




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      :						str		,							A_      :						Union[str, Any]		,							A_      :						List[str]=13		,							A_      :						List[Any]=64		,							A_      :						List[Any]=2		,							A_      :						List[Any]=3		,							A_      :						Tuple=True		,							A_      :						int=True		,							A_      :						List[str]=32		,							A_      :						Dict=5		,							A_      :						List[Any]=4		,							A_      :						Optional[int]=37		,							A_      :						str="gelu"		,							A_      :						Optional[int]=0.1		,							A_      :						List[str]=0.1		,							A_      :						Tuple=10		,							A_      :						Optional[int]=0.02		,							A_      :						List[str]=[1, 16, 4, 4]		,							A_      :						Optional[int]=None		,							)     ->			Optional[int]:

       """simple docstring"""






       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        a__				(       self      :						Any						)     ->			Dict:

       """simple docstring"""






       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        a__				(       self      :						List[Any]						)     ->			Dict:

       """simple docstring"""






       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=UpperCamelCase_		,							initializer_range=self.initializer_range		,							backbone_featmap_shape=self.backbone_featmap_shape		,							backbone_config=UpperCamelCase_		,							)

 def        a__				(       self      :						Optional[int]		,							A_      :						List[str]		,							A_      :						Any		,							A_      :						str						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					ViTHybridModel(config=UpperCamelCase_						)
       model.to(UpperCamelCase_						)
       model.eval()
       lowerCamelCase_	  =					model(UpperCamelCase_						)
       self.parent.assertEqual(result.last_hidden_state.shape		,							(self.batch_size, self.seq_length, self.hidden_size)						)

 def        a__				(       self      :						int		,							A_      :						Union[str, Any]		,							A_      :						str		,							A_      :						Tuple						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					self.type_sequence_label_size
       lowerCamelCase_	  =					ViTHybridForImageClassification(UpperCamelCase_						)
       model.to(UpperCamelCase_						)
       model.eval()
       lowerCamelCase_	  =					model(UpperCamelCase_		,							labels=UpperCamelCase_						)
       self.parent.assertEqual(result.logits.shape		,							(self.batch_size, self.type_sequence_label_size)						)





 def        a__				(       self      :						int						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					self.prepare_config_and_inputs()
       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					config_and_inputs
       lowerCamelCase_	  =					{'pixel_values': pixel_values}
       return config, inputs_dict







@require_torch
class 						A(		__lowerCamelCase							,   __lowerCamelCase							,   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        a__				(       self      :						str						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					ViTHybridModelTester(self						)
       lowerCamelCase_	  =					ConfigTester(self		,							config_class=UpperCamelCase_		,							has_text_modality=UpperCamelCase_		,							hidden_size=37						)

 def        a__				(       self      :						Optional[int]						)     ->			Dict:

       """simple docstring"""






       self.config_tester.run_common_tests()

 @unittest.skip(reason='ViT does not use inputs_embeds'						)
 def        a__				(       self      :						List[str]						)     ->			Optional[Any]:

       """simple docstring"""






       pass

 def        a__				(       self      :						List[Any]						)     ->			Optional[int]:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs_for_common()

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

 def        a__				(       self      :						Dict						)     ->			str:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             lowerCamelCase_	  =					model_class(UpperCamelCase_						)
             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]		,							UpperCamelCase_						)

 def        a__				(       self      :						str						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCamelCase_						)

 def        a__				(       self      :						Tuple						)     ->			Any:

       """simple docstring"""






       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_						)

 def        a__				(       self      :						int						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs_for_common()

       lowerCamelCase_	  =					_config_zero_init(UpperCamelCase_						)
       for model_class in self.all_model_classes:
             lowerCamelCase_	  =					model_class(config=UpperCamelCase_						)
             # 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        a__				(       self      :						Tuple						)     ->			int:

       """simple docstring"""






       for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowerCamelCase_	  =					ViTHybridModel.from_pretrained(UpperCamelCase_						)
             self.assertIsNotNone(UpperCamelCase_						)

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
      return image







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



 '''simple docstring'''


 @cached_property
 def        a__				(       self      :						str						)     ->			Tuple:

       """simple docstring"""






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

 @slow
 def        a__				(       self      :						Union[str, Any]						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(
           UpperCamelCase_						)

       lowerCamelCase_	  =					self.default_image_processor
       lowerCamelCase_	  =					prepare_img()
       lowerCamelCase_	  =					image_processor(images=UpperCamelCase_		,							return_tensors='pt'						).to(UpperCamelCase_						)

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

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

       lowerCamelCase_	  =					torch.tensor([-1.9090, -0.4993, -0.2389]						).to(UpperCamelCase_						)

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





 @slow
 @require_accelerate
 def        a__				(       self      :						Optional[Any]						)     ->			Optional[int]:

       """simple docstring"""






       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=UpperCamelCase_		,							return_tensors='pt'						)
       lowerCamelCase_	  =					model(**UpperCamelCase_						)
       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'						)









from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate







@dataclass(frozen=UpperCamelCase     )
class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		field(default='''language-modeling'''							,   metadata={'''include_in_asdict_even_if_is_default''': True}     )

 UpperCamelCase		     =		Features({'''text''': Value('''string'''     )}     )
 UpperCamelCase		     =		Features({}     )
 UpperCamelCase		     =		"text"

 @property
 def        a__				(       self      :						List[Any]						)     ->			Dict[str, str]:

       """simple docstring"""






       return {self.text_column: "text"}



