


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:	List[Any]						,				a:	List[str]						,				a:	List[Any]=13						,				a:	str=64						,				a:	Optional[Any]=2						,				a:	Dict=3						,				a:	Tuple=True						,				a:	Tuple=True						,				a:	Any=32						,				a:	int=5						,				a:	int=4						,				a:	List[Any]=37						,				a:	List[str]="gelu"						,				a:	Dict=0.1						,				a:	Dict=0.1						,				a:	Tuple=10						,				a:	Optional[int]=0.0_2						,				a:	Union[str, Any]=[1, 16, 4, 4]						,				a:	str=None						,				):
       __lowerCamelCase		:							Tuple		 =						parent
       __lowerCamelCase		:							Optional[Any]		 =						batch_size
       __lowerCamelCase		:							Union[str, Any]		 =						image_size
       __lowerCamelCase		:							Dict		 =						patch_size
       __lowerCamelCase		:							Optional[int]		 =						num_channels
       __lowerCamelCase		:							Optional[Any]		 =						is_training
       __lowerCamelCase		:							str		 =						use_labels
       __lowerCamelCase		:							Optional[Any]		 =						hidden_size
       __lowerCamelCase		:							Any		 =						num_hidden_layers
       __lowerCamelCase		:							str		 =						num_attention_heads
       __lowerCamelCase		:							Any		 =						intermediate_size
       __lowerCamelCase		:							Any		 =						hidden_act
       __lowerCamelCase		:							List[str]		 =						hidden_dropout_prob
       __lowerCamelCase		:							str		 =						attention_probs_dropout_prob
       __lowerCamelCase		:							Optional[Any]		 =						type_sequence_label_size
       __lowerCamelCase		:							Union[str, Any]		 =						initializer_range
       __lowerCamelCase		:							List[Any]		 =						scope
       __lowerCamelCase		:							Optional[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
       __lowerCamelCase		:							int		 =						(self.image_size // 32) ** 2
       __lowerCamelCase		:							List[str]		 =						num_patches + 1




      def 					_snake_case     (    self:	List[Any]    ):
       __lowerCamelCase		:							Any		 =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

       __lowerCamelCase		:							str		 =						self.get_config()

       return config, pixel_values, labels




      def 					_snake_case     (    self:	Optional[Any]    ):
       __lowerCamelCase		:							Optional[int]		 =						{
           '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=a						,				initializer_range=self.initializer_range						,				backbone_featmap_shape=self.backbone_featmap_shape						,				backbone_config=a						,				)




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




      def 					_snake_case     (    self:	Tuple						,				a:	Optional[Any]						,				a:	List[Any]						,				a:	Optional[int]    ):
       __lowerCamelCase		:							Tuple		 =						self.type_sequence_label_size
       __lowerCamelCase		:							Optional[Any]		 =						ViTHybridForImageClassification(a    )
       model.to(a    )
       model.eval()
       __lowerCamelCase		:							int		 =						model(a						,				labels=a    )
       self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.type_sequence_label_size)    )




      def 					_snake_case     (    self:	int    ):
       __lowerCamelCase		:							str		 =						self.prepare_config_and_inputs()
       __lowerCamelCase		:							Union[str, Any]		 =						config_and_inputs
       __lowerCamelCase		:							str		 =						{'pixel_values': pixel_values}
       return config, inputs_dict



@require_torch
class 	A_ (	__UpperCamelCase ,				__UpperCamelCase ,				unittest.TestCase   ):




      '''simple docstring'''


      __snake_case 	=       (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
      __snake_case 	=       (
          {"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
          if is_torch_available()
          else {}
      )
      __snake_case 	=       False
      __snake_case 	=       False
      __snake_case 	=       False




      def 					_snake_case     (    self:	str    ):
       __lowerCamelCase		:							List[Any]		 =						ViTHybridModelTester(self    )
       __lowerCamelCase		:							Dict		 =						ConfigTester(self						,				config_class=a						,				has_text_modality=a						,				hidden_size=37    )




      def 					_snake_case     (    self:	Dict    ):
       self.config_tester.run_common_tests()




      @unittest.skip(reason='ViT does not use inputs_embeds'    )
      def 					_snake_case     (    self:	List[str]    ):
       pass




      def 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase		:							Dict		 =						self.model_tester.prepare_config_and_inputs_for_common()

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




      def 					_snake_case     (    self:	Optional[int]    ):
       __lowerCamelCase		:							str		 =						self.model_tester.prepare_config_and_inputs_for_common()

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

        __lowerCamelCase		:							List[str]		 =						['pixel_values']
        self.assertListEqual(arg_names[:1]						,				a    )




      def 					_snake_case     (    self:	Any    ):
       __lowerCamelCase		:							Union[str, Any]		 =						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a    )




      def 					_snake_case     (    self:	List[Any]    ):
       __lowerCamelCase		:							Tuple		 =						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*a    )




      def 					_snake_case     (    self:	Dict    ):
       __lowerCamelCase		:							str		 =						self.model_tester.prepare_config_and_inputs_for_common()

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



def 				UpperCamelCase__					(  ):
 __lowerCamelCase		:							Optional[int]		 =						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
 return image



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




      '''simple docstring'''


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




      @slow
      def 					_snake_case     (    self:	Optional[Any]    ):
       __lowerCamelCase		:							List[Any]		 =						ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(
           a    )

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

       # forward pass
       with torch.no_grad():
        __lowerCamelCase		:							Union[str, Any]		 =						model(**a    )

       # verify the logits
       __lowerCamelCase		:							Tuple		 =						torch.Size((1, 1000)    )
       self.assertEqual(outputs.logits.shape						,				a    )

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

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




      @slow
      @require_accelerate
      def 					_snake_case     (    self:	Optional[Any]    ):
       __lowerCamelCase		:							Tuple		 =						ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384'    )
       __lowerCamelCase		:							Optional[Any]		 =						ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384'						,				device_map='auto'    )

       __lowerCamelCase		:							str		 =						prepare_img()

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

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





from __future__ import annotations
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[Any]		 =						0.00
 __lowerCamelCase		:							Tuple		 =						0
 for resistor in resistors:
  if resistor <= 0:
   __lowerCamelCase		:							Union[str, Any]		 =						f'Resistor at index {index} has a negative or zero value!'
   raise ValueError(SCREAMING_SNAKE_CASE__						)
  first_sum += 1 / float(SCREAMING_SNAKE_CASE__						)
  index += 1
 return 1 / first_sum
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Union[str, Any]		 =						0.00
 __lowerCamelCase		:							str		 =						0
 for resistor in resistors:
  sum_r += resistor
  if resistor < 0:
   __lowerCamelCase		:							Any		 =						f'Resistor at index {index} has a negative value!'
   raise ValueError(SCREAMING_SNAKE_CASE__						)
  index += 1
 return sum_r


if __name__ == "__main__":
       import doctest

       doctest.testmod()

