



"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
					import numpy
					import tensorflow as tf

					from transformers.models.esm.modeling_tf_esm import (
					    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFEsmForMaskedLM,
					    TFEsmForSequenceClassification,
					    TFEsmForTokenClassification,
					    TFEsmModel,
					)
class 			_a :
			"""simple docstring"""



			def __init__(		self     :    str				,   __UpperCamelCase     :    Tuple				,   )->Dict:
							_UpperCAmelCase					=    parent
							_UpperCAmelCase					=    1_3
							_UpperCAmelCase					=    7
							_UpperCAmelCase					=    True
							_UpperCAmelCase					=    True
							_UpperCAmelCase					=    True
							_UpperCAmelCase					=    9_9
							_UpperCAmelCase					=    3_2
							_UpperCAmelCase					=    2
							_UpperCAmelCase					=    4
							_UpperCAmelCase					=    3_7
							_UpperCAmelCase					=    '''gelu'''
							_UpperCAmelCase					=    0.1
							_UpperCAmelCase					=    0.1
							_UpperCAmelCase					=    5_1_2
							_UpperCAmelCase					=    1_6
							_UpperCAmelCase					=    2
							_UpperCAmelCase					=    0.0_2
							_UpperCAmelCase					=    3
							_UpperCAmelCase					=    4
							_UpperCAmelCase					=    None



			def  lowercase__						(		self     :    Optional[int]   )->int:
							_UpperCAmelCase					=    ids_tensor([self.batch_size, self.seq_length]				,   self.vocab_size   )

							_UpperCAmelCase					=    None
							if self.use_input_mask:
											_UpperCAmelCase					=    random_attention_mask([self.batch_size, self.seq_length]   )

							_UpperCAmelCase					=    None
							_UpperCAmelCase					=    None
							_UpperCAmelCase					=    None
							if self.use_labels:
											_UpperCAmelCase					=    ids_tensor([self.batch_size]				,   self.type_sequence_label_size   )
											_UpperCAmelCase					=    ids_tensor([self.batch_size, self.seq_length]				,   self.num_labels   )
											_UpperCAmelCase					=    ids_tensor([self.batch_size]				,   self.num_choices   )

							_UpperCAmelCase					=    EsmConfig(
							    vocab_size=self.vocab_size				,   hidden_size=self.hidden_size				,   num_hidden_layers=self.num_hidden_layers				,   pad_token_id=1				,   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				,   max_position_embeddings=self.max_position_embeddings				,   type_vocab_size=self.type_vocab_size				,   initializer_range=self.initializer_range				,   )

							return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



			def  lowercase__						(		self     :    str   )->List[Any]:
							(
							    (
							    _UpperCAmelCase
							)		,						(
							    _UpperCAmelCase
							)		,						(
							    _UpperCAmelCase
							)		,						(
							    _UpperCAmelCase
							)		,						(
							    _UpperCAmelCase
							)		,						(
							    _UpperCAmelCase
							)		,						
							)					=    self.prepare_config_and_inputs()

							_UpperCAmelCase					=    True
							_UpperCAmelCase					=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
							_UpperCAmelCase					=    ids_tensor([self.batch_size, self.seq_length]				,   vocab_size=2   )

							return (
							    config,
							    input_ids,
							    input_mask,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    Optional[Any]				,   __UpperCamelCase     :    Any				,   __UpperCamelCase     :    List[Any]				,   __UpperCamelCase     :    Union[str, Any]				,   __UpperCamelCase     :    Dict				,   __UpperCamelCase     :    Tuple   )->Union[str, Any]:
							_UpperCAmelCase					=    TFEsmModel(config=__UpperCamelCase   )
							_UpperCAmelCase					=    {'''input_ids''': input_ids, '''attention_mask''': input_mask}
							_UpperCAmelCase					=    model(__UpperCamelCase   )

							_UpperCAmelCase					=    [input_ids, input_mask]
							_UpperCAmelCase					=    model(__UpperCamelCase   )

							_UpperCAmelCase					=    model(__UpperCamelCase   )

							self.parent.assertEqual(result.last_hidden_state.shape				,   (self.batch_size, self.seq_length, self.hidden_size)   )



			def  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    Any				,   __UpperCamelCase     :    Union[str, Any]				,   __UpperCamelCase     :    Optional[Any]				,   __UpperCamelCase     :    List[Any]				,   __UpperCamelCase     :    Tuple				,   __UpperCamelCase     :    Tuple				,   __UpperCamelCase     :    Tuple				,   __UpperCamelCase     :    Optional[int]				,   )->Optional[int]:
							_UpperCAmelCase					=    True

							_UpperCAmelCase					=    TFEsmModel(config=__UpperCamelCase   )
							_UpperCAmelCase					=    {
							    '''input_ids''': input_ids,
							    '''attention_mask''': input_mask,
							    '''encoder_hidden_states''': encoder_hidden_states,
							    '''encoder_attention_mask''': encoder_attention_mask,
							}
							_UpperCAmelCase					=    model(__UpperCamelCase   )

							_UpperCAmelCase					=    [input_ids, input_mask]
							_UpperCAmelCase					=    model(__UpperCamelCase				,   encoder_hidden_states=__UpperCamelCase   )

							# Also check the case where encoder outputs are not passed
							_UpperCAmelCase					=    model(__UpperCamelCase				,   attention_mask=__UpperCamelCase   )

							self.parent.assertEqual(result.last_hidden_state.shape				,   (self.batch_size, self.seq_length, self.hidden_size)   )



			def  lowercase__						(		self     :    str				,   __UpperCamelCase     :    List[Any]				,   __UpperCamelCase     :    Optional[int]				,   __UpperCamelCase     :    List[str]				,   __UpperCamelCase     :    Any				,   __UpperCamelCase     :    List[str]				,   __UpperCamelCase     :    Dict   )->Union[str, Any]:
							_UpperCAmelCase					=    TFEsmForMaskedLM(config=__UpperCamelCase   )
							_UpperCAmelCase					=    model([input_ids, input_mask]   )
							self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.seq_length, self.vocab_size)   )



			def  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    Union[str, Any]				,   __UpperCamelCase     :    Tuple				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    Dict				,   __UpperCamelCase     :    Union[str, Any]				,   __UpperCamelCase     :    List[Any]   )->List[Any]:
							_UpperCAmelCase					=    self.num_labels
							_UpperCAmelCase					=    TFEsmForTokenClassification(config=__UpperCamelCase   )
							_UpperCAmelCase					=    {'''input_ids''': input_ids, '''attention_mask''': input_mask}
							_UpperCAmelCase					=    model(__UpperCamelCase   )
							self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.seq_length, self.num_labels)   )



			def  lowercase__						(		self     :    Tuple   )->int:
							_UpperCAmelCase					=    self.prepare_config_and_inputs()
							(
							    (
							    _UpperCAmelCase
							)		,						(
							    _UpperCAmelCase
							)		,						(
							    _UpperCAmelCase
							)		,						(
							    _UpperCAmelCase
							)		,						(
							    _UpperCAmelCase
							)		,						(
							    _UpperCAmelCase
							)		,						
							)					=    config_and_inputs
							_UpperCAmelCase					=    {'''input_ids''': input_ids, '''attention_mask''': input_mask}
							return config, inputs_dict



@require_tf
class 			_a (					lowerCAmelCase			,							lowerCAmelCase			,							unittest.TestCase):
			"""simple docstring"""
			UpperCamelCase__			      =	(
			    (
			        TFEsmModel,
			        TFEsmForMaskedLM,
			        TFEsmForSequenceClassification,
			        TFEsmForTokenClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			UpperCamelCase__			      =	(
			    {
			        """feature-extraction""": TFEsmModel,
			        """fill-mask""": TFEsmForMaskedLM,
			        """text-classification""": TFEsmForSequenceClassification,
			        """token-classification""": TFEsmForTokenClassification,
			        """zero-shot""": TFEsmForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			UpperCamelCase__			      =	False
			UpperCamelCase__			      =	False



			def  lowercase__						(		self     :    List[Any]   )->Tuple:
							_UpperCAmelCase					=    TFEsmModelTester(self   )
							_UpperCAmelCase					=    ConfigTester(self				,   config_class=__UpperCamelCase				,   hidden_size=3_7   )



			def  lowercase__						(		self     :    str   )->str:
							self.config_tester.run_common_tests()



			def  lowercase__						(		self     :    Union[str, Any]   )->Tuple:
							_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__UpperCamelCase   )



			def  lowercase__						(		self     :    Any   )->Union[str, Any]:
							_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(*__UpperCamelCase   )



			def  lowercase__						(		self     :    Union[str, Any]   )->List[Any]:
							_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase   )



			def  lowercase__						(		self     :    str   )->Tuple:
							_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase   )



			@slow
			def  lowercase__						(		self     :    Any   )->Optional[Any]:
							for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_UpperCAmelCase					=    TFEsmModel.from_pretrained(__UpperCamelCase   )
											self.assertIsNotNone(__UpperCamelCase   )



			@unittest.skip('''Protein models do not support embedding resizing.'''   )
			def  lowercase__						(		self     :    Any   )->Optional[Any]:
							pass



			@unittest.skip('''Protein models do not support embedding resizing.'''   )
			def  lowercase__						(		self     :    Optional[Any]   )->List[str]:
							pass



			def  lowercase__						(		self     :    Optional[int]   )->Tuple:
							_UpperCAmelCase		,						_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											_UpperCAmelCase					=    model_class(__UpperCamelCase   )
											assert isinstance(model.get_input_embeddings()				,   tf.keras.layers.Layer   )
											if model_class is TFEsmForMaskedLM:
															# Output embedding test differs from the main test because they're a matrix, not a layer
															_UpperCAmelCase					=    model.get_bias()
															assert isinstance(__UpperCamelCase				,   __UpperCamelCase   )
															for k, v in name.items():
																			assert isinstance(__UpperCamelCase				,   tf.Variable   )
											else:
															_UpperCAmelCase					=    model.get_output_embeddings()
															assert x is None
															_UpperCAmelCase					=    model.get_bias()
															assert name is None



@require_tf
class 			_a (					unittest.TestCase):
			"""simple docstring"""



			@slow
			def  lowercase__						(		self     :    Optional[int]   )->Optional[int]:
							_UpperCAmelCase					=    TFEsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''   )

							_UpperCAmelCase					=    tf.constant([[0, 1, 2, 3, 4, 5]]   )
							_UpperCAmelCase					=    model(__UpperCamelCase   )[0]
							_UpperCAmelCase					=    [1, 6, 3_3]
							self.assertEqual(list(output.numpy().shape   )				,   __UpperCamelCase   )
							# compare the actual values for a slice.
							_UpperCAmelCase					=    tf.constant(
							    [
							        [
							            [8.9_2_1_5_1_8, -1_0.5_8_9_8_1_4, -6.4_6_7_1_3_0_7],
							            [-6.3_9_6_7_1_5_6, -1_3.9_1_1_3_7_7, -1.1_2_1_1_9_1_5],
							            [-7.7_8_1_2_4_7, -1_3.9_5_1_5_5_7, -3.7_4_0_5_9_2],
							        ]
							    ]   )
							self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()				,   expected_slice.numpy()				,   atol=1e-2   )   )



			@slow
			def  lowercase__						(		self     :    Dict   )->int:
							_UpperCAmelCase					=    TFEsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D'''   )

							_UpperCAmelCase					=    tf.constant([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]]   )
							_UpperCAmelCase					=    model(__UpperCamelCase   )[0]
							# compare the actual values for a slice.
							_UpperCAmelCase					=    tf.constant(
							    [
							        [
							            [0.1_4_4_4_3_0_9_2, 0.5_4_1_2_5_3_2_7, 0.3_2_4_7_7_3_9],
							            [0.3_0_3_4_0_4_8_4, 0.0_0_5_2_6_6_7_6, 0.3_1_0_7_7_7_2_2],
							            [0.3_2_2_7_8_0_4_3, -0.2_4_9_8_7_0_9_6, 0.3_4_1_4_6_2_8],
							        ]
							    ]   )
							self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()				,   expected_slice.numpy()				,   atol=1e-4   )   )








"""simple docstring"""


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

import datasets


__A   :							Any										=   "\\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"


__A   :							Optional[Any]										=   "\\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"


__A   :							Tuple										=   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 			_a (					datasets.Metric):
			"""simple docstring"""



			def  lowercase__						(		self     :    List[Any]   )->Dict:
							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  lowercase__						(		self     :    int				,   __UpperCamelCase     :    List[str]				,   __UpperCamelCase     :    Union[str, Any]   )->Tuple:
							_UpperCAmelCase					=    0.0
							for i, j in zip(__UpperCamelCase				,   __UpperCamelCase   ):
											n_correct += 1.0 if math_equivalence.is_equiv(__UpperCamelCase				,   __UpperCamelCase   ) else 0.0
							_UpperCAmelCase					=    n_correct / len(__UpperCamelCase   )
							return {
							    "accuracy": accuracy,
							}



