
import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import AlignProcessor, EfficientNetImageProcessor






@require_vision
class     UpperCAmelCase						(		unittest.TestCase					):



   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict					)						->      Dict:


          '''simple docstring'''


          snake_case			:  int     							= tempfile.mkdtemp()

          snake_case			:  Optional[int]     							= [
              "[UNK]",
              "[CLS]",
              "[SEP]",
              "[PAD]",
              "[MASK]",
              "want",
              "##want",
              "##ed",
              "wa",
              "un",
              "runn",
              "##ing",
              ",",
              "low",
              "lowest",
          ]
          snake_case			:  Any     							= os.path.join(self.tmpdirname       ,		VOCAB_FILES_NAMES["vocab_file"]					)
          with open(self.vocab_file       ,		"w"       ,		encoding="utf-8"					) as vocab_writer:
                 vocab_writer.write("".join([x + "\n" for x in vocab_tokens]					)					)

          snake_case			:  int     							= {
              "do_resize": True,
              "size": 20,
              "do_center_crop": True,
              "crop_size": 18,
              "do_normalize": True,
              "image_mean": [0.48145466, 0.4578275, 0.40821073],
              "image_std": [0.26862954, 0.26130258, 0.27577711],
          }
          snake_case			:  int     							= os.path.join(self.tmpdirname       ,		snake_case__					)
          with open(self.image_processor_file       ,		"w"       ,		encoding="utf-8"					) as fp:
                 json.dump(snake_case__       ,		snake_case__					)



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


          '''simple docstring'''


          return BertTokenizer.from_pretrained(self.tmpdirname       ,		**snake_case__					)



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


          '''simple docstring'''


          return BertTokenizerFast.from_pretrained(self.tmpdirname       ,		**snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]       ,		**snake_case__						:   List[str]					)						->      Any:


          '''simple docstring'''


          return EfficientNetImageProcessor.from_pretrained(self.tmpdirname       ,		**snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      Union[str, Any]:


          '''simple docstring'''


          shutil.rmtree(self.tmpdirname					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      Any:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= [np.random.randint(2_55       ,		size=(3, 30, 4_00)       ,		dtype=np.uinta					)]
          snake_case			:  Optional[int]     							= [Image.fromarray(np.moveaxis(snake_case__       ,		0       ,		-1					)					) for x in image_inputs]
          return image_inputs



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


          '''simple docstring'''


          snake_case			:  int     							= self.get_tokenizer()
          snake_case			:  Optional[Any]     							= self.get_rust_tokenizer()
          snake_case			:  Dict     							= self.get_image_processor()

          snake_case			:  Tuple     							= AlignProcessor(tokenizer=snake_case__       ,		image_processor=snake_case__					)
          processor_slow.save_pretrained(self.tmpdirname					)
          snake_case			:  Union[str, Any]     							= AlignProcessor.from_pretrained(self.tmpdirname       ,		use_fast=snake_case__					)

          snake_case			:  List[str]     							= AlignProcessor(tokenizer=snake_case__       ,		image_processor=snake_case__					)
          processor_fast.save_pretrained(self.tmpdirname					)
          snake_case			:  int     							= AlignProcessor.from_pretrained(self.tmpdirname					)

          self.assertEqual(processor_slow.tokenizer.get_vocab()       ,		tokenizer_slow.get_vocab()					)
          self.assertEqual(processor_fast.tokenizer.get_vocab()       ,		tokenizer_fast.get_vocab()					)
          self.assertEqual(tokenizer_slow.get_vocab()       ,		tokenizer_fast.get_vocab()					)
          self.assertIsInstance(processor_slow.tokenizer       ,		snake_case__					)
          self.assertIsInstance(processor_fast.tokenizer       ,		snake_case__					)

          self.assertEqual(processor_slow.image_processor.to_json_string()       ,		image_processor.to_json_string()					)
          self.assertEqual(processor_fast.image_processor.to_json_string()       ,		image_processor.to_json_string()					)
          self.assertIsInstance(processor_slow.image_processor       ,		snake_case__					)
          self.assertIsInstance(processor_fast.image_processor       ,		snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict					)						->      Optional[Any]:


          '''simple docstring'''


          snake_case			:  List[str]     							= AlignProcessor(tokenizer=self.get_tokenizer()       ,		image_processor=self.get_image_processor()					)
          processor.save_pretrained(self.tmpdirname					)

          snake_case			:  List[Any]     							= self.get_tokenizer(bos_token="(BOS)"       ,		eos_token="(EOS)"					)
          snake_case			:  int     							= self.get_image_processor(do_normalize=snake_case__       ,		padding_value=1.0					)

          snake_case			:  Union[str, Any]     							= AlignProcessor.from_pretrained(
              self.tmpdirname       ,		bos_token="(BOS)"       ,		eos_token="(EOS)"       ,		do_normalize=snake_case__       ,		padding_value=1.0					)

          self.assertEqual(processor.tokenizer.get_vocab()       ,		tokenizer_add_kwargs.get_vocab()					)
          self.assertIsInstance(processor.tokenizer       ,		snake_case__					)

          self.assertEqual(processor.image_processor.to_json_string()       ,		image_processor_add_kwargs.to_json_string()					)
          self.assertIsInstance(processor.image_processor       ,		snake_case__					)



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


          '''simple docstring'''


          snake_case			:  Any     							= self.get_image_processor()
          snake_case			:  Tuple     							= self.get_tokenizer()

          snake_case			:  Any     							= AlignProcessor(tokenizer=snake_case__       ,		image_processor=snake_case__					)

          snake_case			:  List[Any]     							= self.prepare_image_inputs()

          snake_case			:  Optional[Any]     							= image_processor(snake_case__       ,		return_tensors="np"					)
          snake_case			:  str     							= processor(images=snake_case__       ,		return_tensors="np"					)

          for key in input_image_proc.keys():
                 self.assertAlmostEqual(input_image_proc[key].sum()       ,		input_processor[key].sum()       ,		delta=1e-2					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any					)						->      int:


          '''simple docstring'''


          snake_case			:  Tuple     							= self.get_image_processor()
          snake_case			:  List[Any]     							= self.get_tokenizer()

          snake_case			:  int     							= AlignProcessor(tokenizer=snake_case__       ,		image_processor=snake_case__					)

          snake_case			:  Any     							= "lower newer"

          snake_case			:  List[Any]     							= processor(text=snake_case__					)

          snake_case			:  Any     							= tokenizer(snake_case__       ,		padding="max_length"       ,		max_length=64					)

          for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]       ,		encoded_processor[key]					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   str					)						->      Any:


          '''simple docstring'''


          snake_case			:  Dict     							= self.get_image_processor()
          snake_case			:  List[Any]     							= self.get_tokenizer()

          snake_case			:  str     							= AlignProcessor(tokenizer=snake_case__       ,		image_processor=snake_case__					)

          snake_case			:  int     							= "lower newer"
          snake_case			:  Tuple     							= self.prepare_image_inputs()

          snake_case			:  Optional[Any]     							= processor(text=snake_case__       ,		images=snake_case__					)

          self.assertListEqual(list(inputs.keys()					)       ,		["input_ids", "token_type_ids", "attention_mask", "pixel_values"]					)

          # test if it raises when no input is passed
          with pytest.raises(snake_case__					):
                 processor()



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


          '''simple docstring'''


          snake_case			:  List[str]     							= self.get_image_processor()
          snake_case			:  Dict     							= self.get_tokenizer()

          snake_case			:  Union[str, Any]     							= AlignProcessor(tokenizer=snake_case__       ,		image_processor=snake_case__					)

          snake_case			:  Optional[int]     							= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          snake_case			:  str     							= processor.batch_decode(snake_case__					)
          snake_case			:  Any     							= tokenizer.batch_decode(snake_case__					)

          self.assertListEqual(snake_case__       ,		snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict					)						->      Dict:


          '''simple docstring'''


          snake_case			:  str     							= self.get_image_processor()
          snake_case			:  Optional[Any]     							= self.get_tokenizer()

          snake_case			:  Optional[int]     							= AlignProcessor(tokenizer=snake_case__       ,		image_processor=snake_case__					)

          snake_case			:  Dict     							= "lower newer"
          snake_case			:  Tuple     							= self.prepare_image_inputs()

          snake_case			:  str     							= processor(text=snake_case__       ,		images=snake_case__					)

          self.assertListEqual(list(inputs.keys()					)       ,		processor.model_input_names					)







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,
          }





