






"""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 			lowerCAmelCase__			:
   def __init__(		self      :	Optional[Any]							,			snake_case__      :	int							,			snake_case__      :	Tuple=1_3							,			snake_case__      :	List[str]=3_0							,			snake_case__      :	Any=2							,			snake_case__      :	Any=3							,			snake_case__      :	str=True							,			snake_case__      :	Optional[Any]=True							,			snake_case__      :	Optional[int]=3_2							,			snake_case__      :	Optional[Any]=2							,			snake_case__      :	int=4							,			snake_case__      :	Optional[int]=3_7							,			snake_case__      :	Tuple="gelu"							,			snake_case__      :	List[str]=0.1							,			snake_case__      :	str=0.1							,			snake_case__      :	str=1_0							,			snake_case__      :	Optional[Any]=0.02							,			snake_case__      :	Optional[int]=3							,			snake_case__      :	Tuple=0.6							,			snake_case__      :	Any=None							,			):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							parent
        UpperCAmelCase__						:    str							=							batch_size
        UpperCAmelCase__						:    Tuple							=							image_size
        UpperCAmelCase__						:    Dict							=							patch_size
        UpperCAmelCase__						:    Optional[Any]							=							num_channels
        UpperCAmelCase__						:    str							=							is_training
        UpperCAmelCase__						:    List[Any]							=							use_labels
        UpperCAmelCase__						:    str							=							hidden_size
        UpperCAmelCase__						:    Optional[int]							=							num_hidden_layers
        UpperCAmelCase__						:    List[Any]							=							num_attention_heads
        UpperCAmelCase__						:    Dict							=							intermediate_size
        UpperCAmelCase__						:    Union[str, Any]							=							hidden_act
        UpperCAmelCase__						:    Dict							=							hidden_dropout_prob
        UpperCAmelCase__						:    List[Any]							=							attention_probs_dropout_prob
        UpperCAmelCase__						:    Dict							=							type_sequence_label_size
        UpperCAmelCase__						:    Any							=							initializer_range
        UpperCAmelCase__						:    Union[str, Any]							=							mask_ratio
        UpperCAmelCase__						:    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)
        UpperCAmelCase__						:    Optional[Any]							=							(image_size // patch_size) ** 2
        UpperCAmelCase__						:    List[Any]							=							int(math.ceil((1 - mask_ratio) * (num_patches + 1)							)							)
   def       __a							(		self      :	Optional[Any]							):


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

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

        UpperCAmelCase__						:    Optional[int]							=							self.get_config()

        return config, pixel_values, labels
   def       __a							(		self      :	str							):


        '''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=snake_case__							,			initializer_range=self.initializer_range							,			mask_ratio=self.mask_ratio							,			)
   def       __a							(		self      :	Dict							,			snake_case__      :	int							,			snake_case__      :	Tuple							,			snake_case__      :	Optional[int]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Dict							=							TFViTMAEModel(config=snake_case__							)
        UpperCAmelCase__						:    Optional[Any]							=							model(snake_case__							,			training=snake_case__							)
        self.parent.assertEqual(result.last_hidden_state.shape							,			(self.batch_size, self.seq_length, self.hidden_size)							)
   def       __a							(		self      :	Optional[Any]							,			snake_case__      :	Tuple							,			snake_case__      :	List[Any]							,			snake_case__      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							TFViTMAEForPreTraining(snake_case__							)
        UpperCAmelCase__						:    str							=							model(snake_case__							,			training=snake_case__							)
        # expected sequence length = num_patches
        UpperCAmelCase__						:    Optional[int]							=							(self.image_size // self.patch_size) ** 2
        UpperCAmelCase__						:    Any							=							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__						:    List[Any]							=							1
        UpperCAmelCase__						:    Any							=							TFViTMAEForPreTraining(snake_case__							)

        UpperCAmelCase__						:    Union[str, Any]							=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
        UpperCAmelCase__						:    str							=							model(snake_case__							,			training=snake_case__							)
        UpperCAmelCase__						:    List[str]							=							self.patch_size**2
        self.parent.assertEqual(result.logits.shape							,			(self.batch_size, num_patches, expected_num_channels)							)




   def       __a							(		self      :	Union[str, Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							self.prepare_config_and_inputs()
        ((UpperCAmelCase__)							,				(UpperCAmelCase__)							,				(UpperCAmelCase__))						:    str							=							config_and_inputs
        UpperCAmelCase__						:    Dict							=							{"pixel_values": pixel_values}
        return config, inputs_dict




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

   SCREAMING_SNAKE_CASE_    =False
   SCREAMING_SNAKE_CASE_    =False
   SCREAMING_SNAKE_CASE_    =False
   SCREAMING_SNAKE_CASE_    =False
   def       __a							(		self      :	List[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							TFViTMAEModelTester(self							)
        UpperCAmelCase__						:    Any							=							ConfigTester(self							,			config_class=snake_case__							,			has_text_modality=snake_case__							,			hidden_size=3_7							)
   def       __a							(		self      :	Optional[int]							):


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


        '''simple docstring'''
        pass
   def       __a							(		self      :	str							):


        '''simple docstring'''
        UpperCAmelCase__							,				UpperCAmelCase__						:    Tuple							=							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             UpperCAmelCase__						:    Dict							=							model_class(snake_case__							)
             self.assertIsInstance(model.get_input_embeddings()							,			(tf.keras.layers.Layer)							)
             UpperCAmelCase__						:    str							=							model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(snake_case__							,			tf.keras.layers.Layer							)							)
   def       __a							(		self      :	List[Any]							):


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

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

             UpperCAmelCase__						:    Optional[int]							=							["pixel_values"]
             self.assertListEqual(arg_names[:1]							,			snake_case__							)
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case__							)
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    int							=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*snake_case__							)
   def       __a							(		self      :	Tuple							):


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

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

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

        for model_class in self.all_model_classes:
             UpperCAmelCase__						:    Optional[Any]							=							model_class(snake_case__							)
             UpperCAmelCase__						:    Any							=							self._prepare_for_class(snake_case__							,			snake_case__							)

             UpperCAmelCase__						:    Optional[Any]							=							model(snake_case__							,			noise=snake_case__							)

             UpperCAmelCase__						:    Optional[Any]							=							copy.deepcopy(self._prepare_for_class(snake_case__							,			snake_case__							)							)
             UpperCAmelCase__						:    List[Any]							=							model(**snake_case__							,			noise=snake_case__							)
             UpperCAmelCase__						:    Tuple							=							outputs_dict[0].numpy()
             UpperCAmelCase__						:    Any							=							outputs_keywords[0].numpy()

             self.assertLess(np.sum(np.abs(output_dict - output_keywords							)							)							,			1e-6							)
   def       __a							(		self      :	int							):


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

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

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

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

             return inputs_np_dict

        for model_class in self.all_model_classes:
             UpperCAmelCase__						:    Dict							=							model_class(snake_case__							)

             UpperCAmelCase__						:    Dict							=							self._prepare_for_class(snake_case__							,			snake_case__							)
             UpperCAmelCase__						:    int							=							prepare_numpy_arrays(snake_case__							)

             UpperCAmelCase__						:    List[str]							=							model(snake_case__							,			noise=snake_case__							)
             UpperCAmelCase__						:    Union[str, Any]							=							model(**snake_case__							,			noise=snake_case__							)
             self.assert_outputs_same(snake_case__							,			snake_case__							)
   def       __a							(		self      :	int							,			snake_case__      :	Optional[int]							,			snake_case__      :	Tuple							,			snake_case__      :	int							):


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

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

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

        super().check_pt_tf_models(snake_case__							,			snake_case__							,			snake_case__							)
   def       __a							(		self      :	Tuple							):


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

        UpperCAmelCase__							,				UpperCAmelCase__						:    Any							=							self.model_tester.prepare_config_and_inputs_for_common()

        UpperCAmelCase__						:    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(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__							)
        }

        UpperCAmelCase__						:    Union[str, Any]							=							int((config.image_size // config.patch_size) ** 2							)
        UpperCAmelCase__						:    Optional[Any]							=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
        UpperCAmelCase__						:    Tuple							=							tf.convert_to_tensor(snake_case__							)
        inputs_dict.update({"noise": noise}							)

        for main_layer_class in tf_main_layer_classes:
             UpperCAmelCase__						:    List[Any]							=							main_layer_class(snake_case__							)

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

             UpperCAmelCase__						:    int							=							tf.keras.Model(snake_case__							,			outputs=main_layer(snake_case__							)							)
             UpperCAmelCase__						:    str							=							model(snake_case__							)

             with tempfile.TemporaryDirectory() as tmpdirname:
                  UpperCAmelCase__						:    List[str]							=							os.path.join(snake_case__							,			"keras_model.h5"							)
                  model.save(snake_case__							)
                  UpperCAmelCase__						:    str							=							tf.keras.models.load_model(
                      snake_case__							,			custom_objects={main_layer_class.__name__: main_layer_class}							)
                  assert isinstance(snake_case__							,			tf.keras.Model							)
                  UpperCAmelCase__						:    Union[str, Any]							=							model(snake_case__							)
                  self.assert_outputs_same(snake_case__							,			snake_case__							)
   @slow
   def       __a							(		self      :	Dict							):


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

        UpperCAmelCase__							,				UpperCAmelCase__						:    List[str]							=							self.model_tester.prepare_config_and_inputs_for_common()

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

        for model_class in self.all_model_classes:
             UpperCAmelCase__						:    Optional[Any]							=							model_class(snake_case__							)
             UpperCAmelCase__						:    Optional[int]							=							self._prepare_for_class(snake_case__							,			snake_case__							)
             UpperCAmelCase__						:    List[str]							=							model(snake_case__							,			noise=snake_case__							)

             if model_class.__name__ == "TFViTMAEModel":
                  UpperCAmelCase__						:    Dict							=							outputs.last_hidden_state.numpy()
                  UpperCAmelCase__						:    Tuple							=							0
             else:
                  UpperCAmelCase__						:    Tuple							=							outputs.logits.numpy()
                  UpperCAmelCase__						:    int							=							0

             with tempfile.TemporaryDirectory() as tmpdirname:
                  model.save_pretrained(snake_case__							,			saved_model=snake_case__							)
                  UpperCAmelCase__						:    Dict							=							model_class.from_pretrained(snake_case__							)
                  UpperCAmelCase__						:    List[str]							=							model(snake_case__							,			noise=snake_case__							)

                  if model_class.__name__ == "TFViTMAEModel":
                       UpperCAmelCase__						:    List[str]							=							after_outputs["last_hidden_state"].numpy()
                       UpperCAmelCase__						:    List[str]							=							0
                  else:
                       UpperCAmelCase__						:    List[str]							=							after_outputs["logits"].numpy()
                       UpperCAmelCase__						:    List[Any]							=							0

                  UpperCAmelCase__						:    List[Any]							=							np.amax(np.abs(out_a - out_a							)							)
                  self.assertLessEqual(snake_case__							,			1e-5							)
   def       __a							(		self      :	Optional[int]							):


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

        UpperCAmelCase__							,				UpperCAmelCase__						:    Any							=							self.model_tester.prepare_config_and_inputs_for_common()

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

        for model_class in self.all_model_classes:
             UpperCAmelCase__						:    Any							=							model_class(snake_case__							)
             UpperCAmelCase__						:    List[Any]							=							self._prepare_for_class(snake_case__							,			snake_case__							)

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


        '''simple docstring'''
        pass




   @slow
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							TFViTMAEModel.from_pretrained("google/vit-base-patch16-224"							)
        self.assertIsNotNone(snake_case__							)




def 	SCREAMING_SNAKE_CASE__       (  )->		Any:






     '''simple docstring'''



     UpperCAmelCase__						:    str							=							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
     return image




@require_tf
@require_vision
class 			lowerCAmelCase__			(    unittest.TestCase   ):
   @cached_property
   def       __a							(		self      :	Optional[int]							):


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




   @slow
   def       __a							(		self      :	Optional[int]							):


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

        UpperCAmelCase__						:    Dict							=							TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"							)

        UpperCAmelCase__						:    int							=							self.default_image_processor
        UpperCAmelCase__						:    Union[str, Any]							=							prepare_img()
        UpperCAmelCase__						:    Tuple							=							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)
        UpperCAmelCase__						:    Any							=							ViTMAEConfig()
        UpperCAmelCase__						:    Any							=							int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2							)
        UpperCAmelCase__						:    List[Any]							=							np.random.uniform(size=(1, num_patches)							)

        # forward pass
        UpperCAmelCase__						:    str							=							model(**snake_case__							,			noise=snake_case__							)

        # verify the logits
        UpperCAmelCase__						:    Union[str, Any]							=							tf.convert_to_tensor([1, 1_9_6, 7_6_8]							)
        self.assertEqual(outputs.logits.shape							,			snake_case__							)

        UpperCAmelCase__						:    Optional[Any]							=							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]							,			snake_case__							,			atol=1e-4							)










"""simple docstring"""



from __future__ import annotations

from collections.abc import Iterator
from typing import Any




class 			lowerCAmelCase__			:
   def __init__(		self      :	Any							,			snake_case__      :	Any							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							data
        UpperCAmelCase__						:    Node | None							=							None




class 			lowerCAmelCase__			:
   def __init__(		self      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							None
        UpperCAmelCase__						:    Optional[int]							=							None
   def __iter__(		self      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							self.head
        while self.head:
             yield node.data
             UpperCAmelCase__						:    Optional[Any]							=							node.next
             if node == self.head:
                  break
   def __len__(		self      :	Tuple							):


        '''simple docstring'''
        return sum(1 for _ in self							)
   def __repr__(		self      :	str							):


        '''simple docstring'''
        return "->".join(str(snake_case__							) for item in iter(self							)							)
   def       __a							(		self      :	Optional[Any]							,			snake_case__      :	Any							):


        '''simple docstring'''
        self.insert_nth(len(self							)							,			snake_case__							)
   def       __a							(		self      :	Optional[Any]							,			snake_case__      :	Any							):


        '''simple docstring'''
        self.insert_nth(0							,			snake_case__							)
   def       __a							(		self      :	Any							,			snake_case__      :	int							,			snake_case__      :	Any							):


        '''simple docstring'''
        if index < 0 or index > len(self							):
             raise IndexError("list index out of range."							)
        UpperCAmelCase__						:    Any							=							Node(snake_case__							)
        if self.head is None:
             UpperCAmelCase__						:    Union[str, Any]							=							new_node  # first node points itself
             UpperCAmelCase__						:    List[str]							=							new_node
        elif index == 0:  # insert at head
             UpperCAmelCase__						:    Optional[Any]							=							self.head
             UpperCAmelCase__						:    List[Any]							=							new_node
        else:
             UpperCAmelCase__						:    Union[str, Any]							=							self.head
             for _ in range(index - 1							):
                  UpperCAmelCase__						:    Dict							=							temp.next
             UpperCAmelCase__						:    Tuple							=							temp.next
             UpperCAmelCase__						:    str							=							new_node
             if index == len(self							) - 1:  # insert at tail
                  UpperCAmelCase__						:    Union[str, Any]							=							new_node
   def       __a							(		self      :	List[Any]							):


        '''simple docstring'''
        return self.delete_nth(0							)
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        return self.delete_nth(len(self							) - 1							)
   def       __a							(		self      :	Tuple							,			snake_case__      :	int = 0							):


        '''simple docstring'''
        if not 0 <= index < len(self							):
             raise IndexError("list index out of range."							)
        UpperCAmelCase__						:    Union[str, Any]							=							self.head
        if self.head == self.tail:  # just one node
             UpperCAmelCase__						:    str							=							None
        elif index == 0:  # delete head node
             UpperCAmelCase__						:    List[Any]							=							self.tail.next.next
             UpperCAmelCase__						:    Dict							=							self.head.next
        else:
             UpperCAmelCase__						:    List[Any]							=							self.head
             for _ in range(index - 1							):
                  UpperCAmelCase__						:    Tuple							=							temp.next
             UpperCAmelCase__						:    Dict							=							temp.next
             UpperCAmelCase__						:    Dict							=							temp.next.next
             if index == len(self							) - 1:  # delete at tail
                  UpperCAmelCase__						:    int							=							temp
        return delete_node.data




   def       __a							(		self      :	Any							):


        '''simple docstring'''
        return len(self							) == 0




def 	SCREAMING_SNAKE_CASE__       (  )->		None:






     '''simple docstring'''



     UpperCAmelCase__						:    str							=							CircularLinkedList()
     assert len(snake_case				) == 0
     assert circular_linked_list.is_empty() is True
     assert str(snake_case				) == ""

     try:
          circular_linked_list.delete_front()
          raise AssertionError  # This should not happen
     except IndexError:
          assert True  # This should happen

     try:
          circular_linked_list.delete_tail()
          raise AssertionError  # This should not happen
     except IndexError:
          assert True  # This should happen

     try:
          circular_linked_list.delete_nth(-1				)
          raise AssertionError
     except IndexError:
          assert True

     try:
          circular_linked_list.delete_nth(0				)
          raise AssertionError
     except IndexError:
          assert True

     assert circular_linked_list.is_empty() is True
     for i in range(5				):
          assert len(snake_case				) == i
          circular_linked_list.insert_nth(snake_case			,					i + 1				)
     assert str(snake_case				) == "->".join(str(snake_case				) for i in range(1			,					6				)				)

     circular_linked_list.insert_tail(6				)
     assert str(snake_case				) == "->".join(str(snake_case				) for i in range(1			,					7				)				)
     circular_linked_list.insert_head(0				)
     assert str(snake_case				) == "->".join(str(snake_case				) for i in range(0			,					7				)				)

     assert circular_linked_list.delete_front() == 0
     assert circular_linked_list.delete_tail() == 6
     assert str(snake_case				) == "->".join(str(snake_case				) for i in range(1			,					6				)				)
     assert circular_linked_list.delete_nth(2				) == 3

     circular_linked_list.insert_nth(2			,					3				)
     assert str(snake_case				) == "->".join(str(snake_case				) for i in range(1			,					6				)				)

     assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
 import doctest

 doctest.testmod()


