




"""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 __lowercase:




					'''simple docstring'''


					def __init__(     self    ,						__a    ,						__a=13    ,						__a=30    ,						__a=2    ,						__a=3    ,						__a=True    ,						__a=True    ,						__a=32    ,						__a=2    ,						__a=4    ,						__a=37    ,						__a="gelu"    ,						__a=0.1    ,						__a=0.1    ,						__a=10    ,						__a=0.02    ,						__a=3    ,						__a=0.6    ,						__a=None    ,						):
								__lowerCamelCase       :						Union[str, Any]													=					parent
								__lowerCamelCase       :						str													=					batch_size
								__lowerCamelCase       :						List[str]													=					image_size
								__lowerCamelCase       :						Any													=					patch_size
								__lowerCamelCase       :						str													=					num_channels
								__lowerCamelCase       :						Tuple													=					is_training
								__lowerCamelCase       :						List[Any]													=					use_labels
								__lowerCamelCase       :						Union[str, Any]													=					hidden_size
								__lowerCamelCase       :						Optional[Any]													=					num_hidden_layers
								__lowerCamelCase       :						Union[str, Any]													=					num_attention_heads
								__lowerCamelCase       :						Optional[Any]													=					intermediate_size
								__lowerCamelCase       :						int													=					hidden_act
								__lowerCamelCase       :						Union[str, Any]													=					hidden_dropout_prob
								__lowerCamelCase       :						Any													=					attention_probs_dropout_prob
								__lowerCamelCase       :						List[Any]													=					type_sequence_label_size
								__lowerCamelCase       :						int													=					initializer_range
								__lowerCamelCase       :						Any													=					mask_ratio
								__lowerCamelCase       :						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)
								__lowerCamelCase       :						Union[str, Any]													=					(image_size // patch_size) ** 2
								__lowerCamelCase       :						Any													=					int(math.ceil((1 - mask_ratio) * (num_patches + 1)  )  )
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						Optional[int]													=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

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

								return config, pixel_values, labels
					def      snake_case_					(     self  ):
								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      snake_case_					(     self    ,						__a    ,						__a    ,						__a  ):
								__lowerCamelCase       :						Tuple													=					TFViTMAEModel(config=__a  )
								__lowerCamelCase       :						List[str]													=					model(__a    ,						training=__a  )
								self.parent.assertEqual(result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length, self.hidden_size)  )
					def      snake_case_					(     self    ,						__a    ,						__a    ,						__a  ):
								__lowerCamelCase       :						str													=					TFViTMAEForPreTraining(__a  )
								__lowerCamelCase       :						int													=					model(__a    ,						training=__a  )
								# expected sequence length = num_patches
								__lowerCamelCase       :						Any													=					(self.image_size // self.patch_size) ** 2
								__lowerCamelCase       :						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
								__lowerCamelCase       :						Union[str, Any]													=					1
								__lowerCamelCase       :						Dict													=					TFViTMAEForPreTraining(__a  )

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





					def      snake_case_					(     self  ):
								__lowerCamelCase       :						Dict													=					self.prepare_config_and_inputs()
								(__lowerCamelCase)       :						List[Any]													=					config_and_inputs
								__lowerCamelCase       :						Optional[Any]													=					{'pixel_values': pixel_values}
								return config, inputs_dict



@require_tf
class __lowercase(   SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_						,						unittest.TestCase					):




					'''simple docstring'''


					__a    :    Any  		=			(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
					__a    :    Tuple  		=			{'feature-extraction': TFViTMAEModel} if is_tf_available() else {}

					__a    :    List[str]  		=			False
					__a    :    List[Any]  		=			False
					__a    :    List[Any]  		=			False
					__a    :    List[Any]  		=			False
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						List[str]													=					TFViTMAEModelTester(self  )
								__lowerCamelCase       :						Optional[Any]													=					ConfigTester(self    ,						config_class=__a    ,						has_text_modality=__a    ,						hidden_size=37  )
					def      snake_case_					(     self  ):
								self.config_tester.run_common_tests()
					@unittest.skip(reason='ViTMAE does not use inputs_embeds'  )
					def      snake_case_					(     self  ):
								pass
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						str													=					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											__lowerCamelCase       :						int													=					model_class(__a  )
											self.assertIsInstance(model.get_input_embeddings()    ,						(tf.keras.layers.Layer)  )
											__lowerCamelCase       :						Optional[Any]													=					model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(__a    ,						tf.keras.layers.Layer  )  )
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						Optional[int]													=					self.model_tester.prepare_config_and_inputs_for_common()

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

											__lowerCamelCase       :						Union[str, Any]													=					['pixel_values']
											self.assertListEqual(arg_names[:1]    ,						__a  )
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						Dict													=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__a  )
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						Any													=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_pretraining(*__a  )
					def      snake_case_					(     self  ):
								np.random.seed(2  )

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

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

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

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

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

											self.assertLess(np.sum(np.abs(output_dict - output_keywords  )  )    ,						1E-6  )
					def      snake_case_					(     self  ):
								np.random.seed(2  )

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

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

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

											return inputs_np_dict

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

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

											__lowerCamelCase       :						Tuple													=					model(__a    ,						noise=__a  )
											__lowerCamelCase       :						str													=					model(**__a    ,						noise=__a  )
											self.assert_outputs_same(__a    ,						__a  )
					def      snake_case_					(     self    ,						__a    ,						__a    ,						__a  ):
								np.random.seed(2  )

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

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

								super().check_pt_tf_models(__a    ,						__a    ,						__a  )
					def      snake_case_					(     self  ):
								np.random.seed(2  )

								__lowerCamelCase       :						Dict													=					self.model_tester.prepare_config_and_inputs_for_common()

								__lowerCamelCase       :						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  )
								}

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

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

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

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

											with tempfile.TemporaryDirectory() as tmpdirname:
														__lowerCamelCase       :						Optional[Any]													=					os.path.join(__a    ,						'keras_model.h5'  )
														model.save(__a  )
														__lowerCamelCase       :						Dict													=					tf.keras.models.load_model(
														    __a    ,						custom_objects={main_layer_class.__name__: main_layer_class}  )
														assert isinstance(__a    ,						tf.keras.Model  )
														__lowerCamelCase       :						Any													=					model(__a  )
														self.assert_outputs_same(__a    ,						__a  )
					@slow
					def      snake_case_					(     self  ):
								np.random.seed(2  )

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

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

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

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

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

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

														__lowerCamelCase       :						int													=					np.amax(np.abs(out_a - out_a  )  )
														self.assertLessEqual(__a    ,						1E-5  )
					def      snake_case_					(     self  ):
								np.random.seed(2  )

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

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

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

											__lowerCamelCase       :						Any													=					model(__a    ,						noise=__a  )
											__lowerCamelCase       :						int													=					model.get_config()
											# make sure that returned config is jsonifiable, which is required by keras
											json.dumps(__a  )
											__lowerCamelCase       :						Union[str, Any]													=					model_class.from_config(model.get_config()  )
											# make sure it also accepts a normal config
											__lowerCamelCase       :						Dict													=					model_class.from_config(model.config  )
											__lowerCamelCase       :						Tuple													=					new_model(__a  )  # Build model
											new_model.set_weights(model.get_weights()  )
											__lowerCamelCase       :						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      snake_case_					(     self  ):
								pass
					@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'  )
					def      snake_case_					(     self  ):
								pass





					@slow
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						Union[str, Any]													=					TFViTMAEModel.from_pretrained('google/vit-base-patch16-224'  )
								self.assertIsNotNone(__a  )





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






@require_tf
@require_vision
class __lowercase(   unittest.TestCase					):




					'''simple docstring'''


					@cached_property
					def      snake_case_					(     self  ):
								return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'  ) if is_vision_available() else None





					@slow
					def      snake_case_					(     self  ):
								np.random.seed(2  )

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

								__lowerCamelCase       :						Dict													=					self.default_image_processor
								__lowerCamelCase       :						Dict													=					prepare_img()
								__lowerCamelCase       :						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)
								__lowerCamelCase       :						Dict													=					ViTMAEConfig()
								__lowerCamelCase       :						Optional[int]													=					int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2  )
								__lowerCamelCase       :						int													=					np.random.uniform(size=(1, num_patches)  )

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

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

								__lowerCamelCase       :						List[Any]													=					tf.convert_to_tensor(
								    [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]  )

								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)}""")



