



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_								=   logging.get_logger(__name__)

a_								=   {
    """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 	__lowerCAmelCase       (		lowerCAmelCase__				):
 lowerCAmelCase__       =					"""m2m_100"""
 lowerCAmelCase__       =					["""past_key_values"""]
 lowerCAmelCase__       =					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




 def __init__(  self		,			__UpperCAmelCase=128112		,			__UpperCAmelCase=1024		,			__UpperCAmelCase=12		,			__UpperCAmelCase=4096		,			__UpperCAmelCase=16		,			__UpperCAmelCase=12		,			__UpperCAmelCase=4096		,			__UpperCAmelCase=16		,			__UpperCAmelCase=0.05		,			__UpperCAmelCase=0.05		,			__UpperCAmelCase=True		,			__UpperCAmelCase=True		,			__UpperCAmelCase="relu"		,			__UpperCAmelCase=1024		,			__UpperCAmelCase=0.1		,			__UpperCAmelCase=0.1		,			__UpperCAmelCase=0.0		,			__UpperCAmelCase=0.02		,			__UpperCAmelCase=2		,			__UpperCAmelCase=True		,			__UpperCAmelCase=1		,			__UpperCAmelCase=0		,			__UpperCAmelCase=2		,			**__UpperCAmelCase		,			):



    '''simple docstring'''
    __lowerCamelCase  							=      vocab_size
    __lowerCamelCase  							=      max_position_embeddings
    __lowerCamelCase  							=      d_model
    __lowerCamelCase  							=      encoder_ffn_dim
    __lowerCamelCase  							=      encoder_layers
    __lowerCamelCase  							=      encoder_attention_heads
    __lowerCamelCase  							=      decoder_ffn_dim
    __lowerCamelCase  							=      decoder_layers
    __lowerCamelCase  							=      decoder_attention_heads
    __lowerCamelCase  							=      dropout
    __lowerCamelCase  							=      attention_dropout
    __lowerCamelCase  							=      activation_dropout
    __lowerCamelCase  							=      activation_function
    __lowerCamelCase  							=      init_std
    __lowerCamelCase  							=      encoder_layerdrop
    __lowerCamelCase  							=      decoder_layerdrop
    __lowerCamelCase  							=      use_cache
    __lowerCamelCase  							=      encoder_layers
    __lowerCamelCase  							=      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		,			**__UpperCAmelCase		,			)



class 	__lowerCAmelCase       (		lowerCAmelCase__				):




 @property
 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      OrderedDict(
        [
            ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
            ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
        ]			)

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

    if self.use_past:
       self.fill_with_past_key_values_(__UpperCAmelCase		,			direction='''inputs'''			)
    return common_inputs




 def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase = -1		,			__UpperCAmelCase = -1		,			__UpperCAmelCase = False		,			__UpperCAmelCase = None		,			):



    '''simple docstring'''
    # 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
    __lowerCamelCase  							=      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
    __lowerCamelCase  							=      tokenizer.num_special_tokens_to_add(__UpperCAmelCase			)
    __lowerCamelCase  							=      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
    __lowerCamelCase  							=      [''' '''.join([tokenizer.unk_token]			) * seq_length] * batch_size
    __lowerCamelCase  							=      dict(tokenizer(__UpperCAmelCase		,			return_tensors=__UpperCAmelCase			)			)
    return common_inputs




 def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase = -1		,			__UpperCAmelCase = -1		,			__UpperCAmelCase = False		,			__UpperCAmelCase = None		,			):



    '''simple docstring'''
    __lowerCamelCase  							=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        __UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase			)

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

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

       __lowerCamelCase  							=      []
       # If the number of encoder and decoder layers are present in the model configuration, both are considered
       __lowerCamelCase							,__lowerCamelCase  							=      self.num_layers
       __lowerCamelCase  							=      min(__UpperCAmelCase		,			__UpperCAmelCase			)
       __lowerCamelCase  							=      max(__UpperCAmelCase		,			__UpperCAmelCase			) - min_num_layers
       __lowerCamelCase  							=      '''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.
       __lowerCamelCase  							=      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
 lowerCAmelCase__       =					_generate_dummy_inputs_for_default_and_seqaseq_lm











def 						a__     (						_UpperCamelCase     :	list	,_UpperCamelCase     :	list	,_UpperCamelCase     :	list	,_UpperCamelCase     :	int	,_UpperCamelCase     :	int							):
   if target == 0:
      answer.append(path.copy()							)
   else:
      for index in range(_UpperCamelCase	,len(_UpperCamelCase							)							):
         if target >= candidates[index]:
            path.append(candidates[index]							)
            backtrack(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,target - candidates[index]	,_UpperCamelCase							)
            path.pop(len(_UpperCamelCase							) - 1							)





def 						a__     (						_UpperCamelCase     :	list	,_UpperCamelCase     :	int							):
   __lowerCamelCase  							=      []  # type: list[int]
   __lowerCamelCase  							=      []  # type: list[int]
   backtrack(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,0							)
   return answer





def 						a__     (						):
   print(combination_sum([-8, 2.3, 0]	,1							)							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()






