



"""simple docstring"""






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     UpperCAmelCase    :



   def __init__(       self    :	List[str]							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	Tuple=1_3							,       __lowerCamelCase    :	str=6_4							,       __lowerCamelCase    :	List[Any]=2							,       __lowerCamelCase    :	Optional[int]=3							,       __lowerCamelCase    :	Tuple=True							,       __lowerCamelCase    :	Optional[int]=True							,       __lowerCamelCase    :	Optional[Any]=3_2							,       __lowerCamelCase    :	Tuple=5							,       __lowerCamelCase    :	List[str]=4							,       __lowerCamelCase    :	List[Any]=3_7							,       __lowerCamelCase    :	str="gelu"							,       __lowerCamelCase    :	Optional[Any]=0.1							,       __lowerCamelCase    :	str=0.1							,       __lowerCamelCase    :	Tuple=1_0							,       __lowerCamelCase    :	List[str]=0.0_2							,       __lowerCamelCase    :	int=[1, 1_6, 4, 4]							,       __lowerCamelCase    :	Optional[int]=None							,       ):



     """simple docstring"""
     _snake_case   	=      parent
     _snake_case   	=      batch_size
     _snake_case   	=      image_size
     _snake_case   	=      patch_size
     _snake_case   	=      num_channels
     _snake_case   	=      is_training
     _snake_case   	=      use_labels
     _snake_case   	=      hidden_size
     _snake_case   	=      num_hidden_layers
     _snake_case   	=      num_attention_heads
     _snake_case   	=      intermediate_size
     _snake_case   	=      hidden_act
     _snake_case   	=      hidden_dropout_prob
     _snake_case   	=      attention_probs_dropout_prob
     _snake_case   	=      type_sequence_label_size
     _snake_case   	=      initializer_range
     _snake_case   	=      scope
     _snake_case   	=      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
     _snake_case   	=      (self.image_size // 3_2) ** 2
     _snake_case   	=      num_patches + 1



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

     _snake_case   	=      self.get_config()

     return config, pixel_values, labels



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     _snake_case   	=      {
         '''global_padding''': '''same''',
         '''layer_type''': '''bottleneck''',
         '''depths''': [3, 4, 9],
         '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
         '''embedding_dynamic_padding''': True,
         '''hidden_sizes''': [4, 8, 1_6, 3_2],
         '''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=__lowerCamelCase							,       initializer_range=self.initializer_range							,       backbone_featmap_shape=self.backbone_featmap_shape							,       backbone_config=__lowerCamelCase							,       )



   def    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	Optional[Any]							,       __lowerCamelCase    :	Optional[Any]							,       __lowerCamelCase    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      ViTHybridModel(config=__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()
     _snake_case   	=      model(__lowerCamelCase				)
     self.parent.assertEqual(result.last_hidden_state.shape							,       (self.batch_size, self.seq_length, self.hidden_size)				)



   def    __UpperCAmelCase    (       self    :	List[Any]							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      self.type_sequence_label_size
     _snake_case   	=      ViTHybridForImageClassification(__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()
     _snake_case   	=      model(__lowerCamelCase							,       labels=__lowerCamelCase				)
     self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.type_sequence_label_size)				)





   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     _snake_case   	=      self.prepare_config_and_inputs()
     _snake_case	,     _snake_case	,     _snake_case   	=      config_and_inputs
     _snake_case   	=      {'''pixel_values''': pixel_values}
     return config, inputs_dict



@require_torch
class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,unittest.TestCase  ):
   A__		:	List[str]           =  (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
   A__		:	Optional[int]           =  (
       {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
       if is_torch_available()
       else {}
   )
   A__		:	Dict           =  False
   A__		:	List[Any]           =  False
   A__		:	Optional[int]           =  False



   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     _snake_case   	=      ViTHybridModelTester(self				)
     _snake_case   	=      ConfigTester(self							,       config_class=__lowerCamelCase							,       has_text_modality=__lowerCamelCase							,       hidden_size=3_7				)



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     self.config_tester.run_common_tests()



   @unittest.skip(reason='''ViT does not use inputs_embeds'''				)
   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     pass



   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

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



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       _snake_case   	=      model_class(__lowerCamelCase				)
       _snake_case   	=      inspect.signature(model.forward				)
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       _snake_case   	=      [*signature.parameters.keys()]

       _snake_case   	=      ['''pixel_values''']
       self.assertListEqual(arg_names[:1]							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

     _snake_case   	=      _config_zero_init(__lowerCamelCase				)
     for model_class in self.all_model_classes:
       _snake_case   	=      model_class(config=__lowerCamelCase				)
       # Skip the check for the backbone
       for name, module in model.named_modules():
         if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
           _snake_case   	=      [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    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _snake_case   	=      ViTHybridModel.from_pretrained(__lowerCamelCase				)
       self.assertIsNotNone(__lowerCamelCase				)




def 			snake_case		(		)						->				List[Any]:
  _snake_case   	=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
  return image







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



   @cached_property
   def    __UpperCAmelCase    (       self    :	str				):



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



   @slow
   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     _snake_case   	=      ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(
         __lowerCamelCase				)

     _snake_case   	=      self.default_image_processor
     _snake_case   	=      prepare_img()
     _snake_case   	=      image_processor(images=__lowerCamelCase							,       return_tensors='''pt'''				).to(__lowerCamelCase				)

     # forward pass
     with torch.no_grad():
       _snake_case   	=      model(**__lowerCamelCase				)

     # verify the logits
     _snake_case   	=      torch.Size((1, 1_0_0_0)				)
     self.assertEqual(outputs.logits.shape							,       __lowerCamelCase				)

     _snake_case   	=      torch.tensor([-1.9_0_9_0, -0.4_9_9_3, -0.2_3_8_9]				).to(__lowerCamelCase				)

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





   @slow
   @require_accelerate
   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384'''				)
     _snake_case   	=      ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384'''							,       device_map='''auto'''				)

     _snake_case   	=      prepare_img()

     _snake_case   	=      image_processor(images=__lowerCamelCase							,       return_tensors='''pt'''				)
     _snake_case   	=      model(**__lowerCamelCase				)
     _snake_case   	=      outputs.logits
     # model predicts one of the 1000 ImageNet classes
     _snake_case   	=      logits.argmax(-1				).item()

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










"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


snake_case   					=				{'''tokenization_herbert''': ['''HerbertTokenizer''']}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case   					=				['''HerbertTokenizerFast''']


if TYPE_CHECKING:
     from .tokenization_herbert import HerbertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_herbert_fast import HerbertTokenizerFast

else:
     import sys

     snake_case   					=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





