


'''simple docstring'''



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

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


A__						:   int				=    logging.get_logger(__name__)

A__						:   List[str]				=    {
    """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json""",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}





class   UpperCAmelCase_    (__a    ):





    """simple docstring"""






    lowerCamelCase    :   List[str]					=     '''blenderbot-small'''
    lowerCamelCase    :   List[Any]					=     ['''past_key_values''']
    lowerCamelCase    :   int					=     {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_=5_02_65     ,	SCREAMING_SNAKE_CASE_=5_12     ,	SCREAMING_SNAKE_CASE_=8     ,	SCREAMING_SNAKE_CASE_=20_48     ,	SCREAMING_SNAKE_CASE_=16     ,	SCREAMING_SNAKE_CASE_=8     ,	SCREAMING_SNAKE_CASE_=20_48     ,	SCREAMING_SNAKE_CASE_=16     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_="gelu"     ,	SCREAMING_SNAKE_CASE_=5_12     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=0     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=2     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	Any:
         __lowerCamelCase  :      Optional[Any]														=						vocab_size
         __lowerCamelCase  :      Dict														=						max_position_embeddings
         __lowerCamelCase  :      int														=						d_model
         __lowerCamelCase  :      int														=						encoder_ffn_dim
         __lowerCamelCase  :      Dict														=						encoder_layers
         __lowerCamelCase  :      List[Any]														=						encoder_attention_heads
         __lowerCamelCase  :      List[str]														=						decoder_ffn_dim
         __lowerCamelCase  :      Any														=						decoder_layers
         __lowerCamelCase  :      Tuple														=						decoder_attention_heads
         __lowerCamelCase  :      Optional[int]														=						dropout
         __lowerCamelCase  :      Optional[int]														=						attention_dropout
         __lowerCamelCase  :      Optional[int]														=						activation_dropout
         __lowerCamelCase  :      Dict														=						activation_function
         __lowerCamelCase  :      List[str]														=						init_std
         __lowerCamelCase  :      int														=						encoder_layerdrop
         __lowerCamelCase  :      Optional[int]														=						decoder_layerdrop
         __lowerCamelCase  :      Optional[Any]														=						use_cache
         __lowerCamelCase  :      Any														=						encoder_layers
         __lowerCamelCase  :      str														=						scale_embedding  # scale factor will be sqrt(d_model) if True

         super().__init__(
             pad_token_id=_lowercase     ,	bos_token_id=_lowercase     ,	eos_token_id=_lowercase     ,	is_encoder_decoder=_lowercase     ,	decoder_start_token_id=_lowercase     ,	forced_eos_token_id=_lowercase     ,	**_lowercase     ,	)







class   UpperCAmelCase_    (__a    ):





    """simple docstring"""






    @property
    def  lowercase_							(				self      )							->	str:



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

              if self.use_past:
                   __lowerCamelCase  :      Tuple														=						{0: '''batch'''}
                   __lowerCamelCase  :      str														=						{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
              else:
                   __lowerCamelCase  :      Optional[Any]														=						{0: '''batch''', 1: '''decoder_sequence'''}
                   __lowerCamelCase  :      Optional[Any]														=						{0: '''batch''', 1: '''decoder_sequence'''}

              if self.use_past:
                   self.fill_with_past_key_values_(_lowercase     ,	direction='inputs'      )
         elif self.task == "causal-lm":
              # TODO: figure this case out.
              __lowerCamelCase  :      List[Any]														=						OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                  ]      )
              if self.use_past:
                   __lowerCamelCase  :      Dict														=						self.num_layers
                   for i in range(_lowercase      ):
                        __lowerCamelCase  :      Optional[Any]														=						{0: '''batch''', 2: '''past_sequence + sequence'''}
                        __lowerCamelCase  :      Dict														=						{0: '''batch''', 2: '''past_sequence + sequence'''}
         else:
              __lowerCamelCase  :      Dict														=						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
    def  lowercase_							(				self      )							->	Any:



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







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = -1     ,	SCREAMING_SNAKE_CASE_ = -1     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = None     ,	)							->	int:
         __lowerCamelCase  :      str														=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowercase     ,	_lowercase     ,	_lowercase     ,	_lowercase     ,	_lowercase      )

         # Generate decoder inputs
         __lowerCamelCase  :      Union[str, Any]														=						seq_length if not self.use_past else 1
         __lowerCamelCase  :      List[Any]														=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowercase     ,	_lowercase     ,	_lowercase     ,	_lowercase     ,	_lowercase      )
         __lowerCamelCase  :      Dict														=						{f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
         __lowerCamelCase  :      int														=						dict(**_lowercase     ,	**_lowercase      )

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

              __lowerCamelCase  :      Dict														=						torch.cat(
                  [common_inputs['decoder_attention_mask'], torch.ones(_lowercase     ,	_lowercase      )]     ,	dim=1      )

              __lowerCamelCase  :      int														=						[]
              # If the number of encoder and decoder layers are present in the model configuration, both are considered
              __lowerCamelCase  :      Optional[Any]														=						self.num_layers
              __lowerCamelCase  :      List[Any]														=						min(_lowercase     ,	_lowercase      )
              __lowerCamelCase  :      Optional[Any]														=						max(_lowercase     ,	_lowercase      ) - min_num_layers
              __lowerCamelCase  :      List[Any]														=						'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

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







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = -1     ,	SCREAMING_SNAKE_CASE_ = -1     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = None     ,	)							->	Optional[Any]:
         __lowerCamelCase  :      str														=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowercase     ,	_lowercase     ,	_lowercase     ,	_lowercase     ,	_lowercase      )

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

              __lowerCamelCase  :      Tuple														=						common_inputs['''attention_mask'''].dtype
              __lowerCamelCase  :      Union[str, Any]														=						torch.cat(
                  [common_inputs['attention_mask'], torch.ones(_lowercase     ,	_lowercase     ,	dtype=_lowercase      )]     ,	dim=1      )
              __lowerCamelCase  :      Optional[Any]														=						[
                  (torch.zeros(_lowercase      ), torch.zeros(_lowercase      )) for _ in range(_lowercase      )
              ]
         return common_inputs







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = -1     ,	SCREAMING_SNAKE_CASE_ = -1     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = None     ,	)							->	int:
         __lowerCamelCase  :      Optional[Any]														=						compute_effective_axis_dimension(
             _lowercase     ,	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
         __lowerCamelCase  :      List[Any]														=						tokenizer.num_special_tokens_to_add(_lowercase      )
         __lowerCamelCase  :      Union[str, Any]														=						compute_effective_axis_dimension(
             _lowercase     ,	fixed_dimension=OnnxConfig.default_fixed_sequence     ,	num_token_to_add=_lowercase      )

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







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = -1     ,	SCREAMING_SNAKE_CASE_ = -1     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = None     ,	)							->	Tuple:



         if self.task in ["default", "seq2seq-lm"]:
              __lowerCamelCase  :      List[str]														=						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                  _lowercase     ,	batch_size=_lowercase     ,	seq_length=_lowercase     ,	is_pair=_lowercase     ,	framework=_lowercase      )

         elif self.task == "causal-lm":
              __lowerCamelCase  :      Optional[Any]														=						self._generate_dummy_inputs_for_causal_lm(
                  _lowercase     ,	batch_size=_lowercase     ,	seq_length=_lowercase     ,	is_pair=_lowercase     ,	framework=_lowercase      )
         else:
              __lowerCamelCase  :      Dict														=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  _lowercase     ,	batch_size=_lowercase     ,	seq_length=_lowercase     ,	is_pair=_lowercase     ,	framework=_lowercase      )

         return common_inputs







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[int]:



         if self.task in ["default", "seq2seq-lm"]:
              __lowerCamelCase  :      Any														=						super()._flatten_past_key_values_(_lowercase     ,	_lowercase     ,	_lowercase     ,	_lowercase      )
         else:
              __lowerCamelCase  :      Dict														=						super(_lowercase     ,	self      )._flatten_past_key_values_(
                  _lowercase     ,	_lowercase     ,	_lowercase     ,	_lowercase      )







def    lowerCAmelCase_  (		lowercase:				Dict		)     ->					Any:







 '''simple docstring'''
 _UpperCamelCase:  int    						=			[]
 _UpperCamelCase:  Dict    						=			set({'''(''', '''[''', '''{'''}		)
 _UpperCamelCase:  int    						=			set({''')''', ''']''', '''}'''}		)
 _UpperCamelCase:  Dict    						=			{'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}

 for i in range(len(lowercase		)		):
  if s[i] in open_brackets:
   stack.append(s[i]		)

  elif s[i] in closed_brackets and (
      len(lowercase		) == 0 or (len(lowercase		) > 0 and open_to_closed[stack.pop()] != s[i])
  ):
   return False

 return len(lowercase		) == 0




def    lowerCAmelCase_  (		)     ->					int:







 '''simple docstring'''
 _UpperCamelCase:  List[str]    						=			input('''Enter sequence of brackets: '''		)
 if is_balanced(lowercase		):
  print(lowercase	,       '''is balanced'''		)
 else:
  print(lowercase	,       '''is not balanced'''		)


if __name__ == "__main__":
  main()