




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      :

						def __init__(		self   :Tuple    , __snake_case   :Any    , __snake_case   :Optional[int]=13    , __snake_case   :str=7    , __snake_case   :str=False    , __snake_case   :Optional[int]=True    , __snake_case   :Optional[int]=False    , __snake_case   :List[str]=True    , __snake_case   :List[str]=33    , __snake_case   :List[Any]=32    , __snake_case   :Tuple=5    , __snake_case   :int=4    , __snake_case   :Tuple=37    , __snake_case   :Union[str, Any]="gelu"    , __snake_case   :int=0.1    , __snake_case   :Tuple=0.1    , __snake_case   :Union[str, Any]=5_12    , __snake_case   :Dict=16    , __snake_case   :Optional[Any]=2    , __snake_case   :str=0.02    , __snake_case   :int=3    , __snake_case   :Optional[Any]=4    , __snake_case   :List[str]=None    , ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=parent
										__magic_name__	:	Optional[int]								=batch_size
										__magic_name__	:	int								=seq_length
										__magic_name__	:	str								=is_training
										__magic_name__	:	Any								=use_input_mask
										__magic_name__	:	List[str]								=use_token_type_ids
										__magic_name__	:	Tuple								=use_labels
										__magic_name__	:	List[str]								=vocab_size
										__magic_name__	:	List[Any]								=hidden_size
										__magic_name__	:	str								=num_hidden_layers
										__magic_name__	:	Optional[Any]								=num_attention_heads
										__magic_name__	:	Dict								=intermediate_size
										__magic_name__	:	Optional[int]								=hidden_act
										__magic_name__	:	int								=hidden_dropout_prob
										__magic_name__	:	Tuple								=attention_probs_dropout_prob
										__magic_name__	:	Dict								=max_position_embeddings
										__magic_name__	:	Optional[Any]								=type_vocab_size
										__magic_name__	:	Union[str, Any]								=type_sequence_label_size
										__magic_name__	:	List[str]								=initializer_range
										__magic_name__	:	str								=num_labels
										__magic_name__	:	str								=num_choices
										__magic_name__	:	List[Any]								=scope

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=ids_tensor([self.batch_size, self.seq_length]    , self.vocab_size )

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

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

										__magic_name__	:	Any								=self.get_config()

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

						def     A__						(		self   :Union[str, Any] ):
										'''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     A__						(		self   :str    , __snake_case   :int    , __snake_case   :Any    , __snake_case   :str    , __snake_case   :List[str]    , __snake_case   :Union[str, Any]    , __snake_case   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=EsmModel(config=UpperCAmelCase__ )
										model.to(UpperCAmelCase__ )
										model.eval()
										__magic_name__	:	int								=model(UpperCAmelCase__    , attention_mask=UpperCAmelCase__ )
										__magic_name__	:	Optional[Any]								=model(UpperCAmelCase__ )
										__magic_name__	:	Any								=model(UpperCAmelCase__ )

										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     A__						(		self   :Optional[Any]    , __snake_case   :int    , __snake_case   :List[str]    , __snake_case   :Dict    , __snake_case   :List[Any]    , __snake_case   :Dict    , __snake_case   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	int								=EsmForMaskedLM(config=UpperCAmelCase__ )
										model.to(UpperCAmelCase__ )
										model.eval()
										__magic_name__	:	int								=model(UpperCAmelCase__    , attention_mask=UpperCAmelCase__    , labels=UpperCAmelCase__ )
										self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.seq_length, self.vocab_size) )

						def     A__						(		self   :Union[str, Any]    , __snake_case   :List[str]    , __snake_case   :Dict    , __snake_case   :Union[str, Any]    , __snake_case   :Tuple    , __snake_case   :Dict    , __snake_case   :List[Any] ):
										'''simple docstring'''

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


						def     A__						(		self   :int ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=self.prepare_config_and_inputs()
										(
										    __magic_name__	
										)	:	str								=config_and_inputs
										__magic_name__	:	str								={'''input_ids''': input_ids, '''attention_mask''': input_mask}
										return config, inputs_dict




@require_torch
class 		__A      (			UpperCamelCase__					,						UpperCamelCase__					,						unittest.TestCase			):
						UpperCamelCase     						=					False

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

						def     A__						(		self   :Any ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=EsmModelTester(self )
										__magic_name__	:	Union[str, Any]								=ConfigTester(self    , config_class=UpperCAmelCase__    , hidden_size=37 )

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										self.config_tester.run_common_tests()

						def     A__						(		self   :int ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*UpperCAmelCase__ )

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	Dict								=self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
														__magic_name__	:	Tuple								=type
														self.model_tester.create_and_check_model(*UpperCAmelCase__ )

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	str								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__ )

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase__ )

						@slow
						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														__magic_name__	:	Dict								=EsmModel.from_pretrained(UpperCAmelCase__ )
														self.assertIsNotNone(UpperCAmelCase__ )

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										__magic_name__	:	str								=self.model_tester.prepare_config_and_inputs()[0]
										__magic_name__	:	Union[str, Any]								=EsmEmbeddings(config=UpperCAmelCase__ )

										__magic_name__	:	Optional[int]								=torch.as_tensor([[12, 31, 13, model.padding_idx]] )
										__magic_name__	:	Any								=torch.as_tensor(
										    [
										        [
										            0 + model.padding_idx + 1,
										            1 + model.padding_idx + 1,
										            2 + model.padding_idx + 1,
										            model.padding_idx,
										        ]
										    ] )
										__magic_name__	:	Optional[int]								=create_position_ids_from_input_ids(UpperCAmelCase__    , model.padding_idx )
										self.assertEqual(position_ids.shape    , expected_positions.shape )
										self.assertTrue(torch.all(torch.eq(UpperCAmelCase__    , UpperCAmelCase__ ) ) )

						def     A__						(		self   :int ):
										'''simple docstring'''

										__magic_name__	:	Any								=self.model_tester.prepare_config_and_inputs()[0]
										__magic_name__	:	str								=EsmEmbeddings(config=UpperCAmelCase__ )

										__magic_name__	:	List[str]								=torch.empty(2    , 4    , 30 )
										__magic_name__	:	Any								=[
										    0 + embeddings.padding_idx + 1,
										    1 + embeddings.padding_idx + 1,
										    2 + embeddings.padding_idx + 1,
										    3 + embeddings.padding_idx + 1,
										]
										__magic_name__	:	Any								=torch.as_tensor([expected_single_positions, expected_single_positions] )
										__magic_name__	:	str								=embeddings.create_position_ids_from_inputs_embeds(UpperCAmelCase__ )
										self.assertEqual(position_ids.shape    , expected_positions.shape )
										self.assertTrue(torch.all(torch.eq(UpperCAmelCase__    , UpperCAmelCase__ ) ) )

						@unittest.skip("""Esm does not support embedding resizing""" )
						def     A__						(		self   :int ):
										'''simple docstring'''

										pass

						@unittest.skip("""Esm does not support embedding resizing""" )
						def     A__						(		self   :str ):
										'''simple docstring'''

										pass


						@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										pass




@require_torch
class 		__A      (			UpperCamelCase__			):

						@slow
						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										with torch.no_grad():
														__magic_name__	:	str								=EsmForMaskedLM.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
														model.eval()
														__magic_name__	:	Dict								=torch.tensor([[0, 1, 2, 3, 4, 5]] )
														__magic_name__	:	Union[str, Any]								=model(UpperCAmelCase__ )[0]

														__magic_name__	:	Tuple								=33

														__magic_name__	:	Any								=torch.Size((1, 6, vocab_size) )
														self.assertEqual(output.shape    , UpperCAmelCase__ )

														__magic_name__	:	List[Any]								=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]    , UpperCAmelCase__    , atol=1E-4 ) )


						@slow
						def     A__						(		self   :int ):
										'''simple docstring'''

										with torch.no_grad():
														__magic_name__	:	Optional[int]								=EsmModel.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
														model.eval()

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









def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Optional[int]								=[]
				__magic_name__	:	int								=[]
				__magic_name__	:	str								={
				    """^""": 3,
				    """*""": 2,
				    """/""": 2,
				    """%""": 2,
				    """+""": 1,
				    """-""": 1,
				}  # Priority of each operator
				__magic_name__	:	Dict								=len(lowerCamelCase   ) if (len(lowerCamelCase   ) > 7) else 7

				# Print table header for output
				print(
				    """Symbol""".center(8   )	,     """Stack""".center(lowerCamelCase   )	,     """Postfix""".center(lowerCamelCase   )	,     sep=""" | """	,     )
				print("""-""" * (print_width * 3 + 7)   )

				for x in infix:
								if x.isalpha() or x.isdigit():
												post_fix.append(lowerCamelCase   )  # if x is Alphabet / Digit, add it to Postfix
								elif x == "(":
												stack.append(lowerCamelCase   )  # if x is "(" push to Stack
								elif x == ")":  # if x is ")" pop stack until "(" is encountered
												while stack[-1] != "(":
																post_fix.append(stack.pop()   )  # Pop stack & add the content to Postfix
												stack.pop()
								else:
												if len(lowerCamelCase   ) == 0:
																stack.append(lowerCamelCase   )  # If stack is empty, push x to stack
												else:  # while priority of x is not > priority of element in the stack
																while len(lowerCamelCase   ) > 0 and priority[x] <= priority[stack[-1]]:
																				post_fix.append(stack.pop()   )  # pop stack & add to Postfix
																stack.append(lowerCamelCase   )  # push x to stack

								print(
								    x.center(8   )	,     ("""""".join(lowerCamelCase   )).ljust(lowerCamelCase   )	,     ("""""".join(lowerCamelCase   )).ljust(lowerCamelCase   )	,     sep=""" | """	,     )  # Output in tabular format

				while len(lowerCamelCase   ) > 0:  # while stack is not empty
								post_fix.append(stack.pop()   )  # pop stack & add to Postfix
								print(
								    """ """.center(8   )	,     ("""""".join(lowerCamelCase   )).ljust(lowerCamelCase   )	,     ("""""".join(lowerCamelCase   )).ljust(lowerCamelCase   )	,     sep=""" | """	,     )  # Output in tabular format

				return "".join(lowerCamelCase   )  # return Postfix as str




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Union[str, Any]								=list(infix[::-1]   )  # reverse the infix equation

				for i in range(len(lowerCamelCase   )   ):
								if infix[i] == "(":
												__magic_name__	:	str								=""")"""  # change "(" to ")"
								elif infix[i] == ")":
												__magic_name__	:	int								="""("""  # change ")" to "("

				return (infix_2_postfix("""""".join(lowerCamelCase   )   ))[
				    ::-1
				]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
							UpperCAmelCase_		:				Tuple									=     input("\nEnter an Infix Equation = ")  # Input an Infix equation
							UpperCAmelCase_		:				Dict									=     "".join(Infix.split())  # Remove spaces from the input
							print("\n\t", Infix, "(Infix) -> ", infix_2_prefix(Infix), "(Prefix)")




