



'''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 			UpperCamelCase_ :





	def __init__(  self		,		A		,		)			->			Optional[int]:
								UpperCAmelCase     :		Tuple							     =		parent
								UpperCAmelCase     :		Optional[Any]							     =		13
								UpperCAmelCase     :		int							     =		7
								UpperCAmelCase     :		Tuple							     =		True
								UpperCAmelCase     :		List[Any]							     =		True
								UpperCAmelCase     :		List[str]							     =		True
								UpperCAmelCase     :		List[Any]							     =		99
								UpperCAmelCase     :		Any							     =		32
								UpperCAmelCase     :		Any							     =		2
								UpperCAmelCase     :		Optional[int]							     =		4
								UpperCAmelCase     :		Dict							     =		37
								UpperCAmelCase     :		Dict							     =		"""gelu"""
								UpperCAmelCase     :		Union[str, Any]							     =		0.1
								UpperCAmelCase     :		Optional[int]							     =		0.1
								UpperCAmelCase     :		Dict							     =		512
								UpperCAmelCase     :		Any							     =		16
								UpperCAmelCase     :		Optional[int]							     =		2
								UpperCAmelCase     :		str							     =		0.0_2
								UpperCAmelCase     :		Optional[Any]							     =		3
								UpperCAmelCase     :		str							     =		4
								UpperCAmelCase     :		Optional[int]							     =		None





	def  _lowercase(  self    )			->			Dict:
								UpperCAmelCase     :		str							     =		ids_tensor([self.batch_size, self.seq_length]		,		self.vocab_size    )

								UpperCAmelCase     :		Optional[Any]							     =		None
								if self.use_input_mask:
															UpperCAmelCase     :		Union[str, Any]							     =		random_attention_mask([self.batch_size, self.seq_length]    )

								UpperCAmelCase     :		List[str]							     =		None
								UpperCAmelCase     :		Dict							     =		None
								UpperCAmelCase     :		Optional[int]							     =		None
								if self.use_labels:
															UpperCAmelCase     :		str							     =		ids_tensor([self.batch_size]		,		self.type_sequence_label_size    )
															UpperCAmelCase     :		Optional[int]							     =		ids_tensor([self.batch_size, self.seq_length]		,		self.num_labels    )
															UpperCAmelCase     :		Dict							     =		ids_tensor([self.batch_size]		,		self.num_choices    )

								UpperCAmelCase     :		List[str]							     =		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    )			->			Optional[Any]:
								(
								    (
								    UpperCAmelCase
								)     ,     (
								    UpperCAmelCase
								)     ,     (
								    UpperCAmelCase
								)     ,     (
								    UpperCAmelCase
								)     ,     (
								    UpperCAmelCase
								)     ,     (
								    UpperCAmelCase
								)     ,     
								)     :		List[str]							     =		self.prepare_config_and_inputs()

								UpperCAmelCase     :		List[Any]							     =		True
								UpperCAmelCase     :		str							     =		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
								UpperCAmelCase     :		List[str]							     =		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		,		A		,		A		,		A		,		A		,		A		,		A    )			->			List[str]:
								UpperCAmelCase     :		List[Any]							     =		TFEsmModel(config=A    )
								UpperCAmelCase     :		int							     =		{"""input_ids""": input_ids, """attention_mask""": input_mask}
								UpperCAmelCase     :		str							     =		model(A    )

								UpperCAmelCase     :		Optional[int]							     =		[input_ids, input_mask]
								UpperCAmelCase     :		int							     =		model(A    )

								UpperCAmelCase     :		List[Any]							     =		model(A    )

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





	def  _lowercase(  self		,		A		,		A		,		A		,		A		,		A		,		A		,		A		,		A		,		)			->			List[str]:
								UpperCAmelCase     :		Dict							     =		True

								UpperCAmelCase     :		Optional[Any]							     =		TFEsmModel(config=A    )
								UpperCAmelCase     :		Any							     =		{
								    """input_ids""": input_ids,
								    """attention_mask""": input_mask,
								    """encoder_hidden_states""": encoder_hidden_states,
								    """encoder_attention_mask""": encoder_attention_mask,
								}
								UpperCAmelCase     :		Any							     =		model(A    )

								UpperCAmelCase     :		Optional[Any]							     =		[input_ids, input_mask]
								UpperCAmelCase     :		Tuple							     =		model(A		,		encoder_hidden_states=A    )

								# Also check the case where encoder outputs are not passed
								UpperCAmelCase     :		int							     =		model(A		,		attention_mask=A    )

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





	def  _lowercase(  self		,		A		,		A		,		A		,		A		,		A		,		A    )			->			Any:
								UpperCAmelCase     :		Optional[Any]							     =		TFEsmForMaskedLM(config=A    )
								UpperCAmelCase     :		Tuple							     =		model([input_ids, input_mask]    )
								self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.seq_length, self.vocab_size)    )





	def  _lowercase(  self		,		A		,		A		,		A		,		A		,		A		,		A    )			->			List[Any]:
								UpperCAmelCase     :		Union[str, Any]							     =		self.num_labels
								UpperCAmelCase     :		str							     =		TFEsmForTokenClassification(config=A    )
								UpperCAmelCase     :		str							     =		{"""input_ids""": input_ids, """attention_mask""": input_mask}
								UpperCAmelCase     :		Union[str, Any]							     =		model(A    )
								self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.seq_length, self.num_labels)    )






	def  _lowercase(  self    )			->			List[Any]:
								UpperCAmelCase     :		List[Any]							     =		self.prepare_config_and_inputs()
								(
								    (
								    UpperCAmelCase
								)     ,     (
								    UpperCAmelCase
								)     ,     (
								    UpperCAmelCase
								)     ,     (
								    UpperCAmelCase
								)     ,     (
								    UpperCAmelCase
								)     ,     (
								    UpperCAmelCase
								)     ,     
								)     :		Union[str, Any]							     =		config_and_inputs
								UpperCAmelCase     :		Optional[Any]							     =		{"""input_ids""": input_ids, """attention_mask""": input_mask}
								return config, inputs_dict




@require_tf
class 			UpperCamelCase_ (							__magic_name__      ,     __magic_name__      ,     unittest.TestCase			):
	lowercase          = (
	    (
	        TFEsmModel,
	        TFEsmForMaskedLM,
	        TFEsmForSequenceClassification,
	        TFEsmForTokenClassification,
	    )
	    if is_tf_available()
	    else ()
	)
	lowercase          = (
	    {
	        'feature-extraction': TFEsmModel,
	        'fill-mask': TFEsmForMaskedLM,
	        'text-classification': TFEsmForSequenceClassification,
	        'token-classification': TFEsmForTokenClassification,
	        'zero-shot': TFEsmForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)
	lowercase          = False
	lowercase          = False





	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Dict							     =		TFEsmModelTester(self    )
								UpperCAmelCase     :		int							     =		ConfigTester(self		,		config_class=A		,		hidden_size=37    )





	def  _lowercase(  self    )			->			Dict:
								self.config_tester.run_common_tests()





	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Dict							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A    )





	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Dict							     =		self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(*A    )





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		str							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*A    )





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Optional[int]							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*A    )





	@slow
	def  _lowercase(  self    )			->			int:
								for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															UpperCAmelCase     :		Any							     =		TFEsmModel.from_pretrained(A    )
															self.assertIsNotNone(A    )





	@unittest.skip("""Protein models do not support embedding resizing."""    )
	def  _lowercase(  self    )			->			List[str]:
								pass





	@unittest.skip("""Protein models do not support embedding resizing."""    )
	def  _lowercase(  self    )			->			Tuple:
								pass






	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     ,     UpperCAmelCase     :		List[Any]							     =		self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															UpperCAmelCase     :		int							     =		model_class(A    )
															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     :		Dict							     =		model.get_bias()
																						assert isinstance(A		,		A    )
																						for k, v in name.items():
																													assert isinstance(A		,		tf.Variable    )
															else:
																						UpperCAmelCase     :		List[Any]							     =		model.get_output_embeddings()
																						assert x is None
																						UpperCAmelCase     :		int							     =		model.get_bias()
																						assert name is None




@require_tf
class 			UpperCamelCase_ (							unittest.TestCase			):





	@slow
	def  _lowercase(  self    )			->			Union[str, Any]:
								UpperCAmelCase     :		Any							     =		TFEsmForMaskedLM.from_pretrained("""facebook/esm2_t6_8M_UR50D"""    )

								UpperCAmelCase     :		List[str]							     =		tf.constant([[0, 1, 2, 3, 4, 5]]    )
								UpperCAmelCase     :		Dict							     =		model(A    )[0]
								UpperCAmelCase     :		Tuple							     =		[1, 6, 33]
								self.assertEqual(list(output.numpy().shape    )		,		A    )
								# compare the actual values for a slice.
								UpperCAmelCase     :		Dict							     =		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    )			->			Any:
								UpperCAmelCase     :		Optional[Any]							     =		TFEsmModel.from_pretrained("""facebook/esm2_t6_8M_UR50D"""    )

								UpperCAmelCase     :		Any							     =		tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]    )
								UpperCAmelCase     :		Optional[Any]							     =		model(A    )[0]
								# compare the actual values for a slice.
								UpperCAmelCase     :		Optional[Any]							     =		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'''




def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			int:
							if b == 0:
														return 1
							if (b % 2) == 0:
														return actual_power(_lowercase	,				int(b / 2		)		) * actual_power(_lowercase	,				int(b / 2		)		)
							else:
														return a * actual_power(_lowercase	,				int(b / 2		)		) * actual_power(_lowercase	,				int(b / 2		)		)



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			float:
							if b < 0:
														return 1 / actual_power(_lowercase	,				_lowercase		)
							return actual_power(_lowercase	,				_lowercase		)


if __name__ == "__main__":
							print(power(-2, -3))

