

"""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


SCREAMING_SNAKE_CASE_					:	int							  =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						{
    '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						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					"blenderbot-small"
    UpperCAmelCase						 =					["past_key_values"]
    UpperCAmelCase						 =					{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

    def __init__(     self: List[str]    ,						UpperCamelCase: str=5_02_65    ,						UpperCamelCase: Dict=5_12    ,						UpperCamelCase: Optional[Any]=8    ,						UpperCamelCase: List[Any]=20_48    ,						UpperCamelCase: Tuple=16    ,						UpperCamelCase: int=8    ,						UpperCamelCase: List[str]=20_48    ,						UpperCamelCase: Tuple=16    ,						UpperCamelCase: List[str]=0.0    ,						UpperCamelCase: str=0.0    ,						UpperCamelCase: Optional[Any]=True    ,						UpperCamelCase: List[Any]=True    ,						UpperCamelCase: Union[str, Any]="gelu"    ,						UpperCamelCase: List[str]=5_12    ,						UpperCamelCase: Dict=0.1    ,						UpperCamelCase: List[Any]=0.0    ,						UpperCamelCase: Dict=0.0    ,						UpperCamelCase: Optional[Any]=0.02    ,						UpperCamelCase: Optional[int]=1    ,						UpperCamelCase: str=False    ,						UpperCamelCase: Optional[Any]=0    ,						UpperCamelCase: Union[str, Any]=1    ,						UpperCamelCase: Tuple=2    ,						UpperCamelCase: Dict=2    ,						**UpperCamelCase: List[Any]    ,						):






         """simple docstring"""


         A__										=  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

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



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    @property
    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         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_(UpperCamelCase    ,						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(UpperCamelCase       ):
                        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
    def 				UpperCamelCase	(     self: List[Any]       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: str    ,						UpperCamelCase: PreTrainedTokenizer    ,						UpperCamelCase: int = -1    ,						UpperCamelCase: int = -1    ,						UpperCamelCase: bool = False    ,						UpperCamelCase: Optional[TensorType] = None    ,						):






         """simple docstring"""


         A__										=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase       )

         # Generate decoder inputs
         A__										=  seq_length if not self.use_past else 1
         A__										=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase       )
         A__										=  {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
         A__										=  dict(**UpperCamelCase    ,						**UpperCamelCase       )

         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(UpperCamelCase    ,						UpperCamelCase       )]    ,						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(UpperCamelCase    ,						UpperCamelCase       )
              A__										=  max(UpperCamelCase    ,						UpperCamelCase       ) - min_num_layers
              A__										=  """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

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

    def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: PreTrainedTokenizer    ,						UpperCamelCase: int = -1    ,						UpperCamelCase: int = -1    ,						UpperCamelCase: bool = False    ,						UpperCamelCase: Optional[TensorType] = None    ,						):






         """simple docstring"""


         A__										=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase       )

         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(UpperCamelCase    ,						UpperCamelCase    ,						dtype=UpperCamelCase       )]    ,						dim=1       )
              A__										=  [
                  (torch.zeros(UpperCamelCase       ), torch.zeros(UpperCamelCase       )) for _ in range(UpperCamelCase       )
              ]
         return common_inputs

    def 				UpperCamelCase	(     self: Union[str, Any]    ,						UpperCamelCase: PreTrainedTokenizer    ,						UpperCamelCase: int = -1    ,						UpperCamelCase: int = -1    ,						UpperCamelCase: bool = False    ,						UpperCamelCase: Optional[TensorType] = None    ,						):






         """simple docstring"""


         A__										=  compute_effective_axis_dimension(
             UpperCamelCase    ,						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(UpperCamelCase       )
         A__										=  compute_effective_axis_dimension(
             UpperCamelCase    ,						fixed_dimension=OnnxConfig.default_fixed_sequence    ,						num_token_to_add=UpperCamelCase       )

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

    def 				UpperCamelCase	(     self: List[Any]    ,						UpperCamelCase: PreTrainedTokenizer    ,						UpperCamelCase: int = -1    ,						UpperCamelCase: int = -1    ,						UpperCamelCase: bool = False    ,						UpperCamelCase: Optional[TensorType] = None    ,						):






         """simple docstring"""


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

         elif self.task == "causal-lm":
              A__										=  self._generate_dummy_inputs_for_causal_lm(
                  UpperCamelCase    ,						batch_size=UpperCamelCase    ,						seq_length=UpperCamelCase    ,						is_pair=UpperCamelCase    ,						framework=UpperCamelCase       )
         else:
              A__										=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  UpperCamelCase    ,						batch_size=UpperCamelCase    ,						seq_length=UpperCamelCase    ,						is_pair=UpperCamelCase    ,						framework=UpperCamelCase       )

         return common_inputs





    def 				UpperCamelCase	(     self: Any    ,						UpperCamelCase: Optional[int]    ,						UpperCamelCase: str    ,						UpperCamelCase: Any    ,						UpperCamelCase: Optional[Any]       ):






         """simple docstring"""


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









"""simple docstring"""



def  _snake_case     (    UpperCAmelCase_  :					Optional[Any]					,   UpperCAmelCase_  :					Optional[Any]			):
     A__										=  [0 for i in range(r + 1			)]
     # nc0 = 1
     A__										=  1
     for i in range(1					,   n + 1			):
          # to compute current row from previous row.
          A__										=  min(UpperCAmelCase_					,   UpperCAmelCase_			)
          while j > 0:
               c[j] += c[j - 1]
               j -= 1
     return c[r]

print(binomial_coefficient(n=1_0, r=5))






