





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    : Dict     ,	UpperCAmelCase    : Optional[Any]     ,	UpperCAmelCase    : int=100     ,	UpperCAmelCase    : List[str]=13     ,	UpperCAmelCase    : str=30     ,	UpperCAmelCase    : Optional[Any]=2     ,	UpperCAmelCase    : Tuple=3     ,	UpperCAmelCase    : Optional[int]=True     ,	UpperCAmelCase    : Union[str, Any]=True     ,	UpperCAmelCase    : List[str]=32     ,	UpperCAmelCase    : Union[str, Any]=5     ,	UpperCAmelCase    : str=4     ,	UpperCAmelCase    : Union[str, Any]=37     ,	UpperCAmelCase    : List[Any]="gelu"     ,	UpperCAmelCase    : Dict=0.1     ,	UpperCAmelCase    : Any=0.1     ,	UpperCAmelCase    : str=10     ,	UpperCAmelCase    : List[str]=0.0_2     ,	UpperCAmelCase    : str=3     ,	)     ->      Any:
     lowerCamelCase__       : List[str]   					=						parent
     lowerCamelCase__       : str   					=						vocab_size
     lowerCamelCase__       : List[str]   					=						batch_size
     lowerCamelCase__       : Dict   					=						image_size
     lowerCamelCase__       : int   					=						patch_size
     lowerCamelCase__       : str   					=						num_channels
     lowerCamelCase__       : Any   					=						is_training
     lowerCamelCase__       : Dict   					=						use_labels
     lowerCamelCase__       : str   					=						hidden_size
     lowerCamelCase__       : int   					=						num_hidden_layers
     lowerCamelCase__       : int   					=						num_attention_heads
     lowerCamelCase__       : Optional[Any]   					=						intermediate_size
     lowerCamelCase__       : Optional[Any]   					=						hidden_act
     lowerCamelCase__       : Dict   					=						hidden_dropout_prob
     lowerCamelCase__       : Tuple   					=						attention_probs_dropout_prob
     lowerCamelCase__       : List[Any]   					=						type_sequence_label_size
     lowerCamelCase__       : Any   					=						initializer_range

     # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
     lowerCamelCase__       : int   					=						(image_size // patch_size) ** 2
     lowerCamelCase__       : Optional[int]   					=						num_patches + 1
 def 	A_      ( self    : Any      )     ->      List[Any]:
     lowerCamelCase__       : List[Any]   					=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

     lowerCamelCase__       : List[str]   					=						None
     if self.use_labels:
         lowerCamelCase__       : Dict   					=						ids_tensor([self.batch_size]     ,	self.type_sequence_label_size      )

     lowerCamelCase__       : Tuple   					=						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=UpperCAmelCase     ,	initializer_range=self.initializer_range     ,	)

     return config, pixel_values, labels
 def 	A_      ( self    : Any     ,	UpperCAmelCase    : int     ,	UpperCAmelCase    : Union[str, Any]     ,	UpperCAmelCase    : Any      )     ->      Optional[int]:
     lowerCamelCase__       : str   					=						FlaxBeitModel(config=UpperCAmelCase      )
     lowerCamelCase__       : str   					=						model(UpperCAmelCase      )
     self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size)      )
 def 	A_      ( self    : int     ,	UpperCAmelCase    : Union[str, Any]     ,	UpperCAmelCase    : Optional[Any]     ,	UpperCAmelCase    : List[str]      )     ->      List[Any]:
     lowerCamelCase__       : List[str]   					=						FlaxBeitForMaskedImageModeling(config=UpperCAmelCase      )
     lowerCamelCase__       : Any   					=						model(UpperCAmelCase      )
     self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.seq_length - 1, self.vocab_size)      )
 def 	A_      ( self    : Dict     ,	UpperCAmelCase    : Any     ,	UpperCAmelCase    : str     ,	UpperCAmelCase    : List[Any]      )     ->      List[Any]:
     lowerCamelCase__       : Dict   					=						self.type_sequence_label_size
     lowerCamelCase__       : Any   					=						FlaxBeitForImageClassification(config=UpperCAmelCase      )
     lowerCamelCase__       : Union[str, Any]   					=						model(UpperCAmelCase      )
     self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.type_sequence_label_size)      )

     # test greyscale images
     lowerCamelCase__       : Optional[Any]   					=						1
     lowerCamelCase__       : Union[str, Any]   					=						FlaxBeitForImageClassification(UpperCAmelCase      )

     lowerCamelCase__       : Any   					=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
     lowerCamelCase__       : Any   					=						model(UpperCAmelCase      )







 def 	A_      ( self    : Optional[int]      )     ->      int:
     lowerCamelCase__       : Any   					=						self.prepare_config_and_inputs()
     (
         (
         lowerCamelCase__
     )					,				(
         lowerCamelCase__
     )					,				(
         lowerCamelCase__
     )					,				
     )       : Optional[Any]   					=						config_and_inputs
     lowerCamelCase__       : Optional[Any]   					=						{'pixel_values': pixel_values}
     return config, inputs_dict


@require_flax
class   lowerCAmelCase				(   __UpperCamelCase,    unittest.TestCase     ):
 UpperCAmelCase__						=						(
     (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
 )
 def 	A_      ( self    : Union[str, Any]      )     ->      None:
     lowerCamelCase__       : Dict   					=						FlaxBeitModelTester(self      )
     lowerCamelCase__       : Union[str, Any]   					=						ConfigTester(self     ,	config_class=UpperCAmelCase     ,	has_text_modality=UpperCAmelCase     ,	hidden_size=37      )
 def 	A_      ( self    : List[str]      )     ->      List[str]:
     self.config_tester.run_common_tests()
 def 	A_      ( self    : Union[str, Any]      )     ->      Optional[int]:
     lowerCamelCase__					,				lowerCamelCase__       : Optional[int]   					=						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         lowerCamelCase__       : Tuple   					=						model_class(UpperCAmelCase      )
         lowerCamelCase__       : Optional[int]   					=						inspect.signature(model.__call__      )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         lowerCamelCase__       : List[Any]   					=						[*signature.parameters.keys()]

         lowerCamelCase__       : List[str]   					=						['pixel_values']
         self.assertListEqual(arg_names[:1]     ,	UpperCAmelCase      )
 def 	A_      ( self    : Tuple      )     ->      Tuple:
     lowerCamelCase__					,				lowerCamelCase__       : int   					=						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__      ):
             lowerCamelCase__       : int   					=						self._prepare_for_class(UpperCAmelCase     ,	UpperCAmelCase      )
             lowerCamelCase__       : Optional[int]   					=						model_class(UpperCAmelCase      )

             @jax.jit
             def model_jitted(UpperCAmelCase    : int     ,	**UpperCAmelCase    : List[str]      ):
                 return model(pixel_values=UpperCAmelCase     ,	**UpperCAmelCase      )

             with self.subTest('JIT Enabled'      ):
                 lowerCamelCase__       : str   					=						model_jitted(**UpperCAmelCase      ).to_tuple()

             with self.subTest('JIT Disabled'      ):
                 with jax.disable_jit():
                     lowerCamelCase__       : List[str]   					=						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      )
 def 	A_      ( self    : Tuple      )     ->      Optional[Any]:
     lowerCamelCase__       : Tuple   					=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*UpperCAmelCase      )
 def 	A_      ( self    : List[Any]      )     ->      Dict:
     lowerCamelCase__       : Union[str, Any]   					=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase      )
 def 	A_      ( self    : Any      )     ->      Union[str, Any]:
     lowerCamelCase__       : Optional[int]   					=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase      )







 @slow
 def 	A_      ( self    : List[str]      )     ->      Tuple:
     for model_class_name in self.all_model_classes:
         lowerCamelCase__       : Optional[Any]   					=						model_class_name.from_pretrained('microsoft/beit-base-patch16-224'      )
         lowerCamelCase__       : Dict   					=						model(np.ones((1, 3, 224, 224)      )      )
         self.assertIsNotNone(UpperCAmelCase      )


def 			SCREAMING_SNAKE_CASE (			)     ->	Dict:
    lowerCamelCase__       : List[str]   					=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
    return image


@require_vision
@require_flax
class   lowerCAmelCase				(   unittest.TestCase     ):
 @cached_property
 def 	A_      ( self    : Union[str, Any]      )     ->      str:
     return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'      ) if is_vision_available() else None
 @slow
 def 	A_      ( self    : Tuple      )     ->      str:
     lowerCamelCase__       : int   					=						FlaxBeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'      )

     lowerCamelCase__       : int   					=						self.default_image_processor
     lowerCamelCase__       : Optional[Any]   					=						prepare_img()
     lowerCamelCase__       : List[Any]   					=						image_processor(images=UpperCAmelCase     ,	return_tensors='np'      ).pixel_values

     # prepare bool_masked_pos
     lowerCamelCase__       : Optional[int]   					=						np.ones((1, 196)     ,	dtype=UpperCAmelCase      )

     # forward pass
     lowerCamelCase__       : Tuple   					=						model(pixel_values=UpperCAmelCase     ,	bool_masked_pos=UpperCAmelCase      )
     lowerCamelCase__       : Dict   					=						outputs.logits

     # verify the logits
     lowerCamelCase__       : str   					=						(1, 196, 8192)
     self.assertEqual(logits.shape     ,	UpperCAmelCase      )

     lowerCamelCase__       : Union[str, Any]   					=						np.array(
         [[-3.2_4_3_7, 0.5_0_7_2, -1_3.9_1_7_4], [-3.2_4_5_6, 0.4_9_4_8, -1_3.9_4_0_1], [-3.2_0_3_3, 0.5_1_2_1, -1_3.8_5_5_0]]      )

     self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]     ,	UpperCAmelCase     ,	atol=1e-2      )      )
 @slow
 def 	A_      ( self    : int      )     ->      Tuple:
     lowerCamelCase__       : Dict   					=						FlaxBeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'      )

     lowerCamelCase__       : List[Any]   					=						self.default_image_processor
     lowerCamelCase__       : List[Any]   					=						prepare_img()
     lowerCamelCase__       : str   					=						image_processor(images=UpperCAmelCase     ,	return_tensors='np'      )

     # forward pass
     lowerCamelCase__       : Tuple   					=						model(**UpperCAmelCase      )
     lowerCamelCase__       : str   					=						outputs.logits

     # verify the logits
     lowerCamelCase__       : List[str]   					=						(1, 1000)
     self.assertEqual(logits.shape     ,	UpperCAmelCase      )

     lowerCamelCase__       : str   					=						np.array([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]      )

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

     lowerCamelCase__       : Tuple   					=						281
     self.assertEqual(logits.argmax(-1      ).item()     ,	UpperCAmelCase      )







 @slow
 def 	A_      ( self    : Dict      )     ->      Tuple:
     lowerCamelCase__       : List[Any]   					=						FlaxBeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'      )

     lowerCamelCase__       : Union[str, Any]   					=						self.default_image_processor
     lowerCamelCase__       : str   					=						prepare_img()
     lowerCamelCase__       : List[str]   					=						image_processor(images=UpperCAmelCase     ,	return_tensors='np'      )

     # forward pass
     lowerCamelCase__       : Optional[int]   					=						model(**UpperCAmelCase      )
     lowerCamelCase__       : str   					=						outputs.logits

     # verify the logits
     lowerCamelCase__       : Any   					=						(1, 21841)
     self.assertEqual(logits.shape     ,	UpperCAmelCase      )

     lowerCamelCase__       : List[Any]   					=						np.array([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]      )

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

     lowerCamelCase__       : Union[str, Any]   					=						2396
     self.assertEqual(logits.argmax(-1      ).item()     ,	UpperCAmelCase      )












def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	str:
    lowerCamelCase__       : Dict   					=						''
    for ch in key:
        if ch == " " or ch not in key_no_dups and ch.isalpha():
            key_no_dups += ch
    return key_no_dups


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	dict[str, str]:
    lowerCamelCase__       : List[str]   					=						[chr(i + 65					) for i in range(26					)]
    # Remove duplicate characters from key
    lowerCamelCase__       : Any   					=						remove_duplicates(key.upper()					)
    lowerCamelCase__       : List[Any]   					=						len(_UpperCAmelCase					)
    # First fill cipher with key characters
    lowerCamelCase__       : Optional[int]   					=						{alphabet[i]: char for i, char in enumerate(_UpperCAmelCase					)}
    # Then map remaining characters in alphabet to
    # the alphabet from the beginning
    for i in range(len(_UpperCAmelCase					)		,      26					):
        lowerCamelCase__       : Any   					=						alphabet[i - offset]
        # Ensure we are not mapping letters to letters previously mapped
        while char in key:
            offset -= 1
            lowerCamelCase__       : Union[str, Any]   					=						alphabet[i - offset]
        lowerCamelCase__       : int   					=						char
    return cipher_alphabet


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	str:
    return "".join(cipher_map.get(_UpperCAmelCase		,      _UpperCAmelCase					) for ch in message.upper()					)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	str:
    lowerCamelCase__       : List[str]   					=						{v: k for k, v in cipher_map.items()}
    return "".join(rev_cipher_map.get(_UpperCAmelCase		,      _UpperCAmelCase					) for ch in message.upper()					)


def 			SCREAMING_SNAKE_CASE (			)     ->	None:
    lowerCamelCase__       : str   					=						input('Enter message to encode or decode: '					).strip()
    lowerCamelCase__       : Union[str, Any]   					=						input('Enter keyword: '					).strip()
    lowerCamelCase__       : Dict   					=						input('Encipher or decipher? E/D:'					).strip()[0].lower()
    try:
        lowerCamelCase__       : Dict   					=						{'e': encipher, 'd': decipher}[option]
    except KeyError:
        raise KeyError('invalid input option'					)
    lowerCamelCase__       : int   					=						create_cipher_map(_UpperCAmelCase					)
    print(func(_UpperCAmelCase		,      _UpperCAmelCase					)					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()





