_RO'		,			keep_accents=a							)

             lowercase__			:		Dict       =	tokenizer.tokenize('This is a test'							)
             self.assertListEqual(a		,			['▁This', '▁is', '▁a', '▁t', 'est']							)

             self.assertListEqual(
                 tokenizer.convert_tokens_to_ids(a							)		,			[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]		,			)

             lowercase__			:		List[Any]       =	tokenizer.tokenize('I was born in 92000, and this is falsé.'							)
             self.assertListEqual(
                 a		,			[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', '.']		,			)
             lowercase__			:		Optional[Any]       =	tokenizer.convert_tokens_to_ids(a							)
             self.assertListEqual(
                 a		,			[
                     value + tokenizer.fairseq_offset
                     for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
                 ]		,			)

             lowercase__			:		Optional[Any]       =	tokenizer.convert_ids_to_tokens(a							)
             self.assertListEqual(
                 a		,			[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 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             lowercase__			:		List[str]       =	{'input_ids': [[2_5_0_0_0_4, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [2_5_0_0_0_4, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 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], [2_5_0_0_0_4, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 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=a		,			model_name='facebook/mbart-large-50'		,			revision='d3913889c59cd5c9e456b269c376325eabad57e2'		,			)


      def 		_UpperCAmelCase       (   self							)       ->					Any:

             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

             lowercase__			:		Dict       =	(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})"""							):
                           lowercase__			:		Tuple       =	self.rust_tokenizer_class.from_pretrained(a		,			**a							)
                           lowercase__			:		List[str]       =	self.tokenizer_class.from_pretrained(a		,			**a							)

                           lowercase__			:		int       =	tempfile.mkdtemp()

                           lowercase__			:		Dict       =	tokenizer_r.save_pretrained(a							)
                           lowercase__			:		Union[str, Any]       =	tokenizer_p.save_pretrained(a							)

                           # 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							)							)
                           lowercase__			:		Any       =	tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f							)
                           self.assertSequenceEqual(a		,			a							)

                           # Checks everything loads correctly in the same way
                           lowercase__			:		Any       =	tokenizer_r.from_pretrained(a							)
                           lowercase__			:		Any       =	tokenizer_p.from_pretrained(a							)

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

                           shutil.rmtree(a							)

                           # Save tokenizer rust, legacy_format=True
                           lowercase__			:		Any       =	tempfile.mkdtemp()

                           lowercase__			:		str       =	tokenizer_r.save_pretrained(a		,			legacy_format=a							)
                           lowercase__			:		Dict       =	tokenizer_p.save_pretrained(a							)

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

                           # Checks everything loads correctly in the same way
                           lowercase__			:		Union[str, Any]       =	tokenizer_r.from_pretrained(a							)
                           lowercase__			:		Optional[Any]       =	tokenizer_p.from_pretrained(a							)

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

                           shutil.rmtree(a							)

                           # Save tokenizer rust, legacy_format=False
                           lowercase__			:		Any       =	tempfile.mkdtemp()

                           lowercase__			:		Optional[int]       =	tokenizer_r.save_pretrained(a		,			legacy_format=a							)
                           lowercase__			:		str       =	tokenizer_p.save_pretrained(a							)

                           # 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
                           lowercase__			:		List[str]       =	tokenizer_r.from_pretrained(a							)
                           lowercase__			:		Dict       =	tokenizer_p.from_pretrained(a							)

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

                           shutil.rmtree(a							)





@require_torch
@require_sentencepiece
@require_tokenizers
class      UpperCAmelCase_	(    unittest.TestCase):
      lowerCamelCase__		:    str   =  '''facebook/mbart-large-50-one-to-many-mmt'''
      lowerCamelCase__		:    Union[str, Any]   =  [
          ''' 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__		:    List[Any]   =  [EN_CODE, 8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2]


      @classmethod
      def 		_UpperCAmelCase       (   cls							)       ->					Any:
             lowercase__			:		MBartaaTokenizer       =	MBartaaTokenizer.from_pretrained(
                 cls.checkpoint_name		,			src_lang='en_XX'		,			tgt_lang='ro_RO'							)
             lowercase__			:		Dict       =	1
             return cls


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']		,			2_5_0_0_0_1							)
             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']		,			2_5_0_0_0_4							)
             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']		,			2_5_0_0_2_0							)
             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN']		,			2_5_0_0_3_8							)


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             lowercase__			:		Optional[Any]       =	self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
             self.assertListEqual(self.expected_src_tokens		,			a							)


      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             self.assertIn(a		,			self.tokenizer.all_special_ids							)
             lowercase__			:		Any       =	[RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
             lowercase__			:		Any       =	self.tokenizer.decode(a		,			skip_special_tokens=a							)
             lowercase__			:		List[str]       =	self.tokenizer.decode(generated_ids[1:]		,			skip_special_tokens=a							)
             self.assertEqual(a		,			a							)
             self.assertNotIn(self.tokenizer.eos_token		,			a							)


      def 		_UpperCAmelCase       (   self							)       ->					int:
             lowercase__			:		Tuple       =	['this is gunna be a long sentence ' * 2_0]
             assert isinstance(src_text[0]		,			a							)
             lowercase__			:		str       =	1_0
             lowercase__			:		Optional[Any]       =	self.tokenizer(a		,			max_length=a		,			truncation=a							).input_ids[0]
             self.assertEqual(ids[0]		,			a							)
             self.assertEqual(ids[-1]		,			2							)
             self.assertEqual(len(a							)		,			a							)


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']							)		,			[2_5_0_0_5_3, 2_5_0_0_0_1]							)


      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             lowercase__			:		List[Any]       =	tempfile.mkdtemp()
             lowercase__			:		Dict       =	self.tokenizer.fairseq_tokens_to_ids
             self.tokenizer.save_pretrained(a							)
             lowercase__			:		Optional[int]       =	MBartaaTokenizer.from_pretrained(a							)
             self.assertDictEqual(new_tok.fairseq_tokens_to_ids		,			a							)


      @require_torch
      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             lowercase__			:		Union[str, Any]       =	self.tokenizer(self.src_text		,			text_target=self.tgt_text		,			padding=a		,			return_tensors='pt'							)
             lowercase__			:		Dict       =	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 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             lowercase__			:		List[Any]       =	self.tokenizer(
                 self.src_text		,			text_target=self.tgt_text		,			padding=a		,			truncation=a		,			max_length=len(self.expected_src_tokens							)		,			return_tensors='pt'		,			)
             lowercase__			:		Optional[int]       =	shift_tokens_right(batch['labels']		,			self.tokenizer.pad_token_id							)

             self.assertIsInstance(a		,			a							)

             self.assertEqual((2, 1_4)		,			batch.input_ids.shape							)
             self.assertEqual((2, 1_4)		,			batch.attention_mask.shape							)
             lowercase__			:		Union[str, Any]       =	batch.input_ids.tolist()[0]
             self.assertListEqual(self.expected_src_tokens		,			a							)
             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 		_UpperCAmelCase       (   self							)       ->					str:
             lowercase__			:		Any       =	self.tokenizer(self.src_text		,			padding=a		,			truncation=a		,			max_length=3		,			return_tensors='pt'							)
             lowercase__			:		int       =	self.tokenizer(
                 text_target=self.tgt_text		,			padding=a		,			truncation=a		,			max_length=1_0		,			return_tensors='pt'							)
             lowercase__			:		Optional[int]       =	targets['input_ids']
             lowercase__			:		Optional[Any]       =	shift_tokens_right(a		,			self.tokenizer.pad_token_id							)

             self.assertEqual(batch.input_ids.shape[1]		,			3							)
             self.assertEqual(batch.decoder_input_ids.shape[1]		,			1_0							)


      @require_torch
      def 		_UpperCAmelCase       (   self							)       ->					int:
             lowercase__			:		Tuple       =	self.tokenizer._build_translation_inputs(
                 'A test'		,			return_tensors='pt'		,			src_lang='en_XX'		,			tgt_lang='ar_AR'							)

             self.assertEqual(
                 nested_simplify(a							)		,			{
                     # en_XX, A, test, EOS
                     'input_ids': [[2_5_0_0_0_4, 6_2, 3_0_3_4, 2]],
                     'attention_mask': [[1, 1, 1, 1]],
                     # ar_AR
                     'forced_bos_token_id': 2_5_0_0_0_1,
                 }		,			)








import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
    from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image





class  A__    :




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase=13					,  lowercase=64					,  lowercase=2					,  lowercase=3					,  lowercase=True					,  lowercase=True					,  lowercase=32					,  lowercase=5					,  lowercase=4					,  lowercase=37					,  lowercase="gelu"					,  lowercase=0.1					,  lowercase=0.1					,  lowercase=10					,  lowercase=0.02					,  lowercase=[1, 16, 4, 4]					,  lowercase=None					,  )			->						List[Any]:







          '''simple docstring'''

          a__						:	Optional[int] =				parent
          a__						:	Optional[int] =				batch_size
          a__						:	Any =				image_size
          a__						:	Optional[Any] =				patch_size
          a__						:	Optional[Any] =				num_channels
          a__						:	int =				is_training
          a__						:	List[str] =				use_labels
          a__						:	List[str] =				hidden_size
          a__						:	Tuple =				num_hidden_layers
          a__						:	Optional[Any] =				num_attention_heads
          a__						:	Union[str, Any] =				intermediate_size
          a__						:	Optional[int] =				hidden_act
          a__						:	Optional[Any] =				hidden_dropout_prob
          a__						:	Any =				attention_probs_dropout_prob
          a__						:	Any =				type_sequence_label_size
          a__						:	Tuple =				initializer_range
          a__						:	Tuple =				scope
          a__						:	int =				backbone_featmap_shape

          # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
          # the number of patches is based on the feature map of the backbone, which by default uses an output stride
          # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
          a__						:	Any =				(self.image_size // 32) ** 2
          a__						:	List[Any] =				num_patches + 1

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	List[str] =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

          a__						:	int =				None
          if self.use_labels:
                 a__						:	int =				ids_tensor([self.batch_size]					,  self.type_sequence_label_size)

          a__						:	List[str] =				self.get_config()

          return config, pixel_values, labels

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	List[str] =				{
              'global_padding': 'same',
              'layer_type': 'bottleneck',
              'depths': [3, 4, 9],
              'out_features': ['stage1', 'stage2', 'stage3'],
              'embedding_dynamic_padding': True,
              'hidden_sizes': [4, 8, 16, 32],
              'num_groups': 2,
          }

          return ViTHybridConfig(
              image_size=self.image_size					,  patch_size=self.patch_size					,  num_channels=self.num_channels					,  hidden_size=self.hidden_size					,  num_hidden_layers=self.num_hidden_layers					,  num_attention_heads=self.num_attention_heads					,  intermediate_size=self.intermediate_size					,  hidden_act=self.hidden_act					,  hidden_dropout_prob=self.hidden_dropout_prob					,  attention_probs_dropout_prob=self.attention_probs_dropout_prob					,  is_decoder=lowercase					,  initializer_range=self.initializer_range					,  backbone_featmap_shape=self.backbone_featmap_shape					,  backbone_config=lowercase					,  )

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						List[str]:







          '''simple docstring'''

          a__						:	List[str] =				ViTHybridModel(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Union[str, Any] =				model(lowercase)
          self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	Dict =				self.type_sequence_label_size
          a__						:	Union[str, Any] =				ViTHybridForImageClassification(lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Tuple =				model(lowercase					,  labels=lowercase)
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.type_sequence_label_size))

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	List[str] =				self.prepare_config_and_inputs()
          a__ ,							a__ ,							a__						:	Union[str, Any] =				config_and_inputs
          a__						:	str =				{'pixel_values': pixel_values}
          return config, inputs_dict





@require_torch
class  A__    (							__UpperCAmelCase ,  __UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				Optional[Any]         =  (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
   __A   :				List[str]         =  (
       {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
       if is_torch_available()
       else {}
   )
   __A   :				Any         =  False
   __A   :				Optional[int]         =  False
   __A   :				Optional[Any]         =  False

   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          a__						:	Any =				ViTHybridModelTester(self)
          a__						:	Any =				ConfigTester(self					,  config_class=lowercase					,  has_text_modality=lowercase					,  hidden_size=37)

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          self.config_tester.run_common_tests()

   @unittest.skip(reason='ViT does not use inputs_embeds')
   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          pass

   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          a__ ,							a__						:	List[str] =				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 a__						:	str =				model_class(lowercase)
                 self.assertIsInstance(model.get_input_embeddings()					,  (nn.Module))
                 a__						:	str =				model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(lowercase					,  nn.Linear))

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__ ,							a__						:	str =				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 a__						:	Union[str, Any] =				model_class(lowercase)
                 a__						:	Union[str, Any] =				inspect.signature(model.forward)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 a__						:	Optional[Any] =				[*signature.parameters.keys()]

                 a__						:	Dict =				['pixel_values']
                 self.assertListEqual(arg_names[:1]					,  lowercase)

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	List[Any] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase)

   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          a__						:	Any =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowercase)

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__ ,							a__						:	Tuple =				self.model_tester.prepare_config_and_inputs_for_common()

          a__						:	Tuple =				_config_zero_init(lowercase)
          for model_class in self.all_model_classes:
                 a__						:	List[Any] =				model_class(config=lowercase)
                 # Skip the check for the backbone
                 for name, module in model.named_modules():
                        if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                               a__						:	Dict =				[F'{name}.{key}' for key in module.state_dict().keys()]
                               break

                 for name, param in model.named_parameters():
                        if param.requires_grad:
                               if name in backbone_params:
                                      continue
                               self.assertIn(
                                   ((param.data.mean() * 1e9).round() / 1e9).item()					,  [0.0, 1.0]					,  msg=F'Parameter {name} of model {model_class} seems not properly initialized'					,  )

   @slow
   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a__						:	Optional[Any] =				ViTHybridModel.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)



def A_	(		)  ->		int:
       a__						:	Dict =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
       return image





@require_torch
@require_vision
class  A__    (							unittest.TestCase	):




   """simple docstring"""



   @cached_property
   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          return (
              ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0])
              if is_vision_available()
              else None
          )

   @slow
   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	List[str] =				ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
              lowercase)

          a__						:	List[str] =				self.default_image_processor
          a__						:	List[Any] =				prepare_img()
          a__						:	Any =				image_processor(images=lowercase					,  return_tensors='pt').to(lowercase)

          # forward pass
          with torch.no_grad():
                 a__						:	Optional[Any] =				model(**lowercase)

          # verify the logits
          a__						:	Optional[Any] =				torch.Size((1, 1000))
          self.assertEqual(outputs.logits.shape					,  lowercase)

          a__						:	Any =				torch.tensor([-1.90_90, -0.49_93, -0.23_89]).to(lowercase)

          self.assertTrue(torch.allclose(outputs.logits[0, :3]					,  lowercase					,  atol=1e-4))

   @slow
   @require_accelerate
   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__						:	List[str] =				ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384')
          a__						:	Union[str, Any] =				ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384'					,  device_map='auto')

          a__						:	Any =				prepare_img()

          a__						:	str =				image_processor(images=lowercase					,  return_tensors='pt')
          a__						:	List[Any] =				model(**lowercase)
          a__						:	int =				outputs.logits
          # model predicts one of the 1000 ImageNet classes
          a__						:	List[str] =				logits.argmax(-1).item()

          self.assertTrue(model.config.idalabel[predicted_class_idx]					,  'tabby, tabby cat')


