





import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
     import jax

     from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel

class      A       (	unittest.TestCase						):



      def __init__(	self:						int		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						Optional[int]=13		,  _lowerCAmelCase:						List[Any]=30		,  _lowerCAmelCase:						Dict=2		,  _lowerCAmelCase:						Tuple=3		,  _lowerCAmelCase:						Optional[Any]=True		,  _lowerCAmelCase:						Dict=True		,  _lowerCAmelCase:						Optional[Any]=32		,  _lowerCAmelCase:						Union[str, Any]=5		,  _lowerCAmelCase:						Dict=4		,  _lowerCAmelCase:						Dict=37		,  _lowerCAmelCase:						List[Any]="gelu"		,  _lowerCAmelCase:						List[Any]=0.1		,  _lowerCAmelCase:						Tuple=0.1		,  _lowerCAmelCase:						int=10		,  _lowerCAmelCase:						int=0.02		,  )						->    List[str]:
         '''simple docstring'''

         UpperCAmelCase_											=parent
         UpperCAmelCase_											=batch_size
         UpperCAmelCase_											=image_size
         UpperCAmelCase_											=patch_size
         UpperCAmelCase_											=num_channels
         UpperCAmelCase_											=is_training
         UpperCAmelCase_											=use_labels
         UpperCAmelCase_											=hidden_size
         UpperCAmelCase_											=num_hidden_layers
         UpperCAmelCase_											=num_attention_heads
         UpperCAmelCase_											=intermediate_size
         UpperCAmelCase_											=hidden_act
         UpperCAmelCase_											=hidden_dropout_prob
         UpperCAmelCase_											=attention_probs_dropout_prob
         UpperCAmelCase_											=type_sequence_label_size
         UpperCAmelCase_											=initializer_range

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



      def 	lowerCAmelCase__    (	self:						Dict       )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_											=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

         UpperCAmelCase_											=ViTConfig(
             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



      def 	lowerCAmelCase__    (	self:						List[str]		,  _lowerCAmelCase:						Optional[int]		,  _lowerCAmelCase:						Any       )						->    List[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=FlaxViTModel(config=_lowerCAmelCase       )
         UpperCAmelCase_											=model(_lowerCAmelCase       )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         UpperCAmelCase_											=(self.image_size, self.image_size)
         UpperCAmelCase_											=(self.patch_size, self.patch_size)
         UpperCAmelCase_											=(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         self.parent.assertEqual(result.last_hidden_state.shape		,  (self.batch_size, num_patches + 1, self.hidden_size)       )



      def 	lowerCAmelCase__    (	self:						Any		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						int       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.type_sequence_label_size
         UpperCAmelCase_											=FlaxViTForImageClassification(config=_lowerCAmelCase       )
         UpperCAmelCase_											=model(_lowerCAmelCase       )
         self.parent.assertEqual(result.logits.shape		,  (self.batch_size, self.type_sequence_label_size)       )

         # test greyscale images
         UpperCAmelCase_											=1
         UpperCAmelCase_											=FlaxViTForImageClassification(_lowerCAmelCase       )

         UpperCAmelCase_											=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
         UpperCAmelCase_											=model(_lowerCAmelCase       )






      def 	lowerCAmelCase__    (	self:						Tuple       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_
         )						,       (
             UpperCAmelCase_
         )						,       
         )											=config_and_inputs
         UpperCAmelCase_											={"pixel_values": pixel_values}
         return config, inputs_dict

@require_flax
class      A       (	__lowercase		,      unittest.TestCase						):
      _snake_case							     =(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											=FlaxViTModelTester(self       )
         UpperCAmelCase_											=ConfigTester(self		,  config_class=_lowerCAmelCase		,  has_text_modality=_lowerCAmelCase		,  hidden_size=37       )



      def 	lowerCAmelCase__    (	self:						int       )						->    Any:
         '''simple docstring'''

         self.config_tester.run_common_tests()



      def 	lowerCAmelCase__    (	self:						int       )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_											=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Dict       )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_						,       UpperCAmelCase_											=self.model_tester.prepare_config_and_inputs_for_common()

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

            UpperCAmelCase_											=["pixel_values"]
            self.assertListEqual(arg_names[:1]		,  _lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_						,       UpperCAmelCase_											=self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__       ):
               UpperCAmelCase_											=self._prepare_for_class(_lowerCAmelCase		,  _lowerCAmelCase       )
               UpperCAmelCase_											=model_class(_lowerCAmelCase       )

               @jax.jit
               def model_jitted(_lowerCAmelCase:						Union[str, Any]		,  **_lowerCAmelCase:						Optional[int]       ):
                  return model(pixel_values=_lowerCAmelCase		,  **_lowerCAmelCase       )

               with self.subTest("JIT Enabled"       ):
                  UpperCAmelCase_											=model_jitted(**_lowerCAmelCase       ).to_tuple()

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






      @slow
      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    List[Any]:
         '''simple docstring'''

         for model_class_name in self.all_model_classes:
            UpperCAmelCase_											=model_class_name.from_pretrained("google/vit-base-patch16-224"       )
            UpperCAmelCase_											=model(np.ones((1, 3, 224, 224)       )       )
            self.assertIsNotNone(_lowerCAmelCase       )











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

import datasets


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


__lowercase  :   List[Any]						      ="""\
This metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.
It first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.
"""


__lowercase  :   str						      =R"""
Calculates accuracy after canonicalizing inputs.

Args:
    predictions: list of predictions to score. Each prediction
        is a string that contains natural language and LaTex.
    references: list of reference for each prediction. Each
        reference is a string that contains natural language
        and LaTex.
Returns:
    accuracy: accuracy after canonicalizing inputs
        (e.g., converting \"1/2\" to \"\\frac{1}{2}\")

Examples:
    >>> metric = datasets.load_metric(\"competition_math\")
    >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])
    >>> print(results)
    {'accuracy': 1.0}
"""

@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION		,      _KWARGS_DESCRIPTION						)
class      A       (	datasets.Metric						):



      def 	lowerCAmelCase__    (	self:						Any       )						->    Optional[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 	lowerCAmelCase__    (	self:						Any		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						Union[str, Any]       )						->    Dict:
         '''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,
         }




