




"""simple docstring"""

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


a							      =     logging.get_logger(__name__)

a							      =     {
    '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json''',
    # See all Marian models at https://huggingface.co/models?filter=marian
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	int     =      '''marian'''
 UpperCAmelCase       :	Any     =      ['''past_key_values''']
 UpperCAmelCase       :	List[Any]     =      {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

 def __init__(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			List[Any]=58_101       ,				_UpperCAmelCase			:			int=None       ,				_UpperCAmelCase			:			List[Any]=1_024       ,				_UpperCAmelCase			:			List[Any]=12       ,				_UpperCAmelCase			:			Optional[int]=4_096       ,				_UpperCAmelCase			:			List[Any]=16       ,				_UpperCAmelCase			:			int=12       ,				_UpperCAmelCase			:			Dict=4_096       ,				_UpperCAmelCase			:			List[Any]=16       ,				_UpperCAmelCase			:			Any=0.0       ,				_UpperCAmelCase			:			List[str]=0.0       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			str=True       ,				_UpperCAmelCase			:			List[Any]="gelu"       ,				_UpperCAmelCase			:			int=1_024       ,				_UpperCAmelCase			:			Optional[Any]=0.1       ,				_UpperCAmelCase			:			Union[str, Any]=0.0       ,				_UpperCAmelCase			:			int=0.0       ,				_UpperCAmelCase			:			str=0.02       ,				_UpperCAmelCase			:			Optional[int]=58_100       ,				_UpperCAmelCase			:			List[str]=False       ,				_UpperCAmelCase			:			List[str]=58_100       ,				_UpperCAmelCase			:			Tuple=0       ,				_UpperCAmelCase			:			Optional[Any]=0       ,				_UpperCAmelCase			:			Any=True       ,				**_UpperCAmelCase			:			Dict       ,				):
       _A     =  vocab_size
       _A     =  decoder_vocab_size or vocab_size
       _A     =  max_position_embeddings
       _A     =  d_model
       _A     =  encoder_ffn_dim
       _A     =  encoder_layers
       _A     =  encoder_attention_heads
       _A     =  decoder_ffn_dim
       _A     =  decoder_layers
       _A     =  decoder_attention_heads
       _A     =  dropout
       _A     =  attention_dropout
       _A     =  activation_dropout
       _A     =  activation_function
       _A     =  init_std
       _A     =  encoder_layerdrop
       _A     =  decoder_layerdrop
       _A     =  use_cache
       _A     =  encoder_layers
       _A     =  scale_embedding  # scale factor will be sqrt(d_model) if True
       _A     =  share_encoder_decoder_embeddings
       super().__init__(
           pad_token_id=__SCREAMING_SNAKE_CASE       ,				eos_token_id=__SCREAMING_SNAKE_CASE       ,				is_encoder_decoder=__SCREAMING_SNAKE_CASE       ,				decoder_start_token_id=__SCREAMING_SNAKE_CASE       ,				forced_eos_token_id=__SCREAMING_SNAKE_CASE       ,				**__SCREAMING_SNAKE_CASE       ,				)





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 @property
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
 def      lowerCAmelCase_				(					self			:			Any						):




       if self.task in ["default", "seq2seq-lm"]:
             _A     =  OrderedDict(
                 [
                     ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                     ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                 ]						)

             if self.use_past:
                   _A     =  {0: 'batch'}
                   _A     =  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
             else:
                   _A     =  {0: 'batch', 1: 'decoder_sequence'}
                   _A     =  {0: 'batch', 1: 'decoder_sequence'}

             if self.use_past:
                   self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE       ,				direction='inputs'						)
       elif self.task == "causal-lm":
             # TODO: figure this case out.
             _A     =  OrderedDict(
                 [
                     ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                     ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                 ]						)
             if self.use_past:
                   _A  ,       _A     =  self.num_layers
                   for i in range(__SCREAMING_SNAKE_CASE						):
                         _A     =  {0: 'batch', 2: 'past_sequence + sequence'}
                         _A     =  {0: 'batch', 2: 'past_sequence + sequence'}
       else:
             _A     =  OrderedDict(
                 [
                     ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                     ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                     ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                     ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                 ]						)

       return common_inputs

 @property
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
 def      lowerCAmelCase_				(					self			:			Any						):




       if self.task in ["default", "seq2seq-lm"]:
             _A     =  super().outputs
       else:
             _A     =  super(__SCREAMING_SNAKE_CASE       ,				self						).outputs
             if self.use_past:
                   _A  ,       _A     =  self.num_layers
                   for i in range(__SCREAMING_SNAKE_CASE						):
                         _A     =  {0: 'batch', 2: 'past_sequence + sequence'}
                         _A     =  {0: 'batch', 2: 'past_sequence + sequence'}
       return common_outputs

 def      lowerCAmelCase_				(					self			:			Dict       ,				_UpperCAmelCase			:			List[Any]       ,				_UpperCAmelCase			:			Optional[int] = -1       ,				_UpperCAmelCase			:			str = -1       ,				_UpperCAmelCase			:			Optional[Any] = False       ,				_UpperCAmelCase			:			str = None       ,				):
       _A     =  self._generate_dummy_inputs_for_encoder_and_decoder(
           __SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE						)

       # Generate decoder inputs
       _A     =  seq_length if not self.use_past else 1
       _A     =  self._generate_dummy_inputs_for_encoder_and_decoder(
           __SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE						)
       _A     =  {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
       _A     =  dict(**__SCREAMING_SNAKE_CASE       ,				**__SCREAMING_SNAKE_CASE						)

       if self.use_past:
             if not is_torch_available():
                   raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'						)
             else:
                   import torch
             _A  ,       _A     =  common_inputs['input_ids'].shape
             _A     =  common_inputs['decoder_input_ids'].shape[1]
             _A  ,       _A     =  self.num_attention_heads
             _A     =  (
                 batch,
                 num_encoder_attention_heads,
                 encoder_seq_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             _A     =  decoder_seq_length + 3
             _A     =  (
                 batch,
                 num_decoder_attention_heads,
                 decoder_past_length,
                 self._config.hidden_size // num_decoder_attention_heads,
             )

             _A     =  torch.cat(
                 [common_inputs['decoder_attention_mask'], torch.ones(__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE						)]       ,				dim=1						)

             _A     =  []
             # If the number of encoder and decoder layers are present in the model configuration, both are considered
             _A  ,       _A     =  self.num_layers
             _A     =  min(__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE						)
             _A     =  max(__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE						) - min_num_layers
             _A     =  'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

             for _ in range(__SCREAMING_SNAKE_CASE						):
                   common_inputs["past_key_values"].append(
                       (
                           torch.zeros(__SCREAMING_SNAKE_CASE						),
                           torch.zeros(__SCREAMING_SNAKE_CASE						),
                           torch.zeros(__SCREAMING_SNAKE_CASE						),
                           torch.zeros(__SCREAMING_SNAKE_CASE						),
                       )						)
             # TODO: test this.
             _A     =  encoder_shape if remaining_side_name == 'encoder' else decoder_shape
             for _ in range(__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE						):
                   common_inputs["past_key_values"].append((torch.zeros(__SCREAMING_SNAKE_CASE						), torch.zeros(__SCREAMING_SNAKE_CASE						))						)
       return common_inputs

 def      lowerCAmelCase_				(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			Optional[Any]       ,				_UpperCAmelCase			:			Union[str, Any] = -1       ,				_UpperCAmelCase			:			Union[str, Any] = -1       ,				_UpperCAmelCase			:			Tuple = False       ,				_UpperCAmelCase			:			Dict = None       ,				):
       _A     =  self._generate_dummy_inputs_for_encoder_and_decoder(
           __SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE						)

       if self.use_past:
             if not is_torch_available():
                   raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'						)
             else:
                   import torch
             _A  ,       _A     =  common_inputs['input_ids'].shape
             # Not using the same length for past_key_values
             _A     =  seqlen + 2
             _A  ,       _A     =  self.num_layers
             _A  ,       _A     =  self.num_attention_heads
             _A     =  (
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )

             _A     =  common_inputs['attention_mask'].dtype
             _A     =  torch.cat(
                 [common_inputs['attention_mask'], torch.ones(__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				dtype=__SCREAMING_SNAKE_CASE						)]       ,				dim=1						)
             _A     =  [
                 (torch.zeros(__SCREAMING_SNAKE_CASE						), torch.zeros(__SCREAMING_SNAKE_CASE						)) for _ in range(__SCREAMING_SNAKE_CASE						)
             ]
       return common_inputs

 def      lowerCAmelCase_				(					self			:			Any       ,				_UpperCAmelCase			:			List[Any]       ,				_UpperCAmelCase			:			Optional[Any] = -1       ,				_UpperCAmelCase			:			List[str] = -1       ,				_UpperCAmelCase			:			Optional[Any] = False       ,				_UpperCAmelCase			:			List[str] = None       ,				):
       _A     =  compute_effective_axis_dimension(
           __SCREAMING_SNAKE_CASE       ,				fixed_dimension=OnnxConfig.default_fixed_batch       ,				num_token_to_add=0						)

       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       _A     =  tokenizer.num_special_tokens_to_add(__SCREAMING_SNAKE_CASE						)
       _A     =  compute_effective_axis_dimension(
           __SCREAMING_SNAKE_CASE       ,				fixed_dimension=OnnxConfig.default_fixed_sequence       ,				num_token_to_add=__SCREAMING_SNAKE_CASE						)

       # Generate dummy inputs according to compute batch and sequence
       _A     =  [' '.join([tokenizer.unk_token]						) * seq_length] * batch_size
       _A     =  dict(tokenizer(__SCREAMING_SNAKE_CASE       ,				return_tensors=__SCREAMING_SNAKE_CASE						)						)
       return common_inputs

 def      lowerCAmelCase_				(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			Tuple       ,				_UpperCAmelCase			:			int = -1       ,				_UpperCAmelCase			:			Union[str, Any] = -1       ,				_UpperCAmelCase			:			Dict = False       ,				_UpperCAmelCase			:			Optional[Any] = None       ,				):




       if self.task in ["default", "seq2seq-lm"]:
             _A     =  self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 __SCREAMING_SNAKE_CASE       ,				batch_size=__SCREAMING_SNAKE_CASE       ,				seq_length=__SCREAMING_SNAKE_CASE       ,				is_pair=__SCREAMING_SNAKE_CASE       ,				framework=__SCREAMING_SNAKE_CASE						)

       else:
             _A     =  self._generate_dummy_inputs_for_causal_lm(
                 __SCREAMING_SNAKE_CASE       ,				batch_size=__SCREAMING_SNAKE_CASE       ,				seq_length=__SCREAMING_SNAKE_CASE       ,				is_pair=__SCREAMING_SNAKE_CASE       ,				framework=__SCREAMING_SNAKE_CASE						)

       return common_inputs

 def      lowerCAmelCase_				(					self			:			List[str]       ,				_UpperCAmelCase			:			List[str]       ,				_UpperCAmelCase			:			int       ,				_UpperCAmelCase			:			List[str]       ,				_UpperCAmelCase			:			Optional[int]						):




       if self.task in ["default", "seq2seq-lm"]:
             _A     =  super()._flatten_past_key_values_(__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE						)
       else:
             _A     =  super(__SCREAMING_SNAKE_CASE       ,				self						)._flatten_past_key_values_(
                 __SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE						)



 @property
 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       return 1E-4







'''simple docstring'''



def       _UpperCamelCase			(_lowerCamelCase				:  Optional[Any]						)-> Dict:




     '''simple docstring'''





     __snake_case            =	[]
     __snake_case            =	[]
     __snake_case            =	{
         '''^''': 3,
         '''*''': 2,
         '''/''': 2,
         '''%''': 2,
         '''+''': 1,
         '''-''': 1,
     }  # Priority of each operator
     __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       _UpperCamelCase			(_lowerCamelCase				:  Optional[Any]						)-> str:




     '''simple docstring'''





     __snake_case            =	list(infix[::-1]						)  # reverse the infix equation

     for i in range(len(_lowerCamelCase						)						):
          if infix[i] == "(":
               __snake_case            =	''')'''  # change "(" to ")"
          elif infix[i] == ")":
               __snake_case            =	'''('''  # change ")" to "("

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


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






