



'''simple docstring'''


import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


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


if is_torch_available():
   from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

_lowerCamelCase	:       Optional[int]      	=	25_6047
_lowerCamelCase	:       Dict      	=	25_6145

@require_sentencepiece
@require_tokenizers
class  SCREAMING_SNAKE_CASE (  _a   ,						unittest.TestCase		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  NllbTokenizer
    _SCREAMING_SNAKE_CASE	  =  NllbTokenizerFast
    _SCREAMING_SNAKE_CASE	  =  True
    _SCREAMING_SNAKE_CASE	  =  True
    _SCREAMING_SNAKE_CASE	  =  {}






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       super().setUp()

       # We have a SentencePiece fixture for testing
       UpperCamelCase					=  NllbTokenizer(UpperCamelCase__  ,						keep_accents=UpperCamelCase__ )
       tokenizer.save_pretrained(self.tmpdirname )






    def  A       (  self      :					int ):


       """simple docstring"""







       UpperCamelCase					=  NllbTokenizer(UpperCamelCase__  ,						keep_accents=UpperCamelCase__ )

       UpperCamelCase					=  tokenizer.tokenize('This is a test' )
       self.assertListEqual(UpperCamelCase__  ,						['▁This', '▁is', '▁a', '▁t', 'est'] )

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

       UpperCamelCase					=  tokenizer.tokenize('I was born in 92000, and this is falsé.' )
       self.assertListEqual(
           UpperCamelCase__  ,						[
               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',
               '.',
           ]  ,						)
       UpperCamelCase					=  tokenizer.convert_tokens_to_ids(UpperCamelCase__ )
       self.assertListEqual(
           UpperCamelCase__  ,						[
               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]
           ]  ,						)

       UpperCamelCase					=  tokenizer.convert_ids_to_tokens(UpperCamelCase__ )
       self.assertListEqual(
           UpperCamelCase__  ,						[
               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>',
               '.',
           ]  ,						)






    def  A       (  self      :					Dict ):


       """simple docstring"""







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

             UpperCamelCase					=  tempfile.mkdtemp()

             UpperCamelCase					=  tokenizer_r.save_pretrained(UpperCamelCase__ )
             UpperCamelCase					=  tokenizer_p.save_pretrained(UpperCamelCase__ )

             # 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 ) )
             UpperCamelCase					=  tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
             self.assertSequenceEqual(UpperCamelCase__  ,						UpperCamelCase__ )

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

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

             shutil.rmtree(UpperCamelCase__ )

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

             UpperCamelCase					=  tokenizer_r.save_pretrained(UpperCamelCase__  ,						legacy_format=UpperCamelCase__ )
             UpperCamelCase					=  tokenizer_p.save_pretrained(UpperCamelCase__ )

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

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

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

             shutil.rmtree(UpperCamelCase__ )

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

             UpperCamelCase					=  tokenizer_r.save_pretrained(UpperCamelCase__  ,						legacy_format=UpperCamelCase__ )
             UpperCamelCase					=  tokenizer_p.save_pretrained(UpperCamelCase__ )

             # 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
             UpperCamelCase					=  tokenizer_r.from_pretrained(UpperCamelCase__ )
             UpperCamelCase					=  tokenizer_p.from_pretrained(UpperCamelCase__ )

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

             shutil.rmtree(UpperCamelCase__ )






    @require_torch
    def  A       (  self      :					Dict ):


       """simple docstring"""







       if not self.test_seqaseq:
          return

       UpperCamelCase					=  self.get_tokenizers()
       for tokenizer in tokenizers:
          with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
             # Longer text that will definitely require truncation.
             UpperCamelCase					=  [
                 ' 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.',
             ]
             UpperCamelCase					=  [
                 '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.',
             ]
             try:
                UpperCamelCase					=  tokenizer.prepare_seqaseq_batch(
                    src_texts=UpperCamelCase__  ,						tgt_texts=UpperCamelCase__  ,						max_length=3  ,						max_target_length=1_0  ,						return_tensors='pt'  ,						src_lang='eng_Latn'  ,						tgt_lang='ron_Latn'  ,						)
             except NotImplementedError:
                return
             self.assertEqual(batch.input_ids.shape[1]  ,						3 )
             self.assertEqual(batch.labels.shape[1]  ,						1_0 )
             # max_target_length will default to max_length if not specified
             UpperCamelCase					=  tokenizer.prepare_seqaseq_batch(
                 UpperCamelCase__  ,						tgt_texts=UpperCamelCase__  ,						max_length=3  ,						return_tensors='pt' )
             self.assertEqual(batch.input_ids.shape[1]  ,						3 )
             self.assertEqual(batch.labels.shape[1]  ,						3 )

             UpperCamelCase					=  tokenizer.prepare_seqaseq_batch(
                 src_texts=UpperCamelCase__  ,						max_length=3  ,						max_target_length=1_0  ,						return_tensors='pt' )
             self.assertEqual(batch_encoder_only.input_ids.shape[1]  ,						3 )
             self.assertEqual(batch_encoder_only.attention_mask.shape[1]  ,						3 )
             self.assertNotIn('decoder_input_ids'  ,						UpperCamelCase__ )






    @unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.' )
    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       pass






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
             UpperCamelCase					=  [AddedToken('<special>'  ,						lstrip=UpperCamelCase__ )]

             UpperCamelCase					=  self.rust_tokenizer_class.from_pretrained(
                 UpperCamelCase__  ,						additional_special_tokens=UpperCamelCase__  ,						**UpperCamelCase__ )
             UpperCamelCase					=  tokenizer_r.encode('Hey this is a <special> token' )

             UpperCamelCase					=  tokenizer_r.encode('<special>'  ,						add_special_tokens=UpperCamelCase__ )[0]

             self.assertTrue(special_token_id in r_output )

             if self.test_slow_tokenizer:
                UpperCamelCase					=  self.rust_tokenizer_class.from_pretrained(
                    UpperCamelCase__  ,						additional_special_tokens=UpperCamelCase__  ,						**UpperCamelCase__  ,						)
                UpperCamelCase					=  self.tokenizer_class.from_pretrained(
                    UpperCamelCase__  ,						additional_special_tokens=UpperCamelCase__  ,						**UpperCamelCase__ )

                UpperCamelCase					=  tokenizer_p.encode('Hey this is a <special> token' )

                UpperCamelCase					=  tokenizer_cr.encode('Hey this is a <special> token' )

                self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
                self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
                self.assertTrue(special_token_id in p_output )
                self.assertTrue(special_token_id in cr_output )

@require_torch
@require_sentencepiece
@require_tokenizers
class  SCREAMING_SNAKE_CASE (  unittest.TestCase		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  """facebook/nllb-200-distilled-600M"""
    _SCREAMING_SNAKE_CASE	  =  [
        """ 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.""",
    ]
    _SCREAMING_SNAKE_CASE	  =  [
        """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.""",
    ]
    _SCREAMING_SNAKE_CASE	  =  [
        256_047,
        16_297,
        134_408,
        8_165,
        248_066,
        14_734,
        950,
        1_135,
        105_721,
        3_573,
        83,
        27_352,
        108,
        49_486,
        2,
    ]






    @classmethod
    def  A       (  cls      :					str ):


       """simple docstring"""







       UpperCamelCase					=  NllbTokenizer.from_pretrained(
           cls.checkpoint_name  ,						src_lang='eng_Latn'  ,						tgt_lang='ron_Latn' )
       UpperCamelCase					=  1
       return cls






    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab']  ,						2_5_6_0_0_1 )
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn']  ,						2_5_6_0_0_2 )
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn']  ,						2_5_6_0_5_7 )






    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
       self.assertListEqual(self.expected_src_tokens  ,						UpperCamelCase__ )






    def  A       (  self      :					Any ):


       """simple docstring"""







       self.assertIn(UpperCamelCase__  ,						self.tokenizer.all_special_ids )
       # fmt: off
       UpperCamelCase					=  [RO_CODE, 4_2_5_4, 9_8_0_6_8, 1_1_2_9_2_3, 3_9_0_7_2, 3_9_0_9, 7_1_3, 1_0_2_7_6_7, 2_6, 1_7_3_1_4, 3_5_6_4_2, 1_4_6_8_3, 3_3_1_1_8, 2_0_2_2, 6_6_9_8_7, 2, 2_5_6_0_4_7]
       # fmt: on

       UpperCamelCase					=  self.tokenizer.decode(UpperCamelCase__  ,						skip_special_tokens=UpperCamelCase__ )
       UpperCamelCase					=  self.tokenizer.decode(generated_ids[1:]  ,						skip_special_tokens=UpperCamelCase__ )
       self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
       self.assertNotIn(self.tokenizer.eos_token  ,						UpperCamelCase__ )






    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  ['this is gunna be a long sentence ' * 2_0]
       assert isinstance(src_text[0]  ,						UpperCamelCase__ )
       UpperCamelCase					=  1_0
       UpperCamelCase					=  self.tokenizer(UpperCamelCase__  ,						max_length=UpperCamelCase__  ,						truncation=UpperCamelCase__ ).input_ids[0]
       self.assertEqual(ids[-1]  ,						2 )
       self.assertEqual(ids[0]  ,						UpperCamelCase__ )
       self.assertEqual(len(UpperCamelCase__ )  ,						UpperCamelCase__ )






    def  A       (  self      :					int ):


       """simple docstring"""







       self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'] )  ,						[2_5_6_2_0_3, 3] )






    def  A       (  self      :					List[str] ):


       """simple docstring"""







       UpperCamelCase					=  tempfile.mkdtemp()
       UpperCamelCase					=  self.tokenizer.fairseq_tokens_to_ids
       self.tokenizer.save_pretrained(UpperCamelCase__ )
       UpperCamelCase					=  NllbTokenizer.from_pretrained(UpperCamelCase__ )
       self.assertDictEqual(new_tok.fairseq_tokens_to_ids  ,						UpperCamelCase__ )






    @require_torch
    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  self.tokenizer(
           self.src_text  ,						text_target=self.tgt_text  ,						padding=UpperCamelCase__  ,						truncation=UpperCamelCase__  ,						max_length=len(self.expected_src_tokens )  ,						return_tensors='pt'  ,						)
       UpperCamelCase					=  shift_tokens_right(
           batch['labels']  ,						self.tokenizer.pad_token_id  ,						self.tokenizer.lang_code_to_id['ron_Latn'] )

       self.assertIsInstance(UpperCamelCase__  ,						UpperCamelCase__ )

       self.assertEqual((2, 1_5)  ,						batch.input_ids.shape )
       self.assertEqual((2, 1_5)  ,						batch.attention_mask.shape )
       UpperCamelCase					=  batch.input_ids.tolist()[0]
       self.assertListEqual(self.expected_src_tokens  ,						UpperCamelCase__ )
       self.assertEqual(UpperCamelCase__  ,						batch.decoder_input_ids[0, 0] )  # EOS
       # 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  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.tokenizer(self.src_text  ,						padding=UpperCamelCase__  ,						truncation=UpperCamelCase__  ,						max_length=3  ,						return_tensors='pt' )
       UpperCamelCase					=  self.tokenizer(
           text_target=self.tgt_text  ,						padding=UpperCamelCase__  ,						truncation=UpperCamelCase__  ,						max_length=1_0  ,						return_tensors='pt' )
       UpperCamelCase					=  targets['input_ids']
       UpperCamelCase					=  shift_tokens_right(
           UpperCamelCase__  ,						self.tokenizer.pad_token_id  ,						decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]  ,						)

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






    @require_torch
    def  A       (  self      :					str ):


       """simple docstring"""







       UpperCamelCase					=  self.tokenizer._build_translation_inputs(
           'A test'  ,						return_tensors='pt'  ,						src_lang='eng_Latn'  ,						tgt_lang='fra_Latn' )

       self.assertEqual(
           nested_simplify(UpperCamelCase__ )  ,						{
               # A, test, EOS, en_XX
               'input_ids': [[2_5_6_0_4_7, 7_0, 7_3_5_6, 2]],
               'attention_mask': [[1, 1, 1, 1]],
               # ar_AR
               'forced_bos_token_id': 2_5_6_0_5_7,
           }  ,						)






    @require_torch
    def  A       (  self      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  True
       UpperCamelCase					=  self.tokenizer(
           'UN Chief says there is no military solution in Syria'  ,						src_lang='eng_Latn'  ,						tgt_lang='fra_Latn' )
       self.assertEqual(
           inputs.input_ids  ,						[1_6_2_9_7, 1_3_4_4_0_8, 2_5_6_5_3, 6_3_7_0, 2_4_8, 2_5_4, 1_0_3_9_2_9, 9_4_9_9_5, 1_0_8, 4_9_4_8_6, 2, 2_5_6_0_4_7] )

       UpperCamelCase					=  False
       UpperCamelCase					=  self.tokenizer(
           'UN Chief says there is no military solution in Syria'  ,						src_lang='eng_Latn'  ,						tgt_lang='fra_Latn' )
       self.assertEqual(
           inputs.input_ids  ,						[2_5_6_0_4_7, 1_6_2_9_7, 1_3_4_4_0_8, 2_5_6_5_3, 6_3_7_0, 2_4_8, 2_5_4, 1_0_3_9_2_9, 9_4_9_9_5, 1_0_8, 4_9_4_8_6, 2] )








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')


