

"""simple docstring"""






import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	import jax.numpy as jnp

	from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
	    FlaxRobertaPreLayerNormForCausalLM,
	    FlaxRobertaPreLayerNormForMaskedLM,
	    FlaxRobertaPreLayerNormForMultipleChoice,
	    FlaxRobertaPreLayerNormForQuestionAnswering,
	    FlaxRobertaPreLayerNormForSequenceClassification,
	    FlaxRobertaPreLayerNormForTokenClassification,
	    FlaxRobertaPreLayerNormModel,
	)


class 			lowerCamelCase_		(			unittest.TestCase   ):





				"""simple docstring"""







				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__=13       ,		UpperCAmelCase__=7       ,		UpperCAmelCase__=True       ,		UpperCAmelCase__=True       ,		UpperCAmelCase__=True       ,		UpperCAmelCase__=True       ,		UpperCAmelCase__=99       ,		UpperCAmelCase__=32       ,		UpperCAmelCase__=5       ,		UpperCAmelCase__=4       ,		UpperCAmelCase__=37       ,		UpperCAmelCase__="gelu"       ,		UpperCAmelCase__=0.1       ,		UpperCAmelCase__=0.1       ,		UpperCAmelCase__=512       ,		UpperCAmelCase__=16       ,		UpperCAmelCase__=2       ,		UpperCAmelCase__=0.02       ,		UpperCAmelCase__=4       ,		):
								SCREAMING_SNAKE_CASE__     							=				parent
								SCREAMING_SNAKE_CASE__     							=				batch_size
								SCREAMING_SNAKE_CASE__     							=				seq_length
								SCREAMING_SNAKE_CASE__     							=				is_training
								SCREAMING_SNAKE_CASE__     							=				use_attention_mask
								SCREAMING_SNAKE_CASE__     							=				use_token_type_ids
								SCREAMING_SNAKE_CASE__     							=				use_labels
								SCREAMING_SNAKE_CASE__     							=				vocab_size
								SCREAMING_SNAKE_CASE__     							=				hidden_size
								SCREAMING_SNAKE_CASE__     							=				num_hidden_layers
								SCREAMING_SNAKE_CASE__     							=				num_attention_heads
								SCREAMING_SNAKE_CASE__     							=				intermediate_size
								SCREAMING_SNAKE_CASE__     							=				hidden_act
								SCREAMING_SNAKE_CASE__     							=				hidden_dropout_prob
								SCREAMING_SNAKE_CASE__     							=				attention_probs_dropout_prob
								SCREAMING_SNAKE_CASE__     							=				max_position_embeddings
								SCREAMING_SNAKE_CASE__     							=				type_vocab_size
								SCREAMING_SNAKE_CASE__     							=				type_sequence_label_size
								SCREAMING_SNAKE_CASE__     							=				initializer_range
								SCREAMING_SNAKE_CASE__     							=				num_choices



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				ids_tensor([self.batch_size, self.seq_length]       ,		self.vocab_size     )

								SCREAMING_SNAKE_CASE__     							=				None
								if self.use_attention_mask:
												SCREAMING_SNAKE_CASE__     							=				random_attention_mask([self.batch_size, self.seq_length]     )

								SCREAMING_SNAKE_CASE__     							=				None
								if self.use_token_type_ids:
												SCREAMING_SNAKE_CASE__     							=				ids_tensor([self.batch_size, self.seq_length]       ,		self.type_vocab_size     )

								SCREAMING_SNAKE_CASE__     							=				RobertaPreLayerNormConfig(
								    vocab_size=self.vocab_size       ,		hidden_size=self.hidden_size       ,		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       ,		is_decoder=UpperCAmelCase__       ,		initializer_range=self.initializer_range       ,		)

								return config, input_ids, token_type_ids, attention_mask



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.prepare_config_and_inputs()
								SCREAMING_SNAKE_CASE__     							=				config_and_inputs
								SCREAMING_SNAKE_CASE__     							=				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
								return config, inputs_dict






				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.prepare_config_and_inputs()
								SCREAMING_SNAKE_CASE__     							=				config_and_inputs

								SCREAMING_SNAKE_CASE__     							=				True
								SCREAMING_SNAKE_CASE__     							=				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
								SCREAMING_SNAKE_CASE__     							=				ids_tensor([self.batch_size, self.seq_length]       ,		vocab_size=2     )

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)


@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 			lowerCamelCase_		(			__lowercase       ,  unittest.TestCase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Union[str, Any]      							=					True

				_lowerCAmelCase      :      Any      							=					(
				    (
				        FlaxRobertaPreLayerNormModel,
				        FlaxRobertaPreLayerNormForCausalLM,
				        FlaxRobertaPreLayerNormForMaskedLM,
				        FlaxRobertaPreLayerNormForSequenceClassification,
				        FlaxRobertaPreLayerNormForTokenClassification,
				        FlaxRobertaPreLayerNormForMultipleChoice,
				        FlaxRobertaPreLayerNormForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				FlaxRobertaPreLayerNormModelTester(self     )






				@slow
				def   lowerCAmelCase__		(						self     ):
								for model_class_name in self.all_model_classes:
												SCREAMING_SNAKE_CASE__     							=				model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"       ,		from_pt=UpperCAmelCase__     )
												SCREAMING_SNAKE_CASE__     							=				model(np.ones((1, 1)     )     )
												self.assertIsNotNone(UpperCAmelCase__     )


@require_flax
class 			lowerCamelCase_		(			unittest.TestCase   ):





				"""simple docstring"""







				@slow
				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"       ,		from_pt=UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]       ,		dtype=jnp.intaa     )
								SCREAMING_SNAKE_CASE__     							=				model(UpperCAmelCase__     )[0]
								SCREAMING_SNAKE_CASE__     							=				[1, 11, 5_0265]
								self.assertEqual(list(output.shape     )       ,		UpperCAmelCase__     )
								# compare the actual values for a slice.
								SCREAMING_SNAKE_CASE__     							=				np.array(
								    [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]]       ,		dtype=np.floataa     )
								self.assertTrue(np.allclose(output[:, :3, :3]       ,		UpperCAmelCase__       ,		atol=1e-4     )     )






				@slow
				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"       ,		from_pt=UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]       ,		dtype=jnp.intaa     )
								SCREAMING_SNAKE_CASE__     							=				model(UpperCAmelCase__     )[0]
								# compare the actual values for a slice.
								SCREAMING_SNAKE_CASE__     							=				np.array(
								    [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]       ,		dtype=np.floataa     )
								self.assertTrue(np.allclose(output[:, :3, :3]       ,		UpperCAmelCase__       ,		atol=1e-4     )     )




"""simple docstring"""






def __lowercase     (      lowerCamelCase_  :      Tuple  ):
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__     							=				{
				    "^": 3,
				    "*": 2,
				    "/": 2,
				    "%": 2,
				    "+": 1,
				    "-": 1,
				}  # Priority of each operator
				SCREAMING_SNAKE_CASE__     							=				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 __lowercase     (      lowerCamelCase_  :      str  ):
				SCREAMING_SNAKE_CASE__     							=				list(infix[::-1]  )  # reverse the infix equation

				for i in range(len(lowerCamelCase_  )  ):
								if infix[i] == "(":
												SCREAMING_SNAKE_CASE__     							=				")"  # change "(" to ")"
								elif infix[i] == ")":
												SCREAMING_SNAKE_CASE__     							=				"("  # change ")" to "("

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


if __name__ == "__main__":
	_lowerCamelCase     =  input('\nEnter an Infix Equation = ')  # Input an Infix equation
	_lowerCamelCase     =  ''.join(Infix.split())  # Remove spaces from the input
	print('\n\t', Infix, '(Infix) -> ', infix_2_prefix(Infix), '(Prefix)')
