import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

   if is_vision_available():
      from transformers import MaskFormerImageProcessor

if is_vision_available():
   from PIL import Image







class __snake_case       :




   def __init__(					self    :       Dict ,			_lowercase    :       List[str] ,			_lowercase    :       int=2 ,			_lowercase    :       Optional[Any]=True ,			_lowercase    :       Optional[int]=False ,			_lowercase    :       Tuple=10 ,			_lowercase    :       Tuple=3 ,			_lowercase    :       Union[str, Any]=32 * 4 ,			_lowercase    :       Any=32 * 6 ,			_lowercase    :       Any=4 ,			_lowercase    :       Any=32 ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   parent
     SCREAMING_SNAKE_CASE__    							=   batch_size
     SCREAMING_SNAKE_CASE__    							=   is_training
     SCREAMING_SNAKE_CASE__    							=   use_auxiliary_loss
     SCREAMING_SNAKE_CASE__    							=   num_queries
     SCREAMING_SNAKE_CASE__    							=   num_channels
     SCREAMING_SNAKE_CASE__    							=   min_size
     SCREAMING_SNAKE_CASE__    							=   max_size
     SCREAMING_SNAKE_CASE__    							=   num_labels
     SCREAMING_SNAKE_CASE__    							=   mask_feature_size




   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]   ).to(
         _lowercase   )

     SCREAMING_SNAKE_CASE__    							=   torch.ones([self.batch_size, self.min_size, self.max_size] ,			device=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   (
         torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] ,			device=_lowercase   ) > 0.5
     ).float()
     SCREAMING_SNAKE_CASE__    							=   (torch.rand((self.batch_size, self.num_labels) ,			device=_lowercase   ) > 0.5).long()

     SCREAMING_SNAKE_CASE__    							=   self.get_config()
     return config, pixel_values, pixel_mask, mask_labels, class_labels




   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     return MaskFormerConfig.from_backbone_and_decoder_configs(
         backbone_config=SwinConfig(
             depths=[1, 1, 1, 1] ,			) ,			decoder_config=DetrConfig(
             decoder_ffn_dim=1_28 ,			num_queries=self.num_queries ,			decoder_attention_heads=2 ,			d_model=self.mask_feature_size ,			) ,			mask_feature_size=self.mask_feature_size ,			fpn_feature_size=self.mask_feature_size ,			num_channels=self.num_channels ,			num_labels=self.num_labels ,			)




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE__    							=   {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
     return config, inputs_dict




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   output.encoder_hidden_states
     SCREAMING_SNAKE_CASE__    							=   output.pixel_decoder_hidden_states
     SCREAMING_SNAKE_CASE__    							=   output.transformer_decoder_hidden_states

     self.parent.assertTrue(len(_lowercase   ) ,			len(config.backbone_config.depths   )   )
     self.parent.assertTrue(len(_lowercase   ) ,			len(config.backbone_config.depths   )   )
     self.parent.assertTrue(len(_lowercase   ) ,			config.decoder_config.decoder_layers   )




   def 			__a		(					self    :       Dict ,			_lowercase    :       Optional[Any] ,			_lowercase    :       Dict ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       List[Any]=False   ):



     """simple docstring"""






     with torch.no_grad():
       SCREAMING_SNAKE_CASE__    							=   MaskFormerModel(config=_lowercase   )
       model.to(_lowercase   )
       model.eval()

       SCREAMING_SNAKE_CASE__    							=   model(pixel_values=_lowercase ,			pixel_mask=_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			output_hidden_states=_lowercase   )
     # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
     # encoder and pixel decoder
     self.parent.assertEqual(
         output.transformer_decoder_last_hidden_state.shape ,			(self.batch_size, self.num_queries, self.mask_feature_size) ,			)
     # let's ensure the other two hidden state exists
     self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None   )
     self.parent.assertTrue(output.encoder_last_hidden_state is not None   )

     if output_hidden_states:
       self.check_output_hidden_state(_lowercase ,			_lowercase   )




   def 			__a		(					self    :       Optional[Any] ,			_lowercase    :       Any ,			_lowercase    :       Any ,			_lowercase    :       List[str] ,			_lowercase    :       List[Any] ,			_lowercase    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   MaskFormerForInstanceSegmentation(config=_lowercase   )
     model.to(_lowercase   )
     model.eval()

     def comm_check_on_output(_lowercase    :       List[str]   ):
       # let's still check that all the required stuff is there
       self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None   )
       self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None   )
       self.parent.assertTrue(result.encoder_last_hidden_state is not None   )
       # okay, now we need to check the logits shape
       # due to the encoder compression, masks have a //4 spatial size
       self.parent.assertEqual(
           result.masks_queries_logits.shape ,			(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) ,			)
       # + 1 for null class
       self.parent.assertEqual(
           result.class_queries_logits.shape ,			(self.batch_size, self.num_queries, self.num_labels + 1)   )

     with torch.no_grad():
       SCREAMING_SNAKE_CASE__    							=   model(pixel_values=_lowercase ,			pixel_mask=_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   model(_lowercase   )

       comm_check_on_output(_lowercase   )

       SCREAMING_SNAKE_CASE__    							=   model(
           pixel_values=_lowercase ,			pixel_mask=_lowercase ,			mask_labels=_lowercase ,			class_labels=_lowercase   )

     comm_check_on_output(_lowercase   )

     self.parent.assertTrue(result.loss is not None   )
     self.parent.assertEqual(result.loss.shape ,			torch.Size([1]   )   )







@require_torch
class __snake_case       (		_UpperCamelCase     ,	_UpperCamelCase     ,	unittest.TestCase	):
   lowerCAmelCase_														=							(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
   lowerCAmelCase_														=							(
       {"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
       if is_torch_available()
       else {}
   )

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




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   MaskFormerModelTester(self   )
     SCREAMING_SNAKE_CASE__    							=   ConfigTester(self ,			config_class=_lowercase ,			has_text_modality=_lowercase   )




   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     self.config_tester.run_common_tests()




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskformer_model(_lowercase ,			**_lowercase ,			output_hidden_states=_lowercase   )




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_lowercase   )




   @unittest.skip(reason="""MaskFormer does not use inputs_embeds"""   )
   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     pass




   @unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method"""   )
   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     pass




   @unittest.skip(reason="""MaskFormer is not a generative model"""   )
   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






     pass




   @unittest.skip(reason="""MaskFormer does not use token embeddings"""   )
   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     pass




   @require_torch_multi_gpu
   @unittest.skip(
       reason="""MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""   )
   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     pass




   @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""   )
   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     pass




   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs_for_common()

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

       SCREAMING_SNAKE_CASE__    							=   ["pixel_values"]
       self.assertListEqual(arg_names[:1] ,			_lowercase   )




   @slow
   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     for model_name in ["facebook/maskformer-swin-small-coco"]:
       SCREAMING_SNAKE_CASE__    							=   MaskFormerModel.from_pretrained(_lowercase   )
       self.assertIsNotNone(_lowercase   )




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   (self.model_tester.min_size,) * 2
     SCREAMING_SNAKE_CASE__    							=   {
         "pixel_values": torch.randn((2, 3, *size) ,			device=_lowercase   ),
         "mask_labels": torch.randn((2, 10, *size) ,			device=_lowercase   ),
         "class_labels": torch.zeros(2 ,			10 ,			device=_lowercase   ).long(),
     }

     SCREAMING_SNAKE_CASE__    							=   MaskFormerForInstanceSegmentation(MaskFormerConfig()   ).to(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   model(**_lowercase   )
     self.assertTrue(outputs.loss is not None   )




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskformer_model(_lowercase ,			**_lowercase ,			output_hidden_states=_lowercase   )




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE__    							=   model_class(_lowercase   ).to(_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   model(**_lowercase ,			output_attentions=_lowercase   )
       self.assertTrue(outputs.attentions is not None   )




   def 			__a		(					self    :       str   ):



     """simple docstring"""






     if not self.model_tester.is_training:
       return
     # only MaskFormerForInstanceSegmentation has the loss
     SCREAMING_SNAKE_CASE__    							=   self.all_model_classes[1]
     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()

     SCREAMING_SNAKE_CASE__    							=   model_class(_lowercase   )
     model.to(_lowercase   )
     model.train()

     SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			mask_labels=_lowercase ,			class_labels=_lowercase   ).loss
     loss.backward()




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.all_model_classes[1]
     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE__    							=   True
     SCREAMING_SNAKE_CASE__    							=   True

     SCREAMING_SNAKE_CASE__    							=   model_class(_lowercase   )
     model.to(_lowercase   )
     model.train()

     SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			mask_labels=_lowercase ,			class_labels=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   outputs.encoder_hidden_states[0]
     encoder_hidden_states.retain_grad()

     SCREAMING_SNAKE_CASE__    							=   outputs.pixel_decoder_hidden_states[0]
     pixel_decoder_hidden_states.retain_grad()
     # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
     SCREAMING_SNAKE_CASE__    							=   outputs.transformer_decoder_hidden_states[0]
     transformer_decoder_hidden_states.retain_grad()

     SCREAMING_SNAKE_CASE__    							=   outputs.attentions[0]
     attentions.retain_grad()

     outputs.loss.backward(retain_graph=_lowercase   )

     self.assertIsNotNone(encoder_hidden_states.grad   )
     self.assertIsNotNone(pixel_decoder_hidden_states.grad   )
     self.assertIsNotNone(transformer_decoder_hidden_states.grad   )
     self.assertIsNotNone(attentions.grad   )





__lowerCamelCase    :				Union[str, Any]							     =	1e-4



def 			__SCREAMING_SNAKE_CASE						(      )							->      List[Any]:







  """simple docstring"""





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







@require_vision
@slow
class __snake_case       (		unittest.TestCase	):




   @cached_property
   def 			__a		(					self    :       int   ):



     """simple docstring"""






     return (
         MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco"""   )
         if is_vision_available()
         else None
     )




   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco"""   ).to(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   self.default_image_processor
     SCREAMING_SNAKE_CASE__    							=   prepare_img()
     SCREAMING_SNAKE_CASE__    							=   image_processor(_lowercase ,			return_tensors="""pt"""   ).to(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   inputs["pixel_values"].shape
     # check size is divisible by 32
     self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0   )
     # check size
     self.assertEqual(_lowercase ,			(1, 3, 8_00, 10_88)   )

     with torch.no_grad():
       SCREAMING_SNAKE_CASE__    							=   model(**_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   torch.tensor(
         [[-0.04_82, 0.92_28, 0.49_51], [-0.25_47, 0.80_17, 0.85_27], [-0.00_69, 0.33_85, -0.00_89]]   ).to(_lowercase   )
     self.assertTrue(
         torch.allclose(
             outputs.encoder_last_hidden_state[0, 0, :3, :3] ,			_lowercase ,			atol=_lowercase   )   )

     SCREAMING_SNAKE_CASE__    							=   torch.tensor(
         [[-0.84_22, -0.84_34, -0.97_18], [-1.01_44, -0.55_65, -0.41_95], [-1.00_38, -0.44_84, -0.19_61]]   ).to(_lowercase   )
     self.assertTrue(
         torch.allclose(
             outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] ,			_lowercase ,			atol=_lowercase   )   )

     SCREAMING_SNAKE_CASE__    							=   torch.tensor(
         [[0.28_52, -0.01_59, 0.97_35], [0.62_54, 0.18_58, 0.85_29], [-0.06_80, -0.41_16, 1.84_13]]   ).to(_lowercase   )
     self.assertTrue(
         torch.allclose(
             outputs.transformer_decoder_last_hidden_state[0, :3, :3] ,			_lowercase ,			atol=_lowercase   )   )




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   (
         MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""   )
         .to(_lowercase   )
         .eval()
     )
     SCREAMING_SNAKE_CASE__    							=   self.default_image_processor
     SCREAMING_SNAKE_CASE__    							=   prepare_img()
     SCREAMING_SNAKE_CASE__    							=   image_processor(_lowercase ,			return_tensors="""pt"""   ).to(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   inputs["pixel_values"].shape
     # check size is divisible by 32
     self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0   )
     # check size
     self.assertEqual(_lowercase ,			(1, 3, 8_00, 10_88)   )

     with torch.no_grad():
       SCREAMING_SNAKE_CASE__    							=   model(**_lowercase   )
     # masks_queries_logits
     SCREAMING_SNAKE_CASE__    							=   outputs.masks_queries_logits
     self.assertEqual(
         masks_queries_logits.shape ,			(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) ,			)
     SCREAMING_SNAKE_CASE__    							=   [
         [-1.3_73_71_24, -1.7_72_49_37, -1.9_36_42_33],
         [-1.5_97_72_81, -1.9_86_79_39, -2.1_52_36_95],
         [-1.5_79_53_98, -1.9_26_98_32, -2.09_39_42],
     ]
     SCREAMING_SNAKE_CASE__    							=   torch.tensor(_lowercase   ).to(_lowercase   )
     self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] ,			_lowercase ,			atol=_lowercase   )   )
     # class_queries_logits
     SCREAMING_SNAKE_CASE__    							=   outputs.class_queries_logits
     self.assertEqual(
         class_queries_logits.shape ,			(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)   )
     SCREAMING_SNAKE_CASE__    							=   torch.tensor(
         [
             [1.6512E00, -5.2572E00, -3.3519E00],
             [3.6169E-02, -5.9025E00, -2.9313E00],
             [1.0766E-04, -7.7630E00, -5.1263E00],
         ]   ).to(_lowercase   )
     self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] ,			_lowercase ,			atol=_lowercase   )   )




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   (
         MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff"""   )
         .to(_lowercase   )
         .eval()
     )
     SCREAMING_SNAKE_CASE__    							=   self.default_image_processor
     SCREAMING_SNAKE_CASE__    							=   prepare_img()
     SCREAMING_SNAKE_CASE__    							=   image_processor(_lowercase ,			return_tensors="""pt"""   ).to(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   inputs["pixel_values"].shape
     # check size is divisible by 32
     self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0   )
     # check size
     self.assertEqual(_lowercase ,			(1, 3, 8_00, 10_88)   )

     with torch.no_grad():
       SCREAMING_SNAKE_CASE__    							=   model(**_lowercase   )
     # masks_queries_logits
     SCREAMING_SNAKE_CASE__    							=   outputs.masks_queries_logits
     self.assertEqual(
         masks_queries_logits.shape ,			(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) ,			)
     SCREAMING_SNAKE_CASE__    							=   [[-0.90_46, -2.63_66, -4.60_62], [-3.41_79, -5.78_90, -8.80_57], [-4.91_79, -7.65_60, -10.77_11]]
     SCREAMING_SNAKE_CASE__    							=   torch.tensor(_lowercase   ).to(_lowercase   )
     self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] ,			_lowercase ,			atol=_lowercase   )   )
     # class_queries_logits
     SCREAMING_SNAKE_CASE__    							=   outputs.class_queries_logits
     self.assertEqual(
         class_queries_logits.shape ,			(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)   )
     SCREAMING_SNAKE_CASE__    							=   torch.tensor(
         [[4.71_88, -3.25_85, -2.88_57], [6.68_71, -2.91_81, -1.24_87], [7.24_49, -2.27_64, -2.18_74]]   ).to(_lowercase   )
     self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] ,			_lowercase ,			atol=_lowercase   )   )




   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   (
         MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""   )
         .to(_lowercase   )
         .eval()
     )
     SCREAMING_SNAKE_CASE__    							=   self.default_image_processor

     SCREAMING_SNAKE_CASE__    							=   image_processor(
         [np.zeros((3, 8_00, 13_33)   ), np.zeros((3, 8_00, 13_33)   )] ,			segmentation_maps=[np.zeros((3_84, 3_84)   ).astype(np.floataa   ), np.zeros((3_84, 3_84)   ).astype(np.floataa   )] ,			return_tensors="""pt""" ,			)

     SCREAMING_SNAKE_CASE__    							=   inputs["pixel_values"].to(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   [el.to(_lowercase   ) for el in inputs["mask_labels"]]
     SCREAMING_SNAKE_CASE__    							=   [el.to(_lowercase   ) for el in inputs["class_labels"]]

     with torch.no_grad():
       SCREAMING_SNAKE_CASE__    							=   model(**_lowercase   )

     self.assertTrue(outputs.loss is not None   )

from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

__lowerCamelCase    :				List[str]							     =	TypeVar('''KEY''')
__lowerCamelCase    :				int							     =	TypeVar('''VAL''')







@dataclass(frozen=lowerCamelCase_     ,	slots=lowerCamelCase_	)
class __snake_case       (		Generic[KEY, VAL]	):
   lowerCAmelCase_														=							42
   lowerCAmelCase_														=							42







class __snake_case       (		_Item	):




   def __init__(					self    :       List[Any]   ):



     """simple docstring"""






     super().__init__(_lowercase ,			_lowercase   )




   def __bool__(					self    :       List[str]   ):



     """simple docstring"""






     return False





__lowerCamelCase    :				str							     =	_DeletedItem()







class __snake_case       (		MutableMapping[KEY, VAL]	):




   def __init__(					self    :       Optional[int] ,			_lowercase    :       int = 8 ,			_lowercase    :       float = 0.75   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   initial_block_size
     SCREAMING_SNAKE_CASE__    							=   [None] * initial_block_size
     assert 0.0 < capacity_factor < 1.0
     SCREAMING_SNAKE_CASE__    							=   capacity_factor
     SCREAMING_SNAKE_CASE__    							=   0




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



     """simple docstring"""






     return hash(_lowercase   ) % len(self._buckets   )




   def 			__a		(					self    :       List[str] ,			_lowercase    :       int   ):



     """simple docstring"""






     return (ind + 1) % len(self._buckets   )




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       int ,			_lowercase    :       KEY ,			_lowercase    :       VAL   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self._buckets[ind]
     if not stored:
       SCREAMING_SNAKE_CASE__    							=   _Item(_lowercase ,			_lowercase   )
       self._len += 1
       return True
     elif stored.key == key:
       SCREAMING_SNAKE_CASE__    							=   _Item(_lowercase ,			_lowercase   )
       return True
     else:
       return False




   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   len(self._buckets   ) * self._capacity_factor
     return len(self   ) >= int(_lowercase   )




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     if len(self._buckets   ) <= self._initial_block_size:
       return False
     SCREAMING_SNAKE_CASE__    							=   len(self._buckets   ) * self._capacity_factor / 2
     return len(self   ) < limit




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self._buckets
     SCREAMING_SNAKE_CASE__    							=   [None] * new_size
     SCREAMING_SNAKE_CASE__    							=   0
     for item in old_buckets:
       if item:
         self._add_item(item.key ,			item.val   )




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     self._resize(len(self._buckets   ) * 2   )




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     self._resize(len(self._buckets   ) // 2   )




   def 			__a		(					self    :       List[str] ,			_lowercase    :       KEY   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self._get_bucket_index(_lowercase   )
     for _ in range(len(self._buckets   )   ):
       yield ind
       SCREAMING_SNAKE_CASE__    							=   self._get_next_ind(_lowercase   )




   def 			__a		(					self    :       List[str] ,			_lowercase    :       KEY ,			_lowercase    :       VAL   ):



     """simple docstring"""






     for ind in self._iterate_buckets(_lowercase   ):
       if self._try_set(_lowercase ,			_lowercase ,			_lowercase   ):
         break




   def __setitem__(					self    :       List[str] ,			_lowercase    :       KEY ,			_lowercase    :       VAL   ):



     """simple docstring"""






     if self._is_full():
       self._size_up()

     self._add_item(_lowercase ,			_lowercase   )




   def __delitem__(					self    :       Tuple ,			_lowercase    :       KEY   ):



     """simple docstring"""






     for ind in self._iterate_buckets(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   self._buckets[ind]
       if item is None:
         raise KeyError(_lowercase   )
       if item is _deleted:
         continue
       if item.key == key:
         SCREAMING_SNAKE_CASE__    							=   _deleted
         self._len -= 1
         break
     if self._is_sparse():
       self._size_down()




   def __getitem__(					self    :       Tuple ,			_lowercase    :       KEY   ):



     """simple docstring"""






     for ind in self._iterate_buckets(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   self._buckets[ind]
       if item is None:
         break
       if item is _deleted:
         continue
       if item.key == key:
         return item.val
     raise KeyError(_lowercase   )




   def __len__(					self    :       List[Any]   ):



     """simple docstring"""






     return self._len




   def __iter__(					self    :       List[str]   ):



     """simple docstring"""






     yield from (item.key for item in self._buckets if item)




   def __repr__(					self    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   """ ,""".join(
         f"""{item.key}: {item.val}""" for item in self._buckets if item   )
     return f"""HashMap({val_string})"""
