






from __future__ import annotations

import unittest
from collections.abc import Iterator


class 		lowerCAmelCase_				:





     def __init__(	self							, _UpperCAmelCase							, _UpperCAmelCase  )		->				None:
        lowerCamelCase									=label
        lowerCamelCase									=parent
        lowerCamelCase									=None
        lowerCamelCase									=None




class 		lowerCAmelCase_				:





     def __init__(	self  )		->				None:
        lowerCamelCase									=None





     def 						lowerCamelCase							(	self  )		->				None:
        lowerCamelCase									=None





     def 						lowerCamelCase							(	self  )		->				bool:
        return self.root is None





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				None:
        lowerCamelCase									=self._put(self.root							, _UpperCAmelCase  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase = None  )		->				Node:
        if node is None:
           lowerCamelCase									=Node(_UpperCAmelCase							, _UpperCAmelCase  )
        else:
           if label < node.label:
              lowerCamelCase									=self._put(node.left							, _UpperCAmelCase							, _UpperCAmelCase  )
           elif label > node.label:
              lowerCamelCase									=self._put(node.right							, _UpperCAmelCase							, _UpperCAmelCase  )
           else:
              lowerCamelCase									=F'''Node with label {label} already exists'''
              raise Exception(_UpperCAmelCase  )

        return node





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				Node:
        return self._search(self.root							, _UpperCAmelCase  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase  )		->				Node:
        if node is None:
           lowerCamelCase									=F'''Node with label {label} does not exist'''
           raise Exception(_UpperCAmelCase  )
        else:
           if label < node.label:
              lowerCamelCase									=self._search(node.left							, _UpperCAmelCase  )
           elif label > node.label:
              lowerCamelCase									=self._search(node.right							, _UpperCAmelCase  )

        return node





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				None:
        lowerCamelCase									=self.search(_UpperCAmelCase  )
        if node.right and node.left:
           lowerCamelCase									=self._get_lowest_node(node.right  )
           lowerCamelCase									=node.left
           lowerCamelCase									=node.right
           lowerCamelCase									=lowest_node
           if node.right:
              lowerCamelCase									=lowest_node
           self._reassign_nodes(_UpperCAmelCase							, _UpperCAmelCase  )
        elif not node.right and node.left:
           self._reassign_nodes(_UpperCAmelCase							, node.left  )
        elif node.right and not node.left:
           self._reassign_nodes(_UpperCAmelCase							, node.right  )
        else:
           self._reassign_nodes(_UpperCAmelCase							, _UpperCAmelCase  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase  )		->				None:
        if new_children:
           lowerCamelCase									=node.parent

        if node.parent:
           if node.parent.right == node:
              lowerCamelCase									=new_children
           else:
              lowerCamelCase									=new_children
        else:
           lowerCamelCase									=new_children





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				Node:
        if node.left:
           lowerCamelCase									=self._get_lowest_node(node.left  )
        else:
           lowerCamelCase									=node
           self._reassign_nodes(_UpperCAmelCase							, node.right  )

        return lowest_node





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				bool:
        try:
           self.search(_UpperCAmelCase  )
           return True
        except Exception:
           return False





     def 						lowerCamelCase							(	self  )		->				int:
        if self.root is None:
           raise Exception("""Binary search tree is empty"""  )

        lowerCamelCase									=self.root
        while node.right is not None:
           lowerCamelCase									=node.right

        return node.label





     def 						lowerCamelCase							(	self  )		->				int:
        if self.root is None:
           raise Exception("""Binary search tree is empty"""  )

        lowerCamelCase									=self.root
        while node.left is not None:
           lowerCamelCase									=node.left

        return node.label





     def 						lowerCamelCase							(	self  )		->				Iterator[Node]:
        return self._inorder_traversal(self.root  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				Iterator[Node]:
        if node is not None:
           yield from self._inorder_traversal(node.left  )
           yield node
           yield from self._inorder_traversal(node.right  )





     def 						lowerCamelCase							(	self  )		->				Iterator[Node]:
        return self._preorder_traversal(self.root  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				Iterator[Node]:
        if node is not None:
           yield node
           yield from self._preorder_traversal(node.left  )
           yield from self._preorder_traversal(node.right  )




class 		lowerCAmelCase_				(  unittest.TestCase ):





     @staticmethod
     def 						lowerCamelCase							(	)		->				BinarySearchTree:
        lowerCamelCase									=BinarySearchTree()
        t.put(8  )
        t.put(3  )
        t.put(6  )
        t.put(1  )
        t.put(10  )
        t.put(14  )
        t.put(13  )
        t.put(4  )
        t.put(7  )
        t.put(5  )

        return t





     def 						lowerCamelCase							(	self  )		->				None:
        lowerCamelCase									=BinarySearchTree()
        assert t.is_empty()

        t.put(8  )
        assert t.root is not None
        assert t.root.parent is None
        assert t.root.label == 8

        t.put(10  )
        assert t.root.right is not None
        assert t.root.right.parent == t.root
        assert t.root.right.label == 10

        t.put(3  )
        assert t.root.left is not None
        assert t.root.left.parent == t.root
        assert t.root.left.label == 3

        t.put(6  )
        assert t.root.left.right is not None
        assert t.root.left.right.parent == t.root.left
        assert t.root.left.right.label == 6

        t.put(1  )
        assert t.root.left.left is not None
        assert t.root.left.left.parent == t.root.left
        assert t.root.left.left.label == 1

        with self.assertRaises(_UpperCAmelCase  ):  # noqa: B017
           t.put(1  )





     def 						lowerCamelCase							(	self  )		->				None:
        lowerCamelCase									=self._get_binary_search_tree()

        lowerCamelCase									=t.search(6  )
        assert node.label == 6

        lowerCamelCase									=t.search(13  )
        assert node.label == 13

        with self.assertRaises(_UpperCAmelCase  ):  # noqa: B017
           t.search(2  )





     def 						lowerCamelCase							(	self  )		->				None:
        lowerCamelCase									=self._get_binary_search_tree()

        t.remove(13  )
        assert t.root is not None
        assert t.root.right is not None
        assert t.root.right.right is not None
        assert t.root.right.right.right is None
        assert t.root.right.right.left is None

        t.remove(7  )
        assert t.root.left is not None
        assert t.root.left.right is not None
        assert t.root.left.right.left is not None
        assert t.root.left.right.right is None
        assert t.root.left.right.left.label == 4

        t.remove(6  )
        assert t.root.left.left is not None
        assert t.root.left.right.right is not None
        assert t.root.left.left.label == 1
        assert t.root.left.right.label == 4
        assert t.root.left.right.right.label == 5
        assert t.root.left.right.left is None
        assert t.root.left.left.parent == t.root.left
        assert t.root.left.right.parent == t.root.left

        t.remove(3  )
        assert t.root is not None
        assert t.root.left.label == 4
        assert t.root.left.right.label == 5
        assert t.root.left.left.label == 1
        assert t.root.left.parent == t.root
        assert t.root.left.left.parent == t.root.left
        assert t.root.left.right.parent == t.root.left

        t.remove(4  )
        assert t.root.left is not None
        assert t.root.left.left is not None
        assert t.root.left.label == 5
        assert t.root.left.right is None
        assert t.root.left.left.label == 1
        assert t.root.left.parent == t.root
        assert t.root.left.left.parent == t.root.left





     def 						lowerCamelCase							(	self  )		->				None:
        lowerCamelCase									=self._get_binary_search_tree()

        t.remove(3  )
        assert t.root is not None
        assert t.root.left is not None
        assert t.root.left.left is not None
        assert t.root.left.right is not None
        assert t.root.left.right.left is not None
        assert t.root.left.right.right is not None
        assert t.root.left.label == 4
        assert t.root.left.right.label == 6
        assert t.root.left.left.label == 1
        assert t.root.left.right.right.label == 7
        assert t.root.left.right.left.label == 5
        assert t.root.left.parent == t.root
        assert t.root.left.right.parent == t.root.left
        assert t.root.left.left.parent == t.root.left
        assert t.root.left.right.left.parent == t.root.left.right





     def 						lowerCamelCase							(	self  )		->				None:
        lowerCamelCase									=self._get_binary_search_tree()
        t.empty()
        assert t.root is None





     def 						lowerCamelCase							(	self  )		->				None:
        lowerCamelCase									=self._get_binary_search_tree()
        assert not t.is_empty()

        t.empty()
        assert t.is_empty()





     def 						lowerCamelCase							(	self  )		->				None:
        lowerCamelCase									=self._get_binary_search_tree()

        assert t.exists(6  )
        assert not t.exists(-1  )





     def 						lowerCamelCase							(	self  )		->				None:
        lowerCamelCase									=self._get_binary_search_tree()

        assert t.get_max_label() == 14

        t.empty()
        with self.assertRaises(_UpperCAmelCase  ):  # noqa: B017
           t.get_max_label()





     def 						lowerCamelCase							(	self  )		->				None:
        lowerCamelCase									=self._get_binary_search_tree()

        assert t.get_min_label() == 1

        t.empty()
        with self.assertRaises(_UpperCAmelCase  ):  # noqa: B017
           t.get_min_label()





     def 						lowerCamelCase							(	self  )		->				None:
        lowerCamelCase									=self._get_binary_search_tree()

        lowerCamelCase									=[i.label for i in t.inorder_traversal()]
        assert inorder_traversal_nodes == [1, 3, 4, 5, 6, 7, 8, 10, 13, 14]





     def 						lowerCamelCase							(	self  )		->				None:
        lowerCamelCase									=self._get_binary_search_tree()

        lowerCamelCase									=[i.label for i in t.preorder_traversal()]
        assert preorder_traversal_nodes == [8, 3, 1, 6, 4, 5, 7, 10, 14, 13]






def  UpperCAmelCase__						(    ):
   lowerCamelCase									=BinarySearchTree()
   t.put(8    )
   t.put(3    )
   t.put(6    )
   t.put(1    )
   t.put(10    )
   t.put(14    )
   t.put(13    )
   t.put(4    )
   t.put(7    )
   t.put(5    )

   print(
       """
            8
           / \\
          3   10
         / \\    \\
        1   6    14
           / \\   /
          4   7 13
           \\
            5
        """    )

   print("""Label 6 exists:"""			,t.exists(6    )    )
   print("""Label 13 exists:"""			,t.exists(13    )    )
   print("""Label -1 exists:"""			,t.exists(-1    )    )
   print("""Label 12 exists:"""			,t.exists(12    )    )

   # Prints all the elements of the list in inorder traversal
   lowerCamelCase									=[i.label for i in t.inorder_traversal()]
   print("""Inorder traversal:"""			,lowerCamelCase    )

   # Prints all the elements of the list in preorder traversal
   lowerCamelCase									=[i.label for i in t.preorder_traversal()]
   print("""Preorder traversal:"""			,lowerCamelCase    )

   print("""Max. label:"""			,t.get_max_label()    )
   print("""Min. label:"""			,t.get_min_label()    )

   # Delete elements
   print("""\nDeleting elements 13, 10, 8, 3, 6, 14"""    )
   print(
       """
          4
         / \\
        1   7
             \\
              5
        """    )
   t.remove(13    )
   t.remove(10    )
   t.remove(8    )
   t.remove(3    )
   t.remove(6    )
   t.remove(14    )

   # Prints all the elements of the list in inorder traversal after delete
   lowerCamelCase									=[i.label for i in t.inorder_traversal()]
   print("""Inorder traversal after delete:"""			,lowerCamelCase    )

   # Prints all the elements of the list in preorder traversal after delete
   lowerCamelCase									=[i.label for i in t.preorder_traversal()]
   print("""Preorder traversal after delete:"""			,lowerCamelCase    )

   print("""Max. label:"""			,t.get_max_label()    )
   print("""Min. label:"""			,t.get_min_label()    )


if __name__ == "__main__":
   binary_search_tree_example()







import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 (
       MODEL_MAPPING,
       DataaVecVisionForImageClassification,
       DataaVecVisionForSemanticSegmentation,
       DataaVecVisionModel,
   )
   from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import BeitImageProcessor


class 		lowerCAmelCase_				:





     def __init__(	self							, _UpperCAmelCase							, _UpperCAmelCase=100							, _UpperCAmelCase=13							, _UpperCAmelCase=30							, _UpperCAmelCase=2							, _UpperCAmelCase=3							, _UpperCAmelCase=True							, _UpperCAmelCase=True							, _UpperCAmelCase=32							, _UpperCAmelCase=4							, _UpperCAmelCase=4							, _UpperCAmelCase=37							, _UpperCAmelCase="gelu"							, _UpperCAmelCase=0.1							, _UpperCAmelCase=0.1							, _UpperCAmelCase=10							, _UpperCAmelCase=0.02							, _UpperCAmelCase=3							, _UpperCAmelCase=None							, _UpperCAmelCase=[0, 1, 2, 3]							, )		->				Dict:
        lowerCamelCase									=parent
        lowerCamelCase									=100
        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									=out_indices
        lowerCamelCase									=num_labels

        # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        lowerCamelCase									=(image_size // patch_size) ** 2
        lowerCamelCase									=num_patches + 1





     def 						lowerCamelCase							(	self  )		->				List[Any]:
        lowerCamelCase									=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

        lowerCamelCase									=None
        lowerCamelCase									=None
        if self.use_labels:
           lowerCamelCase									=ids_tensor([self.batch_size]							, self.type_sequence_label_size  )
           lowerCamelCase									=ids_tensor([self.batch_size, self.image_size, self.image_size]							, self.num_labels  )

        lowerCamelCase									=self.get_config()

        return config, pixel_values, labels, pixel_labels





     def 						lowerCamelCase							(	self  )		->				str:
        return DataaVecVisionConfig(
            vocab_size=self.vocab_size							, 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							, out_indices=self.out_indices							, )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )		->				Union[str, Any]:
        lowerCamelCase									=DataaVecVisionModel(config=_UpperCAmelCase  )
        model.to(_UpperCAmelCase  )
        model.eval()
        lowerCamelCase									=model(_UpperCAmelCase  )
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
        lowerCamelCase									=(self.image_size // self.patch_size) ** 2
        self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, num_patches + 1, self.hidden_size)  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )		->				Any:
        lowerCamelCase									=self.type_sequence_label_size
        lowerCamelCase									=DataaVecVisionForImageClassification(_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 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )		->				str:
        lowerCamelCase									=self.num_labels
        lowerCamelCase									=DataaVecVisionForSemanticSegmentation(_UpperCAmelCase  )
        model.to(_UpperCAmelCase  )
        model.eval()
        lowerCamelCase									=model(_UpperCAmelCase  )
        self.parent.assertEqual(
            result.logits.shape							, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)  )
        lowerCamelCase									=model(_UpperCAmelCase							, labels=_UpperCAmelCase  )
        self.parent.assertEqual(
            result.logits.shape							, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)  )





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




@require_torch
class 		lowerCAmelCase_				(  UpperCamelCase				,     UpperCamelCase				,     unittest.TestCase ):
     __lowerCamelCase       :    Optional[int] 	=     (
         (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
         if is_torch_available()
         else ()
     )
     __lowerCamelCase       :    str 	=     (
         {
             '''feature-extraction''': DataaVecVisionModel,
             '''image-classification''': DataaVecVisionForImageClassification,
             '''image-segmentation''': DataaVecVisionForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     __lowerCamelCase       :    Any 	=     False
     __lowerCamelCase       :    List[str] 	=     False
     __lowerCamelCase       :    int 	=     False





     def 						lowerCamelCase							(	self  )		->				Any:
        lowerCamelCase									=DataaVecVisionModelTester(self  )
        lowerCamelCase									=ConfigTester(
            self							, config_class=_UpperCAmelCase							, has_text_modality=_UpperCAmelCase							, hidden_size=37  )





     def 						lowerCamelCase							(	self  )		->				int:
        self.config_tester.run_common_tests()





     def 						lowerCamelCase							(	self  )		->				Dict:
        # Data2VecVision does not use inputs_embeds
        pass





     @require_torch_multi_gpu
     @unittest.skip(
         reason="""Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""  )
     def 						lowerCamelCase							(	self  )		->				Dict:
        pass





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        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 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        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 						lowerCamelCase							(	self  )		->				str:
        lowerCamelCase									=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_UpperCAmelCase  )





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        lowerCamelCase									=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_segmentation(*_UpperCAmelCase  )





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        if not self.model_tester.is_training:
           return

        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()
        lowerCamelCase									=True

        for model_class in self.all_model_classes:
           if model_class in [*get_values(_UpperCAmelCase  )]:
              continue

           lowerCamelCase									=model_class(_UpperCAmelCase  )
           model.to(_UpperCAmelCase  )
           model.train()
           lowerCamelCase									=self._prepare_for_class(_UpperCAmelCase							, _UpperCAmelCase							, return_labels=_UpperCAmelCase  )
           lowerCamelCase									=model(**_UpperCAmelCase  ).loss
           loss.backward()





     def 						lowerCamelCase							(	self  )		->				int:
        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()
        if not self.model_tester.is_training:
           return

        lowerCamelCase									=False
        lowerCamelCase									=True

        for model_class in self.all_model_classes:
           if model_class in [*get_values(_UpperCAmelCase  )] or not model_class.supports_gradient_checkpointing:
              continue
           # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
           # this can then be incorporated into _prepare_for_class in test_modeling_common.py
           elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
              lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase									=inputs_dict["""pixel_values"""].shape
              lowerCamelCase									=torch.zeros(
                  [self.model_tester.batch_size, height, width]							, device=_UpperCAmelCase  ).long()
           lowerCamelCase									=model_class(_UpperCAmelCase  )
           model.gradient_checkpointing_enable()
           model.to(_UpperCAmelCase  )
           model.train()
           lowerCamelCase									=self._prepare_for_class(_UpperCAmelCase							, _UpperCAmelCase							, return_labels=_UpperCAmelCase  )
           lowerCamelCase									=model(**_UpperCAmelCase  ).loss
           loss.backward()





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        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  )
           for name, param in model.named_parameters():
              # we skip lambda parameters as these require special initial values
              # determined by config.layer_scale_init_value
              if "lambda" in name:
                 continue
              if param.requires_grad:
                 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'''							, )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase=2E-4							, _UpperCAmelCase="outputs"							, _UpperCAmelCase=None  )		->				int:
        # We override with a slightly higher tol value, as semseg models tend to diverge a bit more
        super().check_pt_tf_outputs(_UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )





     def 						lowerCamelCase							(	self  )		->				Dict:
        lowerCamelCase									=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase  )





     @slow
     def 						lowerCamelCase							(	self  )		->				List[Any]:
        for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowerCamelCase									=DataaVecVisionModel.from_pretrained(_UpperCAmelCase  )
           self.assertIsNotNone(_UpperCAmelCase  )






def  UpperCAmelCase__						(    ):
   lowerCamelCase									=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
   return image







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





     @cached_property
     def 						lowerCamelCase							(	self  )		->				List[str]:
        return (
            BeitImageProcessor.from_pretrained("""facebook/data2vec-vision-base-ft1k"""  ) if is_vision_available() else None
        )





     @slow
     def 						lowerCamelCase							(	self  )		->				Optional[int]:
        lowerCamelCase									=DataaVecVisionForImageClassification.from_pretrained("""facebook/data2vec-vision-base-ft1k"""  ).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  )
        lowerCamelCase									=outputs.logits

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

        lowerCamelCase									=torch.tensor([0.3277, -0.1395, 0.0911]  ).to(_UpperCAmelCase  )

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

        lowerCamelCase									=[model.config.labelaid[i] for i in ["""remote control, remote""", """tabby, tabby cat"""]]
        self.assertEqual(logits[0].topk(2  ).indices.cpu().tolist()							, _UpperCAmelCase  )