


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				:
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   )			->	int:




									"""simple docstring"""





									_UpperCAmelCase					=      parent
									_UpperCAmelCase					=      13
									_UpperCAmelCase					=      7
									_UpperCAmelCase					=      True
									_UpperCAmelCase					=      True
									_UpperCAmelCase					=      True
									_UpperCAmelCase					=      99
									_UpperCAmelCase					=      32
									_UpperCAmelCase					=      2
									_UpperCAmelCase					=      4
									_UpperCAmelCase					=      37
									_UpperCAmelCase					=      'gelu'
									_UpperCAmelCase					=      0.1
									_UpperCAmelCase					=      0.1
									_UpperCAmelCase					=      512
									_UpperCAmelCase					=      16
									_UpperCAmelCase					=      2
									_UpperCAmelCase					=      0.02
									_UpperCAmelCase					=      3
									_UpperCAmelCase					=      4
									_UpperCAmelCase					=      None
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									_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      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									(
									    (
									    _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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      TFEsmModel(config=a__						)
									_UpperCAmelCase					=      {'input_ids': input_ids, 'attention_mask': input_mask}
									_UpperCAmelCase					=      model(a__						)

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

									_UpperCAmelCase					=      model(a__						)

									self.parent.assertEqual(result.last_hidden_state.shape      ,   (self.batch_size, self.seq_length, self.hidden_size)						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   )			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      True

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

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

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

									self.parent.assertEqual(result.last_hidden_state.shape      ,   (self.batch_size, self.seq_length, self.hidden_size)						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Dict:




									"""simple docstring"""





									_UpperCAmelCase					=      TFEsmForMaskedLM(config=a__						)
									_UpperCAmelCase					=      model([input_ids, input_mask]						)
									self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.seq_length, self.vocab_size)						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.num_labels
									_UpperCAmelCase					=      TFEsmForTokenClassification(config=a__						)
									_UpperCAmelCase					=      {'input_ids': input_ids, 'attention_mask': input_mask}
									_UpperCAmelCase					=      model(a__						)
									self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.seq_length, self.num_labels)						)






		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									_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				(    lowercase_   ,				lowercase_   ,				unittest.TestCase      ):
		_a     :  List[Any]           =     (
		    (
		        TFEsmModel,
		        TFEsmForMaskedLM,
		        TFEsmForSequenceClassification,
		        TFEsmForTokenClassification,
		    )
		    if is_tf_available()
		    else ()
		)
		_a     :  Optional[Any]           =     (
		    {
		        "feature-extraction": TFEsmModel,
		        "fill-mask": TFEsmForMaskedLM,
		        "text-classification": TFEsmForSequenceClassification,
		        "token-classification": TFEsmForTokenClassification,
		        "zero-shot": TFEsmForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		_a     :  str           =     False
		_a     :  List[str]           =     False
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      TFEsmModelTester(self						)
									_UpperCAmelCase					=      ConfigTester(self      ,   config_class=a__      ,   hidden_size=37						)
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





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




									"""simple docstring"""





									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*a__						)
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs_for_decoder()
									self.model_tester.create_and_check_model_as_decoder(*a__						)
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*a__						)
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*a__						)
		@slow
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCAmelCase					=      TFEsmModel.from_pretrained(a__						)
																self.assertIsNotNone(a__						)
		@unittest.skip('Protein models do not support embedding resizing.'						)
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





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




									"""simple docstring"""





									pass






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




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																_UpperCAmelCase					=      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					=      model.get_bias()
																							assert isinstance(a__      ,   a__						)
																							for k, v in name.items():
																														assert isinstance(a__      ,   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      ):
		@slow
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      TFEsmForMaskedLM.from_pretrained('facebook/esm2_t6_8M_UR50D'						)

									_UpperCAmelCase					=      tf.constant([[0, 1, 2, 3, 4, 5]]						)
									_UpperCAmelCase					=      model(a__						)[0]
									_UpperCAmelCase					=      [1, 6, 33]
									self.assertEqual(list(output.numpy().shape						)      ,   a__						)
									# compare the actual values for a slice.
									_UpperCAmelCase					=      tf.constant(
									    [
									        [
									            [8.921518, -10.589814, -6.4671307],
									            [-6.3967156, -13.911377, -1.1211915],
									            [-7.781247, -13.951557, -3.740592],
									        ]
									    ]						)
									self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()      ,   expected_slice.numpy()      ,   atol=1e-2						)						)






		@slow
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      TFEsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D'						)

									_UpperCAmelCase					=      tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]						)
									_UpperCAmelCase					=      model(a__						)[0]
									# compare the actual values for a slice.
									_UpperCAmelCase					=      tf.constant(
									    [
									        [
									            [0.14443092, 0.54125327, 0.3247739],
									            [0.30340484, 0.00526676, 0.31077722],
									            [0.32278043, -0.24987096, 0.3414628],
									        ]
									    ]						)
									self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()      ,   expected_slice.numpy()      ,   atol=1e-4						)						)




import math
import random

def 	lowerCAmelCase__			(      a__:		float      ,  a__:		bool = False	)  ->		float:


							'''simple docstring'''





							if deriv:
														return value * (1 - value)
							return 1 / (1 + math.exp(-value	))


# Initial Value
lowerCAmelCase__  :Optional[Any]             =							0.02

def 	lowerCAmelCase__			(      a__:		int      ,  a__:		int	)  ->		float:


							'''simple docstring'''





							_UpperCAmelCase					=      float(2 * (random.randint(1      ,  1_0_0	)) - 1	)

							for _ in range(a__	):
														# Forward propagation
														_UpperCAmelCase					=      sigmoid_function(INITIAL_VALUE * weight	)
														# How much did we miss?
														_UpperCAmelCase					=      (expected / 1_0_0) - layer_a
														# Error delta
														_UpperCAmelCase					=      layer_1_error * sigmoid_function(a__      ,  a__	)
														# Update weight
														weight += INITIAL_VALUE * layer_1_delta

							return layer_a * 1_0_0


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		lowerCAmelCase__  :List[Any]             =							int(input('''Expected value: '''))
		lowerCAmelCase__  :Any             =							int(input('''Number of propagations: '''))
		print(forward_propagation(expected, number_propagations))
