




"""simple docstring"""
from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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 (
					    TFDeiTForImageClassification,
					    TFDeiTForImageClassificationWithTeacher,
					    TFDeiTForMaskedImageModeling,
					    TFDeiTModel,
					)
					from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import DeiTImageProcessor

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=None    ,						__a=2    ,						):
								__lowerCamelCase       :						int													=					parent
								__lowerCamelCase       :						List[str]													=					batch_size
								__lowerCamelCase       :						Optional[int]													=					image_size
								__lowerCamelCase       :						Optional[Any]													=					patch_size
								__lowerCamelCase       :						Tuple													=					num_channels
								__lowerCamelCase       :						str													=					is_training
								__lowerCamelCase       :						Optional[Any]													=					use_labels
								__lowerCamelCase       :						Union[str, Any]													=					hidden_size
								__lowerCamelCase       :						Optional[int]													=					num_hidden_layers
								__lowerCamelCase       :						List[Any]													=					num_attention_heads
								__lowerCamelCase       :						Optional[int]													=					intermediate_size
								__lowerCamelCase       :						int													=					hidden_act
								__lowerCamelCase       :						Optional[Any]													=					hidden_dropout_prob
								__lowerCamelCase       :						List[str]													=					attention_probs_dropout_prob
								__lowerCamelCase       :						List[Any]													=					type_sequence_label_size
								__lowerCamelCase       :						Union[str, Any]													=					initializer_range
								__lowerCamelCase       :						int													=					scope
								__lowerCamelCase       :						Optional[int]													=					encoder_stride

								# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
								__lowerCamelCase       :						Tuple													=					(image_size // patch_size) ** 2
								__lowerCamelCase       :						Optional[int]													=					num_patches + 2
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						List[str]													=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

								__lowerCamelCase       :						int													=					self.get_config()

								return config, pixel_values, labels
					def      snake_case_					(     self  ):
								return DeiTConfig(
								    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    ,						encoder_stride=self.encoder_stride    ,						)
					def      snake_case_					(     self    ,						__a    ,						__a    ,						__a  ):
								__lowerCamelCase       :						int													=					TFDeiTModel(config=__a  )
								__lowerCamelCase       :						Optional[int]													=					model(__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       :						List[str]													=					TFDeiTForMaskedImageModeling(config=__a  )
								__lowerCamelCase       :						Union[str, Any]													=					model(__a  )
								self.parent.assertEqual(
								    result.reconstruction.shape    ,						(self.batch_size, self.num_channels, self.image_size, self.image_size)  )

								# test greyscale images
								__lowerCamelCase       :						Tuple													=					1
								__lowerCamelCase       :						Any													=					TFDeiTForMaskedImageModeling(__a  )

								__lowerCamelCase       :						Dict													=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
								__lowerCamelCase       :						Any													=					model(__a  )
								self.parent.assertEqual(result.reconstruction.shape    ,						(self.batch_size, 1, self.image_size, self.image_size)  )
					def      snake_case_					(     self    ,						__a    ,						__a    ,						__a  ):
								__lowerCamelCase       :						str													=					self.type_sequence_label_size
								__lowerCamelCase       :						Optional[int]													=					TFDeiTForImageClassification(__a  )
								__lowerCamelCase       :						Dict													=					model(__a    ,						labels=__a  )
								self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.type_sequence_label_size)  )

								# test greyscale images
								__lowerCamelCase       :						Tuple													=					1
								__lowerCamelCase       :						Tuple													=					TFDeiTForImageClassification(__a  )

								__lowerCamelCase       :						List[str]													=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
								__lowerCamelCase       :						Union[str, Any]													=					model(__a    ,						labels=__a  )
								self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.type_sequence_label_size)  )





					def      snake_case_					(     self  ):
								__lowerCamelCase       :						List[Any]													=					self.prepare_config_and_inputs()
								__lowerCamelCase       :						List[str]													=					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    :    List[Any]  		=			(
					    (
					        TFDeiTModel,
					        TFDeiTForImageClassification,
					        TFDeiTForImageClassificationWithTeacher,
					        TFDeiTForMaskedImageModeling,
					    )
					    if is_tf_available()
					    else ()
					)
					__a    :    Dict  		=			(
					    {
					        'feature-extraction': TFDeiTModel,
					        'image-classification': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
					    }
					    if is_tf_available()
					    else {}
					)

					__a    :    List[Any]  		=			False
					__a    :    str  		=			False
					__a    :    Optional[int]  		=			False
					__a    :    str  		=			False
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						Dict													=					TFDeiTModelTester(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='DeiT does not use inputs_embeds'  )
					def      snake_case_					(     self  ):
								pass
					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       :						str													=					model_class(__a  )
											self.assertIsInstance(model.get_input_embeddings()    ,						(tf.keras.layers.Layer)  )
											__lowerCamelCase       :						List[str]													=					model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(__a    ,						tf.keras.layers.Dense  )  )
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						List[Any]													=					self.model_tester.prepare_config_and_inputs_for_common()

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

											__lowerCamelCase       :						Optional[Any]													=					['pixel_values']
											self.assertListEqual(arg_names[:1]    ,						__a  )
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						List[str]													=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__a  )
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						str													=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_image_modeling(*__a  )
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						int													=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*__a  )
					def      snake_case_					(     self    ,						__a    ,						__a    ,						__a=False  ):
								__lowerCamelCase       :						Dict													=					super()._prepare_for_class(__a    ,						__a    ,						return_labels=__a  )

								if return_labels:
											if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call  ).parameters:
														del inputs_dict["labels"]

								return inputs_dict





					@slow
					def      snake_case_					(     self  ):
								for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowerCamelCase       :						str													=					TFDeiTModel.from_pretrained(__a  )
											self.assertIsNotNone(__a  )





def     UpperCAmelCase					(      )	->  str:
			__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 (
								    DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'  )
								    if is_vision_available()
								    else None
								)





					@slow
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						Optional[Any]													=					TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'  )

								__lowerCamelCase       :						List[str]													=					self.default_image_processor
								__lowerCamelCase       :						Optional[int]													=					prepare_img()
								__lowerCamelCase       :						Optional[int]													=					image_processor(images=__a    ,						return_tensors='tf'  )

								# forward pass
								__lowerCamelCase       :						str													=					model(**__a  )

								# verify the logits
								__lowerCamelCase       :						Any													=					tf.TensorShape((1, 1000)  )
								self.assertEqual(outputs.logits.shape    ,						__a  )

								__lowerCamelCase       :						str													=					tf.constant([-1.0_266, 0.1_912, -1.2_861]  )

								self.assertTrue(np.allclose(outputs.logits[0, :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)}""")



