"""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      _A	:




    """simple docstring"""




    def __init__(		self							:     int	,      A_							:     Union[str, Any]	,      A_							:     List[str]=13	,      A_							:     Any=30	,      A_							:     List[str]=2	,      A_							:     int=3	,      A_							:     Any=True	,      A_							:     Optional[Any]=True	,      A_							:     List[str]=32	,      A_							:     str=2	,      A_							:     List[str]=4	,      A_							:     str=37	,      A_							:     Tuple="gelu"	,      A_							:     str=0.1	,      A_							:     List[str]=0.1	,      A_							:     Union[str, Any]=10	,      A_							:     Optional[Any]=0.02	,      A_							:     Any=3	,      A_							:     Union[str, Any]=0.6	,      A_							:     int=None	,      )    ->       Any:
         __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				=							mask_ratio
         __snake_case				=							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				=							(image_size // patch_size) ** 2
         __snake_case				=							int(math.ceil((1 - mask_ratio) * (num_patches + 1)    )    )
    def  lowercase					(		self							:     Union[str, Any]    )    ->       str:
         __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  lowercase					(		self							:     List[Any]    )    ->       Optional[Any]:
         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=snake_case_	,      initializer_range=self.initializer_range	,      mask_ratio=self.mask_ratio	,      )
    def  lowercase					(		self							:     str	,      A_							:     Any	,      A_							:     Any	,      A_							:     Any    )    ->       int:
         __snake_case				=							TFViTMAEModel(config=snake_case_    )
         __snake_case				=							model(snake_case_	,      training=snake_case_    )
         self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)    )
    def  lowercase					(		self							:     int	,      A_							:     str	,      A_							:     Any	,      A_							:     List[Any]    )    ->       str:
         __snake_case				=							TFViTMAEForPreTraining(snake_case_    )
         __snake_case				=							model(snake_case_	,      training=snake_case_    )
         # expected sequence length = num_patches
         __snake_case				=							(self.image_size // self.patch_size) ** 2
         __snake_case				=							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				=							1
         __snake_case				=							TFViTMAEForPreTraining(snake_case_    )

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







    def  lowercase					(		self							:     Optional[int]    )    ->       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_tf
class      _A	(						_a							, _a							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					int							=					(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
    UpperCamelCase_	:					List[str]							=					{"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}

    UpperCamelCase_	:					List[Any]							=					False
    UpperCamelCase_	:					str							=					False
    UpperCamelCase_	:					Dict							=					False
    UpperCamelCase_	:					Dict							=					False
    def  lowercase					(		self							:     Any    )    ->       Optional[int]:
         __snake_case				=							TFViTMAEModelTester(self    )
         __snake_case				=							ConfigTester(self	,      config_class=snake_case_	,      has_text_modality=snake_case_	,      hidden_size=37    )
    def  lowercase					(		self							:     Tuple    )    ->       Tuple:
         self.config_tester.run_common_tests()
    @unittest.skip(reason='''ViTMAE does not use inputs_embeds'''    )
    def  lowercase					(		self							:     Tuple    )    ->       Union[str, Any]:
         pass
    def  lowercase					(		self							:     Union[str, Any]    )    ->       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(snake_case_    )
              self.assertIsInstance(model.get_input_embeddings()	,      (tf.keras.layers.Layer)    )
              __snake_case				=							model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(snake_case_	,      tf.keras.layers.Layer    )    )
    def  lowercase					(		self							:     Optional[Any]    )    ->       Tuple:
         __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(snake_case_    )
              __snake_case				=							inspect.signature(model.call    )
              # 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]	,      snake_case_    )
    def  lowercase					(		self							:     Optional[int]    )    ->       str:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case_    )
    def  lowercase					(		self							:     List[Any]    )    ->       List[Any]:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*snake_case_    )
    def  lowercase					(		self							:     int    )    ->       str:
         # make the mask reproducible
         np.random.seed(2    )

         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()

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

         for model_class in self.all_model_classes:
              __snake_case				=							model_class(snake_case_    )
              __snake_case				=							self._prepare_for_class(snake_case_	,      snake_case_    )

              __snake_case				=							model(snake_case_	,      noise=snake_case_    )

              __snake_case				=							copy.deepcopy(self._prepare_for_class(snake_case_	,      snake_case_    )    )
              __snake_case				=							model(**snake_case_	,      noise=snake_case_    )
              __snake_case				=							outputs_dict[0].numpy()
              __snake_case				=							outputs_keywords[0].numpy()

              self.assertLess(np.sum(np.abs(output_dict - output_keywords    )    )	,      1E-6    )
    def  lowercase					(		self							:     Any    )    ->       str:
         # make the mask reproducible
         np.random.seed(2    )

         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()

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

         def prepare_numpy_arrays(A_							:     Dict    ):
              __snake_case				=							{}
              for k, v in inputs_dict.items():
                   if tf.is_tensor(snake_case_    ):
                        __snake_case				=							v.numpy()
                   else:
                        __snake_case				=							np.array(snake_case_    )

              return inputs_np_dict

         for model_class in self.all_model_classes:
              __snake_case				=							model_class(snake_case_    )

              __snake_case				=							self._prepare_for_class(snake_case_	,      snake_case_    )
              __snake_case				=							prepare_numpy_arrays(snake_case_    )

              __snake_case				=							model(snake_case_	,      noise=snake_case_    )
              __snake_case				=							model(**snake_case_	,      noise=snake_case_    )
              self.assert_outputs_same(snake_case_	,      snake_case_    )
    def  lowercase					(		self							:     Any	,      A_							:     Optional[int]	,      A_							:     Optional[Any]	,      A_							:     Optional[int]    )    ->       Any:
         # make masks reproducible
         np.random.seed(2    )

         __snake_case				=							int((tf_model.config.image_size // tf_model.config.patch_size) ** 2    )
         __snake_case				=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )
         __snake_case				=							tf.constant(snake_case_    )

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

         super().check_pt_tf_models(snake_case_	,      snake_case_	,      snake_case_    )
    def  lowercase					(		self							:     str    )    ->       int:
         # make mask reproducible
         np.random.seed(2    )

         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()

         __snake_case				=							{
             module_member
             for model_class in self.all_model_classes
             for module in (import_module(model_class.__module__    ),)
             for module_member_name in dir(snake_case_    )
             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(snake_case_	,      snake_case_    ),)
             if isinstance(snake_case_	,      snake_case_    )
             and tf.keras.layers.Layer in module_member.__bases__
             and getattr(snake_case_	,      '''_keras_serializable'''	,      snake_case_    )
         }

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

         for main_layer_class in tf_main_layer_classes:
              __snake_case				=							main_layer_class(snake_case_    )

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

              __snake_case				=							tf.keras.Model(snake_case_	,      outputs=main_layer(snake_case_    )    )
              __snake_case				=							model(snake_case_    )

              with tempfile.TemporaryDirectory() as tmpdirname:
                   __snake_case				=							os.path.join(snake_case_	,      '''keras_model.h5'''    )
                   model.save(snake_case_    )
                   __snake_case				=							tf.keras.models.load_model(
                       snake_case_	,      custom_objects={main_layer_class.__name__: main_layer_class}    )
                   assert isinstance(snake_case_	,      tf.keras.Model    )
                   __snake_case				=							model(snake_case_    )
                   self.assert_outputs_same(snake_case_	,      snake_case_    )
    @slow
    def  lowercase					(		self							:     str    )    ->       Any:
         # make mask reproducible
         np.random.seed(2    )

         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()

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

         for model_class in self.all_model_classes:
              __snake_case				=							model_class(snake_case_    )
              __snake_case				=							self._prepare_for_class(snake_case_	,      snake_case_    )
              __snake_case				=							model(snake_case_	,      noise=snake_case_    )

              if model_class.__name__ == "TFViTMAEModel":
                   __snake_case				=							outputs.last_hidden_state.numpy()
                   __snake_case				=							0
              else:
                   __snake_case				=							outputs.logits.numpy()
                   __snake_case				=							0

              with tempfile.TemporaryDirectory() as tmpdirname:
                   model.save_pretrained(snake_case_	,      saved_model=snake_case_    )
                   __snake_case				=							model_class.from_pretrained(snake_case_    )
                   __snake_case				=							model(snake_case_	,      noise=snake_case_    )

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

                   __snake_case				=							np.amax(np.abs(out_a - out_a    )    )
                   self.assertLessEqual(snake_case_	,      1E-5    )
    def  lowercase					(		self							:     Dict    )    ->       Optional[Any]:
         # make mask reproducible
         np.random.seed(2    )

         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()

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

         for model_class in self.all_model_classes:
              __snake_case				=							model_class(snake_case_    )
              __snake_case				=							self._prepare_for_class(snake_case_	,      snake_case_    )

              __snake_case				=							model(snake_case_	,      noise=snake_case_    )
              __snake_case				=							model.get_config()
              # make sure that returned config is jsonifiable, which is required by keras
              json.dumps(snake_case_    )
              __snake_case				=							model_class.from_config(model.get_config()    )
              # make sure it also accepts a normal config
              __snake_case				=							model_class.from_config(model.config    )
              __snake_case				=							new_model(snake_case_    )  # Build model
              new_model.set_weights(model.get_weights()    )
              __snake_case				=							new_model(snake_case_	,      noise=snake_case_    )

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







    @slow
    def  lowercase					(		self							:     Union[str, Any]    )    ->       Optional[int]:
         __snake_case				=							TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224'''    )
         self.assertIsNotNone(snake_case_    )






def    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
     return image




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




    """simple docstring"""




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







    @slow
    def  lowercase					(		self							:     Optional[int]    )    ->       Dict:
         # make random mask reproducible across the PT and TF model
         np.random.seed(2    )

         __snake_case				=							TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''    )

         __snake_case				=							self.default_image_processor
         __snake_case				=							prepare_img()
         __snake_case				=							image_processor(images=snake_case_	,      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				=							ViTMAEConfig()
         __snake_case				=							int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2    )
         __snake_case				=							np.random.uniform(size=(1, num_patches)    )

         # forward pass
         __snake_case				=							model(**snake_case_	,      noise=snake_case_    )

         # verify the logits
         __snake_case				=							tf.convert_to_tensor([1, 196, 768]    )
         self.assertEqual(outputs.logits.shape	,      snake_case_    )

         __snake_case				=							tf.convert_to_tensor(
             [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]]    )

         tf.debugging.assert_near(outputs.logits[0, :3, :3]	,      snake_case_	,      atol=1E-4    )
"""simple docstring"""



import qiskit





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     __snake_case				=							qiskit.Aer.get_backend('''aer_simulator''')

     # Create a Quantum Circuit acting on the q register
     __snake_case				=							qiskit.QuantumCircuit(snake_case,      snake_case)

     # Map the quantum measurement to the classical bits
     circuit.measure([0],      [0])

     # Execute the circuit on the simulator
     __snake_case				=							qiskit.execute(snake_case,      snake_case,      shots=10_00)

     # Return the histogram data of the results of the experiment.
     return job.result().get_counts(snake_case)


if __name__ == "__main__":
       print(F"""Total count for various states are: {single_qubit_measure(1, 1)}""")