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       ):







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_=1_00		,				lowerCAmelCase_=13		,				lowerCAmelCase_=30		,				lowerCAmelCase_=2		,				lowerCAmelCase_=3		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				lowerCAmelCase_=32		,				lowerCAmelCase_=5		,				lowerCAmelCase_=4		,				lowerCAmelCase_=37		,				lowerCAmelCase_="gelu"		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=10		,				lowerCAmelCase_=0.02		,				lowerCAmelCase_=3		,				)  ->					str:
        _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							)  ->					Tuple:
        _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=lowerCAmelCase_		,				initializer_range=self.initializer_range		,				)

        return config, pixel_values, labels



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Any:
        _A        =  FlaxBeitModel(config=lowerCAmelCase_							)
        _A        =  model(lowerCAmelCase_							)
        self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					List[Any]:
        _A        =  FlaxBeitForMaskedImageModeling(config=lowerCAmelCase_							)
        _A        =  model(lowerCAmelCase_							)
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.seq_length - 1, self.vocab_size)							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					List[str]:
        _A        =  self.type_sequence_label_size
        _A        =  FlaxBeitForImageClassification(config=lowerCAmelCase_							)
        _A        =  model(lowerCAmelCase_							)
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.type_sequence_label_size)							)

        # test greyscale images
        _A        =  1
        _A        =  FlaxBeitForImageClassification(lowerCAmelCase_							)

        _A        =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
        _A        =  model(lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        _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							(			__lowerCAmelCase       ,			unittest.TestCase       ):







 """simple docstring"""






 lowerCamelCase	:str    			=		(
     (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
 )



 def      UpperCAmelCase							(					self							)  ->					None:
        _A        =  FlaxBeitModelTester(self							)
        _A        =  ConfigTester(self		,				config_class=lowerCAmelCase_		,				has_text_modality=lowerCAmelCase_		,				hidden_size=37							)



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        self.config_tester.run_common_tests()



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               _A        =  model_class(lowerCAmelCase_							)
               _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]		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        _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(lowerCAmelCase_		,				lowerCAmelCase_							)
                      _A        =  model_class(lowerCAmelCase_							)

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

                      with self.subTest("""JIT Enabled"""							):
                             _A        =  model_jitted(**lowerCAmelCase_							).to_tuple()

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

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



 def      UpperCAmelCase							(					self							)  ->					str:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_							)



 @slow
 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        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(lowerCAmelCase_							)






def  snake_case  (       )						->   Union[str, Any]:
       _A        =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
       return image







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







 """simple docstring"""






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



 @slow
 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        _A        =  FlaxBeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""							)

        _A        =  self.default_image_processor
        _A        =  prepare_img()
        _A        =  image_processor(images=lowerCAmelCase_		,				return_tensors="""np"""							).pixel_values

        # prepare bool_masked_pos
        _A        =  np.ones((1, 1_96)		,				dtype=lowerCAmelCase_							)

        # forward pass
        _A        =  model(pixel_values=lowerCAmelCase_		,				bool_masked_pos=lowerCAmelCase_							)
        _A        =  outputs.logits

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

        _A        =  np.array(
            [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]							)

        self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]		,				lowerCAmelCase_		,				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=lowerCAmelCase_		,				return_tensors="""np"""							)

        # forward pass
        _A        =  model(**lowerCAmelCase_							)
        _A        =  outputs.logits

        # verify the logits
        _A        =  (1, 10_00)
        self.assertEqual(logits.shape		,				lowerCAmelCase_							)

        _A        =  np.array([-1.2385, -1.0987, -1.0108]							)

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

        _A        =  2_81
        self.assertEqual(logits.argmax(-1							).item()		,				lowerCAmelCase_							)



 @slow
 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _A        =  FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k"""							)

        _A        =  self.default_image_processor
        _A        =  prepare_img()
        _A        =  image_processor(images=lowerCAmelCase_		,				return_tensors="""np"""							)

        # forward pass
        _A        =  model(**lowerCAmelCase_							)
        _A        =  outputs.logits

        # verify the logits
        _A        =  (1, 2_18_41)
        self.assertEqual(logits.shape		,				lowerCAmelCase_							)

        _A        =  np.array([1.6881, -0.2787, 0.5901]							)

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

        _A        =  23_96
        self.assertEqual(logits.argmax(-1							).item()		,				lowerCAmelCase_							)





import random
from typing import Any






def  snake_case  (       snake_case__			:list)						->   list[Any]:
       for _ in range(len(snake_case__)):
              _A        =  random.randint(0   ,	len(snake_case__) - 1)
              _A        =  random.randint(0   ,	len(snake_case__) - 1)
              _A   ,  _A        =  data[b], data[a]
       return data


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE										=						[0, 1, 2, 3, 4, 5, 6, 7]
    _SCREAMING_SNAKE_CASE										=						['python', 'says', 'hello', '!']
    print('Fisher-Yates Shuffle:')
    print('List', integers, strings)
    print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))




