



"""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 			snake_case_						:






							def __init__(   self			,     a_			,     a_=1_3			,     a_=7			,     a_=False			,     a_=True			,     a_=False			,     a_=True			,     a_=3_3			,     a_=3_2			,     a_=5			,     a_=4			,     a_=3_7			,     a_="gelu"			,     a_=0.1			,     a_=0.1			,     a_=5_1_2			,     a_=1_6			,     a_=2			,     a_=0.02			,     a_=3			,     a_=4			,     a_=None			,     ):
									a_     :      List[Any] 						=     parent
									a_     :      Union[str, Any] 						=     batch_size
									a_     :      Optional[Any] 						=     seq_length
									a_     :      Optional[Any] 						=     is_training
									a_     :      Optional[int] 						=     use_input_mask
									a_     :      str 						=     use_token_type_ids
									a_     :      Dict 						=     use_labels
									a_     :      Optional[int] 						=     vocab_size
									a_     :      str 						=     hidden_size
									a_     :      List[Any] 						=     num_hidden_layers
									a_     :      Optional[int] 						=     num_attention_heads
									a_     :      str 						=     intermediate_size
									a_     :      Dict 						=     hidden_act
									a_     :      List[Any] 						=     hidden_dropout_prob
									a_     :      Any 						=     attention_probs_dropout_prob
									a_     :      str 						=     max_position_embeddings
									a_     :      List[Any] 						=     type_vocab_size
									a_     :      Optional[Any] 						=     type_sequence_label_size
									a_     :      str 						=     initializer_range
									a_     :      Tuple 						=     num_labels
									a_     :      Any 						=     num_choices
									a_     :      List[str] 						=     scope






							def snake_case_  (   self      ):
									a_     :      int 						=     ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size      )

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

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

									a_     :      List[str] 						=     self.get_config()

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






							def snake_case_  (   self      ):
									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 snake_case_  (   self			,     a_			,     a_			,     a_			,     a_			,     a_			,     a_      ):
									a_     :      Tuple 						=     EsmModel(config=a_      )
									model.to(a_      )
									model.eval()
									a_     :      Tuple 						=     model(a_			,     attention_mask=a_      )
									a_     :      Any 						=     model(a_      )
									a_     :      List[Any] 						=     model(a_      )

									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 snake_case_  (   self			,     a_			,     a_			,     a_			,     a_			,     a_			,     a_      ):
									a_     :      str 						=     EsmForMaskedLM(config=a_      )
									model.to(a_      )
									model.eval()
									a_     :      Any 						=     model(a_			,     attention_mask=a_			,     labels=a_      )
									self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.vocab_size)      )






							def snake_case_  (   self			,     a_			,     a_			,     a_			,     a_			,     a_			,     a_      ):
									a_     :      int 						=     self.num_labels
									a_     :      Tuple 						=     EsmForTokenClassification(config=a_      )
									model.to(a_      )
									model.eval()
									a_     :      Optional[Any] 						=     model(a_			,     attention_mask=a_			,     labels=a_      )
									self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.num_labels)      )






							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 						=     self.prepare_config_and_inputs()
									(
									    (
									    a_
									)						,				(
									    a_
									)						,				(
									    a_
									)						,				(
									    a_
									)						,				(
									    a_
									)						,				(
									    a_
									)						,				
									)     :      Any 						=     config_and_inputs
									a_     :      str 						=     {"input_ids": input_ids, "attention_mask": input_mask}
									return config, inputs_dict




@require_torch
class 			snake_case_						(						a_     ,a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					False

							__lowerCAmelCase    					=					(
							    (
							        EsmForMaskedLM,
							        EsmModel,
							        EsmForSequenceClassification,
							        EsmForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							__lowerCAmelCase    					=					()
							__lowerCAmelCase    					=					(
							    {
							        "feature-extraction": EsmModel,
							        "fill-mask": EsmForMaskedLM,
							        "text-classification": EsmForSequenceClassification,
							        "token-classification": EsmForTokenClassification,
							        "zero-shot": EsmForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							__lowerCAmelCase    					=					True






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     EsmModelTester(self      )
									a_     :      Union[str, Any] 						=     ConfigTester(self			,     config_class=a_			,     hidden_size=3_7      )






							def snake_case_  (   self      ):
									self.config_tester.run_common_tests()






							def snake_case_  (   self      ):
									a_     :      Dict 						=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*a_      )






							def snake_case_  (   self      ):
									a_     :      int 						=     self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
											a_     :      List[Any] 						=     type
											self.model_tester.create_and_check_model(*a_      )






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*a_      )






							def snake_case_  (   self      ):
									a_     :      Dict 						=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*a_      )






							@slow
							def snake_case_  (   self      ):
									for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											a_     :      Tuple 						=     EsmModel.from_pretrained(a_      )
											self.assertIsNotNone(a_      )






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     self.model_tester.prepare_config_and_inputs()[0]
									a_     :      Optional[Any] 						=     EsmEmbeddings(config=a_      )

									a_     :      Tuple 						=     torch.as_tensor([[1_2, 3_1, 1_3, model.padding_idx]]      )
									a_     :      List[Any] 						=     torch.as_tensor(
									    [
									        [
									            0 + model.padding_idx + 1,
									            1 + model.padding_idx + 1,
									            2 + model.padding_idx + 1,
									            model.padding_idx,
									        ]
									    ]      )
									a_     :      Tuple 						=     create_position_ids_from_input_ids(a_			,     model.padding_idx      )
									self.assertEqual(position_ids.shape			,     expected_positions.shape      )
									self.assertTrue(torch.all(torch.eq(a_			,     a_      )      )      )






							def snake_case_  (   self      ):
									a_     :      Any 						=     self.model_tester.prepare_config_and_inputs()[0]
									a_     :      Any 						=     EsmEmbeddings(config=a_      )

									a_     :      List[str] 						=     torch.empty(2			,     4			,     3_0      )
									a_     :      Dict 						=     [
									    0 + embeddings.padding_idx + 1,
									    1 + embeddings.padding_idx + 1,
									    2 + embeddings.padding_idx + 1,
									    3 + embeddings.padding_idx + 1,
									]
									a_     :      str 						=     torch.as_tensor([expected_single_positions, expected_single_positions]      )
									a_     :      Any 						=     embeddings.create_position_ids_from_inputs_embeds(a_      )
									self.assertEqual(position_ids.shape			,     expected_positions.shape      )
									self.assertTrue(torch.all(torch.eq(a_			,     a_      )      )      )






							@unittest.skip("Esm does not support embedding resizing"      )
							def snake_case_  (   self      ):
									pass






							@unittest.skip("Esm does not support embedding resizing"      )
							def snake_case_  (   self      ):
									pass






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




@require_torch
class 			snake_case_						(						a_				):






							@slow
							def snake_case_  (   self      ):
									with torch.no_grad():
											a_     :      int 						=     EsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D"      )
											model.eval()
											a_     :      Optional[int] 						=     torch.tensor([[0, 1, 2, 3, 4, 5]]      )
											a_     :      Optional[int] 						=     model(a_      )[0]

											a_     :      List[Any] 						=     3_3

											a_     :      List[Any] 						=     torch.Size((1, 6, vocab_size)      )
											self.assertEqual(output.shape			,     a_      )

											a_     :      Dict 						=     torch.tensor(
											    [[[8.9_215, -10.5_898, -6.4_671], [-6.3_967, -13.9_114, -1.1_212], [-7.7_812, -13.9_516, -3.7_406]]]      )
											self.assertTrue(torch.allclose(output[:, :3, :3]			,     a_			,     atol=1e-4      )      )






							@slow
							def snake_case_  (   self      ):
									with torch.no_grad():
											a_     :      str 						=     EsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D"      )
											model.eval()

											a_     :      Optional[Any] 						=     torch.tensor([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]]      )
											a_     :      Dict 						=     model(a_      )[0]
											# compare the actual values for a slice.
											a_     :      List[str] 						=     torch.tensor(
											    [[[0.1_444, 0.5_413, 0.3_248], [0.3_034, 0.0_053, 0.3_108], [0.3_228, -0.2_499, 0.3_415]]]      )
											self.assertTrue(torch.allclose(output[:, :3, :3]			,     a_			,     atol=1e-4      )      )




"""simple docstring"""
SCREAMING_SNAKE_CASE_				= 0  # The first color of the flag.
SCREAMING_SNAKE_CASE_				= 1  # The second color of the flag.
SCREAMING_SNAKE_CASE_				= 2  # The third color of the flag.
SCREAMING_SNAKE_CASE_				= (red, white, blue)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      list:
		if not sequence:
				return []
		if len(SCREAMING_SNAKE_CASE__	) == 1:
				return list(SCREAMING_SNAKE_CASE__	)
		a_     :      Dict 						=     0
		a_     :      List[Any] 						=     len(SCREAMING_SNAKE_CASE__	) - 1
		a_     :      str 						=     0
		while mid <= high:
				if sequence[mid] == colors[0]:
						a_						,				a_     :      int 						=     sequence[mid], sequence[low]
						low += 1
						mid += 1
				elif sequence[mid] == colors[1]:
						mid += 1
				elif sequence[mid] == colors[2]:
						a_						,				a_     :      List[Any] 						=     sequence[high], sequence[mid]
						high -= 1
				else:
						a_     :      Dict 						=     F"""The elements inside the sequence must contains only {colors} values"""
						raise ValueError(SCREAMING_SNAKE_CASE__	)
		return sequence


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					SCREAMING_SNAKE_CASE_				= input("""Enter numbers separated by commas:\n""").strip()
					SCREAMING_SNAKE_CASE_				= [int(item.strip()) for item in user_input.split(""",""")]
					print(F"""{dutch_national_flag_sort(unsorted)}""")