






"""simple docstring"""




from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFResNetForImageClassification, TFResNetModel
 from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor






class 			UpperCamelCase     :


 def __init__(	self						:		List[Any]				,		UpperCAmelCase__						:		str				,		UpperCAmelCase__						:		int=3				,		UpperCAmelCase__						:		Any=32				,		UpperCAmelCase__						:		Dict=3				,		UpperCAmelCase__						:		int=10				,		UpperCAmelCase__						:		Optional[Any]=[10, 20, 30, 40]				,		UpperCAmelCase__						:		str=[1, 1, 2, 1]				,		UpperCAmelCase__						:		str=True				,		UpperCAmelCase__						:		Dict=True				,		UpperCAmelCase__						:		int="relu"				,		UpperCAmelCase__						:		Any=3				,		UpperCAmelCase__						:		str=None				,		)   ->			List[Any]:
   _a    :			int			 =      parent
   _a    :			Any			 =      batch_size
   _a    :			Optional[Any]			 =      image_size
   _a    :			List[Any]			 =      num_channels
   _a    :			Optional[int]			 =      embeddings_size
   _a    :			Tuple			 =      hidden_sizes
   _a    :			str			 =      depths
   _a    :			int			 =      is_training
   _a    :			List[str]			 =      use_labels
   _a    :			Union[str, Any]			 =      hidden_act
   _a    :			Optional[Any]			 =      num_labels
   _a    :			str			 =      scope
   _a    :			str			 =      len(snake_case__				)


 def _lowercase      (	self						:		Optional[int]				)   ->			str:
   _a    :			List[str]			 =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

   _a    :			Union[str, Any]			 =      None
   if self.use_labels:
     _a    :			Any			 =      ids_tensor([self.batch_size]				,		self.num_labels				)

   _a    :			Optional[Any]			 =      self.get_config()

   return config, pixel_values, labels


 def _lowercase      (	self						:		Dict				)   ->			List[Any]:
   return ResNetConfig(
       num_channels=self.num_channels				,		embeddings_size=self.embeddings_size				,		hidden_sizes=self.hidden_sizes				,		depths=self.depths				,		hidden_act=self.hidden_act				,		num_labels=self.num_labels				,		image_size=self.image_size				,		)


 def _lowercase      (	self						:		List[Any]				,		UpperCAmelCase__						:		int				,		UpperCAmelCase__						:		Tuple				,		UpperCAmelCase__						:		int				)   ->			Dict:
   _a    :			List[str]			 =      TFResNetModel(config=snake_case__				)
   _a    :			List[str]			 =      model(snake_case__				)
   # expected last hidden states: B, C, H // 32, W // 32
   self.parent.assertEqual(
       result.last_hidden_state.shape				,		(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)				,		)


 def _lowercase      (	self						:		Optional[Any]				,		UpperCAmelCase__						:		List[str]				,		UpperCAmelCase__						:		Tuple				,		UpperCAmelCase__						:		Any				)   ->			Tuple:
   _a    :			Tuple			 =      self.num_labels
   _a    :			int			 =      TFResNetForImageClassification(snake_case__				)
   _a    :			Optional[int]			 =      model(snake_case__				,		labels=snake_case__				)
   self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.num_labels)				)







 def _lowercase      (	self						:		Dict				)   ->			List[Any]:
   _a    :			Optional[Any]			 =      self.prepare_config_and_inputs()
   _a    :			int			 =      config_and_inputs
   _a    :			Union[str, Any]			 =      {"pixel_values": pixel_values}
   return config, inputs_dict







@require_tf
class 			UpperCamelCase     (      A_						,	A_						,	unittest.TestCase ):
 UpperCamelCase   :			List[Any]     =   (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
 UpperCamelCase   :			Dict     =   (
     {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
     if is_tf_available()
     else {}
 )

 UpperCamelCase   :			List[Any]     =   False
 UpperCamelCase   :			Any     =   False
 UpperCamelCase   :			List[str]     =   False
 UpperCamelCase   :			Optional[int]     =   False
 UpperCamelCase   :			Optional[Any]     =   False


 def _lowercase      (	self						:		Optional[int]				)   ->			Optional[int]:
   _a    :			Union[str, Any]			 =      TFResNetModelTester(self				)
   _a    :			Dict			 =      ConfigTester(self				,		config_class=snake_case__				,		has_text_modality=snake_case__				)


 def _lowercase      (	self						:		int				)   ->			Optional[Any]:
   self.create_and_test_config_common_properties()
   self.config_tester.create_and_test_config_to_json_string()
   self.config_tester.create_and_test_config_to_json_file()
   self.config_tester.create_and_test_config_from_and_save_pretrained()
   self.config_tester.create_and_test_config_with_num_labels()
   self.config_tester.check_config_can_be_init_without_params()
   self.config_tester.check_config_arguments_init()


 def _lowercase      (	self						:		Optional[int]				)   ->			List[str]:
   return


 @unittest.skip(reason="""ResNet does not use inputs_embeds"""				)
 def _lowercase      (	self						:		str				)   ->			Optional[int]:
   pass


 @unittest.skip(reason="""ResNet does not support input and output embeddings"""				)
 def _lowercase      (	self						:		int				)   ->			Dict:
   pass


 def _lowercase      (	self						:		Tuple				)   ->			List[str]:
   _a    :			Optional[int]			 =      self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     _a    :			int			 =      model_class(snake_case__				)
     _a    :			Union[str, Any]			 =      inspect.signature(model.call				)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     _a    :			Union[str, Any]			 =      [*signature.parameters.keys()]

     _a    :			str			 =      ["pixel_values"]
     self.assertListEqual(arg_names[:1]				,		snake_case__				)


 def _lowercase      (	self						:		Union[str, Any]				)   ->			Dict:
   _a    :			Optional[Any]			 =      self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*snake_case__				)


 def _lowercase      (	self						:		Union[str, Any]				)   ->			int:


   def check_hidden_states_output(UpperCAmelCase__						:		int				,		UpperCAmelCase__						:		List[str]				,		UpperCAmelCase__						:		Optional[Any]				):
     _a    :			Optional[int]			 =      model_class(snake_case__				)
     _a    :			Any			 =      model(**self._prepare_for_class(snake_case__				,		snake_case__				)				)

     _a    :			Union[str, Any]			 =      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

     _a    :			List[Any]			 =      self.model_tester.num_stages
     self.assertEqual(len(snake_case__				)				,		expected_num_stages + 1				)

     # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
     self.assertListEqual(
         list(hidden_states[0].shape[-2:]				)				,		[self.model_tester.image_size // 4, self.model_tester.image_size // 4]				,		)

   _a    :			Tuple			 =      self.model_tester.prepare_config_and_inputs_for_common()
   _a    :			Optional[Any]			 =      ["basic", "bottleneck"]
   for model_class in self.all_model_classes:
     for layer_type in layers_type:
       _a    :			List[Any]			 =      layer_type
       _a    :			List[Any]			 =      True
       check_hidden_states_output(snake_case__				,		snake_case__				,		snake_case__				)

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       _a    :			List[str]			 =      True

       check_hidden_states_output(snake_case__				,		snake_case__				,		snake_case__				)


 def _lowercase      (	self						:		str				)   ->			Any:
   _a    :			str			 =      self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*snake_case__				)







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


   for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _a    :			str			 =      TFResNetModel.from_pretrained(snake_case__				)
     self.assertIsNotNone(snake_case__				)







def      lowerCAmelCase__	(	):



  '''simple docstring'''
  _a    :			List[Any]			 =      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
  return image






@require_tf
@require_vision
class 			UpperCamelCase     (      unittest.TestCase ):


 @cached_property
 def _lowercase      (	self						:		str				)   ->			Optional[int]:
   return (
       AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
       if is_vision_available()
       else None
   )







 @slow
 def _lowercase      (	self						:		List[str]				)   ->			List[str]:
   _a    :			Tuple			 =      TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)

   _a    :			str			 =      self.default_image_processor
   _a    :			Dict			 =      prepare_img()
   _a    :			List[str]			 =      image_processor(images=snake_case__				,		return_tensors="""tf"""				)

   # forward pass
   _a    :			List[str]			 =      model(**snake_case__				)

   # verify the logits
   _a    :			Any			 =      tf.TensorShape((1, 1000)				)
   self.assertEqual(outputs.logits.shape				,		snake_case__				)

   _a    :			str			 =      tf.constant([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]				)

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



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

import datasets


__lowerCamelCase				=			"""\
@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}
}
"""


__lowerCamelCase				=			"""\
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.
"""


__lowerCamelCase				=			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     UpperCAmelCase						(		datasets.Metric					):



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      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 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]       ,		snake_case__						:   str       ,		snake_case__						:   List[Any]					)						->      Optional[Any]:


          '''simple docstring'''


          snake_case			:  Optional[int]     							= 0.0
          for i, j in zip(snake_case__       ,		snake_case__					):
                 n_correct += 1.0 if math_equivalence.is_equiv(snake_case__       ,		snake_case__					) else 0.0
          snake_case			:  Optional[int]     							= n_correct / len(snake_case__					)
          return {
              "accuracy": accuracy,
          }





