
"""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 __lowerCAmelCase					:




			"""simple docstring"""







			def __init__(					self    :       Any							, _snake_case    :       int							, )   ->			Optional[Any]:







								"""simple docstring"""
								A_								=    parent
								A_								=    13
								A_								=    7
								A_								=    True
								A_								=    True
								A_								=    True
								A_								=    99
								A_								=    32
								A_								=    2
								A_								=    4
								A_								=    37
								A_								=    'gelu'
								A_								=    0.1
								A_								=    0.1
								A_								=    512
								A_								=    16
								A_								=    2
								A_								=    0.0_2
								A_								=    3
								A_								=    4
								A_								=    None




			def   lowerCamelCase__  (					self    :       int						)   ->			Tuple:







								"""simple docstring"""
								A_								=    ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size						)

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

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

								A_								=    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   lowerCamelCase__  (					self    :       Any						)   ->			Optional[Any]:







								"""simple docstring"""
								(
								    A_  
								)								=    self.prepare_config_and_inputs()

								A_								=    True
								A_								=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
								A_								=    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   lowerCamelCase__  (					self    :       Dict							, _snake_case    :       List[Any]							, _snake_case    :       Optional[int]							, _snake_case    :       str							, _snake_case    :       Any							, _snake_case    :       Optional[int]							, _snake_case    :       List[Any]						)   ->			Dict:







								"""simple docstring"""
								A_								=    TFEsmModel(config=snake_case__						)
								A_								=    {'input_ids': input_ids, 'attention_mask': input_mask}
								A_								=    model(snake_case__						)

								A_								=    [input_ids, input_mask]
								A_								=    model(snake_case__						)

								A_								=    model(snake_case__						)

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




			def   lowerCamelCase__  (					self    :       Union[str, Any]							, _snake_case    :       List[Any]							, _snake_case    :       Dict							, _snake_case    :       Optional[int]							, _snake_case    :       int							, _snake_case    :       Dict							, _snake_case    :       List[Any]							, _snake_case    :       Dict							, _snake_case    :       int							, )   ->			Tuple:







								"""simple docstring"""
								A_								=    True

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

								A_								=    [input_ids, input_mask]
								A_								=    model(snake_case__							, encoder_hidden_states=snake_case__						)

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

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




			def   lowerCamelCase__  (					self    :       List[str]							, _snake_case    :       Any							, _snake_case    :       List[str]							, _snake_case    :       List[str]							, _snake_case    :       Any							, _snake_case    :       Union[str, Any]							, _snake_case    :       Union[str, Any]						)   ->			int:







								"""simple docstring"""
								A_								=    TFEsmForMaskedLM(config=snake_case__						)
								A_								=    model([input_ids, input_mask]						)
								self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.vocab_size)						)




			def   lowerCamelCase__  (					self    :       Any							, _snake_case    :       Any							, _snake_case    :       Any							, _snake_case    :       Optional[int]							, _snake_case    :       Any							, _snake_case    :       Tuple							, _snake_case    :       List[Any]						)   ->			Tuple:







								"""simple docstring"""
								A_								=    self.num_labels
								A_								=    TFEsmForTokenClassification(config=snake_case__						)
								A_								=    {'input_ids': input_ids, 'attention_mask': input_mask}
								A_								=    model(snake_case__						)
								self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.num_labels)						)






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







								"""simple docstring"""
								A_								=    self.prepare_config_and_inputs()
								(
								    A_  
								)								=    config_and_inputs
								A_								=    {'input_ids': input_ids, 'attention_mask': input_mask}
								return config, inputs_dict





@require_tf
class __lowerCAmelCase					(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					unittest.TestCase   ):




			"""simple docstring"""







			snake_case						   =					(
			    (
			        TFEsmModel,
			        TFEsmForMaskedLM,
			        TFEsmForSequenceClassification,
			        TFEsmForTokenClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			snake_case						   =					(
			    {
			        "feature-extraction": TFEsmModel,
			        "fill-mask": TFEsmForMaskedLM,
			        "text-classification": TFEsmForSequenceClassification,
			        "token-classification": TFEsmForTokenClassification,
			        "zero-shot": TFEsmForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			snake_case						   =					False
			snake_case						   =					False




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







								"""simple docstring"""
								A_								=    TFEsmModelTester(self						)
								A_								=    ConfigTester(self							, config_class=snake_case__							, hidden_size=37						)




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







								"""simple docstring"""
								self.config_tester.run_common_tests()




			def   lowerCamelCase__  (					self    :       Optional[Any]						)   ->			str:







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*snake_case__						)




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







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(*snake_case__						)




			def   lowerCamelCase__  (					self    :       int						)   ->			Optional[Any]:







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*snake_case__						)




			def   lowerCamelCase__  (					self    :       Dict						)   ->			Tuple:







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*snake_case__						)




			@slow
			def   lowerCamelCase__  (					self    :       List[str]						)   ->			Optional[Any]:







								"""simple docstring"""

								for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													A_								=    TFEsmModel.from_pretrained(snake_case__						)
													self.assertIsNotNone(snake_case__						)




			@unittest.skip("Protein models do not support embedding resizing."						)
			def   lowerCamelCase__  (					self    :       List[Any]						)   ->			Union[str, Any]:







								"""simple docstring"""
								pass




			@unittest.skip("Protein models do not support embedding resizing."						)
			def   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			Tuple:







								"""simple docstring"""
								pass






			def   lowerCamelCase__  (					self    :       int						)   ->			Dict:







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													A_								=    model_class(snake_case__						)
													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
																		A_								=    model.get_bias()
																		assert isinstance(snake_case__							, snake_case__						)
																		for k, v in name.items():
																							assert isinstance(snake_case__							, tf.Variable						)
													else:
																		A_								=    model.get_output_embeddings()
																		assert x is None
																		A_								=    model.get_bias()
																		assert name is None





@require_tf
class __lowerCAmelCase					(       unittest.TestCase   ):




			"""simple docstring"""







			@slow
			def   lowerCamelCase__  (					self    :       Any						)   ->			int:







								"""simple docstring"""
								A_								=    TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D"						)

								A_								=    tf.constant([[0, 1, 2, 3, 4, 5]]						)
								A_								=    model(snake_case__						)[0]
								A_								=    [1, 6, 33]
								self.assertEqual(list(output.numpy().shape						)							, snake_case__						)
								# compare the actual values for a slice.
								A_								=    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   lowerCamelCase__  (					self    :       Optional[int]						)   ->			Any:







								"""simple docstring"""
								A_								=    TFEsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D"						)

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







from __future__ import annotations

from collections.abc import Iterator
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							value
            _lowerCAmelCase     :				Node | None       =							None
            _lowerCAmelCase     :				Node | None       =							None
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							tree







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            if node is None:
                   return 0
            return node.value + (
                self.depth_first_search(node.left ) + self.depth_first_search(node.right )
            )







     def __iter__(			self ):

            '''simple docstring'''

            yield self.depth_first_search(self.tree )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

