






"""simple docstring"""



import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     MODEL_MAPPING,
     SegformerForImageClassification,
     SegformerForSemanticSegmentation,
     SegformerModel,
 )
 from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import SegformerImageProcessor




class 			lowerCAmelCase__			(    __magic_name__   ):
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							self.config_class(**self.inputs_dict							)
        self.parent.assertTrue(hasattr(snake_case__							,			"hidden_sizes"							)							)
        self.parent.assertTrue(hasattr(snake_case__							,			"num_attention_heads"							)							)
        self.parent.assertTrue(hasattr(snake_case__							,			"num_encoder_blocks"							)							)




class 			lowerCAmelCase__			:
   def __init__(		self      :	Dict							,			snake_case__      :	int							,			snake_case__      :	str=1_3							,			snake_case__      :	Tuple=6_4							,			snake_case__      :	int=3							,			snake_case__      :	int=4							,			snake_case__      :	Union[str, Any]=[2, 2, 2, 2]							,			snake_case__      :	Union[str, Any]=[8, 4, 2, 1]							,			snake_case__      :	Dict=[1_6, 3_2, 6_4, 1_2_8]							,			snake_case__      :	Union[str, Any]=[1, 4, 8, 1_6]							,			snake_case__      :	int=[1, 2, 4, 8]							,			snake_case__      :	Optional[int]=True							,			snake_case__      :	Union[str, Any]=True							,			snake_case__      :	int="gelu"							,			snake_case__      :	Dict=0.1							,			snake_case__      :	Any=0.1							,			snake_case__      :	Dict=0.02							,			snake_case__      :	Dict=3							,			snake_case__      :	Optional[int]=None							,			):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							parent
        UpperCAmelCase__						:    Optional[Any]							=							batch_size
        UpperCAmelCase__						:    int							=							image_size
        UpperCAmelCase__						:    Any							=							num_channels
        UpperCAmelCase__						:    List[Any]							=							num_encoder_blocks
        UpperCAmelCase__						:    str							=							sr_ratios
        UpperCAmelCase__						:    List[str]							=							depths
        UpperCAmelCase__						:    Tuple							=							hidden_sizes
        UpperCAmelCase__						:    int							=							downsampling_rates
        UpperCAmelCase__						:    Tuple							=							num_attention_heads
        UpperCAmelCase__						:    Union[str, Any]							=							is_training
        UpperCAmelCase__						:    Tuple							=							use_labels
        UpperCAmelCase__						:    Any							=							hidden_act
        UpperCAmelCase__						:    List[str]							=							hidden_dropout_prob
        UpperCAmelCase__						:    Any							=							attention_probs_dropout_prob
        UpperCAmelCase__						:    Optional[Any]							=							initializer_range
        UpperCAmelCase__						:    str							=							num_labels
        UpperCAmelCase__						:    Optional[int]							=							scope
   def       __a							(		self      :	Union[str, Any]							):


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

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

        UpperCAmelCase__						:    Optional[Any]							=							self.get_config()
        return config, pixel_values, labels
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        return SegformerConfig(
            image_size=self.image_size							,			num_channels=self.num_channels							,			num_encoder_blocks=self.num_encoder_blocks							,			depths=self.depths							,			hidden_sizes=self.hidden_sizes							,			num_attention_heads=self.num_attention_heads							,			hidden_act=self.hidden_act							,			hidden_dropout_prob=self.hidden_dropout_prob							,			attention_probs_dropout_prob=self.attention_probs_dropout_prob							,			initializer_range=self.initializer_range							,			)
   def       __a							(		self      :	int							,			snake_case__      :	List[Any]							,			snake_case__      :	Union[str, Any]							,			snake_case__      :	List[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							SegformerModel(config=snake_case__							)
        model.to(snake_case__							)
        model.eval()
        UpperCAmelCase__						:    str							=							model(snake_case__							)
        UpperCAmelCase__						:    Optional[Any]							=							self.image_size // (self.downsampling_rates[-1] * 2)
        self.parent.assertEqual(
            result.last_hidden_state.shape							,			(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)							)
   def       __a							(		self      :	Optional[int]							,			snake_case__      :	Tuple							,			snake_case__      :	Union[str, Any]							,			snake_case__      :	int							):


        '''simple docstring'''
        UpperCAmelCase__						:    Dict							=							self.num_labels
        UpperCAmelCase__						:    List[str]							=							SegformerForSemanticSegmentation(snake_case__							)
        model.to(snake_case__							)
        model.eval()
        UpperCAmelCase__						:    Any							=							model(snake_case__							)
        self.parent.assertEqual(
            result.logits.shape							,			(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)							)
        UpperCAmelCase__						:    List[str]							=							model(snake_case__							,			labels=snake_case__							)
        self.parent.assertEqual(
            result.logits.shape							,			(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)							)
        self.parent.assertGreater(result.loss							,			0.0							)
   def       __a							(		self      :	Optional[int]							,			snake_case__      :	Any							,			snake_case__      :	int							,			snake_case__      :	Union[str, Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							1
        UpperCAmelCase__						:    Dict							=							SegformerForSemanticSegmentation(config=snake_case__							)
        model.to(snake_case__							)
        model.eval()
        UpperCAmelCase__						:    Optional[Any]							=							torch.randint(0							,			1							,			(self.batch_size, self.image_size, self.image_size)							).to(snake_case__							)
        UpperCAmelCase__						:    Optional[Any]							=							model(snake_case__							,			labels=snake_case__							)
        self.parent.assertGreater(result.loss							,			0.0							)




   def       __a							(		self      :	List[Any]							):


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




@require_torch
class 			lowerCAmelCase__			(    __magic_name__							,						__magic_name__							,						unittest.TestCase   ):
   SCREAMING_SNAKE_CASE_    =(
       (
           SegformerModel,
           SegformerForSemanticSegmentation,
           SegformerForImageClassification,
       )
       if is_torch_available()
       else ()
   )
   SCREAMING_SNAKE_CASE_    =(
       {
           '''feature-extraction''': SegformerModel,
           '''image-classification''': SegformerForImageClassification,
           '''image-segmentation''': SegformerForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

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


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							SegformerModelTester(self							)
        UpperCAmelCase__						:    Any							=							SegformerConfigTester(self							,			config_class=snake_case__							)
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        self.config_tester.run_common_tests()
   def       __a							(		self      :	List[Any]							):


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


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_binary_image_segmentation(*snake_case__							)
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_segmentation(*snake_case__							)
   @unittest.skip("SegFormer does not use inputs_embeds"							)
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        pass
   @unittest.skip("SegFormer does not have get_input_embeddings method and get_output_embeddings methods"							)
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        pass
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__							,				UpperCAmelCase__						:    Union[str, Any]							=							self.model_tester.prepare_config_and_inputs_for_common()

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

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


        '''simple docstring'''
        UpperCAmelCase__							,				UpperCAmelCase__						:    Any							=							self.model_tester.prepare_config_and_inputs_for_common()
        UpperCAmelCase__						:    List[str]							=							True

        for model_class in self.all_model_classes:
             UpperCAmelCase__						:    Dict							=							True
             UpperCAmelCase__						:    Union[str, Any]							=							False
             UpperCAmelCase__						:    Tuple							=							True
             UpperCAmelCase__						:    Dict							=							model_class(snake_case__							)
             model.to(snake_case__							)
             model.eval()
             with torch.no_grad():
                  UpperCAmelCase__						:    Union[str, Any]							=							model(**self._prepare_for_class(snake_case__							,			snake_case__							)							)
             UpperCAmelCase__						:    int							=							outputs.attentions

             UpperCAmelCase__						:    Dict							=							sum(self.model_tester.depths							)
             self.assertEqual(len(snake_case__							)							,			snake_case__							)

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             UpperCAmelCase__						:    Any							=							True
             UpperCAmelCase__						:    Tuple							=							model_class(snake_case__							)
             model.to(snake_case__							)
             model.eval()
             with torch.no_grad():
                  UpperCAmelCase__						:    Dict							=							model(**self._prepare_for_class(snake_case__							,			snake_case__							)							)
             UpperCAmelCase__						:    str							=							outputs.attentions

             self.assertEqual(len(snake_case__							)							,			snake_case__							)

             # verify the first attentions (first block, first layer)
             UpperCAmelCase__						:    List[str]							=							(self.model_tester.image_size // 4) ** 2
             UpperCAmelCase__						:    Dict							=							(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
             self.assertListEqual(
                 list(attentions[0].shape[-3:]							)							,			[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]							,			)

             # verify the last attentions (last block, last layer)
             UpperCAmelCase__						:    Any							=							(self.model_tester.image_size // 3_2) ** 2
             UpperCAmelCase__						:    Optional[Any]							=							(self.model_tester.image_size // (3_2 * self.model_tester.sr_ratios[-1])) ** 2
             self.assertListEqual(
                 list(attentions[-1].shape[-3:]							)							,			[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]							,			)
             UpperCAmelCase__						:    Optional[int]							=							len(snake_case__							)

             # Check attention is always last and order is fine
             UpperCAmelCase__						:    Optional[int]							=							True
             UpperCAmelCase__						:    Any							=							True
             UpperCAmelCase__						:    Union[str, Any]							=							model_class(snake_case__							)
             model.to(snake_case__							)
             model.eval()
             with torch.no_grad():
                  UpperCAmelCase__						:    Tuple							=							model(**self._prepare_for_class(snake_case__							,			snake_case__							)							)

             self.assertEqual(out_len + 1							,			len(snake_case__							)							)

             UpperCAmelCase__						:    List[str]							=							outputs.attentions

             self.assertEqual(len(snake_case__							)							,			snake_case__							)
             # verify the first attentions (first block, first layer)
             UpperCAmelCase__						:    List[Any]							=							(self.model_tester.image_size // 4) ** 2
             UpperCAmelCase__						:    str							=							(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]							)							,			[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]							,			)
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        def check_hidden_states_output(snake_case__      :	str							,			snake_case__      :	int							,			snake_case__      :	Any							):
             UpperCAmelCase__						:    List[Any]							=							model_class(snake_case__							)
             model.to(snake_case__							)
             model.eval()

             with torch.no_grad():
                  UpperCAmelCase__						:    int							=							model(**self._prepare_for_class(snake_case__							,			snake_case__							)							)

             UpperCAmelCase__						:    Optional[int]							=							outputs.hidden_states

             UpperCAmelCase__						:    int							=							self.model_tester.num_encoder_blocks
             self.assertEqual(len(snake_case__							)							,			snake_case__							)

             # verify the first hidden states (first block)
             self.assertListEqual(
                 list(hidden_states[0].shape[-3:]							)							,			[
                     self.model_tester.hidden_sizes[0],
                     self.model_tester.image_size // 4,
                     self.model_tester.image_size // 4,
                 ]							,			)

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

        for model_class in self.all_model_classes:
             UpperCAmelCase__						:    List[str]							=							True
             check_hidden_states_output(snake_case__							,			snake_case__							,			snake_case__							)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             UpperCAmelCase__						:    Union[str, Any]							=							True

             check_hidden_states_output(snake_case__							,			snake_case__							,			snake_case__							)
   def       __a							(		self      :	List[Any]							):


        '''simple docstring'''
        if not self.model_tester.is_training:
             return

        UpperCAmelCase__							,				UpperCAmelCase__						:    str							=							self.model_tester.prepare_config_and_inputs_for_common()
        UpperCAmelCase__						:    str							=							True

        for model_class in self.all_model_classes:
             if model_class in get_values(snake_case__							):
                  continue

             UpperCAmelCase__						:    Union[str, Any]							=							model_class(snake_case__							)
             model.to(snake_case__							)
             model.train()
             UpperCAmelCase__						:    List[Any]							=							self._prepare_for_class(snake_case__							,			snake_case__							,			return_labels=snake_case__							)
             UpperCAmelCase__						:    List[str]							=							model(**snake_case__							).loss
             loss.backward()
   @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."							)
   def       __a							(		self      :	Union[str, Any]							):


        '''simple docstring'''
        pass




   @slow
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCAmelCase__						:    int							=							SegformerModel.from_pretrained(snake_case__							)
             self.assertIsNotNone(snake_case__							)




def 	SCREAMING_SNAKE_CASE__       (  )->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    Optional[int]							=							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
     return image




@require_torch
class 			lowerCAmelCase__			(    unittest.TestCase   ):
   @slow
   def       __a							(		self      :	Optional[int]							):


        '''simple docstring'''
        # only resize + normalize
        UpperCAmelCase__						:    Tuple							=							SegformerImageProcessor(
            image_scale=(5_1_2, 5_1_2)							,			keep_ratio=snake_case__							,			align=snake_case__							,			do_random_crop=snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512"							).to(
            snake_case__							)

        UpperCAmelCase__						:    Tuple							=							prepare_img()
        UpperCAmelCase__						:    str							=							image_processor(images=snake_case__							,			return_tensors="pt"							)
        UpperCAmelCase__						:    Dict							=							encoded_inputs.pixel_values.to(snake_case__							)

        with torch.no_grad():
             UpperCAmelCase__						:    Any							=							model(snake_case__							)

        UpperCAmelCase__						:    List[str]							=							torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8)							)
        self.assertEqual(outputs.logits.shape							,			snake_case__							)

        UpperCAmelCase__						:    str							=							torch.tensor(
            [
                [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
                [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
                [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
            ]							).to(snake_case__							)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]							,			snake_case__							,			atol=1e-4							)							)
   @slow
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        # only resize + normalize
        UpperCAmelCase__						:    Tuple							=							SegformerImageProcessor(
            image_scale=(5_1_2, 5_1_2)							,			keep_ratio=snake_case__							,			align=snake_case__							,			do_random_crop=snake_case__							)
        UpperCAmelCase__						:    str							=							SegformerForSemanticSegmentation.from_pretrained(
            "nvidia/segformer-b1-finetuned-cityscapes-1024-1024"							).to(snake_case__							)

        UpperCAmelCase__						:    Optional[Any]							=							prepare_img()
        UpperCAmelCase__						:    Dict							=							image_processor(images=snake_case__							,			return_tensors="pt"							)
        UpperCAmelCase__						:    int							=							encoded_inputs.pixel_values.to(snake_case__							)

        with torch.no_grad():
             UpperCAmelCase__						:    Any							=							model(snake_case__							)

        UpperCAmelCase__						:    int							=							torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8)							)
        self.assertEqual(outputs.logits.shape							,			snake_case__							)

        UpperCAmelCase__						:    List[str]							=							torch.tensor(
            [
                [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
                [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
                [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
            ]							).to(snake_case__							)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]							,			snake_case__							,			atol=1e-1							)							)




   @slow
   def       __a							(		self      :	List[Any]							):


        '''simple docstring'''
        # only resize + normalize
        UpperCAmelCase__						:    Any							=							SegformerImageProcessor(
            image_scale=(5_1_2, 5_1_2)							,			keep_ratio=snake_case__							,			align=snake_case__							,			do_random_crop=snake_case__							)
        UpperCAmelCase__						:    Dict							=							SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512"							).to(
            snake_case__							)

        UpperCAmelCase__						:    List[str]							=							prepare_img()
        UpperCAmelCase__						:    Dict							=							image_processor(images=snake_case__							,			return_tensors="pt"							)
        UpperCAmelCase__						:    List[Any]							=							encoded_inputs.pixel_values.to(snake_case__							)

        with torch.no_grad():
             UpperCAmelCase__						:    List[Any]							=							model(snake_case__							)

        UpperCAmelCase__						:    List[str]							=							outputs.logits.detach().cpu()

        UpperCAmelCase__						:    List[str]							=							image_processor.post_process_semantic_segmentation(outputs=snake_case__							,			target_sizes=[(5_0_0, 3_0_0)]							)
        UpperCAmelCase__						:    Dict							=							torch.Size((5_0_0, 3_0_0)							)
        self.assertEqual(segmentation[0].shape							,			snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							image_processor.post_process_semantic_segmentation(outputs=snake_case__							)
        UpperCAmelCase__						:    Optional[Any]							=							torch.Size((1_2_8, 1_2_8)							)
        self.assertEqual(segmentation[0].shape							,			snake_case__							)










"""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()


