






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 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase=13		,		_UpperCAmelCase=30		,		_UpperCAmelCase=2		,		_UpperCAmelCase=3		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=32		,		_UpperCAmelCase=5		,		_UpperCAmelCase=4		,		_UpperCAmelCase=37		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=10		,		_UpperCAmelCase=0.0_2		,		)			-> List[str]:
     UpperCamelCase_									=     parent
     UpperCamelCase_									=     batch_size
     UpperCamelCase_									=     image_size
     UpperCamelCase_									=     patch_size
     UpperCamelCase_									=     num_channels
     UpperCamelCase_									=     is_training
     UpperCamelCase_									=     use_labels
     UpperCamelCase_									=     hidden_size
     UpperCamelCase_									=     num_hidden_layers
     UpperCamelCase_									=     num_attention_heads
     UpperCamelCase_									=     intermediate_size
     UpperCamelCase_									=     hidden_act
     UpperCamelCase_									=     hidden_dropout_prob
     UpperCamelCase_									=     attention_probs_dropout_prob
     UpperCamelCase_									=     type_sequence_label_size
     UpperCamelCase_									=     initializer_range

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




  def    _UpperCAmelCase     (     self )			-> int:
     UpperCamelCase_									=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

     UpperCamelCase_									=     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=_UpperCAmelCase		,		initializer_range=self.initializer_range		,		)

     return config, pixel_values




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> Tuple:
     UpperCamelCase_									=     FlaxViTModel(config=_UpperCAmelCase )
     UpperCamelCase_									=     model(_UpperCAmelCase )
     # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
     UpperCamelCase_									=     (self.image_size, self.image_size)
     UpperCamelCase_									=     (self.patch_size, self.patch_size)
     UpperCamelCase_									=     (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		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> str:
     UpperCamelCase_									=     self.type_sequence_label_size
     UpperCamelCase_									=     FlaxViTForImageClassification(config=_UpperCAmelCase )
     UpperCamelCase_									=     model(_UpperCAmelCase )
     self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.type_sequence_label_size) )

     # test greyscale images
     UpperCamelCase_									=     1
     UpperCamelCase_									=     FlaxViTForImageClassification(_UpperCAmelCase )

     UpperCamelCase_									=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
     UpperCamelCase_									=     model(_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     self.prepare_config_and_inputs()
     (
         (
         UpperCamelCase_
     )		,      (
         UpperCamelCase_
     )		,      
     )									=     config_and_inputs
     UpperCamelCase_									=     {'pixel_values': pixel_values}
     return config, inputs_dict




@require_flax
class 						_a       (  UpperCAmelCase__						,       unittest.TestCase    ):






  """simple docstring"""





  A_						=   (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()




  def    _UpperCAmelCase     (     self )			-> None:
     UpperCamelCase_									=     FlaxViTModelTester(self )
     UpperCamelCase_									=     ConfigTester(self		,		config_class=_UpperCAmelCase		,		has_text_modality=_UpperCAmelCase		,		hidden_size=37 )




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     self.config_tester.run_common_tests()




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()

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

        UpperCamelCase_									=     ['pixel_values']
        self.assertListEqual(arg_names[:1]		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> int:
     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__ ):
           UpperCamelCase_									=     self._prepare_for_class(_UpperCAmelCase		,		_UpperCAmelCase )
           UpperCamelCase_									=     model_class(_UpperCAmelCase )

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

           with self.subTest('JIT Enabled' ):
              UpperCamelCase_									=     model_jitted(**_UpperCAmelCase ).to_tuple()

           with self.subTest('JIT Disabled' ):
              with jax.disable_jit():
                 UpperCamelCase_									=     model_jitted(**_UpperCAmelCase ).to_tuple()

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




  @slow
  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     for model_class_name in self.all_model_classes:
        UpperCamelCase_									=     model_class_name.from_pretrained('google/vit-base-patch16-224' )
        UpperCamelCase_									=     model(np.ones((1, 3, 224, 224) ) )
        self.assertIsNotNone(_UpperCAmelCase )













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






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase = "x"    ,      __lowercase = 10**-10    ,      __lowercase = 1    ,      ):
   UpperCamelCase_									=     symbols(__lowercase)
   UpperCamelCase_									=     lambdify(__lowercase    ,      __lowercase)
   UpperCamelCase_									=     lambdify(__lowercase    ,      diff(__lowercase    ,      __lowercase))

   UpperCamelCase_									=     starting_point

   while True:
      if diff_function(__lowercase) != 0:
         UpperCamelCase_									=     prev_guess - multiplicity * func(__lowercase) / diff_function(
             __lowercase)
      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_									=     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", 1_0, precision=0.005)}',
 )

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





