

'''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			:					str					,      lowercase_			:					List[Any]					,      lowercase_			:					List[str]=13					,      lowercase_			:					Optional[Any]=7					,      lowercase_			:					int=False					,      lowercase_			:					Tuple=True					,      lowercase_			:					str=False					,      lowercase_			:					List[Any]=True					,      lowercase_			:					int=33					,      lowercase_			:					Optional[int]=32					,      lowercase_			:					Union[str, Any]=5					,      lowercase_			:					Union[str, Any]=4					,      lowercase_			:					Any=37					,      lowercase_			:					Any="gelu"					,      lowercase_			:					Optional[Any]=0.1					,      lowercase_			:					Optional[int]=0.1					,      lowercase_			:					Tuple=512					,      lowercase_			:					Any=16					,      lowercase_			:					List[str]=2					,      lowercase_			:					Optional[int]=0.02					,      lowercase_			:					Optional[int]=3					,      lowercase_			:					Dict=4					,      lowercase_			:					Optional[int]=None					,      )						->     Dict:
						UpperCAmelCase	:     Dict						 =			parent
						UpperCAmelCase	:     List[Any]						 =			batch_size
						UpperCAmelCase	:     Tuple						 =			seq_length
						UpperCAmelCase	:     Optional[Any]						 =			is_training
						UpperCAmelCase	:     str						 =			use_input_mask
						UpperCAmelCase	:     Optional[int]						 =			use_token_type_ids
						UpperCAmelCase	:     List[str]						 =			use_labels
						UpperCAmelCase	:     Union[str, Any]						 =			vocab_size
						UpperCAmelCase	:     List[Any]						 =			hidden_size
						UpperCAmelCase	:     Optional[Any]						 =			num_hidden_layers
						UpperCAmelCase	:     Dict						 =			num_attention_heads
						UpperCAmelCase	:     Dict						 =			intermediate_size
						UpperCAmelCase	:     List[Any]						 =			hidden_act
						UpperCAmelCase	:     str						 =			hidden_dropout_prob
						UpperCAmelCase	:     str						 =			attention_probs_dropout_prob
						UpperCAmelCase	:     Dict						 =			max_position_embeddings
						UpperCAmelCase	:     Optional[int]						 =			type_vocab_size
						UpperCAmelCase	:     Optional[int]						 =			type_sequence_label_size
						UpperCAmelCase	:     List[Any]						 =			initializer_range
						UpperCAmelCase	:     Union[str, Any]						 =			num_labels
						UpperCAmelCase	:     int						 =			num_choices
						UpperCAmelCase	:     Union[str, Any]						 =			scope


					def        UpperCAmelCase_	(		self			:					Dict	)						->     List[Any]:
						UpperCAmelCase	:     int						 =			ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size	)

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

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

						UpperCAmelCase	:     str						 =			self.get_config()

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


					def        UpperCAmelCase_	(		self			:					str	)						->     Tuple:
						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        UpperCAmelCase_	(		self			:					List[Any]					,      lowercase_			:					int					,      lowercase_			:					Tuple					,      lowercase_			:					str					,      lowercase_			:					Tuple					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					Any	)						->     str:
						UpperCAmelCase	:     str						 =			EsmModel(config=__A	)
						model.to(__A	)
						model.eval()
						UpperCAmelCase	:     Dict						 =			model(__A					,      attention_mask=__A	)
						UpperCAmelCase	:     Any						 =			model(__A	)
						UpperCAmelCase	:     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        UpperCAmelCase_	(		self			:					List[Any]					,      lowercase_			:					Any					,      lowercase_			:					str					,      lowercase_			:					Any					,      lowercase_			:					str					,      lowercase_			:					List[Any]					,      lowercase_			:					Any	)						->     List[str]:
						UpperCAmelCase	:     List[Any]						 =			EsmForMaskedLM(config=__A	)
						model.to(__A	)
						model.eval()
						UpperCAmelCase	:     Optional[int]						 =			model(__A					,      attention_mask=__A					,      labels=__A	)
						self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size)	)


					def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					Any					,      lowercase_			:					str					,      lowercase_			:					Optional[int]					,      lowercase_			:					Tuple					,      lowercase_			:					Union[str, Any]	)						->     int:
						UpperCAmelCase	:     List[Any]						 =			self.num_labels
						UpperCAmelCase	:     Optional[int]						 =			EsmForTokenClassification(config=__A	)
						model.to(__A	)
						model.eval()
						UpperCAmelCase	:     Dict						 =			model(__A					,      attention_mask=__A					,      labels=__A	)
						self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.num_labels)	)




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




@require_torch
class 	A_			(    A__	,       A__	,       unittest.TestCase							):






					'''simple docstring'''



					UpperCAmelCase_						:     Dict  		=  False

					UpperCAmelCase_						:     List[Any]  		=  (
					    (
					        EsmForMaskedLM,
					        EsmModel,
					        EsmForSequenceClassification,
					        EsmForTokenClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					UpperCAmelCase_						:     Union[str, Any]  		=  ()
					UpperCAmelCase_						:     List[str]  		=  (
					    {
					        "feature-extraction": EsmModel,
					        "fill-mask": EsmForMaskedLM,
					        "text-classification": EsmForSequenceClassification,
					        "token-classification": EsmForTokenClassification,
					        "zero-shot": EsmForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					UpperCAmelCase_						:     Any  		=  True


					def        UpperCAmelCase_	(		self			:					int	)						->     str:
						UpperCAmelCase	:     Any						 =			EsmModelTester(self	)
						UpperCAmelCase	:     List[str]						 =			ConfigTester(self					,      config_class=__A					,      hidden_size=37	)


					def        UpperCAmelCase_	(		self			:					List[str]	)						->     List[Any]:
						self.config_tester.run_common_tests()


					def        UpperCAmelCase_	(		self			:					str	)						->     str:
						UpperCAmelCase	:     Union[str, Any]						 =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__A	)


					def        UpperCAmelCase_	(		self			:					List[str]	)						->     Dict:
						UpperCAmelCase	:     Optional[int]						 =			self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
							UpperCAmelCase	:     List[Any]						 =			type
							self.model_tester.create_and_check_model(*__A	)


					def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Tuple:
						UpperCAmelCase	:     Any						 =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*__A	)


					def        UpperCAmelCase_	(		self			:					str	)						->     Any:
						UpperCAmelCase	:     Optional[int]						 =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*__A	)


					@slow
					def        UpperCAmelCase_	(		self			:					Dict	)						->     Union[str, Any]:
						for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase	:     Any						 =			EsmModel.from_pretrained(__A	)
							self.assertIsNotNone(__A	)


					def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     str:
						UpperCAmelCase	:     Union[str, Any]						 =			self.model_tester.prepare_config_and_inputs()[0]
						UpperCAmelCase	:     Dict						 =			EsmEmbeddings(config=__A	)

						UpperCAmelCase	:     Optional[Any]						 =			torch.as_tensor([[12, 31, 13, model.padding_idx]]	)
						UpperCAmelCase	:     str						 =			torch.as_tensor(
						    [
						        [
						            0 + model.padding_idx + 1,
						            1 + model.padding_idx + 1,
						            2 + model.padding_idx + 1,
						            model.padding_idx,
						        ]
						    ]	)
						UpperCAmelCase	:     List[Any]						 =			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        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     List[str]:
						UpperCAmelCase	:     Tuple						 =			self.model_tester.prepare_config_and_inputs()[0]
						UpperCAmelCase	:     List[str]						 =			EsmEmbeddings(config=__A	)

						UpperCAmelCase	:     int						 =			torch.empty(2					,      4					,      30	)
						UpperCAmelCase	:     Tuple						 =			[
						    0 + embeddings.padding_idx + 1,
						    1 + embeddings.padding_idx + 1,
						    2 + embeddings.padding_idx + 1,
						    3 + embeddings.padding_idx + 1,
						]
						UpperCAmelCase	:     Dict						 =			torch.as_tensor([expected_single_positions, expected_single_positions]	)
						UpperCAmelCase	:     List[str]						 =			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        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     Dict:
						pass


					@unittest.skip('Esm does not support embedding resizing'	)
					def        UpperCAmelCase_	(		self			:					Dict	)						->     Dict:
						pass




					@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'	)
					def        UpperCAmelCase_	(		self			:					List[Any]	)						->     str:
						pass




@require_torch
class 	A_			(    A__							):






					'''simple docstring'''



					@slow
					def        UpperCAmelCase_	(		self			:					Tuple	)						->     List[str]:
						with torch.no_grad():
							UpperCAmelCase	:     int						 =			EsmForMaskedLM.from_pretrained('facebook/esm2_t6_8M_UR50D'	)
							model.eval()
							UpperCAmelCase	:     List[Any]						 =			torch.tensor([[0, 1, 2, 3, 4, 5]]	)
							UpperCAmelCase	:     Optional[int]						 =			model(__A	)[0]

							UpperCAmelCase	:     Optional[Any]						 =			33

							UpperCAmelCase	:     Union[str, Any]						 =			torch.Size((1, 6, vocab_size)	)
							self.assertEqual(output.shape					,      __A	)

							UpperCAmelCase	:     List[str]						 =			torch.tensor(
							    [[[8.9215, -10.5_898, -6.4671], [-6.3967, -13.9_114, -1.1212], [-7.7812, -13.9_516, -3.7406]]]	)
							self.assertTrue(torch.allclose(output[:, :3, :3]					,      __A					,      atol=1E-4	)	)




					@slow
					def        UpperCAmelCase_	(		self			:					List[str]	)						->     List[str]:
						with torch.no_grad():
							UpperCAmelCase	:     Any						 =			EsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D'	)
							model.eval()

							UpperCAmelCase	:     Dict						 =			torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]	)
							UpperCAmelCase	:     Union[str, Any]						 =			model(__A	)[0]
							# compare the actual values for a slice.
							UpperCAmelCase	:     List[str]						 =			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]					,      __A					,      atol=1E-4	)	)







'''simple docstring'''

def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     int						 =			len(UpperCAmelCase_     )
 UpperCAmelCase	:     int						 =			len(UpperCAmelCase_     )
 UpperCAmelCase	:     int						 =			(
     first_str_length if first_str_length > second_str_length else second_str_length
 )
 UpperCAmelCase	:     list						 =			[]
 for char_count in range(UpperCAmelCase_     ):
  if char_count < first_str_length:
   output_list.append(first_str[char_count]     )
  if char_count < second_str_length:
   output_list.append(second_str[char_count]     )
 return "".join(UpperCAmelCase_     )


if __name__ == "__main__":
 print(alternative_string_arrange("AB", "XYZ"), end=" ")




