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  A_			(							unittest.TestCase			):

  def __init__(		self			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=1_00  ,				__SCREAMING_SNAKE_CASE			:   List[str]=13  ,				__SCREAMING_SNAKE_CASE			:   Any=30  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=2  ,				__SCREAMING_SNAKE_CASE			:   str=3  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=True  ,				__SCREAMING_SNAKE_CASE			:   int=True  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=32  ,				__SCREAMING_SNAKE_CASE			:   List[str]=5  ,				__SCREAMING_SNAKE_CASE			:   Any=4  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=37  ,				__SCREAMING_SNAKE_CASE			:   str="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=0.1  ,				__SCREAMING_SNAKE_CASE			:   List[str]=0.1  ,				__SCREAMING_SNAKE_CASE			:   Tuple=10  ,				__SCREAMING_SNAKE_CASE			:   Tuple=0.02  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=3  ,				):
   __a							=	parent
   __a							=	vocab_size
   __a							=	batch_size
   __a							=	image_size
   __a							=	patch_size
   __a							=	num_channels
   __a							=	is_training
   __a							=	use_labels
   __a							=	hidden_size
   __a							=	num_hidden_layers
   __a							=	num_attention_heads
   __a							=	intermediate_size
   __a							=	hidden_act
   __a							=	hidden_dropout_prob
   __a							=	attention_probs_dropout_prob
   __a							=	type_sequence_label_size
   __a							=	initializer_range

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

  def 		_UpperCAmelCase (		self			:   str							):
   __a							=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

   __a							=	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=__SCREAMING_SNAKE_CASE  ,				initializer_range=self.initializer_range  ,				)

   return config, pixel_values, labels

  def 		_UpperCAmelCase (		self			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   int  ,				__SCREAMING_SNAKE_CASE			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]							):
   __a							=	FlaxBeitModel(config=__SCREAMING_SNAKE_CASE							)
   __a							=	model(__SCREAMING_SNAKE_CASE							)
   self.parent.assertEqual(result.last_hidden_state.shape  ,				(self.batch_size, self.seq_length, self.hidden_size)							)

  def 		_UpperCAmelCase (		self			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   Tuple							):
   __a							=	FlaxBeitForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE							)
   __a							=	model(__SCREAMING_SNAKE_CASE							)
   self.parent.assertEqual(result.logits.shape  ,				(self.batch_size, self.seq_length - 1, self.vocab_size)							)

  def 		_UpperCAmelCase (		self			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   Any  ,				__SCREAMING_SNAKE_CASE			:   int  ,				__SCREAMING_SNAKE_CASE			:   Tuple							):
   __a							=	self.type_sequence_label_size
   __a							=	FlaxBeitForImageClassification(config=__SCREAMING_SNAKE_CASE							)
   __a							=	model(__SCREAMING_SNAKE_CASE							)
   self.parent.assertEqual(result.logits.shape  ,				(self.batch_size, self.type_sequence_label_size)							)

   # test greyscale images
   __a							=	1
   __a							=	FlaxBeitForImageClassification(__SCREAMING_SNAKE_CASE							)

   __a							=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
   __a							=	model(__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   str							):
   __a							=	self.prepare_config_and_inputs()
   (
       (
       __a
   )							,  (
       __a
   )							,  (
       __a
   )							,  
   )							=	config_and_inputs
   __a							=	{"pixel_values": pixel_values}
   return config, inputs_dict
@require_flax
class  A_			(							a_      ,							unittest.TestCase			):
  _SCREAMING_SNAKE_CASE     					=      (
      (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
  )

  def 		_UpperCAmelCase (		self			:   Tuple							):
   __a							=	FlaxBeitModelTester(self							)
   __a							=	ConfigTester(self  ,				config_class=__SCREAMING_SNAKE_CASE  ,				has_text_modality=__SCREAMING_SNAKE_CASE  ,				hidden_size=37							)

  def 		_UpperCAmelCase (		self			:   Tuple							):
   self.config_tester.run_common_tests()

  def 		_UpperCAmelCase (		self			:   Dict							):
   __a							,  __a							=	self.model_tester.prepare_config_and_inputs_for_common()

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

    __a							=	["pixel_values"]
    self.assertListEqual(arg_names[:1]  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Tuple							):
   __a							,  __a							=	self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    with self.subTest(model_class.__name__							):
     __a							=	self._prepare_for_class(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
     __a							=	model_class(__SCREAMING_SNAKE_CASE							)

     @jax.jit
     def model_jitted(__SCREAMING_SNAKE_CASE			:   int  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]							):
      return model(pixel_values=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

     with self.subTest("JIT Enabled"							):
      __a							=	model_jitted(**__SCREAMING_SNAKE_CASE							).to_tuple()

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

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

  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Tuple							):
   __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE							)

  @slow
  def 		_UpperCAmelCase (		self			:   Any							):
   for model_class_name in self.all_model_classes:
    __a							=	model_class_name.from_pretrained("microsoft/beit-base-patch16-224"							)
    __a							=	model(np.ones((1, 3, 2_24, 2_24)							)							)
    self.assertIsNotNone(__SCREAMING_SNAKE_CASE							)



def 	__A		(      ):



 """simple docstring"""


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






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

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

  @slow
  def 		_UpperCAmelCase (		self			:   int							):
   __a							=	FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"							)

   __a							=	self.default_image_processor
   __a							=	prepare_img()
   __a							=	image_processor(images=__SCREAMING_SNAKE_CASE  ,				return_tensors="np"							).pixel_values

   # prepare bool_masked_pos
   __a							=	np.ones((1, 1_96)  ,				dtype=__SCREAMING_SNAKE_CASE							)

   # forward pass
   __a							=	model(pixel_values=__SCREAMING_SNAKE_CASE  ,				bool_masked_pos=__SCREAMING_SNAKE_CASE							)
   __a							=	outputs.logits

   # verify the logits
   __a							=	(1, 1_96, 81_92)
   self.assertEqual(logits.shape  ,				__SCREAMING_SNAKE_CASE							)

   __a							=	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]  ,				__SCREAMING_SNAKE_CASE  ,				atol=1E-2							)							)

  @slow
  def 		_UpperCAmelCase (		self			:   Dict							):
   __a							=	FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"							)

   __a							=	self.default_image_processor
   __a							=	prepare_img()
   __a							=	image_processor(images=__SCREAMING_SNAKE_CASE  ,				return_tensors="np"							)

   # forward pass
   __a							=	model(**__SCREAMING_SNAKE_CASE							)
   __a							=	outputs.logits

   # verify the logits
   __a							=	(1, 10_00)
   self.assertEqual(logits.shape  ,				__SCREAMING_SNAKE_CASE							)

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

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

   __a							=	2_81
   self.assertEqual(logits.argmax(-1							).item()  ,				__SCREAMING_SNAKE_CASE							)

  @slow
  def 		_UpperCAmelCase (		self			:   Dict							):
   __a							=	FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"							)

   __a							=	self.default_image_processor
   __a							=	prepare_img()
   __a							=	image_processor(images=__SCREAMING_SNAKE_CASE  ,				return_tensors="np"							)

   # forward pass
   __a							=	model(**__SCREAMING_SNAKE_CASE							)
   __a							=	outputs.logits

   # verify the logits
   __a							=	(1, 2_18_41)
   self.assertEqual(logits.shape  ,				__SCREAMING_SNAKE_CASE							)

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

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

   __a							=	23_96
   self.assertEqual(logits.argmax(-1							).item()  ,				__SCREAMING_SNAKE_CASE							)



from typing import Any



def 	__A		(      _A       ):



 """simple docstring"""


 if not input_list:
  return []
 __a							=	[input_list.count(_A       ) for value in input_list]
 __a							=	max(_A       )  # Gets the maximum count in the input list.
 # Gets values of modes
 return sorted({input_list[i] for i, value in enumerate(_A       ) if value == y}       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()


