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

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


__snake_case							 =  logging.get_logger(__name__)

__snake_case							 =  {
    """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json""",
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}







class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : List[Any]									='m2m_100'
      UpperCamelCase_    : Union[str, Any]									=['past_key_values']
      UpperCamelCase_    : Union[str, Any]									={'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
      def __init__(    self   ,    SCREAMING_SNAKE_CASE_=12_8112   ,    SCREAMING_SNAKE_CASE_=1024   ,    SCREAMING_SNAKE_CASE_=12   ,    SCREAMING_SNAKE_CASE_=4096   ,    SCREAMING_SNAKE_CASE_=16   ,    SCREAMING_SNAKE_CASE_=12   ,    SCREAMING_SNAKE_CASE_=4096   ,    SCREAMING_SNAKE_CASE_=16   ,    SCREAMING_SNAKE_CASE_=0.05   ,    SCREAMING_SNAKE_CASE_=0.05   ,    SCREAMING_SNAKE_CASE_=True   ,    SCREAMING_SNAKE_CASE_=True   ,    SCREAMING_SNAKE_CASE_="relu"   ,    SCREAMING_SNAKE_CASE_=1024   ,    SCREAMING_SNAKE_CASE_=0.1   ,    SCREAMING_SNAKE_CASE_=0.1   ,    SCREAMING_SNAKE_CASE_=0.0   ,    SCREAMING_SNAKE_CASE_=0.02   ,    SCREAMING_SNAKE_CASE_=2   ,    SCREAMING_SNAKE_CASE_=True   ,    SCREAMING_SNAKE_CASE_=1   ,    SCREAMING_SNAKE_CASE_=0   ,    SCREAMING_SNAKE_CASE_=2   ,    **SCREAMING_SNAKE_CASE_   ,    )    ->				Any:
          UpperCamelCase	:List[str]     							=       vocab_size
          UpperCamelCase	:Tuple     							=       max_position_embeddings
          UpperCamelCase	:int     							=       d_model
          UpperCamelCase	:Optional[int]     							=       encoder_ffn_dim
          UpperCamelCase	:Any     							=       encoder_layers
          UpperCamelCase	:Optional[Any]     							=       encoder_attention_heads
          UpperCamelCase	:List[Any]     							=       decoder_ffn_dim
          UpperCamelCase	:Union[str, Any]     							=       decoder_layers
          UpperCamelCase	:Optional[Any]     							=       decoder_attention_heads
          UpperCamelCase	:Optional[int]     							=       dropout
          UpperCamelCase	:Optional[Any]     							=       attention_dropout
          UpperCamelCase	:Optional[int]     							=       activation_dropout
          UpperCamelCase	:Any     							=       activation_function
          UpperCamelCase	:Optional[Any]     							=       init_std
          UpperCamelCase	:Tuple     							=       encoder_layerdrop
          UpperCamelCase	:Optional[Any]     							=       decoder_layerdrop
          UpperCamelCase	:List[Any]     							=       use_cache
          UpperCamelCase	:Dict     							=       encoder_layers
          UpperCamelCase	:Tuple     							=       scale_embedding  # scale factor will be sqrt(d_model) if True

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







class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      @property
      def    UpperCAmelCase						(    self			)    ->				Dict:
          UpperCamelCase	:List[Any]     							=       OrderedDict(
              [
                  ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                  ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
              ]			)

          if self.use_past:
              UpperCamelCase	:Optional[int]     							=       {0: "batch"}
              UpperCamelCase	:str     							=       {0: "batch", 1: "past_decoder_sequence + sequence"}
          else:
              UpperCamelCase	:Optional[Any]     							=       {0: "batch", 1: "decoder_sequence"}
              UpperCamelCase	:Union[str, Any]     							=       {0: "batch", 1: "decoder_sequence"}

          if self.use_past:
              self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_   ,    direction='''inputs'''			)
          return common_inputs
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_ = -1   ,    SCREAMING_SNAKE_CASE_ = -1   ,    SCREAMING_SNAKE_CASE_ = False   ,    SCREAMING_SNAKE_CASE_ = None   ,    )    ->				Tuple:
          UpperCamelCase	:int     							=       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
          UpperCamelCase	:Dict     							=       tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:str     							=       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
          UpperCamelCase	:Union[str, Any]     							=       [" ".join([tokenizer.unk_token]			) * seq_length] * batch_size
          UpperCamelCase	:Tuple     							=       dict(tokenizer(SCREAMING_SNAKE_CASE_   ,    return_tensors=SCREAMING_SNAKE_CASE_			)			)
          return common_inputs
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_ = -1   ,    SCREAMING_SNAKE_CASE_ = -1   ,    SCREAMING_SNAKE_CASE_ = False   ,    SCREAMING_SNAKE_CASE_ = None   ,    )    ->				Optional[int]:
          UpperCamelCase	:Optional[Any]     							=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

          # Generate decoder inputs
          UpperCamelCase	:List[str]     							=       seq_length if not self.use_past else 1
          UpperCamelCase	:Dict     							=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Union[str, Any]     							=       {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
          UpperCamelCase	:Union[str, Any]     							=       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
              UpperCamelCase	:Union[str, Any]     							=       common_inputs["input_ids"].shape
              UpperCamelCase	:List[Any]     							=       common_inputs["decoder_input_ids"].shape[1]
              UpperCamelCase	:Optional[Any]     							=       self.num_attention_heads
              UpperCamelCase	:Any     							=       (
                  batch,
                  num_encoder_attention_heads,
                  encoder_seq_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              UpperCamelCase	:Optional[Any]     							=       decoder_seq_length + 3
              UpperCamelCase	:int     							=       (
                  batch,
                  num_decoder_attention_heads,
                  decoder_past_length,
                  self._config.hidden_size // num_decoder_attention_heads,
              )

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

              UpperCamelCase	:Tuple     							=       []
              # If the number of encoder and decoder layers are present in the model configuration, both are considered
              UpperCamelCase	:Tuple     							=       self.num_layers
              UpperCamelCase	:Optional[Any]     							=       min(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)
              UpperCamelCase	:Tuple     							=       max(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			) - min_num_layers
              UpperCamelCase	:Optional[int]     							=       "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.
              UpperCamelCase	:Union[str, Any]     							=       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




      UpperCamelCase_    : str									=_generate_dummy_inputs_for_default_and_seqaseq_lm













def   _A       (    SCREAMING_SNAKE_CASE__  :     int	):
    UpperCamelCase	:Optional[Any]     							=       9

    while num > 0:
        if num % 10 != digit:
            return False
        num //= 100
        digit -= 1

    return True


def   _A       (    ):
    UpperCamelCase	:List[str]     							=       138902663

    while not is_square_form(num * num	):
        if num % 10 == 3:
            num -= 6  # (3 - 6) % 10 = 7
        else:
            num -= 4  # (7 - 4) % 10 = 3

    return num * 10


if __name__ == "__main__":
 print(f'''{solution() = }''')





