





'''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 		UpperCAmelCase   :


  """simple docstring"""







  def __init__(				self    :					Optional[Any]							,	UpperCamelCase__    :					Dict							,	UpperCamelCase__    :					List[Any]=13							,	UpperCamelCase__    :					Tuple=30							,	UpperCamelCase__    :					List[str]=2							,	UpperCamelCase__    :					Optional[Any]=3							,	UpperCamelCase__    :					Optional[int]=True							,	UpperCamelCase__    :					Any=True							,	UpperCamelCase__    :					Union[str, Any]=32							,	UpperCamelCase__    :					Union[str, Any]=2							,	UpperCamelCase__    :					Dict=4							,	UpperCamelCase__    :					Optional[int]=37							,	UpperCamelCase__    :					Tuple="gelu"							,	UpperCamelCase__    :					Tuple=0.1							,	UpperCamelCase__    :					Union[str, Any]=0.1							,	UpperCamelCase__    :					List[Any]=10							,	UpperCamelCase__    :					str=0.02							,	UpperCamelCase__    :					Union[str, Any]=3							,	UpperCamelCase__    :					Optional[Any]=0.6							,	UpperCamelCase__    :					Union[str, Any]=None							,	)     ->						List[Any]:
    _UpperCamelCase       			=parent
    _UpperCamelCase       			=batch_size
    _UpperCamelCase       			=image_size
    _UpperCamelCase       			=patch_size
    _UpperCamelCase       			=num_channels
    _UpperCamelCase       			=is_training
    _UpperCamelCase       			=use_labels
    _UpperCamelCase       			=hidden_size
    _UpperCamelCase       			=num_hidden_layers
    _UpperCamelCase       			=num_attention_heads
    _UpperCamelCase       			=intermediate_size
    _UpperCamelCase       			=hidden_act
    _UpperCamelCase       			=hidden_dropout_prob
    _UpperCamelCase       			=attention_probs_dropout_prob
    _UpperCamelCase       			=type_sequence_label_size
    _UpperCamelCase       			=initializer_range
    _UpperCamelCase       			=mask_ratio
    _UpperCamelCase       			=scope

    # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
    # (we add 1 for the [CLS] token)
    _UpperCamelCase       			=(image_size // patch_size) ** 2
    _UpperCamelCase       			=int(math.ceil((1 - mask_ratio) * (num_patches + 1)  )  )


  def   UpperCamelCase__      (				self    :					Any  )     ->						int:
    _UpperCamelCase       			=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

    _UpperCamelCase       			=None
    if self.use_labels:
      _UpperCamelCase       			=ids_tensor([self.batch_size]							,	self.type_sequence_label_size  )

    _UpperCamelCase       			=self.get_config()

    return config, pixel_values, labels


  def   UpperCamelCase__      (				self    :					Dict  )     ->						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=UpperCamelCase__							,	initializer_range=self.initializer_range							,	mask_ratio=self.mask_ratio							,	)


  def   UpperCamelCase__      (				self    :					Optional[int]							,	UpperCamelCase__    :					int							,	UpperCamelCase__    :					Tuple							,	UpperCamelCase__    :					Dict  )     ->						Optional[Any]:
    _UpperCamelCase       			=TFViTMAEModel(config=UpperCamelCase__  )
    _UpperCamelCase       			=model(UpperCamelCase__							,	training=UpperCamelCase__  )
    self.parent.assertEqual(result.last_hidden_state.shape							,	(self.batch_size, self.seq_length, self.hidden_size)  )


  def   UpperCamelCase__      (				self    :					Any							,	UpperCamelCase__    :					Optional[Any]							,	UpperCamelCase__    :					str							,	UpperCamelCase__    :					List[str]  )     ->						Tuple:
    _UpperCamelCase       			=TFViTMAEForPreTraining(UpperCamelCase__  )
    _UpperCamelCase       			=model(UpperCamelCase__							,	training=UpperCamelCase__  )
    # expected sequence length = num_patches
    _UpperCamelCase       			=(self.image_size // self.patch_size) ** 2
    _UpperCamelCase       			=self.patch_size**2 * self.num_channels
    self.parent.assertEqual(result.logits.shape							,	(self.batch_size, num_patches, expected_num_channels)  )

    # test greyscale images
    _UpperCamelCase       			=1
    _UpperCamelCase       			=TFViTMAEForPreTraining(UpperCamelCase__  )

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




  def   UpperCamelCase__      (				self    :					str  )     ->						Any:
    _UpperCamelCase       			=self.prepare_config_and_inputs()
    ((_UpperCamelCase)			,     (_UpperCamelCase)			,     (_UpperCamelCase))       			=config_and_inputs
    _UpperCamelCase       			={'''pixel_values''': pixel_values}
    return config, inputs_dict







@require_tf
class 		UpperCAmelCase   (				lowercase_   ,     lowercase_   ,     unittest.TestCase):


  """simple docstring"""







  lowerCAmelCase_					=				(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
  lowerCAmelCase_					=				{"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}

  lowerCAmelCase_					=				False
  lowerCAmelCase_					=				False
  lowerCAmelCase_					=				False
  lowerCAmelCase_					=				False


  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						Tuple:
    _UpperCamelCase       			=TFViTMAEModelTester(self  )
    _UpperCamelCase       			=ConfigTester(self							,	config_class=UpperCamelCase__							,	has_text_modality=UpperCamelCase__							,	hidden_size=37  )


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						int:
    self.config_tester.run_common_tests()


  @unittest.skip(reason='''ViTMAE does not use inputs_embeds'''  )
  def   UpperCamelCase__      (				self    :					Tuple  )     ->						List[str]:
    pass


  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						Dict:
    _UpperCamelCase			,     _UpperCamelCase       			=self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      _UpperCamelCase       			=model_class(UpperCamelCase__  )
      self.assertIsInstance(model.get_input_embeddings()							,	(tf.keras.layers.Layer)  )
      _UpperCamelCase       			=model.get_output_embeddings()
      self.assertTrue(x is None or isinstance(UpperCamelCase__							,	tf.keras.layers.Layer  )  )


  def   UpperCamelCase__      (				self    :					List[Any]  )     ->						Optional[int]:
    _UpperCamelCase			,     _UpperCamelCase       			=self.model_tester.prepare_config_and_inputs_for_common()

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

      _UpperCamelCase       			=['''pixel_values''']
      self.assertListEqual(arg_names[:1]							,	UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Any  )     ->						Optional[int]:
    _UpperCamelCase       			=self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					int  )     ->						List[Any]:
    _UpperCamelCase       			=self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_pretraining(*UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Tuple  )     ->						int:
    # make the mask reproducible
    np.random.seed(2  )

    _UpperCamelCase			,     _UpperCamelCase       			=self.model_tester.prepare_config_and_inputs_for_common()

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

    for model_class in self.all_model_classes:
      _UpperCamelCase       			=model_class(UpperCamelCase__  )
      _UpperCamelCase       			=self._prepare_for_class(UpperCamelCase__							,	UpperCamelCase__  )

      _UpperCamelCase       			=model(UpperCamelCase__							,	noise=UpperCamelCase__  )

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

      self.assertLess(np.sum(np.abs(output_dict - output_keywords  )  )							,	1E-6  )


  def   UpperCamelCase__      (				self    :					int  )     ->						Any:
    # make the mask reproducible
    np.random.seed(2  )

    _UpperCamelCase			,     _UpperCamelCase       			=self.model_tester.prepare_config_and_inputs_for_common()

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

    def prepare_numpy_arrays(UpperCamelCase__    :					int  ):
      _UpperCamelCase       			={}
      for k, v in inputs_dict.items():
        if tf.is_tensor(UpperCamelCase__  ):
          _UpperCamelCase       			=v.numpy()
        else:
          _UpperCamelCase       			=np.array(UpperCamelCase__  )

      return inputs_np_dict

    for model_class in self.all_model_classes:
      _UpperCamelCase       			=model_class(UpperCamelCase__  )

      _UpperCamelCase       			=self._prepare_for_class(UpperCamelCase__							,	UpperCamelCase__  )
      _UpperCamelCase       			=prepare_numpy_arrays(UpperCamelCase__  )

      _UpperCamelCase       			=model(UpperCamelCase__							,	noise=UpperCamelCase__  )
      _UpperCamelCase       			=model(**UpperCamelCase__							,	noise=UpperCamelCase__  )
      self.assert_outputs_same(UpperCamelCase__							,	UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Union[str, Any]							,	UpperCamelCase__    :					int							,	UpperCamelCase__    :					List[str]							,	UpperCamelCase__    :					List[Any]  )     ->						str:
    # make masks reproducible
    np.random.seed(2  )

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

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

    super().check_pt_tf_models(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Dict  )     ->						Optional[int]:
    # make mask reproducible
    np.random.seed(2  )

    _UpperCamelCase			,     _UpperCamelCase       			=self.model_tester.prepare_config_and_inputs_for_common()

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

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

    for main_layer_class in tf_main_layer_classes:
      _UpperCamelCase       			=main_layer_class(UpperCamelCase__  )

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

      _UpperCamelCase       			=tf.keras.Model(UpperCamelCase__							,	outputs=main_layer(UpperCamelCase__  )  )
      _UpperCamelCase       			=model(UpperCamelCase__  )

      with tempfile.TemporaryDirectory() as tmpdirname:
        _UpperCamelCase       			=os.path.join(UpperCamelCase__							,	'''keras_model.h5'''  )
        model.save(UpperCamelCase__  )
        _UpperCamelCase       			=tf.keras.models.load_model(
            UpperCamelCase__							,	custom_objects={main_layer_class.__name__: main_layer_class}  )
        assert isinstance(UpperCamelCase__							,	tf.keras.Model  )
        _UpperCamelCase       			=model(UpperCamelCase__  )
        self.assert_outputs_same(UpperCamelCase__							,	UpperCamelCase__  )


  @slow
  def   UpperCamelCase__      (				self    :					List[Any]  )     ->						Tuple:
    # make mask reproducible
    np.random.seed(2  )

    _UpperCamelCase			,     _UpperCamelCase       			=self.model_tester.prepare_config_and_inputs_for_common()

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

    for model_class in self.all_model_classes:
      _UpperCamelCase       			=model_class(UpperCamelCase__  )
      _UpperCamelCase       			=self._prepare_for_class(UpperCamelCase__							,	UpperCamelCase__  )
      _UpperCamelCase       			=model(UpperCamelCase__							,	noise=UpperCamelCase__  )

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

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

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

        _UpperCamelCase       			=np.amax(np.abs(out_a - out_a  )  )
        self.assertLessEqual(UpperCamelCase__							,	1E-5  )


  def   UpperCamelCase__      (				self    :					Tuple  )     ->						Any:
    # make mask reproducible
    np.random.seed(2  )

    _UpperCamelCase			,     _UpperCamelCase       			=self.model_tester.prepare_config_and_inputs_for_common()

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

    for model_class in self.all_model_classes:
      _UpperCamelCase       			=model_class(UpperCamelCase__  )
      _UpperCamelCase       			=self._prepare_for_class(UpperCamelCase__							,	UpperCamelCase__  )

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

      self.assert_outputs_same(UpperCamelCase__							,	UpperCamelCase__  )


  @unittest.skip(
      reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''  )
  def   UpperCamelCase__      (				self    :					List[Any]  )     ->						Any:
    pass


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




  @slow
  def   UpperCamelCase__      (				self    :					Any  )     ->						List[str]:
    _UpperCamelCase       			=TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224'''  )
    self.assertIsNotNone(UpperCamelCase__  )




def 					_a   ():


  """simple docstring"""

  _UpperCamelCase       			=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
  return image







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


  """simple docstring"""







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




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

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

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

    # forward pass
    _UpperCamelCase       			=model(**UpperCamelCase__							,	noise=UpperCamelCase__  )

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

    _UpperCamelCase       			=tf.convert_to_tensor(
        [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]  )

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













'''simple docstring'''






import argparse
import datetime


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			={
      '''0''': '''Sunday''',
      '''1''': '''Monday''',
      '''2''': '''Tuesday''',
      '''3''': '''Wednesday''',
      '''4''': '''Thursday''',
      '''5''': '''Friday''',
      '''6''': '''Saturday''',
  }

  _UpperCamelCase       			={0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}

  # Validate
  if not 0 < len(__SCREAMING_SNAKE_CASE   ) < 11:
    raise ValueError('''Must be 10 characters long'''   )

  # Get month
  _UpperCamelCase       			=int(date_input[0] + date_input[1]   )
  # Validate
  if not 0 < m < 13:
    raise ValueError('''Month must be between 1 - 12'''   )

  _UpperCamelCase       			=date_input[2]
  # Validate
  if sep_a not in ["-", "/"]:
    raise ValueError('''Date separator must be \'-\' or \'/\''''   )

  # Get day
  _UpperCamelCase       			=int(date_input[3] + date_input[4]   )
  # Validate
  if not 0 < d < 32:
    raise ValueError('''Date must be between 1 - 31'''   )

  # Get second separator
  _UpperCamelCase       			=date_input[5]
  # Validate
  if sep_a not in ["-", "/"]:
    raise ValueError('''Date separator must be \'-\' or \'/\''''   )

  # Get year
  _UpperCamelCase       			=int(date_input[6] + date_input[7] + date_input[8] + date_input[9]   )
  # Arbitrary year range
  if not 45 < y < 8500:
    raise ValueError(
        '''Year out of range. There has to be some sort of limit...right?'''   )

  # Get datetime obj for validation
  _UpperCamelCase       			=datetime.date(int(__SCREAMING_SNAKE_CASE   )  ,      int(__SCREAMING_SNAKE_CASE   )  ,      int(__SCREAMING_SNAKE_CASE   )   )

  # Start math
  if m <= 2:
    _UpperCamelCase       			=y - 1
    _UpperCamelCase       			=m + 12
  # maths var
  _UpperCamelCase       			=int(str(__SCREAMING_SNAKE_CASE   )[:2]   )
  _UpperCamelCase       			=int(str(__SCREAMING_SNAKE_CASE   )[2:]   )
  _UpperCamelCase       			=int(2.6 * m - 5.3_9   )
  _UpperCamelCase       			=int(c / 4   )
  _UpperCamelCase       			=int(k / 4   )
  _UpperCamelCase       			=int(d + k   )
  _UpperCamelCase       			=int(t + u + v + x   )
  _UpperCamelCase       			=int(z - (2 * c)   )
  _UpperCamelCase       			=round(w % 7   )
  # End math

  # Validate math
  if f != convert_datetime_days[dt_ck.weekday()]:
    raise AssertionError('''The date was evaluated incorrectly. Contact developer.'''   )

  # Response
  _UpperCamelCase       			=f'''Your date {date_input}, is a {days[str(__SCREAMING_SNAKE_CASE   )]}!'''
  return response


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      __lowerCamelCase						: List[Any]		=   argparse.ArgumentParser(
          description=(
              'Find out what day of the week nearly any date is or was. Enter '
              'date as a string in the mm-dd-yyyy or mm/dd/yyyy format'
          )
      )
      parser.add_argument(
          'date_input', type=str, help='Date as a string (mm-dd-yyyy or mm/dd/yyyy)'
      )
      __lowerCamelCase						: Union[str, Any]		=   parser.parse_args()
      zeller(args.date_input)






