



'''simple docstring'''





from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor



class  snake_case__						:
 def __init__(   self						:							List[Any]      ,     __a						:							Dict      ,     __a						:							Union[str, Any]=13      ,     __a						:							Optional[int]=30      ,     __a						:							Optional[Any]=2      ,     __a						:							int=3      ,     __a						:							Tuple=True      ,     __a						:							Any=True      ,     __a						:							str=32      ,     __a						:							List[Any]=2      ,     __a						:							List[Any]=4      ,     __a						:							Dict=37      ,     __a						:							List[Any]="gelu"      ,     __a						:							List[Any]=0.1      ,     __a						:							str=0.1      ,     __a						:							Union[str, Any]=10      ,     __a						:							int=0.0_2      ,     __a						:							int=3      ,     __a						:							List[str]=0.6      ,     __a						:							Union[str, Any]=None      ,     )      ->   Dict:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    parent
       __snake_case :   str													=    batch_size
       __snake_case :   List[str]													=    image_size
       __snake_case :   Any													=    patch_size
       __snake_case :   str													=    num_channels
       __snake_case :   Tuple													=    is_training
       __snake_case :   List[Any]													=    use_labels
       __snake_case :   Union[str, Any]													=    hidden_size
       __snake_case :   Optional[Any]													=    num_hidden_layers
       __snake_case :   Union[str, Any]													=    num_attention_heads
       __snake_case :   Optional[Any]													=    intermediate_size
       __snake_case :   int													=    hidden_act
       __snake_case :   Union[str, Any]													=    hidden_dropout_prob
       __snake_case :   Any													=    attention_probs_dropout_prob
       __snake_case :   List[Any]													=    type_sequence_label_size
       __snake_case :   int													=    initializer_range
       __snake_case :   Any													=    mask_ratio
       __snake_case :   List[str]													=    scope

       # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
       # (we add 1 for the [CLS] token)
       __snake_case :   Union[str, Any]													=    (image_size // patch_size) ** 2
       __snake_case :   Any													=    int(math.ceil((1 - mask_ratio) * (num_patches + 1)  )  )
 def A_   (   self						:							int  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   Optional[int]													=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

       __snake_case :   str													=    None
       if self.use_labels:
             __snake_case :   Optional[Any]													=    ids_tensor([self.batch_size]      ,     self.type_sequence_label_size  )

       __snake_case :   Union[str, Any]													=    self.get_config()

       return config, pixel_values, labels
 def A_   (   self						:							List[Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       return ViTMAEConfig(
           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      ,     decoder_hidden_size=self.hidden_size      ,     decoder_num_hidden_layers=self.num_hidden_layers      ,     decoder_num_attention_heads=self.num_attention_heads      ,     decoder_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      ,     mask_ratio=self.mask_ratio      ,     )
 def A_   (   self						:							Any      ,     __a						:							Any      ,     __a						:							str      ,     __a						:							Union[str, Any]  )      ->   Tuple:







       '''simple docstring'''
       __snake_case :   Tuple													=    TFViTMAEModel(config=__a  )
       __snake_case :   List[str]													=    model(__a      ,     training=__a  )
       self.parent.assertEqual(result.last_hidden_state.shape      ,     (self.batch_size, self.seq_length, self.hidden_size)  )
 def A_   (   self						:							Optional[Any]      ,     __a						:							Tuple      ,     __a						:							Any      ,     __a						:							str  )      ->   int:







       '''simple docstring'''
       __snake_case :   str													=    TFViTMAEForPreTraining(__a  )
       __snake_case :   int													=    model(__a      ,     training=__a  )
       # expected sequence length = num_patches
       __snake_case :   Any													=    (self.image_size // self.patch_size) ** 2
       __snake_case :   Dict													=    self.patch_size**2 * self.num_channels
       self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, num_patches, expected_num_channels)  )

       # test greyscale images
       __snake_case :   Union[str, Any]													=    1
       __snake_case :   Dict													=    TFViTMAEForPreTraining(__a  )

       __snake_case :   Any													=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
       __snake_case :   Optional[Any]													=    model(__a      ,     training=__a  )
       __snake_case :   Tuple													=    self.patch_size**2
       self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, num_patches, expected_num_channels)  )





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







       '''simple docstring'''
       __snake_case :   Dict													=    self.prepare_config_and_inputs()
       ((__snake_case)							,     (__snake_case)							,     (__snake_case)) :   List[Any]													=    config_and_inputs
       __snake_case :   Optional[Any]													=    {'pixel_values': pixel_values}
       return config, inputs_dict



@require_tf
class  snake_case__						(    SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     unittest.TestCase				):
 A__				= (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
 A__				= {'''feature-extraction''': TFViTMAEModel} if is_tf_available() else {}

 A__				= False
 A__				= False
 A__				= False
 A__				= False
 def A_   (   self						:							List[Any]  )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   List[str]													=    TFViTMAEModelTester(self  )
       __snake_case :   Optional[Any]													=    ConfigTester(self      ,     config_class=__a      ,     has_text_modality=__a      ,     hidden_size=37  )
 def A_   (   self						:							List[Any]  )      ->   Union[str, Any]:







       '''simple docstring'''
       self.config_tester.run_common_tests()
 @unittest.skip(reason='ViTMAE does not use inputs_embeds'  )
 def A_   (   self						:							Optional[int]  )      ->   Dict:







       '''simple docstring'''
       pass
 def A_   (   self						:							Union[str, Any]  )      ->   str:







       '''simple docstring'''
       __snake_case							,     __snake_case :   str													=    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             __snake_case :   int													=    model_class(__a  )
             self.assertIsInstance(model.get_input_embeddings()      ,     (tf.keras.layers.Layer)  )
             __snake_case :   Optional[Any]													=    model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(__a      ,     tf.keras.layers.Layer  )  )
 def A_   (   self						:							List[str]  )      ->   str:







       '''simple docstring'''
       __snake_case							,     __snake_case :   Optional[int]													=    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             __snake_case :   List[str]													=    model_class(__a  )
             __snake_case :   Any													=    inspect.signature(model.call  )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             __snake_case :   List[Any]													=    [*signature.parameters.keys()]

             __snake_case :   Union[str, Any]													=    ['pixel_values']
             self.assertListEqual(arg_names[:1]      ,     __a  )
 def A_   (   self						:							str  )      ->   str:







       '''simple docstring'''
       __snake_case :   Dict													=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__a  )
 def A_   (   self						:							List[str]  )      ->   Tuple:







       '''simple docstring'''
       __snake_case :   Any													=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*__a  )
 def A_   (   self						:							List[Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       # make the mask reproducible
       np.random.seed(2  )

       __snake_case							,     __snake_case :   List[Any]													=    self.model_tester.prepare_config_and_inputs_for_common()

       __snake_case :   Union[str, Any]													=    int((config.image_size // config.patch_size) ** 2  )
       __snake_case :   Tuple													=    np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

       for model_class in self.all_model_classes:
             __snake_case :   Tuple													=    model_class(__a  )
             __snake_case :   Optional[int]													=    self._prepare_for_class(__a      ,     __a  )

             __snake_case :   Optional[int]													=    model(__a      ,     noise=__a  )

             __snake_case :   List[str]													=    copy.deepcopy(self._prepare_for_class(__a      ,     __a  )  )
             __snake_case :   Union[str, Any]													=    model(**__a      ,     noise=__a  )
             __snake_case :   Tuple													=    outputs_dict[0].numpy()
             __snake_case :   Optional[Any]													=    outputs_keywords[0].numpy()

             self.assertLess(np.sum(np.abs(output_dict - output_keywords  )  )      ,     1e-6  )
 def A_   (   self						:							Any  )      ->   Union[str, Any]:







       '''simple docstring'''
       # make the mask reproducible
       np.random.seed(2  )

       __snake_case							,     __snake_case :   Union[str, Any]													=    self.model_tester.prepare_config_and_inputs_for_common()

       __snake_case :   List[Any]													=    int((config.image_size // config.patch_size) ** 2  )
       __snake_case :   int													=    np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

       def prepare_numpy_arrays(__a						:							List[str]  ):
             __snake_case :   Optional[int]													=    {}
             for k, v in inputs_dict.items():
                   if tf.is_tensor(__a  ):
                         __snake_case :   str													=    v.numpy()
                   else:
                         __snake_case :   Optional[Any]													=    np.array(__a  )

             return inputs_np_dict

       for model_class in self.all_model_classes:
             __snake_case :   int													=    model_class(__a  )

             __snake_case :   List[str]													=    self._prepare_for_class(__a      ,     __a  )
             __snake_case :   List[str]													=    prepare_numpy_arrays(__a  )

             __snake_case :   Tuple													=    model(__a      ,     noise=__a  )
             __snake_case :   str													=    model(**__a      ,     noise=__a  )
             self.assert_outputs_same(__a      ,     __a  )
 def A_   (   self						:							List[Any]      ,     __a						:							str      ,     __a						:							str      ,     __a						:							Dict  )      ->   Optional[int]:







       '''simple docstring'''
       # make masks reproducible
       np.random.seed(2  )

       __snake_case :   Optional[Any]													=    int((tf_model.config.image_size // tf_model.config.patch_size) ** 2  )
       __snake_case :   List[Any]													=    np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )
       __snake_case :   Any													=    tf.constant(__a  )

       # Add `noise` argument.
       # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
       __snake_case :   Optional[Any]													=    tf_noise

       super().check_pt_tf_models(__a      ,     __a      ,     __a  )
 def A_   (   self						:							Optional[int]  )      ->   int:







       '''simple docstring'''
       # make mask reproducible
       np.random.seed(2  )

       __snake_case							,     __snake_case :   Dict													=    self.model_tester.prepare_config_and_inputs_for_common()

       __snake_case :   Any													=    {
           module_member
           for model_class in self.all_model_classes
           for module in (import_module(model_class.__module__  ),)
           for module_member_name in dir(__a  )
           if module_member_name.endswith('MainLayer'  )
           # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
           and module_member_name[: -len('MainLayer'  )] == model_class.__name__[: -len('Model'  )]
           for module_member in (getattr(__a      ,     __a  ),)
           if isinstance(__a      ,     __a  )
           and tf.keras.layers.Layer in module_member.__bases__
           and getattr(__a      ,     '_keras_serializable'      ,     __a  )
       }

       __snake_case :   Dict													=    int((config.image_size // config.patch_size) ** 2  )
       __snake_case :   int													=    np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )
       __snake_case :   int													=    tf.convert_to_tensor(__a  )
       inputs_dict.update({'noise': noise}  )

       for main_layer_class in tf_main_layer_classes:
             __snake_case :   List[str]													=    main_layer_class(__a  )

             __snake_case :   List[Any]													=    {
                 name: tf.keras.Input(tensor.shape[1:]      ,     dtype=tensor.dtype  ) for name, tensor in inputs_dict.items()
             }

             __snake_case :   Optional[Any]													=    tf.keras.Model(__a      ,     outputs=main_layer(__a  )  )
             __snake_case :   Union[str, Any]													=    model(__a  )

             with tempfile.TemporaryDirectory() as tmpdirname:
                   __snake_case :   Optional[Any]													=    os.path.join(__a      ,     'keras_model.h5'  )
                   model.save(__a  )
                   __snake_case :   Dict													=    tf.keras.models.load_model(
                       __a      ,     custom_objects={main_layer_class.__name__: main_layer_class}  )
                   assert isinstance(__a      ,     tf.keras.Model  )
                   __snake_case :   Any													=    model(__a  )
                   self.assert_outputs_same(__a      ,     __a  )
 @slow
 def A_   (   self						:							Dict  )      ->   Optional[int]:







       '''simple docstring'''
       # make mask reproducible
       np.random.seed(2  )

       __snake_case							,     __snake_case :   Optional[Any]													=    self.model_tester.prepare_config_and_inputs_for_common()

       __snake_case :   List[str]													=    int((config.image_size // config.patch_size) ** 2  )
       __snake_case :   Any													=    np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

       for model_class in self.all_model_classes:
             __snake_case :   Dict													=    model_class(__a  )
             __snake_case :   int													=    self._prepare_for_class(__a      ,     __a  )
             __snake_case :   Optional[int]													=    model(__a      ,     noise=__a  )

             if model_class.__name__ == "TFViTMAEModel":
                   __snake_case :   Any													=    outputs.last_hidden_state.numpy()
                   __snake_case :   Tuple													=    0
             else:
                   __snake_case :   str													=    outputs.logits.numpy()
                   __snake_case :   Optional[int]													=    0

             with tempfile.TemporaryDirectory() as tmpdirname:
                   model.save_pretrained(__a      ,     saved_model=__a  )
                   __snake_case :   Optional[Any]													=    model_class.from_pretrained(__a  )
                   __snake_case :   Optional[Any]													=    model(__a      ,     noise=__a  )

                   if model_class.__name__ == "TFViTMAEModel":
                         __snake_case :   int													=    after_outputs['last_hidden_state'].numpy()
                         __snake_case :   Optional[Any]													=    0
                   else:
                         __snake_case :   Optional[Any]													=    after_outputs['logits'].numpy()
                         __snake_case :   Any													=    0

                   __snake_case :   int													=    np.amax(np.abs(out_a - out_a  )  )
                   self.assertLessEqual(__a      ,     1e-5  )
 def A_   (   self						:							str  )      ->   Optional[int]:







       '''simple docstring'''
       # make mask reproducible
       np.random.seed(2  )

       __snake_case							,     __snake_case :   Optional[int]													=    self.model_tester.prepare_config_and_inputs_for_common()

       __snake_case :   Optional[Any]													=    int((config.image_size // config.patch_size) ** 2  )
       __snake_case :   str													=    np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

       for model_class in self.all_model_classes:
             __snake_case :   Optional[Any]													=    model_class(__a  )
             __snake_case :   Tuple													=    self._prepare_for_class(__a      ,     __a  )

             __snake_case :   Any													=    model(__a      ,     noise=__a  )
             __snake_case :   int													=    model.get_config()
             # make sure that returned config is jsonifiable, which is required by keras
             json.dumps(__a  )
             __snake_case :   Union[str, Any]													=    model_class.from_config(model.get_config()  )
             # make sure it also accepts a normal config
             __snake_case :   Dict													=    model_class.from_config(model.config  )
             __snake_case :   Tuple													=    new_model(__a  )  # Build model
             new_model.set_weights(model.get_weights()  )
             __snake_case :   List[str]													=    new_model(__a      ,     noise=__a  )

             self.assert_outputs_same(__a      ,     __a  )
 @unittest.skip(
     reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'  )
 def A_   (   self						:							Tuple  )      ->   Optional[int]:







       '''simple docstring'''
       pass
 @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'  )
 def A_   (   self						:							Union[str, Any]  )      ->   Tuple:







       '''simple docstring'''
       pass





 @slow
 def A_   (   self						:							str  )      ->   Tuple:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    TFViTMAEModel.from_pretrained('google/vit-base-patch16-224'  )
       self.assertIsNotNone(__a  )





def   a_   (		)    ->  Union[str, Any]:
      __snake_case :   Optional[int]													=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
      return image





@require_tf
@require_vision
class  snake_case__						(    unittest.TestCase				):
 @cached_property
 def A_   (   self						:							List[str]  )      ->   List[str]:







       '''simple docstring'''
       return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'  ) if is_vision_available() else None





 @slow
 def A_   (   self						:							Dict  )      ->   Any:







       '''simple docstring'''
       # make random mask reproducible across the PT and TF model
       np.random.seed(2  )

       __snake_case :   Optional[int]													=    TFViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'  )

       __snake_case :   Dict													=    self.default_image_processor
       __snake_case :   Dict													=    prepare_img()
       __snake_case :   List[Any]													=    image_processor(images=__a      ,     return_tensors='tf'  )

       # prepare a noise vector that will be also used for testing the TF model
       # (this way we can ensure that the PT and TF models operate on the same inputs)
       __snake_case :   Dict													=    ViTMAEConfig()
       __snake_case :   Optional[int]													=    int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2  )
       __snake_case :   int													=    np.random.uniform(size=(1, num_patches)  )

       # forward pass
       __snake_case :   Any													=    model(**__a      ,     noise=__a  )

       # verify the logits
       __snake_case :   Union[str, Any]													=    tf.convert_to_tensor([1, 196, 768]  )
       self.assertEqual(outputs.logits.shape      ,     __a  )

       __snake_case :   List[Any]													=    tf.convert_to_tensor(
           [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]  )

       tf.debugging.assert_near(outputs.logits[0, :3, :3]      ,     __a      ,     atol=1e-4  )








'''simple docstring'''





import qiskit





def   a_   (		_UpperCAmelCase    :      int = 2				)    ->  qiskit.result.counts.Counts:
      __snake_case :   Union[str, Any]													=    qubits

      # Using Aer's simulator
      __snake_case :   List[Any]													=    qiskit.Aer.get_backend('aer_simulator'				)

      # Creating a Quantum Circuit acting on the q register
      __snake_case :   Dict													=    qiskit.QuantumCircuit(_UpperCAmelCase ,_UpperCAmelCase				)

      # Adding a H gate on qubit 0 (now q0 in superposition)
      circuit.h(0				)

      for i in range(1 ,_UpperCAmelCase				):
            # Adding CX (CNOT) gate
            circuit.cx(i - 1 ,_UpperCAmelCase				)

      # Mapping the quantum measurement to the classical bits
      circuit.measure(list(range(_UpperCAmelCase				)				) ,list(range(_UpperCAmelCase				)				)				)

      # Now measuring any one qubit would affect other qubits to collapse
      # their super position and have same state as the measured one.

      # Executing the circuit on the simulator
      __snake_case :   Optional[Any]													=    qiskit.execute(_UpperCAmelCase ,_UpperCAmelCase ,shots=10_00				)

      return job.result().get_counts(_UpperCAmelCase				)


if __name__ == "__main__":
    print(F"""Total count for various states are: {quantum_entanglement(3)}""")



