

import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
	    SpeechaTextaDecoder,
	    SpeechaTextaForCausalLM,
	)

@require_torch
class    a__  :
			"""simple docstring"""







			def __init__(      self , lowercase , lowercase=99 , lowercase=13 , lowercase=16 , lowercase=7 , lowercase=True , lowercase=True , lowercase=True , lowercase=False , lowercase=True , lowercase=2 , lowercase=32 , lowercase=4 , lowercase=4 , lowercase=30 , lowercase=0 , lowercase=1 , lowercase=2 , lowercase=None , )					->  List[Any]:
							'''simple docstring'''






							A__            =     parent
							A__            =     batch_size
							A__            =     decoder_seq_length
							# For common tests
							A__            =     self.decoder_seq_length
							A__            =     is_training
							A__            =     use_attention_mask
							A__            =     use_labels

							A__            =     vocab_size
							A__            =     d_model
							A__            =     d_model
							A__            =     decoder_layers
							A__            =     decoder_layers
							A__            =     decoder_ffn_dim
							A__            =     decoder_attention_heads
							A__            =     decoder_attention_heads
							A__            =     eos_token_id
							A__            =     bos_token_id
							A__            =     pad_token_id
							A__            =     decoder_start_token_id
							A__            =     use_cache
							A__            =     max_position_embeddings

							A__            =     None
							A__            =     decoder_seq_length
							A__            =     2
							A__            =     1


			def        UpperCamelCase						(      self    )					->  List[str]:
							'''simple docstring'''






							A__            =     ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size    )

							A__            =     None
							if self.use_attention_mask:
											A__            =     ids_tensor([self.batch_size, self.decoder_seq_length] , vocab_size=2    )

							A__            =     None
							if self.use_labels:
											A__            =     ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size    )

							A__            =     SpeechaTextaConfig(
							    vocab_size=self.vocab_size , d_model=self.d_model , decoder_layers=self.decoder_layers , decoder_ffn_dim=self.decoder_ffn_dim , decoder_attention_heads=self.decoder_attention_heads , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , use_cache=self.use_cache , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , max_position_embeddings=self.max_position_embeddings , )

							return (
							    config,
							    input_ids,
							    attention_mask,
							    lm_labels,
							)


			def        UpperCamelCase						(      self , lowercase , lowercase , lowercase , lowercase , )					->  List[str]:
							'''simple docstring'''






							A__            =     True
							A__            =     SpeechaTextaDecoder(config=lowercase    ).to(lowercase    ).eval()
							A__            =     input_ids[:2]

							input_ids[input_ids == 0] += 1
							# first forward pass
							A__            =     model(lowercase , use_cache=lowercase    )
							A__            =     model(lowercase    )
							A__            =     model(lowercase , use_cache=lowercase    )

							self.parent.assertTrue(len(lowercase    ) == len(lowercase    )    )
							self.parent.assertTrue(len(lowercase    ) == len(lowercase    ) + 1    )

							A__            =     outputs["past_key_values"]

							# create hypothetical next token and extent to next_input_ids
							A__            =     ids_tensor((2, 1) , config.vocab_size - 1    ) + 1

							# append to next input_ids and
							A__            =     torch.cat([input_ids, next_tokens] , dim=-1    )

							print(lowercase    )

							A__            =     model(lowercase    )["last_hidden_state"]
							A__            =     model(lowercase , past_key_values=lowercase    )["last_hidden_state"]

							# select random slice
							A__            =     ids_tensor((1,) , output_from_past.shape[-1]    ).item()
							A__            =     output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
							A__            =     output_from_past[:, 0, random_slice_idx].detach()

							# test that outputs are equal for slice
							assert torch.allclose(lowercase , lowercase , atol=1e-3    )


			def        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






							A__            =     self.prepare_config_and_inputs()
							(
							    (
							    A__
							)      ,			(
							    A__
							)      ,			(
							    A__
							)      ,			(
							    A__
							)      ,			
							)            =     config_and_inputs

							A__            =     {
							    "input_ids": input_ids,
							    "attention_mask": attention_mask,
							}
							return config, inputs_dict

@require_torch
class    a__  (   snake_case							,		snake_case							,		snake_case							,		unittest.TestCase     ):
			"""simple docstring"""







			__lowerCamelCase	    =    (SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
			__lowerCamelCase	    =    (SpeechaTextaForCausalLM,) if is_torch_available() else ()
			__lowerCamelCase	    =    {'text-generation': SpeechaTextaForCausalLM} if is_torch_available() else {}
			__lowerCamelCase	    =    True
			__lowerCamelCase	    =    False


			def        UpperCamelCase						(      self , )					->  Tuple:
							'''simple docstring'''






							A__            =     SpeechaTextaStandaloneDecoderModelTester(self , is_training=lowercase    )
							A__            =     ConfigTester(self , config_class=lowercase    )


			def        UpperCamelCase						(      self    )					->  Tuple:
							'''simple docstring'''






							pass


			def        UpperCamelCase						(      self    )					->  Any:
							'''simple docstring'''






							pass


			def        UpperCamelCase						(      self    )					->  Optional[int]:
							'''simple docstring'''






							pass


			def        UpperCamelCase						(      self    )					->  Tuple:
							'''simple docstring'''






							self.config_tester.run_common_tests()


			def        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






							A__            =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_decoder_model_past(*lowercase    )


			def        UpperCamelCase						(      self    )					->  Tuple:
							'''simple docstring'''






							return




from .balanced_parentheses import balanced_parentheses
from .stack import Stack



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							str )     -> int:




				'''simple docstring'''





				return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(SCREAMING_SNAKE_CASE_		,    -1 )



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							str )     -> str:




				'''simple docstring'''





				if not balanced_parentheses(SCREAMING_SNAKE_CASE_ ):
								raise ValueError("Mismatched parentheses" )
				A__            =     Stack()
				A__            =     []
				for char in expression_str:
								if char.isalpha() or char.isdigit():
												postfix.append(SCREAMING_SNAKE_CASE_ )
								elif char == "(":
												stack.push(SCREAMING_SNAKE_CASE_ )
								elif char == ")":
												while not stack.is_empty() and stack.peek() != "(":
																postfix.append(stack.pop() )
												stack.pop()
								else:
												while not stack.is_empty() and precedence(SCREAMING_SNAKE_CASE_ ) <= precedence(stack.peek() ):
																postfix.append(stack.pop() )
												stack.push(SCREAMING_SNAKE_CASE_ )
				while not stack.is_empty():
								postfix.append(stack.pop() )
				return " ".join(SCREAMING_SNAKE_CASE_ )


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	lowerCAmelCase__						=	"""a+b*(c^d-e)^(f+g*h)-i"""

	print("""Infix to Postfix Notation demonstration:\n""")
	print("""Infix notation: """ + expression)
	print("""Postfix notation: """ + infix_to_postfix(expression))

