





import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
    from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import MobileNetVaImageProcessor

class    lowerCAmelCase							(					__UpperCamelCase			):
 def      A_	(      self  :      Tuple	)			->	Optional[Any]:
     lowerCamelCase__    :      Tuple					 =		self.config_class(**self.inputs_dict	)
     self.parent.assertTrue(hasattr(UpperCAmelCase			,     'tf_padding'	)	)
     self.parent.assertTrue(hasattr(UpperCAmelCase			,     'depth_multiplier'	)	)


class    lowerCAmelCase							:
 def __init__(      self  :      List[Any]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Optional[Any]=13			,     UpperCAmelCase  :      str=3			,     UpperCAmelCase  :      Optional[int]=32			,     UpperCAmelCase  :      Union[str, Any]=0.2_5			,     UpperCAmelCase  :      Union[str, Any]=8			,     UpperCAmelCase  :      List[Any]=8			,     UpperCAmelCase  :      List[str]=6			,     UpperCAmelCase  :      Dict=32			,     UpperCAmelCase  :      Optional[int]=True			,     UpperCAmelCase  :      List[str]=True			,     UpperCAmelCase  :      Union[str, Any]=True			,     UpperCAmelCase  :      List[str]="relu6"			,     UpperCAmelCase  :      Dict=1280			,     UpperCAmelCase  :      int=0.1			,     UpperCAmelCase  :      Optional[Any]=0.0_2			,     UpperCAmelCase  :      Optional[int]=True			,     UpperCAmelCase  :      Dict=True			,     UpperCAmelCase  :      int=10			,     UpperCAmelCase  :      str=None			,     )			->	Optional[Any]:
     lowerCamelCase__    :      Tuple					 =		parent
     lowerCamelCase__    :      Any					 =		batch_size
     lowerCamelCase__    :      Optional[int]					 =		num_channels
     lowerCamelCase__    :      Optional[Any]					 =		image_size
     lowerCamelCase__    :      List[Any]					 =		depth_multiplier
     lowerCamelCase__    :      List[Any]					 =		depth_divisible_by
     lowerCamelCase__    :      Dict					 =		min_depth
     lowerCamelCase__    :      Optional[Any]					 =		expand_ratio
     lowerCamelCase__    :      Union[str, Any]					 =		tf_padding
     lowerCamelCase__    :      Any					 =		output_stride
     lowerCamelCase__    :      List[str]					 =		first_layer_is_expansion
     lowerCamelCase__    :      Union[str, Any]					 =		finegrained_output
     lowerCamelCase__    :      Union[str, Any]					 =		hidden_act
     lowerCamelCase__    :      Any					 =		last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier	)
     lowerCamelCase__    :      Union[str, Any]					 =		classifier_dropout_prob
     lowerCamelCase__    :      Tuple					 =		use_labels
     lowerCamelCase__    :      int					 =		is_training
     lowerCamelCase__    :      List[str]					 =		num_labels
     lowerCamelCase__    :      str					 =		initializer_range
     lowerCamelCase__    :      Optional[Any]					 =		scope
 def      A_	(      self  :      Optional[Any]	)			->	Dict:
     lowerCamelCase__    :      Any					 =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

     lowerCamelCase__    :      Tuple					 =		None
     lowerCamelCase__    :      Dict					 =		None
     if self.use_labels:
         lowerCamelCase__    :      Tuple					 =		ids_tensor([self.batch_size]			,     self.num_labels	)
         lowerCamelCase__    :      Dict					 =		ids_tensor([self.batch_size, self.image_size, self.image_size]			,     self.num_labels	)

     lowerCamelCase__    :      List[str]					 =		self.get_config()

     return config, pixel_values, labels, pixel_labels
 def      A_	(      self  :      Any	)			->	Union[str, Any]:
     return MobileNetVaConfig(
         num_channels=self.num_channels			,     image_size=self.image_size			,     depth_multiplier=self.depth_multiplier			,     depth_divisible_by=self.depth_divisible_by			,     min_depth=self.min_depth			,     expand_ratio=self.expand_ratio			,     output_stride=self.output_stride			,     first_layer_is_expansion=self.first_layer_is_expansion			,     finegrained_output=self.finegrained_output			,     hidden_act=self.hidden_act			,     tf_padding=self.tf_padding			,     classifier_dropout_prob=self.classifier_dropout_prob			,     initializer_range=self.initializer_range			,     )
 def      A_	(      self  :      Dict			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      List[Any]			,     UpperCAmelCase  :      Optional[int]			,     UpperCAmelCase  :      Optional[int]	)			->	Any:
     lowerCamelCase__    :      int					 =		MobileNetVaModel(config=UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()
     lowerCamelCase__    :      Optional[int]					 =		model(UpperCAmelCase	)
     self.parent.assertEqual(
         result.last_hidden_state.shape			,     (
             self.batch_size,
             self.last_hidden_size,
             self.image_size // self.output_stride,
             self.image_size // self.output_stride,
         )			,     )
     self.parent.assertEqual(
         result.pooler_output.shape			,     (self.batch_size, self.last_hidden_size)			,     )
 def      A_	(      self  :      str			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      Dict			,     UpperCAmelCase  :      Any			,     UpperCAmelCase  :      Union[str, Any]	)			->	Union[str, Any]:
     lowerCamelCase__    :      Optional[int]					 =		self.num_labels
     lowerCamelCase__    :      Any					 =		MobileNetVaForImageClassification(UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()
     lowerCamelCase__    :      int					 =		model(UpperCAmelCase			,     labels=UpperCAmelCase	)
     self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)	)
 def      A_	(      self  :      str			,     UpperCAmelCase  :      List[Any]			,     UpperCAmelCase  :      Optional[int]			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      int	)			->	Tuple:
     lowerCamelCase__    :      Any					 =		self.num_labels
     lowerCamelCase__    :      Optional[Any]					 =		MobileNetVaForSemanticSegmentation(UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()
     lowerCamelCase__    :      str					 =		model(UpperCAmelCase	)
     self.parent.assertEqual(
         result.logits.shape			,     (
             self.batch_size,
             self.num_labels,
             self.image_size // self.output_stride,
             self.image_size // self.output_stride,
         )			,     )
     lowerCamelCase__    :      Optional[int]					 =		model(UpperCAmelCase			,     labels=UpperCAmelCase	)
     self.parent.assertEqual(
         result.logits.shape			,     (
             self.batch_size,
             self.num_labels,
             self.image_size // self.output_stride,
             self.image_size // self.output_stride,
         )			,     )







 def      A_	(      self  :      Optional[int]	)			->	Union[str, Any]:
     lowerCamelCase__    :      Dict					 =		self.prepare_config_and_inputs()
     lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__    :      Dict					 =		config_and_inputs
     lowerCamelCase__    :      str					 =		{'pixel_values': pixel_values}
     return config, inputs_dict


@require_torch
class    lowerCAmelCase							(					__UpperCamelCase,  __UpperCamelCase,  unittest.TestCase			):
 UpperCAmelCase__													=		(
     (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 UpperCAmelCase__													=		(
     {
         """feature-extraction""": MobileNetVaModel,
         """image-classification""": MobileNetVaForImageClassification,
         """image-segmentation""": MobileNetVaForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 UpperCAmelCase__													=		False
 UpperCAmelCase__													=		False
 UpperCAmelCase__													=		False
 UpperCAmelCase__													=		False
 def      A_	(      self  :      Tuple	)			->	int:
     lowerCamelCase__    :      Optional[Any]					 =		MobileNetVaModelTester(self	)
     lowerCamelCase__    :      int					 =		MobileNetVaConfigTester(self			,     config_class=UpperCAmelCase			,     has_text_modality=UpperCAmelCase	)
 def      A_	(      self  :      List[str]	)			->	List[str]:
     self.config_tester.run_common_tests()
 @unittest.skip(reason='MobileNetV2 does not use inputs_embeds'	)
 def      A_	(      self  :      List[Any]	)			->	List[Any]:
     pass
 @unittest.skip(reason='MobileNetV2 does not support input and output embeddings'	)
 def      A_	(      self  :      Any	)			->	Optional[Any]:
     pass
 @unittest.skip(reason='MobileNetV2 does not output attentions'	)
 def      A_	(      self  :      str	)			->	Optional[Any]:
     pass
 def      A_	(      self  :      Union[str, Any]	)			->	Tuple:
     lowerCamelCase__ ,							lowerCamelCase__    :      Any					 =		self.model_tester.prepare_config_and_inputs_for_common()

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

         lowerCamelCase__    :      List[str]					 =		['pixel_values']
         self.assertListEqual(arg_names[:1]			,     UpperCAmelCase	)
 def      A_	(      self  :      str	)			->	Tuple:
     lowerCamelCase__    :      Tuple					 =		self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*UpperCAmelCase	)
 def      A_	(      self  :      str	)			->	Optional[int]:
     def check_hidden_states_output(UpperCAmelCase  :      List[str]			,     UpperCAmelCase  :      Any			,     UpperCAmelCase  :      List[Any]	):
         lowerCamelCase__    :      Any					 =		model_class(UpperCAmelCase	)
         model.to(UpperCAmelCase	)
         model.eval()

         with torch.no_grad():
             lowerCamelCase__    :      Any					 =		model(**self._prepare_for_class(UpperCAmelCase			,     UpperCAmelCase	)	)

         lowerCamelCase__    :      Optional[int]					 =		outputs.hidden_states

         lowerCamelCase__    :      Dict					 =		16
         self.assertEqual(len(UpperCAmelCase	)			,     UpperCAmelCase	)

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

     for model_class in self.all_model_classes:
         lowerCamelCase__    :      List[Any]					 =		True
         check_hidden_states_output(UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         lowerCamelCase__    :      Optional[Any]					 =		True

         check_hidden_states_output(UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	)
 def      A_	(      self  :      Union[str, Any]	)			->	str:
     lowerCamelCase__    :      str					 =		self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase	)
 def      A_	(      self  :      Optional[int]	)			->	int:
     lowerCamelCase__    :      List[str]					 =		self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase	)







 @slow
 def      A_	(      self  :      Optional[Any]	)			->	Tuple:
     for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowerCamelCase__    :      Tuple					 =		MobileNetVaModel.from_pretrained(UpperCAmelCase	)
         self.assertIsNotNone(UpperCAmelCase	)


def        SCREAMING_SNAKE_CASE      (       )			->     List[Any]:
    lowerCamelCase__    :      Any					 =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
    return image


@require_torch
@require_vision
class    lowerCAmelCase							(					unittest.TestCase			):
 @cached_property
 def      A_	(      self  :      Dict	)			->	List[str]:
     return (
         MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v2_1.0_224'	) if is_vision_available() else None
     )
 @slow
 def      A_	(      self  :      Any	)			->	Union[str, Any]:
     lowerCamelCase__    :      List[str]					 =		MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v2_1.0_224'	).to(UpperCAmelCase	)

     lowerCamelCase__    :      Any					 =		self.default_image_processor
     lowerCamelCase__    :      Any					 =		prepare_img()
     lowerCamelCase__    :      Dict					 =		image_processor(images=UpperCAmelCase			,     return_tensors='pt'	).to(UpperCAmelCase	)

     # forward pass
     with torch.no_grad():
         lowerCamelCase__    :      Dict					 =		model(**UpperCAmelCase	)

     # verify the logits
     lowerCamelCase__    :      List[str]					 =		torch.Size((1, 1001)	)
     self.assertEqual(outputs.logits.shape			,     UpperCAmelCase	)

     lowerCamelCase__    :      Dict					 =		torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]	).to(UpperCAmelCase	)

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







 @slow
 def      A_	(      self  :      Union[str, Any]	)			->	Optional[Any]:
     lowerCamelCase__    :      str					 =		MobileNetVaForSemanticSegmentation.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'	)
     lowerCamelCase__    :      List[str]					 =		model.to(UpperCAmelCase	)

     lowerCamelCase__    :      Dict					 =		MobileNetVaImageProcessor.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'	)

     lowerCamelCase__    :      Any					 =		prepare_img()
     lowerCamelCase__    :      Optional[int]					 =		image_processor(images=UpperCAmelCase			,     return_tensors='pt'	).to(UpperCAmelCase	)

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

     # verify the logits
     lowerCamelCase__    :      Optional[Any]					 =		torch.Size((1, 21, 65, 65)	)
     self.assertEqual(logits.shape			,     UpperCAmelCase	)

     lowerCamelCase__    :      Optional[int]					 =		torch.tensor(
         [
             [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
             [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
             [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
         ]			,     device=UpperCAmelCase			,     )

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












from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff





def      lowerCAmelCase_ (       __a	,    __a	,    __a = 10**-10		)     ->		float:







  """simple docstring"""





  lowerCamelCase__:      str			  =a
  while True:
    lowerCamelCase__:      Optional[Any]			  =Decimal(__a		) - (
        Decimal(eval(__a		)		) / Decimal(eval(str(diff(__a		)		)		)		)  # noqa: S307
    )
    # This number dictates the accuracy of the answer
    if abs(eval(__a		)		) < precision:  # noqa: S307
      return float(__a		)


# Let's Execute
if __name__ == "__main__":
     # Find root of trigonometric function
     # Find value of pi
     print(f'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')
     # Find root of polynomial
     print(f'The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}')
     # Find Square Root of 5
     print(f'The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}')
     # Exponential Roots
     print(f'The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}')

