



'''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    lowerCamelCase__				:






      """simple docstring"""




      def __init__(       self							:     List[str]			,a__							:     Any			,a__							:     Tuple=13			,a__							:     str=64			,a__							:     List[Any]=2			,a__							:     Optional[int]=3			,a__							:     Tuple=True			,a__							:     Optional[int]=True			,a__							:     Optional[Any]=32			,a__							:     Tuple=5			,a__							:     List[str]=4			,a__							:     List[Any]=37			,a__							:     str="gelu"			,a__							:     Optional[Any]=0.1			,a__							:     str=0.1			,a__							:     Tuple=10			,a__							:     List[str]=0.02			,a__							:     int=[1, 16, 4, 4]			,a__							:     Optional[int]=None			,):
             a__								= parent
             a__								= batch_size
             a__								= image_size
             a__								= patch_size
             a__								= num_channels
             a__								= is_training
             a__								= use_labels
             a__								= hidden_size
             a__								= num_hidden_layers
             a__								= num_attention_heads
             a__								= intermediate_size
             a__								= hidden_act
             a__								= hidden_dropout_prob
             a__								= attention_probs_dropout_prob
             a__								= type_sequence_label_size
             a__								= initializer_range
             a__								= scope
             a__								= 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__								= (self.image_size // 32) ** 2
             a__								= num_patches + 1




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

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

             a__								= self.get_config()

             return config, pixel_values, labels




      def lowerCAmelCase_					(       self							:     Any   ):
             a__								= {
                 "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=__lowerCamelCase			,initializer_range=self.initializer_range			,backbone_featmap_shape=self.backbone_featmap_shape			,backbone_config=__lowerCamelCase			,)




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




      def lowerCAmelCase_					(       self							:     List[Any]			,a__							:     int			,a__							:     Optional[int]			,a__							:     Union[str, Any]   ):
             a__								= self.type_sequence_label_size
             a__								= ViTHybridForImageClassification(__lowerCamelCase   )
             model.to(__lowerCamelCase   )
             model.eval()
             a__								= model(__lowerCamelCase			,labels=__lowerCamelCase   )
             self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.type_sequence_label_size)   )




      def lowerCAmelCase_					(       self							:     Tuple   ):
             a__								= self.prepare_config_and_inputs()
             a__						,					a__						,					a__								= config_and_inputs
             a__								= {"pixel_values": pixel_values}
             return config, inputs_dict



@require_torch
class    lowerCamelCase__				(			__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    unittest.TestCase						):






      """simple docstring"""
      UpperCamelCase__			 =     (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
      UpperCamelCase__			 =     (
          {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
          if is_torch_available()
          else {}
      )
      UpperCamelCase__			 =     False
      UpperCamelCase__			 =     False
      UpperCamelCase__			 =     False




      def lowerCAmelCase_					(       self							:     Tuple   ):
             a__								= ViTHybridModelTester(self   )
             a__								= ConfigTester(self			,config_class=__lowerCamelCase			,has_text_modality=__lowerCamelCase			,hidden_size=37   )




      def lowerCAmelCase_					(       self							:     List[str]   ):
             self.config_tester.run_common_tests()




      @unittest.skip(reason="ViT does not use inputs_embeds"   )
      def lowerCAmelCase_					(       self							:     Any   ):
             pass




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

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




      def lowerCAmelCase_					(       self							:     Dict   ):
             a__						,					a__								= self.model_tester.prepare_config_and_inputs_for_common()

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

                    a__								= ["pixel_values"]
                    self.assertListEqual(arg_names[:1]			,__lowerCamelCase   )




      def lowerCAmelCase_					(       self							:     Dict   ):
             a__								= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__lowerCamelCase   )




      def lowerCAmelCase_					(       self							:     List[Any]   ):
             a__								= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase   )




      def lowerCAmelCase_					(       self							:     List[Any]   ):
             a__						,					a__								= self.model_tester.prepare_config_and_inputs_for_common()

             a__								= _config_zero_init(__lowerCamelCase   )
             for model_class in self.all_model_classes:
                    a__								= model_class(config=__lowerCamelCase   )
                    # Skip the check for the backbone
                    for name, module in model.named_modules():
                           if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                                  a__								= [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 lowerCAmelCase_					(       self							:     List[str]   ):
             for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    a__								= ViTHybridModel.from_pretrained(__lowerCamelCase   )
                    self.assertIsNotNone(__lowerCamelCase   )






def 		_lowerCAmelCase				():

       """simple docstring"""

       a__								= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
       return image



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






      """simple docstring"""




      @cached_property
      def lowerCAmelCase_					(       self							:     str   ):
             return (
                 ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
                 if is_vision_available()
                 else None
             )




      @slow
      def lowerCAmelCase_					(       self							:     Dict   ):
             a__								= ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(
                 __lowerCamelCase   )

             a__								= self.default_image_processor
             a__								= prepare_img()
             a__								= image_processor(images=__lowerCamelCase			,return_tensors="pt"   ).to(__lowerCamelCase   )

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

             # verify the logits
             a__								= torch.Size((1, 10_00)   )
             self.assertEqual(outputs.logits.shape			,__lowerCamelCase   )

             a__								= torch.tensor([-1.9090, -0.4993, -0.2389]   ).to(__lowerCamelCase   )

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




      @slow
      @require_accelerate
      def lowerCAmelCase_					(       self							:     int   ):
             a__								= ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384"   )
             a__								= ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384"			,device_map="auto"   )

             a__								= prepare_img()

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

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





"""simple docstring"""






from copy import deepcopy



class     UpperCAmelCase    :



   def __init__(       self    :	Optional[Any]							,       __lowerCamelCase    :	list[int] | None = None							,       __lowerCamelCase    :	int | None = None				):



     """simple docstring"""

     if arr is None and size is not None:
       _snake_case   	=      size
       _snake_case   	=      [0] * size
     elif arr is not None:
       self.init(__lowerCamelCase				)
     else:
       raise ValueError('''Either arr or size must be specified'''				)



   def    __UpperCAmelCase    (       self    :	Any							,       __lowerCamelCase    :	list[int]				):



     """simple docstring"""
     _snake_case   	=      len(__lowerCamelCase				)
     _snake_case   	=      deepcopy(__lowerCamelCase				)
     for i in range(1							,       self.size				):
       _snake_case   	=      self.next_(__lowerCamelCase				)
       if j < self.size:
         self.tree[j] += self.tree[i]



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



     """simple docstring"""
     _snake_case   	=      self.tree[:]
     for i in range(self.size - 1							,       0							,       -1				):
       _snake_case   	=      self.next_(__lowerCamelCase				)
       if j < self.size:
         arr[j] -= arr[i]
     return arr



   @staticmethod
   def    __UpperCAmelCase    (       __lowerCamelCase    :	int				):



     """simple docstring"""
     return index + (index & (-index))



   @staticmethod
   def    __UpperCAmelCase    (       __lowerCamelCase    :	int				):



     """simple docstring"""
     return index - (index & (-index))



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     if index == 0:
       self.tree[0] += value
       return
     while index < self.size:
       self.tree[index] += value
       _snake_case   	=      self.next_(__lowerCamelCase				)



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



     """simple docstring"""
     self.add(__lowerCamelCase							,       value - self.get(__lowerCamelCase				)				)



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



     """simple docstring"""
     if right == 0:
       return 0
     _snake_case   	=      self.tree[0]
     right -= 1  # make right inclusive
     while right > 0:
       result += self.tree[right]
       _snake_case   	=      self.prev(__lowerCamelCase				)
     return result



   def    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     return self.prefix(__lowerCamelCase				) - self.prefix(__lowerCamelCase				)



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



     """simple docstring"""
     return self.query(__lowerCamelCase							,       index + 1				)





   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     value -= self.tree[0]
     if value < 0:
       return -1

     _snake_case   	=      1  # Largest power of 2 <= size
     while j * 2 < self.size:
       j *= 2

     _snake_case   	=      0

     while j > 0:
       if i + j < self.size and self.tree[i + j] <= value:
         value -= self.tree[i + j]
         i += j
       j //= 2
     return i


if __name__ == "__main__":
     import doctest

     doctest.testmod()





