





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     ):



      """simple docstring"""






      def __init__(			self					,    UpperCAmelCase					,    UpperCAmelCase=100					,    UpperCAmelCase=13					,    UpperCAmelCase=30					,    UpperCAmelCase=2					,    UpperCAmelCase=3					,    UpperCAmelCase=True					,    UpperCAmelCase=True					,    UpperCAmelCase=32					,    UpperCAmelCase=5					,    UpperCAmelCase=4					,    UpperCAmelCase=37					,    UpperCAmelCase="gelu"					,    UpperCAmelCase=0.1					,    UpperCAmelCase=0.1					,    UpperCAmelCase=10					,    UpperCAmelCase=0.02					,    UpperCAmelCase=3					,    )							-> Optional[int]:






        '''simple docstring'''






        __snake_case :					Optional[int]          =							parent
        __snake_case :					int          =							vocab_size
        __snake_case :					Union[str, Any]          =							batch_size
        __snake_case :					str          =							image_size
        __snake_case :					str          =							patch_size
        __snake_case :					Optional[Any]          =							num_channels
        __snake_case :					int          =							is_training
        __snake_case :					List[str]          =							use_labels
        __snake_case :					Any          =							hidden_size
        __snake_case :					str          =							num_hidden_layers
        __snake_case :					Dict          =							num_attention_heads
        __snake_case :					str          =							intermediate_size
        __snake_case :					List[Any]          =							hidden_act
        __snake_case :					Tuple          =							hidden_dropout_prob
        __snake_case :					List[str]          =							attention_probs_dropout_prob
        __snake_case :					Tuple          =							type_sequence_label_size
        __snake_case :					int          =							initializer_range

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







      def     UpperCAmelCase		(			self )							-> List[str]:






        '''simple docstring'''






        __snake_case :					Union[str, Any]          =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

        __snake_case :					Union[str, 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=lowerCAmelCase__					,    initializer_range=self.initializer_range					,    )

        return config, pixel_values, labels







      def     UpperCAmelCase		(			self					,    UpperCAmelCase					,    UpperCAmelCase					,    UpperCAmelCase )							-> Any:






        '''simple docstring'''






        __snake_case :					Optional[Any]          =							FlaxBeitModel(config=lowerCAmelCase__ )
        __snake_case :					Optional[int]          =							model(lowerCAmelCase__ )
        self.parent.assertEqual(result.last_hidden_state.shape					,    (self.batch_size, self.seq_length, self.hidden_size) )







      def     UpperCAmelCase		(			self					,    UpperCAmelCase					,    UpperCAmelCase					,    UpperCAmelCase )							-> List[str]:






        '''simple docstring'''






        __snake_case :					Optional[Any]          =							FlaxBeitForMaskedImageModeling(config=lowerCAmelCase__ )
        __snake_case :					List[str]          =							model(lowerCAmelCase__ )
        self.parent.assertEqual(result.logits.shape					,    (self.batch_size, self.seq_length - 1, self.vocab_size) )







      def     UpperCAmelCase		(			self					,    UpperCAmelCase					,    UpperCAmelCase					,    UpperCAmelCase )							-> Any:






        '''simple docstring'''






        __snake_case :					str          =							self.type_sequence_label_size
        __snake_case :					List[str]          =							FlaxBeitForImageClassification(config=lowerCAmelCase__ )
        __snake_case :					Optional[Any]          =							model(lowerCAmelCase__ )
        self.parent.assertEqual(result.logits.shape					,    (self.batch_size, self.type_sequence_label_size) )

        # test greyscale images
        __snake_case :					List[str]          =							1
        __snake_case :					Dict          =							FlaxBeitForImageClassification(lowerCAmelCase__ )

        __snake_case :					Tuple          =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
        __snake_case :					Union[str, Any]          =							model(lowerCAmelCase__ )







      def     UpperCAmelCase		(			self )							-> Dict:






        '''simple docstring'''






        __snake_case :					int          =							self.prepare_config_and_inputs()
        (
            (
            __snake_case							
        )     , (
            __snake_case							
        )     , (
            __snake_case							
        )     , 							
        ) :					Optional[int]          =							config_and_inputs
        __snake_case :					Optional[Any]          =							{"pixel_values": pixel_values}
        return config, inputs_dict




@require_flax
class 					_lowerCamelCase			(     a			, unittest.TestCase     ):



      """simple docstring"""






      UpperCAmelCase_    :   Optional[int]      =(
          (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
      )







      def     UpperCAmelCase		(			self )							-> None:






        '''simple docstring'''






        __snake_case :					Tuple          =							FlaxBeitModelTester(self )
        __snake_case :					int          =							ConfigTester(self					,    config_class=lowerCAmelCase__					,    has_text_modality=lowerCAmelCase__					,    hidden_size=37 )







      def     UpperCAmelCase		(			self )							-> List[str]:






        '''simple docstring'''






        self.config_tester.run_common_tests()







      def     UpperCAmelCase		(			self )							-> str:






        '''simple docstring'''






        __snake_case     , __snake_case :					Union[str, Any]          =							self.model_tester.prepare_config_and_inputs_for_common()

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

          __snake_case :					List[Any]          =							["pixel_values"]
          self.assertListEqual(arg_names[:1]					,    lowerCAmelCase__ )







      def     UpperCAmelCase		(			self )							-> Optional[Any]:






        '''simple docstring'''






        __snake_case     , __snake_case :					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__ ):
            __snake_case :					Union[str, Any]          =							self._prepare_for_class(lowerCAmelCase__					,    lowerCAmelCase__ )
            __snake_case :					Optional[Any]          =							model_class(lowerCAmelCase__ )

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

            with self.subTest("JIT Enabled" ):
              __snake_case :					List[str]          =							model_jitted(**lowerCAmelCase__ ).to_tuple()

            with self.subTest("JIT Disabled" ):
              with jax.disable_jit():
                __snake_case :					str          =							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:






        '''simple docstring'''






        __snake_case :					int          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase__ )







      def     UpperCAmelCase		(			self )							-> List[Any]:






        '''simple docstring'''






        __snake_case :					Optional[Any]          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__ )







      def     UpperCAmelCase		(			self )							-> Tuple:






        '''simple docstring'''






        __snake_case :					str          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__ )







      @slow
      def     UpperCAmelCase		(			self )							-> int:






        '''simple docstring'''






        for model_class_name in self.all_model_classes:
          __snake_case :					List[str]          =							model_class_name.from_pretrained("microsoft/beit-base-patch16-224" )
          __snake_case :					List[Any]          =							model(np.ones((1, 3, 224, 224) ) )
          self.assertIsNotNone(lowerCAmelCase__ )







def    lowerCAmelCase__(						)			->     Optional[Any]:
  __snake_case :					Optional[Any]          =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
  return image




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



      """simple docstring"""






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






        '''simple docstring'''






        return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224" ) if is_vision_available() else None







      @slow
      def     UpperCAmelCase		(			self )							-> Dict:






        '''simple docstring'''






        __snake_case :					List[Any]          =							FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k" )

        __snake_case :					Any          =							self.default_image_processor
        __snake_case :					Optional[int]          =							prepare_img()
        __snake_case :					str          =							image_processor(images=lowerCAmelCase__					,    return_tensors="np" ).pixel_values

        # prepare bool_masked_pos
        __snake_case :					int          =							np.ones((1, 196)					,    dtype=lowerCAmelCase__ )

        # forward pass
        __snake_case :					Any          =							model(pixel_values=lowerCAmelCase__					,    bool_masked_pos=lowerCAmelCase__ )
        __snake_case :					Tuple          =							outputs.logits

        # verify the logits
        __snake_case :					int          =							(1, 196, 8192)
        self.assertEqual(logits.shape					,    lowerCAmelCase__ )

        __snake_case :					List[Any]          =							np.array(
            [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]] )

        self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]					,    lowerCAmelCase__					,    atol=1E-2 ) )







      @slow
      def     UpperCAmelCase		(			self )							-> List[Any]:






        '''simple docstring'''






        __snake_case :					int          =							FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224" )

        __snake_case :					Optional[Any]          =							self.default_image_processor
        __snake_case :					Optional[int]          =							prepare_img()
        __snake_case :					int          =							image_processor(images=lowerCAmelCase__					,    return_tensors="np" )

        # forward pass
        __snake_case :					Optional[int]          =							model(**lowerCAmelCase__ )
        __snake_case :					List[str]          =							outputs.logits

        # verify the logits
        __snake_case :					List[Any]          =							(1, 1000)
        self.assertEqual(logits.shape					,    lowerCAmelCase__ )

        __snake_case :					Optional[int]          =							np.array([-1.2_385, -1.0_987, -1.0_108] )

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

        __snake_case :					str          =							281
        self.assertEqual(logits.argmax(-1 ).item()					,    lowerCAmelCase__ )







      @slow
      def     UpperCAmelCase		(			self )							-> Tuple:






        '''simple docstring'''






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

        __snake_case :					Optional[Any]          =							self.default_image_processor
        __snake_case :					Dict          =							prepare_img()
        __snake_case :					Union[str, Any]          =							image_processor(images=lowerCAmelCase__					,    return_tensors="np" )

        # forward pass
        __snake_case :					int          =							model(**lowerCAmelCase__ )
        __snake_case :					List[str]          =							outputs.logits

        # verify the logits
        __snake_case :					Dict          =							(1, 21841)
        self.assertEqual(logits.shape					,    lowerCAmelCase__ )

        __snake_case :					Optional[int]          =							np.array([1.6_881, -0.2_787, 0.5_901] )

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

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








'''simple docstring'''

import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


lowercase__		:     str			=							'\\n@article{hendrycksmath2021,\n  title={Measuring Mathematical Problem Solving With the MATH Dataset},\n  author={Dan Hendrycks\n    and Collin Burns\n    and Saurav Kadavath\n    and Akul Arora\n    and Steven Basart\n    and Eric Tang\n    and Dawn Song\n    and Jacob Steinhardt},\n  journal={arXiv preprint arXiv:2103.03874},\n  year={2021}\n}\n'


lowercase__		:     str			=							'\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.\n'


lowercase__		:     Dict			=							R'\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n    predictions: list of predictions to score. Each prediction\n        is a string that contains natural language and LaTex.\n    references: list of reference for each prediction. Each\n        reference is a string that contains natural language\n        and LaTex.\nReturns:\n    accuracy: accuracy after canonicalizing inputs\n        (e.g., converting "1/2" to "\\frac{1}{2}")\n\nExamples:\n    >>> metric = datasets.load_metric("competition_math")\n    >>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])\n    >>> print(results)\n    {\'accuracy\': 1.0}\n'
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION		,		_KWARGS_DESCRIPTION					)
class 							__lowerCAmelCase	(       datasets.Metric					):






       """simple docstring"""





       def snake_case__		(				self  :						Dict	)       ->	Any:

            '''simple docstring'''




            return datasets.MetricInfo(
                description=_DESCRIPTION			,					citation=_CITATION			,					inputs_description=_KWARGS_DESCRIPTION			,					features=datasets.Features(
                    {
                        '''predictions''': datasets.Value('''string'''	),
                        '''references''': datasets.Value('''string'''	),
                    }	)			,					homepage='''https://github.com/hendrycks/math'''			,					codebase_urls=['''https://github.com/hendrycks/math''']			,					)







       def snake_case__		(				self  :						List[Any]			,					lowerCAmelCase__  :						Dict			,					lowerCAmelCase__  :						int	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase							       =			0.0
            for i, j in zip(lowerCAmelCase__			,					lowerCAmelCase__	):
                 n_correct += 1.0 if math_equivalence.is_equiv(lowerCAmelCase__			,					lowerCAmelCase__	) else 0.0
            _UpperCamelCase							       =			n_correct / len(lowerCAmelCase__	)
            return {
                "accuracy": accuracy,
            }



