

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


lowerCamelCase 	=   logging.get_logger(__name__)

lowerCamelCase 	=   {
    '''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 		__magic_name__     (				snake_case__				):

   '''simple docstring'''




   lowerCamelCase__					:      List[Any]        =  'm2m_100'
   lowerCamelCase__					:      str        =  ['past_key_values']
   lowerCamelCase__					:      List[Any]        =  {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



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






     """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=lowercase_,       bos_token_id=lowercase_,       eos_token_id=lowercase_,       is_encoder_decoder=lowercase_,       decoder_start_token_id=lowercase_,       **lowercase_,       )







class 		__magic_name__     (				snake_case__				):

   '''simple docstring'''




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






     """simple docstring"""






     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_(lowercase_,       direction='''inputs'''			)
     return common_inputs



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_ = -1,       lowercase_ = -1,       lowercase_ = False,       lowercase_ = None,       )				->       Mapping[str, Any]:






     """simple docstring"""






     a__    =compute_effective_axis_dimension(
         lowercase_,       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(lowercase_			)
     a__    =compute_effective_axis_dimension(
         lowercase_,       fixed_dimension=OnnxConfig.default_fixed_sequence,       num_token_to_add=lowercase_			)

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



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_ = -1,       lowercase_ = -1,       lowercase_ = False,       lowercase_ = None,       )				->       Mapping[str, Any]:






     """simple docstring"""






     a__    =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_			)

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

     if self.use_past:
       if not is_torch_available():
         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''			)
       else:
         import torch
       a__    =common_inputs['input_ids'].shape
       a__    =common_inputs['decoder_input_ids'].shape[1]
       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(lowercase_,       lowercase_			)],       dim=1			)

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

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


   lowerCamelCase__					:      int        =  _generate_dummy_inputs_for_default_and_seqaseq_lm



import numpy as np







def _a					(			SCREAMING_SNAKE_CASE						:     Union[str, Any]						)       ->       List[str]:

 """simple docstring"""






 __lowerCAmelCase:  Union[str, Any]        = np.exp(SCREAMING_SNAKE_CASE						)

 # Add up the all the exponentials
 __lowerCAmelCase:  Dict        = np.sum(SCREAMING_SNAKE_CASE						)

 # Divide every exponent by the sum of all exponents
 __lowerCAmelCase:  int        = exponent_vector / sum_of_exponents

 return softmax_vector


if __name__ == "__main__":
  print(softmax((0,)))





