





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

import datasets


snake_case								=							"""\
@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}
}
"""


snake_case								=							"""\
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.
"""


snake_case								=							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						):







       """simple docstring"""





       def  __UpperCAmelCase			(     self						:						List[Any]	)    ->       Optional[int]:
         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  __UpperCAmelCase			(     self						:						Optional[Any]    ,__A						:						List[Any]    ,__A						:						List[Any]	)    ->       str:
         _lowercase						    =			0.0
         for i, j in zip(__A    ,__A	):
           n_correct += 1.0 if math_equivalence.is_equiv(__A    ,__A	) else 0.0
         _lowercase						    =			n_correct / len(__A	)
         return {
             "accuracy": accuracy,
         }
'''simple docstring'''


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel





def    lowerCAmelCase       (		UpperCamelCase__					: BertModel     ,       UpperCamelCase__					: str     ,       UpperCamelCase__					: str							):


	"""simple docstring"""
	__UpperCAmelCase      					=      ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

	__UpperCAmelCase      					=      (
	    ('''layer.''', '''layer_'''),
	    ('''word_embeddings.weight''', '''word_embeddings'''),
	    ('''position_embeddings.weight''', '''position_embeddings'''),
	    ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
	    ('''.''', '''/'''),
	    ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
	    ('''LayerNorm/bias''', '''LayerNorm/beta'''),
	    ('''weight''', '''kernel'''),
	)

	if not os.path.isdir(UpperCamelCase__							):
		os.makedirs(UpperCamelCase__							)

	__UpperCAmelCase      					=      model.state_dict()

	def to_tf_var_name(UpperCamelCase__					: str							):
		for patt, repl in iter(UpperCamelCase__							):
			__UpperCAmelCase      					=      name.replace(UpperCamelCase__     ,       UpperCamelCase__							)
		return f"""bert/{name}"""

	def create_tf_var(UpperCamelCase__					: np.ndarray     ,       UpperCamelCase__					: str     ,       UpperCamelCase__					: tf.Session							):
		__UpperCAmelCase      					=      tf.dtypes.as_dtype(tensor.dtype							)
		__UpperCAmelCase      					=      tf.get_variable(dtype=UpperCamelCase__     ,       shape=tensor.shape     ,       name=UpperCamelCase__     ,       initializer=tf.zeros_initializer()							)
		session.run(tf.variables_initializer([tf_var]							)							)
		session.run(UpperCamelCase__							)
		return tf_var

	tf.reset_default_graph()
	with tf.Session() as session:
		for var_name in state_dict:
			__UpperCAmelCase      					=      to_tf_var_name(UpperCamelCase__							)
			__UpperCAmelCase      					=      state_dict[var_name].numpy()
			if any(x in var_name for x in tensors_to_transpose							):
				__UpperCAmelCase      					=      torch_tensor.T
			__UpperCAmelCase      					=      create_tf_var(tensor=UpperCamelCase__     ,       name=UpperCamelCase__     ,       session=UpperCamelCase__							)
			tf.keras.backend.set_value(UpperCamelCase__     ,       UpperCamelCase__							)
			__UpperCAmelCase      					=      session.run(UpperCamelCase__							)
			print(f"""Successfully created {tf_name}: {np.allclose(UpperCamelCase__     ,       UpperCamelCase__							)}"""							)

		__UpperCAmelCase      					=      tf.train.Saver(tf.trainable_variables()							)
		saver.save(UpperCamelCase__     ,       os.path.join(UpperCamelCase__     ,       model_name.replace('''-'''     ,       '''_'''							) + '''.ckpt'''							)							)





def    lowerCAmelCase       (		UpperCamelCase__					: List[str]=None							):


	"""simple docstring"""
	__UpperCAmelCase      					=      argparse.ArgumentParser()
	parser.add_argument('''--model_name'''     ,       type=UpperCamelCase__     ,       required=UpperCamelCase__     ,       help='''model name e.g. bert-base-uncased'''							)
	parser.add_argument(
	    '''--cache_dir'''     ,       type=UpperCamelCase__     ,       default=UpperCamelCase__     ,       required=UpperCamelCase__     ,       help='''Directory containing pytorch model'''							)
	parser.add_argument('''--pytorch_model_path'''     ,       type=UpperCamelCase__     ,       required=UpperCamelCase__     ,       help='''/path/to/<pytorch-model-name>.bin'''							)
	parser.add_argument('''--tf_cache_dir'''     ,       type=UpperCamelCase__     ,       required=UpperCamelCase__     ,       help='''Directory in which to save tensorflow model'''							)
	__UpperCAmelCase      					=      parser.parse_args(UpperCamelCase__							)

	__UpperCAmelCase      					=      BertModel.from_pretrained(
	    pretrained_model_name_or_path=args.model_name     ,       state_dict=torch.load(args.pytorch_model_path							)     ,       cache_dir=args.cache_dir     ,       )

	convert_pytorch_checkpoint_to_tf(model=UpperCamelCase__     ,       ckpt_dir=args.tf_cache_dir     ,       model_name=args.model_name							)


if __name__ == "__main__":
					main()
