
'''simple docstring'''




import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
      import torch
      from torch import nn

      from transformers import (
          MODEL_MAPPING,
          BeitForImageClassification,
          BeitForMaskedImageModeling,
          BeitForSemanticSegmentation,
          BeitModel,
      )
      from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      import PIL
      from PIL import Image

      from transformers import BeitImageProcessor





class  A				:





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=100						,			SCREAMING_SNAKE_CASE=13						,			SCREAMING_SNAKE_CASE=30						,			SCREAMING_SNAKE_CASE=2						,			SCREAMING_SNAKE_CASE=3						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=32						,			SCREAMING_SNAKE_CASE=4						,			SCREAMING_SNAKE_CASE=4						,			SCREAMING_SNAKE_CASE=37						,			SCREAMING_SNAKE_CASE="gelu"						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=10						,			SCREAMING_SNAKE_CASE=0.02						,			SCREAMING_SNAKE_CASE=3						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=[0, 1, 2, 3]						,			) ->				Union[str, Any]:






         """simple docstring"""






         A       :   Union[str, Any]					=						parent
         A       :   Optional[int]					=						100
         A       :   Optional[int]					=						batch_size
         A       :   Any					=						image_size
         A       :   Tuple					=						patch_size
         A       :   Union[str, Any]					=						num_channels
         A       :   str					=						is_training
         A       :   Dict					=						use_labels
         A       :   Any					=						hidden_size
         A       :   Dict					=						num_hidden_layers
         A       :   List[Any]					=						num_attention_heads
         A       :   Dict					=						intermediate_size
         A       :   Tuple					=						hidden_act
         A       :   Union[str, Any]					=						hidden_dropout_prob
         A       :   Any					=						attention_probs_dropout_prob
         A       :   List[Any]					=						type_sequence_label_size
         A       :   Dict					=						initializer_range
         A       :   int					=						scope
         A       :   Any					=						out_indices
         A       :   Any					=						num_labels

         # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         A       :   List[Any]					=						(image_size // patch_size) ** 2
         A       :   Union[str, Any]					=						num_patches + 1





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         A       :   Any					=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

         A       :   Tuple					=						None
         A       :   Any					=						None
         if self.use_labels:
             A       :   Optional[int]					=						ids_tensor([self.batch_size]						,			self.type_sequence_label_size	)
             A       :   Optional[int]					=						ids_tensor([self.batch_size, self.image_size, self.image_size]						,			self.num_labels	)

         A       :   Dict					=						self.get_config()

         return config, pixel_values, labels, pixel_labels





     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         return BeitConfig(
             vocab_size=self.vocab_size						,			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						,			hidden_act=self.hidden_act						,			hidden_dropout_prob=self.hidden_dropout_prob						,			attention_probs_dropout_prob=self.attention_probs_dropout_prob						,			is_decoder=SCREAMING_SNAKE_CASE						,			initializer_range=self.initializer_range						,			out_indices=self.out_indices						,			)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Optional[int]:






         """simple docstring"""






         A       :   Union[str, Any]					=						BeitModel(config=SCREAMING_SNAKE_CASE	)
         model.to(SCREAMING_SNAKE_CASE	)
         model.eval()
         A       :   str					=						model(SCREAMING_SNAKE_CASE	)
         self.parent.assertEqual(result.last_hidden_state.shape						,			(self.batch_size, self.seq_length, self.hidden_size)	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Tuple:






         """simple docstring"""






         A       :   List[Any]					=						BeitForMaskedImageModeling(config=SCREAMING_SNAKE_CASE	)
         model.to(SCREAMING_SNAKE_CASE	)
         model.eval()
         A       :   Dict					=						model(SCREAMING_SNAKE_CASE	)
         self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.seq_length - 1, self.vocab_size)	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				str:






         """simple docstring"""






         A       :   int					=						self.type_sequence_label_size
         A       :   Dict					=						BeitForImageClassification(SCREAMING_SNAKE_CASE	)
         model.to(SCREAMING_SNAKE_CASE	)
         model.eval()
         A       :   List[Any]					=						model(SCREAMING_SNAKE_CASE						,			labels=SCREAMING_SNAKE_CASE	)
         self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.type_sequence_label_size)	)

         # test greyscale images
         A       :   Dict					=						1
         A       :   Optional[Any]					=						BeitForImageClassification(SCREAMING_SNAKE_CASE	)
         model.to(SCREAMING_SNAKE_CASE	)
         model.eval()

         A       :   List[Any]					=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
         A       :   Dict					=						model(SCREAMING_SNAKE_CASE						,			labels=SCREAMING_SNAKE_CASE	)
         self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.type_sequence_label_size)	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				str:






         """simple docstring"""






         A       :   List[str]					=						self.num_labels
         A       :   int					=						BeitForSemanticSegmentation(SCREAMING_SNAKE_CASE	)
         model.to(SCREAMING_SNAKE_CASE	)
         model.eval()
         A       :   Optional[int]					=						model(SCREAMING_SNAKE_CASE	)
         self.parent.assertEqual(
             result.logits.shape						,			(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)
         A       :   List[Any]					=						model(SCREAMING_SNAKE_CASE						,			labels=SCREAMING_SNAKE_CASE	)
         self.parent.assertEqual(
             result.logits.shape						,			(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         A       :   Optional[int]					=						self.prepare_config_and_inputs()
         A,  A,  A,  A       :   int					=						config_and_inputs
         A       :   Union[str, Any]					=						{'''pixel_values''': pixel_values}
         return config, inputs_dict





@require_torch
class  A				(							__snake_case    ,			__snake_case    ,			unittest.TestCase						):
     __magic_name__						=		(
         (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
         if is_torch_available()
         else ()
     )
     __magic_name__						=		(
         {
             '''feature-extraction''': BeitModel,
             '''image-classification''': BeitForImageClassification,
             '''image-segmentation''': BeitForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		False





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         A       :   str					=						BeitModelTester(self	)
         A       :   List[Any]					=						ConfigTester(self						,			config_class=SCREAMING_SNAKE_CASE						,			has_text_modality=SCREAMING_SNAKE_CASE						,			hidden_size=37	)





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         self.config_tester.run_common_tests()





     @unittest.skip(reason='''BEiT does not use inputs_embeds'''	)
     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         pass





     @require_torch_multi_gpu
     @unittest.skip(reason='''BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''	)
     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         pass





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






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

         for model_class in self.all_model_classes:
             A       :   Dict					=						model_class(SCREAMING_SNAKE_CASE	)
             self.assertIsInstance(model.get_input_embeddings()						,			(nn.Module)	)
             A       :   int					=						model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE						,			nn.Linear	)	)





     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






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

         for model_class in self.all_model_classes:
             A       :   Dict					=						model_class(SCREAMING_SNAKE_CASE	)
             A       :   Union[str, Any]					=						inspect.signature(model.forward	)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             A       :   Any					=						[*signature.parameters.keys()]

             A       :   Tuple					=						['''pixel_values''']
             self.assertListEqual(arg_names[:1]						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   Optional[int]					=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         A       :   str					=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         A       :   Optional[int]					=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   Union[str, Any]					=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				Dict:






         """simple docstring"""






         if not self.model_tester.is_training:
             return

         A,  A       :   Dict					=						self.model_tester.prepare_config_and_inputs_for_common()
         A       :   Optional[Any]					=						True

         for model_class in self.all_model_classes:
             # we don't test BeitForMaskedImageModeling
             if model_class in [*get_values(SCREAMING_SNAKE_CASE	), BeitForMaskedImageModeling]:
                 continue

             A       :   Optional[int]					=						model_class(SCREAMING_SNAKE_CASE	)
             model.to(SCREAMING_SNAKE_CASE	)
             model.train()
             A       :   Union[str, Any]					=						self._prepare_for_class(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			return_labels=SCREAMING_SNAKE_CASE	)
             A       :   List[str]					=						model(**SCREAMING_SNAKE_CASE	).loss
             loss.backward()





     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         A,  A       :   Optional[int]					=						self.model_tester.prepare_config_and_inputs_for_common()
         if not self.model_tester.is_training:
             return

         A       :   Union[str, Any]					=						False
         A       :   Tuple					=						True

         for model_class in self.all_model_classes:
             # we don't test BeitForMaskedImageModeling
             if (
                 model_class in [*get_values(SCREAMING_SNAKE_CASE	), BeitForMaskedImageModeling]
                 or not model_class.supports_gradient_checkpointing
             ):
                 continue

             A       :   int					=						model_class(SCREAMING_SNAKE_CASE	)
             model.gradient_checkpointing_enable()
             model.to(SCREAMING_SNAKE_CASE	)
             model.train()
             A       :   Dict					=						self._prepare_for_class(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			return_labels=SCREAMING_SNAKE_CASE	)
             A       :   Dict					=						model(**SCREAMING_SNAKE_CASE	).loss
             loss.backward()





     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         A,  A       :   int					=						self.model_tester.prepare_config_and_inputs_for_common()

         A       :   List[Any]					=						_config_zero_init(SCREAMING_SNAKE_CASE	)
         for model_class in self.all_model_classes:
             A       :   List[str]					=						model_class(config=SCREAMING_SNAKE_CASE	)
             for name, param in model.named_parameters():
                 # we skip lambda parameters as these require special initial values
                 # determined by config.layer_scale_init_value
                 if "lambda" in name:
                     continue
                 if param.requires_grad:
                     self.assertIn(
                         ((param.data.mean() * 1e9).round() / 1e9).item()						,			[0.0, 1.0]						,			msg=F'Parameter {name} of model {model_class} seems not properly initialized'						,			)





     @slow
     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             A       :   int					=						BeitModel.from_pretrained(SCREAMING_SNAKE_CASE	)
             self.assertIsNotNone(SCREAMING_SNAKE_CASE	)







def 		lowerCAmelCase_				(   ):




    '''simple docstring'''



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







@require_torch
@require_vision
class  A				(							unittest.TestCase						):





     @cached_property
     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''	) if is_vision_available() else None





     @slow
     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         A       :   Tuple					=						BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''	).to(SCREAMING_SNAKE_CASE	)

         A       :   int					=						self.default_image_processor
         A       :   Optional[int]					=						prepare_img()
         A       :   List[Any]					=						image_processor(images=SCREAMING_SNAKE_CASE						,			return_tensors='''pt'''	).pixel_values.to(SCREAMING_SNAKE_CASE	)

         # prepare bool_masked_pos
         A       :   Optional[int]					=						torch.ones((1, 196)						,			dtype=torch.bool	).to(SCREAMING_SNAKE_CASE	)

         # forward pass
         with torch.no_grad():
             A       :   int					=						model(pixel_values=SCREAMING_SNAKE_CASE						,			bool_masked_pos=SCREAMING_SNAKE_CASE	)
         A       :   Optional[int]					=						outputs.logits

         # verify the logits
         A       :   str					=						torch.Size((1, 196, 8192)	)
         self.assertEqual(logits.shape						,			SCREAMING_SNAKE_CASE	)

         A       :   Union[str, Any]					=						torch.tensor(
             [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]	).to(SCREAMING_SNAKE_CASE	)

         self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]						,			SCREAMING_SNAKE_CASE						,			atol=1e-2	)	)





     @slow
     def 				__lowerCAmelCase							(							self	) ->				Dict:






         """simple docstring"""






         A       :   List[Any]					=						BeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''	).to(SCREAMING_SNAKE_CASE	)

         A       :   Tuple					=						self.default_image_processor
         A       :   Tuple					=						prepare_img()
         A       :   Any					=						image_processor(images=SCREAMING_SNAKE_CASE						,			return_tensors='''pt'''	).to(SCREAMING_SNAKE_CASE	)

         # forward pass
         with torch.no_grad():
             A       :   Tuple					=						model(**SCREAMING_SNAKE_CASE	)
         A       :   Optional[int]					=						outputs.logits

         # verify the logits
         A       :   List[str]					=						torch.Size((1, 1000)	)
         self.assertEqual(logits.shape						,			SCREAMING_SNAKE_CASE	)

         A       :   Optional[Any]					=						torch.tensor([-1.2_385, -1.0_987, -1.0_108]	).to(SCREAMING_SNAKE_CASE	)

         self.assertTrue(torch.allclose(logits[0, :3]						,			SCREAMING_SNAKE_CASE						,			atol=1e-4	)	)

         A       :   List[Any]					=						281
         self.assertEqual(logits.argmax(-1	).item()						,			SCREAMING_SNAKE_CASE	)





     @slow
     def 				__lowerCAmelCase							(							self	) ->				List[str]:






         """simple docstring"""






         A       :   List[str]					=						BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''	).to(
             SCREAMING_SNAKE_CASE	)

         A       :   Union[str, Any]					=						self.default_image_processor
         A       :   List[Any]					=						prepare_img()
         A       :   Optional[Any]					=						image_processor(images=SCREAMING_SNAKE_CASE						,			return_tensors='''pt'''	).to(SCREAMING_SNAKE_CASE	)

         # forward pass
         with torch.no_grad():
             A       :   str					=						model(**SCREAMING_SNAKE_CASE	)
         A       :   Dict					=						outputs.logits

         # verify the logits
         A       :   Union[str, Any]					=						torch.Size((1, 21841)	)
         self.assertEqual(logits.shape						,			SCREAMING_SNAKE_CASE	)

         A       :   List[Any]					=						torch.tensor([1.6_881, -0.2_787, 0.5_901]	).to(SCREAMING_SNAKE_CASE	)

         self.assertTrue(torch.allclose(logits[0, :3]						,			SCREAMING_SNAKE_CASE						,			atol=1e-4	)	)

         A       :   str					=						2396
         self.assertEqual(logits.argmax(-1	).item()						,			SCREAMING_SNAKE_CASE	)





     @slow
     def 				__lowerCAmelCase							(							self	) ->				List[Any]:






         """simple docstring"""






         A       :   Tuple					=						BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''	)
         A       :   Tuple					=						model.to(SCREAMING_SNAKE_CASE	)

         A       :   int					=						BeitImageProcessor(do_resize=SCREAMING_SNAKE_CASE						,			size=640						,			do_center_crop=SCREAMING_SNAKE_CASE	)

         A       :   Union[str, Any]					=						load_dataset('''hf-internal-testing/fixtures_ade20k'''						,			split='''test'''	)
         A       :   List[Any]					=						Image.open(ds[0]['''file''']	)
         A       :   Optional[Any]					=						image_processor(images=SCREAMING_SNAKE_CASE						,			return_tensors='''pt'''	).to(SCREAMING_SNAKE_CASE	)

         # forward pass
         with torch.no_grad():
             A       :   List[Any]					=						model(**SCREAMING_SNAKE_CASE	)
         A       :   Union[str, Any]					=						outputs.logits

         # verify the logits
         A       :   Union[str, Any]					=						torch.Size((1, 150, 160, 160)	)
         self.assertEqual(logits.shape						,			SCREAMING_SNAKE_CASE	)

         A       :   int					=						version.parse(PIL.__version__	) < version.parse('''9.0.0'''	)

         if is_pillow_less_than_a:
             A       :   Any					=						torch.tensor(
                 [
                     [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
                     [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
                     [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
                 ]						,			device=SCREAMING_SNAKE_CASE						,			)
         else:
             A       :   int					=						torch.tensor(
                 [
                     [[-4.8_960, -2.3_688, -3.0_355], [-2.8_478, -0.9_836, -1.7_418], [-2.9_449, -1.3_332, -2.1_456]],
                     [[-5.8_081, -3.4_124, -4.1_006], [-3.8_561, -2.2_081, -3.0_323], [-3.8_365, -2.4_601, -3.3_669]],
                     [[-0.0_309, 3.9_868, 4.0_540], [2.9_640, 4.6_877, 4.9_976], [3.2_081, 4.7_690, 4.9_942]],
                 ]						,			device=SCREAMING_SNAKE_CASE						,			)

         self.assertTrue(torch.allclose(logits[0, :3, :3, :3]						,			SCREAMING_SNAKE_CASE						,			atol=1e-4	)	)





     @slow
     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         A       :   int					=						BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''	)
         A       :   Dict					=						model.to(SCREAMING_SNAKE_CASE	)

         A       :   Optional[int]					=						BeitImageProcessor(do_resize=SCREAMING_SNAKE_CASE						,			size=640						,			do_center_crop=SCREAMING_SNAKE_CASE	)

         A       :   Optional[int]					=						load_dataset('''hf-internal-testing/fixtures_ade20k'''						,			split='''test'''	)
         A       :   Optional[int]					=						Image.open(ds[0]['''file''']	)
         A       :   List[str]					=						image_processor(images=SCREAMING_SNAKE_CASE						,			return_tensors='''pt'''	).to(SCREAMING_SNAKE_CASE	)

         # forward pass
         with torch.no_grad():
             A       :   Optional[Any]					=						model(**SCREAMING_SNAKE_CASE	)

         A       :   Any					=						outputs.logits.detach().cpu()

         A       :   List[str]					=						image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE						,			target_sizes=[(500, 300)]	)
         A       :   int					=						torch.Size((500, 300)	)
         self.assertEqual(segmentation[0].shape						,			SCREAMING_SNAKE_CASE	)

         A       :   Dict					=						image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE	)
         A       :   str					=						torch.Size((160, 160)	)
         self.assertEqual(segmentation[0].shape						,			SCREAMING_SNAKE_CASE	)






"""simple docstring"""





# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets



def       lowerCamelCase__		(			__snake_case	)							->		Tuple:


					"""simple docstring"""





					return 1 / (1 + np.exp(-z	))



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		List[str]:


					"""simple docstring"""





					return (-y * np.log(__snake_case	) - (1 - y) * np.log(1 - h	)).mean()



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case	)							->		int:


					"""simple docstring"""





					_UpperCamelCase							 =  np.dot(__snake_case,     __snake_case	)
					return np.sum(y * scores - np.log(1 + np.exp(__snake_case	)	)	)



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case=7_00_00	)							->		Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase							 =  np.zeros(x.shape[1]	)

					for iterations in range(__snake_case	):
										_UpperCamelCase							 =  np.dot(__snake_case,     __snake_case	)
										_UpperCamelCase							 =  sigmoid_function(__snake_case	)
										_UpperCamelCase							 =  np.dot(x.T,     h - y	) / y.size
										_UpperCamelCase							 =  theta - alpha * gradient  # updating the weights
										_UpperCamelCase							 =  np.dot(__snake_case,     __snake_case	)
										_UpperCamelCase							 =  sigmoid_function(__snake_case	)
										_UpperCamelCase							 =  cost_function(__snake_case,     __snake_case	)
										if iterations % 1_00 == 0:
															print(F'''loss: {j} \t'''	)  # printing the loss after every 100 iterations
					return theta


# In[68]:

if __name__ == "__main__":
				_a														=		datasets.load_iris()
				_a														=		iris.data[:, :2]
				_a														=		(iris.target != 0) * 1

				_a														=		0.1
				_a														=		logistic_reg(alpha, x, y, max_iterations=7_0000)
				print("""theta: """, theta)  # printing the theta i.e our weights vector



				def       lowerCamelCase__		(			__snake_case	)							->		Tuple:


									"""simple docstring"""





									return sigmoid_function(
									    np.dot(__snake_case,     __snake_case	)	)  # predicting the value of probability from the logistic regression algorithm

				plt.figure(figsize=(10, 6))
				plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="""b""", label="""0""")
				plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="""r""", label="""1""")
				((_a)							,							(_a))														=		(x[:, 0].min(), x[:, 0].max())
				((_a)							,							(_a))														=		(x[:, 1].min(), x[:, 1].max())
				((_a)							,							(_a))														=		np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
				_a														=		np.c_[xxa.ravel(), xxa.ravel()]
				_a														=		predict_prob(grid).reshape(xxa.shape)
				plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

				plt.legend()
				plt.show()






