





'''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_		:




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




 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

     _lowerCAmelCase				=      self.get_config()

     return config, pixel_values, labels




 def 							_snake_case   (	self		)    ->							List[str]:
     _lowerCAmelCase				=      {
         "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 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Dict:
     _lowerCAmelCase				=      ViTHybridModel(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase				=      model(_lowerCAmelCase		)
     self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)		)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							List[str]:
     _lowerCAmelCase				=      self.type_sequence_label_size
     _lowerCAmelCase				=      ViTHybridForImageClassification(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase				=      model(_lowerCAmelCase			,     labels=_lowerCAmelCase		)
     self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.type_sequence_label_size)		)





 def 							_snake_case   (	self		)    ->							List[str]:
     _lowerCAmelCase				=      self.prepare_config_and_inputs()
     _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      config_and_inputs
     _lowerCAmelCase				=      {"pixel_values": pixel_values}
     return config, inputs_dict







@require_torch
class    lowerCAmelCase_		(		__magic_name__						,__magic_name__						,unittest.TestCase		):
 __lowerCamelCase						:							Optional[int]         =					(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
 __lowerCamelCase						:							Any         =					(
     {"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
     if is_torch_available()
     else {}
 )
 __lowerCamelCase						:							Tuple         =					False
 __lowerCamelCase						:							str         =					False
 __lowerCamelCase						:							Dict         =					False




 def 							_snake_case   (	self		)    ->							List[Any]:
     _lowerCAmelCase				=      ViTHybridModelTester(self		)
     _lowerCAmelCase				=      ConfigTester(self			,     config_class=_lowerCAmelCase			,     has_text_modality=_lowerCAmelCase			,     hidden_size=37		)




 def 							_snake_case   (	self		)    ->							List[str]:
     self.config_tester.run_common_tests()




 @unittest.skip(reason="ViT does not use inputs_embeds"		)
 def 							_snake_case   (	self		)    ->							Dict:
     pass




 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()

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




 def 							_snake_case   (	self		)    ->							List[str]:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()

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

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




 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Tuple:
     _lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							List[str]:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()

     _lowerCAmelCase				=      _config_zero_init(_lowerCAmelCase		)
     for model_class in self.all_model_classes:
         _lowerCAmelCase				=      model_class(config=_lowerCAmelCase		)
         # Skip the check for the backbone
         for name, module in model.named_modules():
             if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                 _lowerCAmelCase				=      [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 							_snake_case   (	self		)    ->							int:
     for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase				=      ViTHybridModel.from_pretrained(_lowerCAmelCase		)
         self.assertIsNotNone(_lowerCAmelCase		)







def 	__a():



    '''simple docstring'''





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





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




 @cached_property
 def 							_snake_case   (	self		)    ->							List[Any]:
     return (
         ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
         if is_vision_available()
         else None
     )




 @slow
 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(
         _lowerCAmelCase		)

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

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

     # verify the logits
     _lowerCAmelCase				=      torch.Size((1, 1000)		)
     self.assertEqual(outputs.logits.shape			,     _lowerCAmelCase		)

     _lowerCAmelCase				=      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 							_snake_case   (	self		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384"		)
     _lowerCAmelCase				=      ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384"			,     device_map="auto"		)

     _lowerCAmelCase				=      prepare_img()

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

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









'''simple docstring'''


from __future__ import annotations


def 	__a(SCREAMING_SNAKE_CASE_  :					float		,       SCREAMING_SNAKE_CASE_  :					float		,       SCREAMING_SNAKE_CASE_  :					float    ):



    '''simple docstring'''





    if (voltage, current, resistance).count(0    ) != 1:
        raise ValueError("One and only one argument must be 0"    )
    if resistance < 0:
        raise ValueError("Resistance cannot be negative"    )
    if voltage == 0:
        return {"voltage": float(current * resistance    )}
    elif current == 0:
        return {"current": voltage / resistance}
    elif resistance == 0:
        return {"resistance": voltage / current}
    else:
        raise ValueError("Exactly one argument must be 0"    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


