



"""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


_UpperCAmelCase   =	logging.get_logger(__name__)

_UpperCAmelCase   =	{
    "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     a		(     _a				):
  UpperCamelCase		: Any     =    """blenderbot-small"""
  UpperCamelCase		: List[Any]     =    ["""past_key_values"""]
  UpperCamelCase		: Union[str, Any]     =    {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



  def __init__(  self       :    Tuple				,						lowerCAmelCase       :    Any=5_0265				,						lowerCAmelCase       :    Any=512				,						lowerCAmelCase       :    Dict=8				,						lowerCAmelCase       :    List[Any]=2048				,						lowerCAmelCase       :    Any=16				,						lowerCAmelCase       :    Union[str, Any]=8				,						lowerCAmelCase       :    List[str]=2048				,						lowerCAmelCase       :    Optional[int]=16				,						lowerCAmelCase       :    Tuple=0.0				,						lowerCAmelCase       :    Optional[int]=0.0				,						lowerCAmelCase       :    Optional[Any]=True				,						lowerCAmelCase       :    int=True				,						lowerCAmelCase       :    str="gelu"				,						lowerCAmelCase       :    Union[str, Any]=512				,						lowerCAmelCase       :    Tuple=0.1				,						lowerCAmelCase       :    Any=0.0				,						lowerCAmelCase       :    List[Any]=0.0				,						lowerCAmelCase       :    Optional[int]=0.0_2				,						lowerCAmelCase       :    List[str]=1				,						lowerCAmelCase       :    str=False				,						lowerCAmelCase       :    List[Any]=0				,						lowerCAmelCase       :    Tuple=1				,						lowerCAmelCase       :    Any=2				,						lowerCAmelCase       :    Tuple=2				,						**lowerCAmelCase       :    str				,						)   ->    List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Dict   	=vocab_size
     SCREAMING_SNAKE_CASE_:       Dict   	=max_position_embeddings
     SCREAMING_SNAKE_CASE_:       Optional[int]   	=d_model
     SCREAMING_SNAKE_CASE_:       str   	=encoder_ffn_dim
     SCREAMING_SNAKE_CASE_:       str   	=encoder_layers
     SCREAMING_SNAKE_CASE_:       Any   	=encoder_attention_heads
     SCREAMING_SNAKE_CASE_:       Any   	=decoder_ffn_dim
     SCREAMING_SNAKE_CASE_:       List[str]   	=decoder_layers
     SCREAMING_SNAKE_CASE_:       str   	=decoder_attention_heads
     SCREAMING_SNAKE_CASE_:       Dict   	=dropout
     SCREAMING_SNAKE_CASE_:       str   	=attention_dropout
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=activation_dropout
     SCREAMING_SNAKE_CASE_:       Tuple   	=activation_function
     SCREAMING_SNAKE_CASE_:       Optional[int]   	=init_std
     SCREAMING_SNAKE_CASE_:       Any   	=encoder_layerdrop
     SCREAMING_SNAKE_CASE_:       Optional[int]   	=decoder_layerdrop
     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=use_cache
     SCREAMING_SNAKE_CASE_:       Dict   	=encoder_layers
     SCREAMING_SNAKE_CASE_:       List[str]   	=scale_embedding  # scale factor will be sqrt(d_model) if True

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







class     a		(     _a				):



  @property
  def 		lowerCamelCase__				(  self       :    Any						)   ->    Mapping[str, Mapping[int, str]]:


     '''simple docstring'''




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

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

        if self.use_past:
           self.fill_with_past_key_values_(lowerCAmelCase				,						direction="""inputs"""						)
     elif self.task == "causal-lm":
        # TODO: figure this case out.
        SCREAMING_SNAKE_CASE_:       Tuple   	=OrderedDict(
            [
                ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
            ]						)
        if self.use_past:
           SCREAMING_SNAKE_CASE_:       List[str]   	=self.num_layers
           for i in range(lowerCAmelCase						):
              SCREAMING_SNAKE_CASE_:       int   	={0: 'batch', 2: 'past_sequence + sequence'}
              SCREAMING_SNAKE_CASE_:       str   	={0: 'batch', 2: 'past_sequence + sequence'}
     else:
        SCREAMING_SNAKE_CASE_:       str   	=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 		lowerCamelCase__				(  self       :    List[Any]						)   ->    Mapping[str, Mapping[int, str]]:


     '''simple docstring'''




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



  def 		lowerCamelCase__				(  self       :    Dict				,						lowerCAmelCase       :    str				,						lowerCAmelCase       :    List[Any] = -1				,						lowerCAmelCase       :    Tuple = -1				,						lowerCAmelCase       :    str = False				,						lowerCAmelCase       :    int = None				,						)   ->    Mapping[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Optional[int]   	=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowerCAmelCase				,						lowerCAmelCase				,						lowerCAmelCase				,						lowerCAmelCase				,						lowerCAmelCase						)

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

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

        SCREAMING_SNAKE_CASE_:       int   	=torch.cat(
            [common_inputs["""decoder_attention_mask"""], torch.ones(lowerCAmelCase				,						lowerCAmelCase						)]				,						dim=1						)

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

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



  def 		lowerCamelCase__				(  self       :    int				,						lowerCAmelCase       :    Optional[int]				,						lowerCAmelCase       :    Optional[int] = -1				,						lowerCAmelCase       :    Any = -1				,						lowerCAmelCase       :    List[Any] = False				,						lowerCAmelCase       :    List[str] = None				,						)   ->    Mapping[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Any   	=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowerCAmelCase				,						lowerCAmelCase				,						lowerCAmelCase				,						lowerCAmelCase				,						lowerCAmelCase						)

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

        SCREAMING_SNAKE_CASE_:       Tuple   	=common_inputs['attention_mask'].dtype
        SCREAMING_SNAKE_CASE_:       Optional[int]   	=torch.cat(
            [common_inputs["""attention_mask"""], torch.ones(lowerCAmelCase				,						lowerCAmelCase				,						dtype=lowerCAmelCase						)]				,						dim=1						)
        SCREAMING_SNAKE_CASE_:       Any   	=[
            (torch.zeros(lowerCAmelCase						), torch.zeros(lowerCAmelCase						)) for _ in range(lowerCAmelCase						)
        ]
     return common_inputs



  def 		lowerCamelCase__				(  self       :    List[str]				,						lowerCAmelCase       :    Dict				,						lowerCAmelCase       :    Optional[int] = -1				,						lowerCAmelCase       :    int = -1				,						lowerCAmelCase       :    List[Any] = False				,						lowerCAmelCase       :    Tuple = None				,						)   ->    Mapping[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Tuple   	=compute_effective_axis_dimension(
         lowerCAmelCase				,						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
     SCREAMING_SNAKE_CASE_:       List[str]   	=tokenizer.num_special_tokens_to_add(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:       int   	=compute_effective_axis_dimension(
         lowerCAmelCase				,						fixed_dimension=OnnxConfig.default_fixed_sequence				,						num_token_to_add=lowerCAmelCase						)

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



  def 		lowerCamelCase__				(  self       :    Tuple				,						lowerCAmelCase       :    Any				,						lowerCAmelCase       :    List[str] = -1				,						lowerCAmelCase       :    Optional[Any] = -1				,						lowerCAmelCase       :    int = False				,						lowerCAmelCase       :    Tuple = None				,						)   ->    Mapping[str, Any]:


     '''simple docstring'''




     if self.task in ["default", "seq2seq-lm"]:
        SCREAMING_SNAKE_CASE_:       Optional[Any]   	=self._generate_dummy_inputs_for_default_and_seqaseq_lm(
            lowerCAmelCase				,						batch_size=lowerCAmelCase				,						seq_length=lowerCAmelCase				,						is_pair=lowerCAmelCase				,						framework=lowerCAmelCase						)

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

     return common_inputs





  def 		lowerCamelCase__				(  self       :    Tuple				,						lowerCAmelCase       :    Optional[int]				,						lowerCAmelCase       :    int				,						lowerCAmelCase       :    Any				,						lowerCAmelCase       :    int						)   ->    int:


     '''simple docstring'''




     if self.task in ["default", "seq2seq-lm"]:
        SCREAMING_SNAKE_CASE_:       List[Any]   	=super()._flatten_past_key_values_(lowerCAmelCase				,						lowerCAmelCase				,						lowerCAmelCase				,						lowerCAmelCase						)
     else:
        SCREAMING_SNAKE_CASE_:       Tuple   	=super(lowerCAmelCase				,						self						)._flatten_past_key_values_(
            lowerCAmelCase				,						lowerCAmelCase				,						lowerCAmelCase				,						lowerCAmelCase						)







from __future__ import annotations



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						bool:
    if len(lowerCamelCase_ ) < 2:
        raise ValueError('Monogons and Digons are not polygons in the Euclidean space' )
    if any(i <= 0 for i in nums ):
        raise ValueError('All values must be greater than 0' )
    _lowercase       :							Tuple      		=		nums.copy()
    copy_nums.sort()
    return copy_nums[-1] < sum(copy_nums[:-1] )


if __name__ == "__main__":
 import doctest

 doctest.testmod()


