


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    lowerCamelCase__						(		unittest.TestCase    ):
      def __init__(		self	:	List[str]      ,       A_	:	Optional[int]      ,       A_	:	int=1_0_0      ,       A_	:	Optional[int]=1_3      ,       A_	:	int=3_0      ,       A_	:	Optional[int]=2      ,       A_	:	Union[str, Any]=3      ,       A_	:	str=True      ,       A_	:	Optional[Any]=True      ,       A_	:	Union[str, Any]=3_2      ,       A_	:	Optional[int]=5      ,       A_	:	List[str]=4      ,       A_	:	Dict=3_7      ,       A_	:	Optional[Any]="gelu"      ,       A_	:	str=0.1      ,       A_	:	List[str]=0.1      ,       A_	:	Any=1_0      ,       A_	:	List[str]=0.02      ,       A_	:	List[Any]=3      ,       ):






       '''simple docstring'''


       __lowercase									=	parent
       __lowercase									=	vocab_size
       __lowercase									=	batch_size
       __lowercase									=	image_size
       __lowercase									=	patch_size
       __lowercase									=	num_channels
       __lowercase									=	is_training
       __lowercase									=	use_labels
       __lowercase									=	hidden_size
       __lowercase									=	num_hidden_layers
       __lowercase									=	num_attention_heads
       __lowercase									=	intermediate_size
       __lowercase									=	hidden_act
       __lowercase									=	hidden_dropout_prob
       __lowercase									=	attention_probs_dropout_prob
       __lowercase									=	type_sequence_label_size
       __lowercase									=	initializer_range

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






       '''simple docstring'''


       __lowercase									=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

       __lowercase									=	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 SCREAMING_SNAKE_CASE_	(		self	:	List[str]      ,       A_	:	List[str]      ,       A_	:	Optional[Any]      ,       A_	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	FlaxBeitModel(config=snake_case_							)
       __lowercase									=	model(snake_case_							)
       self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, self.seq_length, self.hidden_size)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]      ,       A_	:	Tuple      ,       A_	:	Optional[Any]      ,       A_	:	Union[str, Any]							):






       '''simple docstring'''


       __lowercase									=	FlaxBeitForMaskedImageModeling(config=snake_case_							)
       __lowercase									=	model(snake_case_							)
       self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.seq_length - 1, self.vocab_size)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]      ,       A_	:	Dict      ,       A_	:	int      ,       A_	:	str							):






       '''simple docstring'''


       __lowercase									=	self.type_sequence_label_size
       __lowercase									=	FlaxBeitForImageClassification(config=snake_case_							)
       __lowercase									=	model(snake_case_							)
       self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.type_sequence_label_size)							)

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

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






      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


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






@require_flax
class    lowerCamelCase__						(		_a			,   unittest.TestCase    ):
      a :     int											=		(
          (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
      )
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''simple docstring'''


       __lowercase									=	FlaxBeitModelTester(self							)
       __lowercase									=	ConfigTester(self      ,       config_class=snake_case_      ,       has_text_modality=snake_case_      ,       hidden_size=3_7							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       self.config_tester.run_common_tests()
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       __lowercase							,		__lowercase									=	self.model_tester.prepare_config_and_inputs_for_common()

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

        __lowercase									=	["""pixel_values"""]
        self.assertListEqual(arg_names[:1]      ,       snake_case_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       __lowercase							,		__lowercase									=	self.model_tester.prepare_config_and_inputs_for_common()

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

         @jax.jit
         def model_jitted(A_	:	Dict      ,       **A_	:	Optional[Any]							):
          return model(pixel_values=snake_case_      ,       **snake_case_							)

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

         with self.subTest("""JIT Disabled"""							):
          with jax.disable_jit():
           __lowercase									=	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 SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*snake_case_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*snake_case_							)






      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       for model_class_name in self.all_model_classes:
        __lowercase									=	model_class_name.from_pretrained("""microsoft/beit-base-patch16-224"""							)
        __lowercase									=	model(np.ones((1, 3, 2_2_4, 2_2_4)							)							)
        self.assertIsNotNone(snake_case_							)






def lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
 return image





@require_vision
@require_flax
class    lowerCamelCase__						(		unittest.TestCase    ):
      @cached_property
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224"""							) if is_vision_available() else None
      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       __lowercase									=	FlaxBeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""							)

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

       # prepare bool_masked_pos
       __lowercase									=	np.ones((1, 1_9_6)      ,       dtype=snake_case_							)

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

       # verify the logits
       __lowercase									=	(1, 1_9_6, 8_1_9_2)
       self.assertEqual(logits.shape      ,       snake_case_							)

       __lowercase									=	np.array(
           [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]							)

       self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]      ,       snake_case_      ,       atol=1e-2							)							)
      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       __lowercase									=	FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""							)

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

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

       # verify the logits
       __lowercase									=	(1, 1_0_0_0)
       self.assertEqual(logits.shape      ,       snake_case_							)

       __lowercase									=	np.array([-1.23_85, -1.09_87, -1.01_08]							)

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

       __lowercase									=	2_8_1
       self.assertEqual(logits.argmax(-1							).item()      ,       snake_case_							)






      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


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

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

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

       # verify the logits
       __lowercase									=	(1, 2_1_8_4_1)
       self.assertEqual(logits.shape      ,       snake_case_							)

       __lowercase									=	np.array([1.68_81, -0.27_87, 0.59_01]							)

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

       __lowercase									=	2_3_9_6
       self.assertEqual(logits.argmax(-1							).item()      ,       snake_case_							)




"""simple docstring"""

def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list ):






 """simple docstring"""







 _enforce_args(UpperCamelCase__				,   UpperCamelCase__ )
 if n == 0:
  return 0
 __lowercase									=	float("""-inf""" )
 for i in range(1				,   n + 1 ):
  __lowercase									=	max(
      UpperCamelCase__				,   prices[i - 1] + naive_cut_rod_recursive(n - i				,   UpperCamelCase__ ) )

 return max_revue


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list ):






 """simple docstring"""







 _enforce_args(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	[float("""-inf""" ) for _ in range(n + 1 )]
 return _top_down_cut_rod_recursive(UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__ )


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list				,   UpperCamelCase__	: list ):






 """simple docstring"""







 if max_rev[n] >= 0:
  return max_rev[n]
 elif n == 0:
  return 0
 else:
  __lowercase									=	float("""-inf""" )
  for i in range(1				,   n + 1 ):
   __lowercase									=	max(
       UpperCamelCase__				,   prices[i - 1] + _top_down_cut_rod_recursive(n - i				,   UpperCamelCase__				,   UpperCamelCase__ )				,   )

  __lowercase									=	max_revenue

 return max_rev[n]


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list ):






 """simple docstring"""







 _enforce_args(UpperCamelCase__				,   UpperCamelCase__ )

 # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
 # length 0.
 __lowercase									=	[float("""-inf""" ) for _ in range(n + 1 )]
 __lowercase									=	0

 for i in range(1				,   n + 1 ):
  __lowercase									=	max_rev[i]
  for j in range(1				,   i + 1 ):
   __lowercase									=	max(UpperCamelCase__				,   prices[j - 1] + max_rev[i - j] )

  __lowercase									=	max_revenue_i

 return max_rev[n]


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list ):






 """simple docstring"""







 if n < 0:
  __lowercase									=	f'''n must be greater than or equal to 0. Got n = {n}'''
  raise ValueError(UpperCamelCase__ )

 if n > len(UpperCamelCase__ ):
  __lowercase									=	(
      """Each integral piece of rod must have a corresponding price. """
      f'''Got n = {n} but length of prices = {len(UpperCamelCase__ )}'''
  )
  raise ValueError(UpperCamelCase__ )


def lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	[6, 10, 12, 15, 20, 23]
 __lowercase									=	len(UpperCamelCase__ )

 # the best revenue comes from cutting the rod into 6 pieces, each
 # of length 1 resulting in a revenue of 6 * 6 = 36.
 __lowercase									=	36

 __lowercase									=	top_down_cut_rod(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	bottom_up_cut_rod(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	naive_cut_rod_recursive(UpperCamelCase__				,   UpperCamelCase__ )

 assert expected_max_revenue == max_rev_top_down
 assert max_rev_top_down == max_rev_bottom_up
 assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
       main()
