





import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
  from ... import PreTrainedTokenizerBase, TensorType

_A					:				Dict				=			logging.get_logger(__name__)



class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		Optional[Any]				=	"""vision-encoder-decoder"""
    lowerCamelCase__    :		Union[str, Any]				=	True



    def __init__(       self   ,				**A_     ):





        '''simple docstring'''

        super().__init__(**A_     )
        if "encoder" not in kwargs or "decoder" not in kwargs:
            raise ValueError(
                f'''A configuraton of type {self.model_type} cannot be instantiated because '''
                f'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'''     )

        SCREAMING_SNAKE_CASE__											=			kwargs.pop('''encoder'''     )
        SCREAMING_SNAKE_CASE__											=			encoder_config.pop('''model_type'''     )
        SCREAMING_SNAKE_CASE__											=			kwargs.pop('''decoder'''     )
        SCREAMING_SNAKE_CASE__											=			decoder_config.pop('''model_type'''     )

        SCREAMING_SNAKE_CASE__											=			AutoConfig.for_model(A_   ,				**A_     )
        SCREAMING_SNAKE_CASE__											=			AutoConfig.for_model(A_   ,				**A_     )
        SCREAMING_SNAKE_CASE__											=			True



    @classmethod
    def  lowercase_					(       cls   ,				A_   ,				A_   ,				**A_     ):





        '''simple docstring'''

        logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''     )
        SCREAMING_SNAKE_CASE__											=			True
        SCREAMING_SNAKE_CASE__											=			True

        return cls(encoder=encoder_config.to_dict()   ,				decoder=decoder_config.to_dict()   ,				**A_     )




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			copy.deepcopy(self.__dict__     )
        SCREAMING_SNAKE_CASE__											=			self.encoder.to_dict()
        SCREAMING_SNAKE_CASE__											=			self.decoder.to_dict()
        SCREAMING_SNAKE_CASE__											=			self.__class__.model_type
        return output





class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		int				=	version.parse("""1.11"""    )



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return OrderedDict(
            [
                ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
            ]     )



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return 1E-4




    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return OrderedDict({'''last_hidden_state''': {0: '''batch''', 1: '''encoder_sequence'''}}     )





class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			OrderedDict()
        SCREAMING_SNAKE_CASE__											=			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
        SCREAMING_SNAKE_CASE__											=			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
        SCREAMING_SNAKE_CASE__											=			{0: '''batch''', 1: '''encoder_sequence'''}

        return common_inputs




    def  lowercase_					(       self   ,				A_   ,				A_ = -1   ,				A_ = -1   ,				A_ = False   ,				A_ = None   ,				):





        '''simple docstring'''

        import torch

        SCREAMING_SNAKE_CASE__											=			OrderedDict()

        SCREAMING_SNAKE_CASE__											=			super().generate_dummy_inputs(
            A_   ,				batch_size=A_   ,				seq_length=A_   ,				is_pair=A_   ,				framework=A_     )

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			dummy_input['''input_ids'''].shape
        SCREAMING_SNAKE_CASE__											=			(batch, encoder_sequence, self._config.encoder_hidden_size)
        SCREAMING_SNAKE_CASE__											=			dummy_input.pop('''input_ids'''     )
        SCREAMING_SNAKE_CASE__											=			dummy_input.pop('''attention_mask'''     )
        SCREAMING_SNAKE_CASE__											=			torch.zeros(A_     )

        return common_inputs





class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        return VisionEncoderDecoderEncoderOnnxConfig(A_     )




    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_ = "default"     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			encoder_config.hidden_size
        return VisionEncoderDecoderDecoderOnnxConfig(A_   ,				A_     )





"""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 					A_(unittest.TestCase      ):







			"""simple docstring"""



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

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

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

						_lowerCamelCase					:					List[Any]	       =    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=A				,   initializer_range=self.initializer_range				,   )

						return config, pixel_values, labels
			def    _lowerCAmelCase		(	self				,   A				,   A				,   A							):
						_lowerCamelCase					:					Tuple	       =    FlaxBeitModel(config=A							)
						_lowerCamelCase					:					List[str]	       =    model(A							)
						self.parent.assertEqual(result.last_hidden_state.shape				,   (self.batch_size, self.seq_length, self.hidden_size)							)
			def    _lowerCAmelCase		(	self				,   A				,   A				,   A							):
						_lowerCamelCase					:					List[str]	       =    FlaxBeitForMaskedImageModeling(config=A							)
						_lowerCamelCase					:					Any	       =    model(A							)
						self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.seq_length - 1, self.vocab_size)							)
			def    _lowerCAmelCase		(	self				,   A				,   A				,   A							):
						_lowerCamelCase					:					Optional[Any]	       =    self.type_sequence_label_size
						_lowerCamelCase					:					Tuple	       =    FlaxBeitForImageClassification(config=A							)
						_lowerCamelCase					:					Tuple	       =    model(A							)
						self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.type_sequence_label_size)							)

						# test greyscale images
						_lowerCamelCase					:					List[Any]	       =    1
						_lowerCamelCase					:					str	       =    FlaxBeitForImageClassification(A							)

						_lowerCamelCase					:					Optional[int]	       =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
						_lowerCamelCase					:					List[Any]	       =    model(A							)

			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					str	       =    self.prepare_config_and_inputs()
						(
						    (
						    _lowerCamelCase
						)       ,							(
						    _lowerCamelCase
						)       ,							(
						    _lowerCamelCase
						)       ,							
						)					:					Optional[Any]	       =    config_and_inputs
						_lowerCamelCase					:					int	       =    {'pixel_values': pixel_values}
						return config, inputs_dict







@require_flax
class 					A_(SCREAMING_SNAKE_CASE_   ,   unittest.TestCase      ):







			"""simple docstring"""



			a_       :			List[Any]												=			(
			    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
			)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Tuple	       =    FlaxBeitModelTester(self							)
						_lowerCamelCase					:					List[str]	       =    ConfigTester(self				,   config_class=A				,   has_text_modality=A				,   hidden_size=37							)
			def    _lowerCAmelCase		(	self							):
						self.config_tester.run_common_tests()
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase       ,							_lowerCamelCase					:					Tuple	       =    self.model_tester.prepare_config_and_inputs_for_common()

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

									_lowerCamelCase					:					List[Any]	       =    ['pixel_values']
									self.assertListEqual(arg_names[:1]				,   A							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase       ,							_lowerCamelCase					:					List[str]	       =    self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__							):
												_lowerCamelCase					:					Dict	       =    self._prepare_for_class(A				,   A							)
												_lowerCamelCase					:					str	       =    model_class(A							)

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

												with self.subTest('JIT Enabled'							):
															_lowerCamelCase					:					List[Any]	       =    model_jitted(**A							).to_tuple()

												with self.subTest('JIT Disabled'							):
															with jax.disable_jit():
																		_lowerCamelCase					:					Optional[int]	       =    model_jitted(**A							).to_tuple()

												self.assertEqual(len(A							)				,   len(A							)							)
												for jitted_output, output in zip(A				,   A							):
															self.assertEqual(jitted_output.shape				,   output.shape							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Tuple	       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					int	       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*A							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Optional[int]	       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A							)

			@slow
			def    _lowerCAmelCase		(	self							):
						for model_class_name in self.all_model_classes:
									_lowerCamelCase					:					str	       =    model_class_name.from_pretrained('microsoft/beit-base-patch16-224'							)
									_lowerCamelCase					:					Dict	       =    model(np.ones((1, 3, 224, 224)							)							)
									self.assertIsNotNone(A							)






def     UpperCAmelCase_					(				):




			'''simple docstring'''



			_lowerCamelCase					:					Union[str, Any]	       =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
			return image







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







			"""simple docstring"""



			@cached_property
			def    _lowerCAmelCase		(	self							):
						return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'							) if is_vision_available() else None
			@slow
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					List[str]	       =    FlaxBeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'							)

						_lowerCamelCase					:					int	       =    self.default_image_processor
						_lowerCamelCase					:					List[str]	       =    prepare_img()
						_lowerCamelCase					:					Any	       =    image_processor(images=A				,   return_tensors='np'							).pixel_values

						# prepare bool_masked_pos
						_lowerCamelCase					:					Optional[Any]	       =    np.ones((1, 196)				,   dtype=A							)

						# forward pass
						_lowerCamelCase					:					Optional[int]	       =    model(pixel_values=A				,   bool_masked_pos=A							)
						_lowerCamelCase					:					Optional[int]	       =    outputs.logits

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

						_lowerCamelCase					:					Dict	       =    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]				,   A				,   atol=1E-2							)							)
			@slow
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Any	       =    FlaxBeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'							)

						_lowerCamelCase					:					Tuple	       =    self.default_image_processor
						_lowerCamelCase					:					Dict	       =    prepare_img()
						_lowerCamelCase					:					Optional[Any]	       =    image_processor(images=A				,   return_tensors='np'							)

						# forward pass
						_lowerCamelCase					:					Tuple	       =    model(**A							)
						_lowerCamelCase					:					Optional[int]	       =    outputs.logits

						# verify the logits
						_lowerCamelCase					:					int	       =    (1, 1000)
						self.assertEqual(logits.shape				,   A							)

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

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

						_lowerCamelCase					:					str	       =    281
						self.assertEqual(logits.argmax(-1							).item()				,   A							)

			@slow
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Optional[Any]	       =    FlaxBeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'							)

						_lowerCamelCase					:					Any	       =    self.default_image_processor
						_lowerCamelCase					:					Union[str, Any]	       =    prepare_img()
						_lowerCamelCase					:					Tuple	       =    image_processor(images=A				,   return_tensors='np'							)

						# forward pass
						_lowerCamelCase					:					Union[str, Any]	       =    model(**A							)
						_lowerCamelCase					:					Optional[int]	       =    outputs.logits

						# verify the logits
						_lowerCamelCase					:					List[Any]	       =    (1, 2_1841)
						self.assertEqual(logits.shape				,   A							)

						_lowerCamelCase					:					Tuple	       =    np.array([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]							)

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

						_lowerCamelCase					:					List[Any]	       =    2396
						self.assertEqual(logits.argmax(-1							).item()				,   A							)





