
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				:   Dict    ,   _A				:   Any    ,   _A				:   Tuple=1_3    ,   _A				:   Any=6_4    ,   _A				:   Optional[int]=2    ,   _A				:   Union[str, Any]=3    ,   _A				:   str=True    ,   _A				:   List[str]=True    ,   _A				:   Union[str, Any]=3_2    ,   _A				:   Dict=5    ,   _A				:   Dict=4    ,   _A				:   Dict=3_7    ,   _A				:   Optional[int]="gelu"    ,   _A				:   int=0.1    ,   _A				:   Union[str, Any]=0.1    ,   _A				:   List[Any]=1_0    ,   _A				:   Any=0.02    ,   _A				:   Tuple=[1, 1_6, 4, 4]    ,   _A				:   Union[str, Any]=None    ,   )						->						str:
         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				:   Optional[Any]       )						->						Any:
         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				:   Union[str, Any]       )						->						Optional[Any]:
         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=_A    ,   initializer_range=self.initializer_range    ,   backbone_featmap_shape=self.backbone_featmap_shape    ,   backbone_config=_A    ,   )





     def 		UpperCAmelCase(self				:   Optional[Any]    ,   _A				:   int    ,   _A				:   str    ,   _A				:   Any       )						->						List[Any]:
         snake_case								= ViTHybridModel(config=_A       )
         model.to(_A       )
         model.eval()
         snake_case								= model(_A       )
         self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)       )





     def 		UpperCAmelCase(self				:   Optional[Any]    ,   _A				:   List[Any]    ,   _A				:   Optional[int]    ,   _A				:   int       )						->						Union[str, Any]:
         snake_case								= self.type_sequence_label_size
         snake_case								= ViTHybridForImageClassification(_A       )
         model.to(_A       )
         model.eval()
         snake_case								= model(_A    ,   labels=_A       )
         self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)       )





     def 		UpperCAmelCase(self				:   Union[str, Any]       )						->						Optional[int]:
         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 					lowerCamelCase		(  A_							,			A_							,			unittest.TestCase  ):
     UpperCAmelCase__    :							str									=		(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
     UpperCAmelCase__    :							int									=		(
         {"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
         if is_torch_available()
         else {}
     )
     UpperCAmelCase__    :							Union[str, Any]									=		False
     UpperCAmelCase__    :							Any									=		False
     UpperCAmelCase__    :							Union[str, Any]									=		False





     def 		UpperCAmelCase(self				:   List[str]       )						->						Dict:
         snake_case								= ViTHybridModelTester(self       )
         snake_case								= ConfigTester(self    ,   config_class=_A    ,   has_text_modality=_A    ,   hidden_size=3_7       )





     def 		UpperCAmelCase(self				:   List[Any]       )						->						List[Any]:
         self.config_tester.run_common_tests()





     @unittest.skip(reason="ViT does not use inputs_embeds"       )
     def 		UpperCAmelCase(self				:   Any       )						->						Optional[Any]:
         pass





     def 		UpperCAmelCase(self				:   Optional[Any]       )						->						List[Any]:
         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(_A       )
             self.assertIsInstance(model.get_input_embeddings()    ,   (nn.Module)       )
             snake_case								= model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(_A    ,   nn.Linear       )       )





     def 		UpperCAmelCase(self				:   Union[str, Any]       )						->						Optional[int]:
         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(_A       )
             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]    ,   _A       )





     def 		UpperCAmelCase(self				:   List[Any]       )						->						List[Any]:
         snake_case								= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_A       )





     def 		UpperCAmelCase(self				:   Any       )						->						Union[str, Any]:
         snake_case								= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_A       )





     def 		UpperCAmelCase(self				:   Union[str, Any]       )						->						List[Any]:
         snake_case					,							snake_case								= self.model_tester.prepare_config_and_inputs_for_common()

         snake_case								= _config_zero_init(_A       )
         for model_class in self.all_model_classes:
             snake_case								= model_class(config=_A       )
             # 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				:   Dict       )						->						int:
         for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             snake_case								= ViTHybridModel.from_pretrained(_A       )
             self.assertIsNotNone(_A       )



def 						lowercase_      (       )							->							Any:
    """simple docstring"""

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







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





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





     @slow
     def 		UpperCAmelCase(self				:   Dict       )						->						List[Any]:
         snake_case								= ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(
             _A       )

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

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

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

         snake_case								= torch.tensor([-1.90_90, -0.49_93, -0.23_89]       ).to(_A       )

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





     @slow
     @require_accelerate
     def 		UpperCAmelCase(self				:   int       )						->						int:
         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=_A    ,   return_tensors="pt"       )
         snake_case								= model(**_A       )
         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"       )



from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap



def 						lowercase_      (       A__		)							->							str:
    """simple docstring"""

    return getitem, k



def 						lowercase_      (       A__			,		A__		)							->							str:
    """simple docstring"""

    return setitem, k, v



def 						lowercase_      (       A__		)							->							List[Any]:
    """simple docstring"""

    return delitem, k



def 						lowercase_      (       A__			,		A__			,		*A__		)							->							str:
    """simple docstring"""

    try:
        return fun(A__			,		*A__		), None
    except Exception as e:
        return None, e


_A   =			(
    _set("key_a", "val_a"),
    _set("key_b", "val_b"),
)

_A   =			[
    _set("key_a", "val_a"),
    _set("key_a", "val_b"),
]

_A   =			[
    _set("key_a", "val_a"),
    _set("key_b", "val_b"),
    _del("key_a"),
    _del("key_b"),
    _set("key_a", "val_a"),
    _del("key_a"),
]

_A   =			[
    _get("key_a"),
    _del("key_a"),
    _set("key_a", "val_a"),
    _del("key_a"),
    _del("key_a"),
    _get("key_a"),
]

_A   =			[
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

_A   =			[
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set("key_a", "val_b"),
]



@pytest.mark.parametrize(
    "operations"			,		(
        pytest.param(_add_items			,		id="add items"		),
        pytest.param(_overwrite_items			,		id="overwrite items"		),
        pytest.param(_delete_items			,		id="delete items"		),
        pytest.param(_access_absent_items			,		id="access absent items"		),
        pytest.param(_add_with_resize_up			,		id="add with resize up"		),
        pytest.param(_add_with_resize_down			,		id="add with resize down"		),
    )			,		)
def 						lowercase_      (       A__		)							->							List[Any]:
    """simple docstring"""

    snake_case								= HashMap(initial_block_size=4		)
    snake_case								= {}
    for _, (fun, *args) in enumerate(A__		):
        snake_case					,							snake_case								= _run_operation(A__			,		A__			,		*A__		)
        snake_case					,							snake_case								= _run_operation(A__			,		A__			,		*A__		)
        assert my_res == py_res
        assert str(A__		) == str(A__		)
        assert set(A__		) == set(A__		)
        assert len(A__		) == len(A__		)
        assert set(my.items()		) == set(py.items()		)





def 						lowercase_      (       )							->							Optional[int]:
    """simple docstring"""

    def is_public(A__		) -> bool:
        return not name.startswith("_"		)

    snake_case								= {name for name in dir({}		) if is_public(A__		)}
    snake_case								= {name for name in dir(HashMap()		) if is_public(A__		)}

    assert dict_public_names > hash_public_names

