


'''simple docstring'''

import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

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


if is_flax_available():
 import jax

 from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
 from PIL import Image

 from transformers import BeitImageProcessor





class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase=100    ,						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=0.02    ,						UpperCamelCase=3    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						parent
   lowerCamelCase_  				=						vocab_size
   lowerCamelCase_  				=						batch_size
   lowerCamelCase_  				=						image_size
   lowerCamelCase_  				=						patch_size
   lowerCamelCase_  				=						num_channels
   lowerCamelCase_  				=						is_training
   lowerCamelCase_  				=						use_labels
   lowerCamelCase_  				=						hidden_size
   lowerCamelCase_  				=						num_hidden_layers
   lowerCamelCase_  				=						num_attention_heads
   lowerCamelCase_  				=						intermediate_size
   lowerCamelCase_  				=						hidden_act
   lowerCamelCase_  				=						hidden_dropout_prob
   lowerCamelCase_  				=						attention_probs_dropout_prob
   lowerCamelCase_  				=						type_sequence_label_size
   lowerCamelCase_  				=						initializer_range

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






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

   lowerCamelCase_  				=						None
   if self.use_labels:
    lowerCamelCase_  				=						ids_tensor([self.batch_size]    ,						self.type_sequence_label_size  )

   lowerCamelCase_  				=						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=snake_case_    ,						initializer_range=self.initializer_range    ,						)

   return config, pixel_values, labels






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						FlaxBeitModel(config=snake_case_  )
   lowerCamelCase_  				=						model(snake_case_  )
   self.parent.assertEqual(result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length, self.hidden_size)  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						FlaxBeitForMaskedImageModeling(config=snake_case_  )
   lowerCamelCase_  				=						model(snake_case_  )
   self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.seq_length - 1, self.vocab_size)  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.type_sequence_label_size
   lowerCamelCase_  				=						FlaxBeitForImageClassification(config=snake_case_  )
   lowerCamelCase_  				=						model(snake_case_  )
   self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.type_sequence_label_size)  )

   # test greyscale images
   lowerCamelCase_  				=						1
   lowerCamelCase_  				=						FlaxBeitForImageClassification(snake_case_  )

   lowerCamelCase_  				=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
   lowerCamelCase_  				=						model(snake_case_  )







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.prepare_config_and_inputs()
   (
       (
       lowerCamelCase_						
   )				,(
       lowerCamelCase_						
   )				,(
       lowerCamelCase_						
   )				,						
   )  				=						config_and_inputs
   lowerCamelCase_  				=						{"pixel_values": pixel_values}
   return config, inputs_dict





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





  """simple docstring"""




  _lowerCamelCase						=						(
      (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						FlaxBeitModelTester(self  )
   lowerCamelCase_  				=						ConfigTester(self    ,						config_class=snake_case_    ,						has_text_modality=snake_case_    ,						hidden_size=37  )






  def 						snake_case	(					self  ):







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






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_				,lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs_for_common()

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

    lowerCamelCase_  				=						["pixel_values"]
    self.assertListEqual(arg_names[:1]    ,						snake_case_  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_				,lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    with self.subTest(model_class.__name__  ):
     lowerCamelCase_  				=						self._prepare_for_class(snake_case_    ,						snake_case_  )
     lowerCamelCase_  				=						model_class(snake_case_  )

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

     with self.subTest("JIT Enabled"  ):
      lowerCamelCase_  				=						model_jitted(**snake_case_  ).to_tuple()

     with self.subTest("JIT Disabled"  ):
      with jax.disable_jit():
       lowerCamelCase_  				=						model_jitted(**snake_case_  ).to_tuple()

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






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*snake_case_  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*snake_case_  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*snake_case_  )







  @slow
  def 						snake_case	(					self  ):







   """simple docstring"""
   for model_class_name in self.all_model_classes:
    lowerCamelCase_  				=						model_class_name.from_pretrained("microsoft/beit-base-patch16-224"  )
    lowerCamelCase_  				=						model(np.ones((1, 3, 224, 224)  )  )
    self.assertIsNotNone(snake_case_  )





def __snake_case      (   ):
 lowerCamelCase_  				=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
 return image







@require_vision
@require_flax
class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  @cached_property
  def 						snake_case	(					self  ):







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






  @slow
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"  )

   lowerCamelCase_  				=						self.default_image_processor
   lowerCamelCase_  				=						prepare_img()
   lowerCamelCase_  				=						image_processor(images=snake_case_    ,						return_tensors="np"  ).pixel_values

   # prepare bool_masked_pos
   lowerCamelCase_  				=						np.ones((1, 196)    ,						dtype=snake_case_  )

   # forward pass
   lowerCamelCase_  				=						model(pixel_values=snake_case_    ,						bool_masked_pos=snake_case_  )
   lowerCamelCase_  				=						outputs.logits

   # verify the logits
   lowerCamelCase_  				=						(1, 196, 8192)
   self.assertEqual(logits.shape    ,						snake_case_  )

   lowerCamelCase_  				=						np.array(
       [[-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]]  )

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






  @slow
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"  )

   lowerCamelCase_  				=						self.default_image_processor
   lowerCamelCase_  				=						prepare_img()
   lowerCamelCase_  				=						image_processor(images=snake_case_    ,						return_tensors="np"  )

   # forward pass
   lowerCamelCase_  				=						model(**snake_case_  )
   lowerCamelCase_  				=						outputs.logits

   # verify the logits
   lowerCamelCase_  				=						(1, 1000)
   self.assertEqual(logits.shape    ,						snake_case_  )

   lowerCamelCase_  				=						np.array([-1.2_385, -1.0_987, -1.0_108]  )

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

   lowerCamelCase_  				=						281
   self.assertEqual(logits.argmax(-1  ).item()    ,						snake_case_  )







  @slow
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"  )

   lowerCamelCase_  				=						self.default_image_processor
   lowerCamelCase_  				=						prepare_img()
   lowerCamelCase_  				=						image_processor(images=snake_case_    ,						return_tensors="np"  )

   # forward pass
   lowerCamelCase_  				=						model(**snake_case_  )
   lowerCamelCase_  				=						outputs.logits

   # verify the logits
   lowerCamelCase_  				=						(1, 2_1841)
   self.assertEqual(logits.shape    ,						snake_case_  )

   lowerCamelCase_  				=						np.array([1.6_881, -0.2_787, 0.5_901]  )

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

   lowerCamelCase_  				=						2396
   self.assertEqual(logits.argmax(-1  ).item()    ,						snake_case_  )







'''simple docstring'''

import math





def __snake_case      (   UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		float	):
 return math.pow(UpperCAmelCase_      ,       2	) - a





def __snake_case      (   UpperCAmelCase_		:		float	):
 return 2 * x





def __snake_case      (   UpperCAmelCase_		:		float	):
 lowerCamelCase_  				=						2.0

 while start <= a:
  lowerCamelCase_  				=						math.pow(UpperCAmelCase_      ,       2	)

 return start





def __snake_case      (   UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		int = 9999      ,       UpperCAmelCase_		:		float = 0.00_0000_0000_0001	):

 if a < 0:
  raise ValueError("math domain error"	)

 lowerCamelCase_  				=						get_initial_point(UpperCAmelCase_	)

 for _ in range(UpperCAmelCase_	):
  lowerCamelCase_  				=						value
  lowerCamelCase_  				=						value - fx(UpperCAmelCase_      ,       UpperCAmelCase_	) / fx_derivative(UpperCAmelCase_	)
  if abs(prev_value - value	) < tolerance:
   return value

 return value


if __name__ == "__main__":
     from doctest import testmod

     testmod()



