





import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
  import jax

  from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel






class     lowercase__       (	unittest.TestCase				):

   '''simple docstring'''


   def __init__(    self,		__magic_name__,		__magic_name__=13,		__magic_name__=30,		__magic_name__=2,		__magic_name__=3,		__magic_name__=True,		__magic_name__=True,		__magic_name__=32,		__magic_name__=5,		__magic_name__=4,		__magic_name__=37,		__magic_name__="gelu",		__magic_name__=0.1,		__magic_name__=0.1,		__magic_name__=10,		__magic_name__=0.02,		) ->      List[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Any          =      parent
       UpperCamelCase__							:				Any          =      batch_size
       UpperCamelCase__							:				Tuple          =      image_size
       UpperCamelCase__							:				Tuple          =      patch_size
       UpperCamelCase__							:				int          =      num_channels
       UpperCamelCase__							:				int          =      is_training
       UpperCamelCase__							:				Dict          =      use_labels
       UpperCamelCase__							:				List[str]          =      hidden_size
       UpperCamelCase__							:				Tuple          =      num_hidden_layers
       UpperCamelCase__							:				int          =      num_attention_heads
       UpperCamelCase__							:				List[str]          =      intermediate_size
       UpperCamelCase__							:				List[Any]          =      hidden_act
       UpperCamelCase__							:				Optional[Any]          =      hidden_dropout_prob
       UpperCamelCase__							:				Optional[Any]          =      attention_probs_dropout_prob
       UpperCamelCase__							:				str          =      type_sequence_label_size
       UpperCamelCase__							:				Optional[Any]          =      initializer_range

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




   def       UpperCamelCase__  (    self							) ->      Any:






       """simple docstring"""
       UpperCamelCase__							:				List[Any]          =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

       UpperCamelCase__							:				int          =      ViTConfig(
           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=__magic_name__,		initializer_range=self.initializer_range,		)

       return config, pixel_values




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__							) ->      Dict:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      FlaxViTModel(config=__magic_name__							)
       UpperCamelCase__							:				int          =      model(__magic_name__							)
       # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
       UpperCamelCase__							:				Any          =      (self.image_size, self.image_size)
       UpperCamelCase__							:				Union[str, Any]          =      (self.patch_size, self.patch_size)
       UpperCamelCase__							:				str          =      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
       self.parent.assertEqual(result.last_hidden_state.shape,		(self.batch_size, num_patches + 1, self.hidden_size)							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__							) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				int          =      self.type_sequence_label_size
       UpperCamelCase__							:				List[str]          =      FlaxViTForImageClassification(config=__magic_name__							)
       UpperCamelCase__							:				int          =      model(__magic_name__							)
       self.parent.assertEqual(result.logits.shape,		(self.batch_size, self.type_sequence_label_size)							)

       # test greyscale images
       UpperCamelCase__							:				List[str]          =      1
       UpperCamelCase__							:				Optional[int]          =      FlaxViTForImageClassification(__magic_name__							)

       UpperCamelCase__							:				Optional[int]          =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
       UpperCamelCase__							:				int          =      model(__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      Dict:






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






@require_flax
class     lowercase__       (	__lowerCamelCase			,				unittest.TestCase				):

   '''simple docstring'''


   a						:							Optional[int]   		=		(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()




   def       UpperCamelCase__  (    self							) ->      None:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      FlaxViTModelTester(self							)
       UpperCamelCase__							:				Optional[Any]          =      ConfigTester(self,		config_class=__magic_name__,		has_text_modality=__magic_name__,		hidden_size=37							)




   def       UpperCamelCase__  (    self							) ->      Optional[Any]:






       """simple docstring"""
       self.config_tester.run_common_tests()




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






       """simple docstring"""
       UpperCamelCase__							:				str          =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      Any:






       """simple docstring"""
       UpperCamelCase__							:				int          =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      int:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				List[str]          =      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCamelCase__							:				Optional[int]          =      model_class(__magic_name__							)
           UpperCamelCase__							:				int          =      inspect.signature(model.__call__							)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           UpperCamelCase__							:				Any          =      [*signature.parameters.keys()]

           UpperCamelCase__							:				str          =      ['''pixel_values''']
           self.assertListEqual(arg_names[:1],		__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      str:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				int          =      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__							):
               UpperCamelCase__							:				List[str]          =      self._prepare_for_class(__magic_name__,		__magic_name__							)
               UpperCamelCase__							:				Dict          =      model_class(__magic_name__							)

               @jax.jit
               def model_jitted(__magic_name__,		**__magic_name__							):
                   return model(pixel_values=__magic_name__,		**__magic_name__							)

               with self.subTest('''JIT Enabled'''							):
                   UpperCamelCase__							:				Tuple          =      model_jitted(**__magic_name__							).to_tuple()

               with self.subTest('''JIT Disabled'''							):
                   with jax.disable_jit():
                       UpperCamelCase__							:				str          =      model_jitted(**__magic_name__							).to_tuple()

               self.assertEqual(len(__magic_name__							),		len(__magic_name__							)							)
               for jitted_output, output in zip(__magic_name__,		__magic_name__							):
                   self.assertEqual(jitted_output.shape,		output.shape							)




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






       """simple docstring"""
       for model_class_name in self.all_model_classes:
           UpperCamelCase__							:				List[str]          =      model_class_name.from_pretrained('''google/vit-base-patch16-224'''							)
           UpperCamelCase__							:				List[Any]          =      model(np.ones((1, 3, 224, 224)							)							)
           self.assertIsNotNone(__magic_name__							)








from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403




def 		lowerCAmelCase_    (  __UpperCAmelCase:			str		,   __UpperCAmelCase:			complex		,   __UpperCAmelCase:			str = "x"		,   __UpperCAmelCase:			float = 10**-10		,   __UpperCAmelCase:			int = 1		,   )      ->	complex:
    UpperCamelCase__							:				Optional[int]          =      symbols(__UpperCAmelCase      )
    UpperCamelCase__							:				Dict          =      lambdify(__UpperCAmelCase		,   __UpperCAmelCase      )
    UpperCamelCase__							:				List[str]          =      lambdify(__UpperCAmelCase		,   diff(__UpperCAmelCase		,   __UpperCAmelCase      )      )

    UpperCamelCase__							:				int          =      starting_point

    while True:
        if diff_function(__UpperCAmelCase      ) != 0:
            UpperCamelCase__							:				Optional[int]          =      prev_guess - multiplicity * func(__UpperCAmelCase      ) / diff_function(
                __UpperCAmelCase      )
        else:
            raise ZeroDivisionError('''Could not find root'''      ) from None

        # Precision is checked by comparing the difference of consecutive guesses
        if abs(next_guess - prev_guess      ) < precision:
            return next_guess

        UpperCamelCase__							:				Tuple          =      next_guess


# 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
  # Find fourth Root of 5
  print(F'''The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5J)}''')

  # Find value of e
  print(
      'The root of log(y) - 1 = 0 is ',
      F'''{newton_raphson("log(y) - 1", 2, variable="y")}''',
  )

  # Exponential Roots
  print(
      'The root of exp(x) - 1 = 0 is',
      F'''{newton_raphson("exp(x) - 1", 10, precision=0.005)}''',
  )

  # Find root of cos(x)
  print(F'''The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}''')

