


'''simple docstring'''







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


a__    :   Tuple								=			logging.get_logger(__name__)

a__    :   Dict								=			{
    '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    lowercase_	(       a__  ):
    __UpperCAmelCase       =      'm2m_100'
    __UpperCAmelCase       =      ['past_key_values']
    __UpperCAmelCase       =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


    def __init__(	self    ,   a=12_81_12    ,   a=10_24    ,   a=12    ,   a=40_96    ,   a=16    ,   a=12    ,   a=40_96    ,   a=16    ,   a=0.05    ,   a=0.05    ,   a=True    ,   a=True    ,   a="relu"    ,   a=10_24    ,   a=0.1    ,   a=0.1    ,   a=0.0    ,   a=0.02    ,   a=2    ,   a=True    ,   a=1    ,   a=0    ,   a=2    ,   **a    ,   ):
          UpperCamelCase__      =							vocab_size
          UpperCamelCase__      =							max_position_embeddings
          UpperCamelCase__      =							d_model
          UpperCamelCase__      =							encoder_ffn_dim
          UpperCamelCase__      =							encoder_layers
          UpperCamelCase__      =							encoder_attention_heads
          UpperCamelCase__      =							decoder_ffn_dim
          UpperCamelCase__      =							decoder_layers
          UpperCamelCase__      =							decoder_attention_heads
          UpperCamelCase__      =							dropout
          UpperCamelCase__      =							attention_dropout
          UpperCamelCase__      =							activation_dropout
          UpperCamelCase__      =							activation_function
          UpperCamelCase__      =							init_std
          UpperCamelCase__      =							encoder_layerdrop
          UpperCamelCase__      =							decoder_layerdrop
          UpperCamelCase__      =							use_cache
          UpperCamelCase__      =							encoder_layers
          UpperCamelCase__      =							scale_embedding  # scale factor will be sqrt(d_model) if True

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

class    lowercase_	(       a__  ):


    @property
    def   __a				(	self       ):
          UpperCamelCase__      =							OrderedDict(
              [
                  ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                  ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
              ]       )

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

          if self.use_past:
                self.fill_with_past_key_values_(a    ,   direction="inputs"       )
          return common_inputs


    def   __a				(	self    ,   a    ,   a = -1    ,   a = -1    ,   a = False    ,   a = None    ,   ):
          # Copied from OnnxConfig.generate_dummy_inputs
          # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
          # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
          UpperCamelCase__      =							compute_effective_axis_dimension(
              a    ,   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__      =							tokenizer.num_special_tokens_to_add(a       )
          UpperCamelCase__      =							compute_effective_axis_dimension(
              a    ,   fixed_dimension=OnnxConfig.default_fixed_sequence    ,   num_token_to_add=a       )

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


    def   __a				(	self    ,   a    ,   a = -1    ,   a = -1    ,   a = False    ,   a = None    ,   ):
          UpperCamelCase__      =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              a    ,   a    ,   a    ,   a    ,   a       )

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

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

                UpperCamelCase__      =							torch.cat(
                    [common_inputs["decoder_attention_mask"], torch.ones(a    ,   a       )]    ,   dim=1       )

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

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

    __UpperCAmelCase       =      _generate_dummy_inputs_for_default_and_seqaseq_lm









'''simple docstring'''







def _UpperCamelCase			(	__A ,     __A       )				->						int:


      '''simple docstring'''



      return (input_a, input_a).count(0       ) % 2







def _UpperCamelCase			(	)				->						None:


      '''simple docstring'''



      assert xor_gate(0 ,     0       ) == 0
      assert xor_gate(0 ,     1       ) == 1
      assert xor_gate(1 ,     0       ) == 1
      assert xor_gate(1 ,     1       ) == 0


if __name__ == "__main__":
  print(xor_gate(0, 0))
  print(xor_gate(0, 1))





