
"""simple docstring"""






import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
					from transformers.models.esm.modeling_esm import (
					    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
					    EsmEmbeddings,
					    create_position_ids_from_input_ids,
					)

class 					__a     :



							'''simple docstring'''







							def __init__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase=13  ,    _lowerCamelCase=7  ,    _lowerCamelCase=False  ,    _lowerCamelCase=True  ,    _lowerCamelCase=False  ,    _lowerCamelCase=True  ,    _lowerCamelCase=33  ,    _lowerCamelCase=32  ,    _lowerCamelCase=5  ,    _lowerCamelCase=4  ,    _lowerCamelCase=37  ,    _lowerCamelCase="gelu"  ,    _lowerCamelCase=0.1  ,    _lowerCamelCase=0.1  ,    _lowerCamelCase=512  ,    _lowerCamelCase=16  ,    _lowerCamelCase=2  ,    _lowerCamelCase=0.02  ,    _lowerCamelCase=3  ,    _lowerCamelCase=4  ,    _lowerCamelCase=None  ,    )			->							Union[str, Any]:




										'''simple docstring'''
										__lowercase       =       parent
										__lowercase       =       batch_size
										__lowercase       =       seq_length
										__lowercase       =       is_training
										__lowercase       =       use_input_mask
										__lowercase       =       use_token_type_ids
										__lowercase       =       use_labels
										__lowercase       =       vocab_size
										__lowercase       =       hidden_size
										__lowercase       =       num_hidden_layers
										__lowercase       =       num_attention_heads
										__lowercase       =       intermediate_size
										__lowercase       =       hidden_act
										__lowercase       =       hidden_dropout_prob
										__lowercase       =       attention_probs_dropout_prob
										__lowercase       =       max_position_embeddings
										__lowercase       =       type_vocab_size
										__lowercase       =       type_sequence_label_size
										__lowercase       =       initializer_range
										__lowercase       =       num_labels
										__lowercase       =       num_choices
										__lowercase       =       scope
							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''
										__lowercase       =       ids_tensor([self.batch_size, self.seq_length]  ,    self.vocab_size       )

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

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

										__lowercase       =       self.get_config()

										return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''
										return EsmConfig(
										    vocab_size=self.vocab_size  ,    hidden_size=self.hidden_size  ,    pad_token_id=1  ,    num_hidden_layers=self.num_hidden_layers  ,    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  ,    )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase       )			->							int:




										'''simple docstring'''
										__lowercase       =       EsmModel(config=_lowerCamelCase       )
										model.to(_lowerCamelCase       )
										model.eval()
										__lowercase       =       model(_lowerCamelCase  ,    attention_mask=_lowerCamelCase       )
										__lowercase       =       model(_lowerCamelCase       )
										__lowercase       =       model(_lowerCamelCase       )

										self.parent.assertEqual(result.last_hidden_state.shape  ,    (self.batch_size, self.seq_length, self.hidden_size)       )
										self.parent.assertEqual(result.pooler_output.shape  ,    (self.batch_size, self.hidden_size)       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase       )			->							Optional[int]:




										'''simple docstring'''
										__lowercase       =       EsmForMaskedLM(config=_lowerCamelCase       )
										model.to(_lowerCamelCase       )
										model.eval()
										__lowercase       =       model(_lowerCamelCase  ,    attention_mask=_lowerCamelCase  ,    labels=_lowerCamelCase       )
										self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.seq_length, self.vocab_size)       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase       )			->							List[Any]:




										'''simple docstring'''
										__lowercase       =       self.num_labels
										__lowercase       =       EsmForTokenClassification(config=_lowerCamelCase       )
										model.to(_lowerCamelCase       )
										model.eval()
										__lowercase       =       model(_lowerCamelCase  ,    attention_mask=_lowerCamelCase  ,    labels=_lowerCamelCase       )
										self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.seq_length, self.num_labels)       )





							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




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





@require_torch
class 					__a     ( __a					,   __a					,   unittest.TestCase       ):



							'''simple docstring'''







							_lowerCamelCase					:			Dict          =   False

							_lowerCamelCase					:			List[Any]          =   (
							    (
							        EsmForMaskedLM,
							        EsmModel,
							        EsmForSequenceClassification,
							        EsmForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							_lowerCamelCase					:			Dict          =   ()
							_lowerCamelCase					:			Tuple          =   (
							    {
							        """feature-extraction""": EsmModel,
							        """fill-mask""": EsmForMaskedLM,
							        """text-classification""": EsmForSequenceClassification,
							        """token-classification""": EsmForTokenClassification,
							        """zero-shot""": EsmForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							_lowerCamelCase					:			Optional[int]          =   True
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[str]:




										'''simple docstring'''
										__lowercase       =       EsmModelTester(self       )
										__lowercase       =       ConfigTester(self  ,    config_class=_lowerCamelCase  ,    hidden_size=37       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Optional[int]:




										'''simple docstring'''
										self.config_tester.run_common_tests()
							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''
										__lowercase       =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Tuple:




										'''simple docstring'''
										__lowercase       =       self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
													__lowercase       =       type
													self.model_tester.create_and_check_model(*_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Tuple:




										'''simple docstring'''
										__lowercase       =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Dict:




										'''simple docstring'''
										__lowercase       =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase       )
							@slow
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__lowercase       =       EsmModel.from_pretrained(_lowerCamelCase       )
													self.assertIsNotNone(_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Union[str, Any]:




										'''simple docstring'''
										__lowercase       =       self.model_tester.prepare_config_and_inputs()[0]
										__lowercase       =       EsmEmbeddings(config=_lowerCamelCase       )

										__lowercase       =       torch.as_tensor([[12, 31, 13, model.padding_idx]]       )
										__lowercase       =       torch.as_tensor(
										    [
										        [
										            0 + model.padding_idx + 1,
										            1 + model.padding_idx + 1,
										            2 + model.padding_idx + 1,
										            model.padding_idx,
										        ]
										    ]       )
										__lowercase       =       create_position_ids_from_input_ids(_lowerCamelCase  ,    model.padding_idx       )
										self.assertEqual(position_ids.shape  ,    expected_positions.shape       )
										self.assertTrue(torch.all(torch.eq(_lowerCamelCase  ,    _lowerCamelCase       )       )       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[Any]:




										'''simple docstring'''
										__lowercase       =       self.model_tester.prepare_config_and_inputs()[0]
										__lowercase       =       EsmEmbeddings(config=_lowerCamelCase       )

										__lowercase       =       torch.empty(2  ,    4  ,    30       )
										__lowercase       =       [
										    0 + embeddings.padding_idx + 1,
										    1 + embeddings.padding_idx + 1,
										    2 + embeddings.padding_idx + 1,
										    3 + embeddings.padding_idx + 1,
										]
										__lowercase       =       torch.as_tensor([expected_single_positions, expected_single_positions]       )
										__lowercase       =       embeddings.create_position_ids_from_inputs_embeds(_lowerCamelCase       )
										self.assertEqual(position_ids.shape  ,    expected_positions.shape       )
										self.assertTrue(torch.all(torch.eq(_lowerCamelCase  ,    _lowerCamelCase       )       )       )
							@unittest.skip("Esm does not support embedding resizing"       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[Any]:




										'''simple docstring'''
										pass
							@unittest.skip("Esm does not support embedding resizing"       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Any:




										'''simple docstring'''
										pass





							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Dict:




										'''simple docstring'''
										pass





@require_torch
class 					__a     ( __a       ):



							'''simple docstring'''







							@slow
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Any:




										'''simple docstring'''
										with torch.no_grad():
													__lowercase       =       EsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D"       )
													model.eval()
													__lowercase       =       torch.tensor([[0, 1, 2, 3, 4, 5]]       )
													__lowercase       =       model(_lowerCamelCase       )[0]

													__lowercase       =       33

													__lowercase       =       torch.Size((1, 6, vocab_size)       )
													self.assertEqual(output.shape  ,    _lowerCamelCase       )

													__lowercase       =       torch.tensor(
													    [[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]]       )
													self.assertTrue(torch.allclose(output[:, :3, :3]  ,    _lowerCamelCase  ,    atol=1e-4       )       )





							@slow
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Tuple:




										'''simple docstring'''
										with torch.no_grad():
													__lowercase       =       EsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D"       )
													model.eval()

													__lowercase       =       torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]       )
													__lowercase       =       model(_lowerCamelCase       )[0]
													# compare the actual values for a slice.
													__lowercase       =       torch.tensor(
													    [[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]]       )
													self.assertTrue(torch.allclose(output[:, :3, :3]  ,    _lowerCamelCase  ,    atol=1e-4       )       )







"""simple docstring"""






def      lowerCAmelCase__			(		__magic_name__      )						->int:

			if not isinstance(__magic_name__			,   __magic_name__      ):
						raise ValueError("multiplicative_persistence() only accepts integral values"      )
			if num < 0:
						raise ValueError("multiplicative_persistence() does not accept negative values"      )

			__lowercase       =       0
			__lowercase       =       str(__magic_name__      )

			while len(__magic_name__      ) != 1:
						__lowercase       =       [int(__magic_name__      ) for i in num_string]

						__lowercase       =       1
						for i in range(0			,   len(__magic_name__      )      ):
									total *= numbers[i]

						__lowercase       =       str(__magic_name__      )

						steps += 1
			return steps



def      lowerCAmelCase__			(		__magic_name__      )						->int:

			if not isinstance(__magic_name__			,   __magic_name__      ):
						raise ValueError("additive_persistence() only accepts integral values"      )
			if num < 0:
						raise ValueError("additive_persistence() does not accept negative values"      )

			__lowercase       =       0
			__lowercase       =       str(__magic_name__      )

			while len(__magic_name__      ) != 1:
						__lowercase       =       [int(__magic_name__      ) for i in num_string]

						__lowercase       =       0
						for i in range(0			,   len(__magic_name__      )      ):
									total += numbers[i]

						__lowercase       =       str(__magic_name__      )

						steps += 1
			return steps


if __name__ == "__main__":
					import doctest

					doctest.testmod()





