





"""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


_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       snake_case		(       UpperCAmelCase_       ):
      lowerCamelCase__      =			'''m2m_100'''
      lowerCamelCase__      =			['''past_key_values''']
      lowerCamelCase__      =			{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

      def __init__(      self       :str						,							_lowerCamelCase       :Dict=1_2_8_1_1_2						,							_lowerCamelCase       :Optional[int]=1_0_2_4						,							_lowerCamelCase       :int=1_2						,							_lowerCamelCase       :Union[str, Any]=4_0_9_6						,							_lowerCamelCase       :Optional[Any]=1_6						,							_lowerCamelCase       :Union[str, Any]=1_2						,							_lowerCamelCase       :Optional[Any]=4_0_9_6						,							_lowerCamelCase       :Any=1_6						,							_lowerCamelCase       :Tuple=0.0_5						,							_lowerCamelCase       :Optional[Any]=0.0_5						,							_lowerCamelCase       :Dict=True						,							_lowerCamelCase       :Dict=True						,							_lowerCamelCase       :List[str]="relu"						,							_lowerCamelCase       :Optional[Any]=1_0_2_4						,							_lowerCamelCase       :Tuple=0.1						,							_lowerCamelCase       :Tuple=0.1						,							_lowerCamelCase       :Union[str, Any]=0.0						,							_lowerCamelCase       :List[Any]=0.0_2						,							_lowerCamelCase       :Optional[int]=2						,							_lowerCamelCase       :str=True						,							_lowerCamelCase       :Any=1						,							_lowerCamelCase       :Dict=0						,							_lowerCamelCase       :str=2						,							**_lowerCamelCase       :Dict						,							):
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	vocab_size
       __SCREAMING_SNAKE_CASE			:      Any 							=	max_position_embeddings
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	d_model
       __SCREAMING_SNAKE_CASE			:      Dict 							=	encoder_ffn_dim
       __SCREAMING_SNAKE_CASE			:      Any 							=	encoder_layers
       __SCREAMING_SNAKE_CASE			:      str 							=	encoder_attention_heads
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	decoder_ffn_dim
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	decoder_layers
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	decoder_attention_heads
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	dropout
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	attention_dropout
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	activation_dropout
       __SCREAMING_SNAKE_CASE			:      str 							=	activation_function
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	init_std
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	encoder_layerdrop
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	decoder_layerdrop
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	use_cache
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	encoder_layers
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	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       snake_case		(       UpperCAmelCase_       ):

      @property
      def 						SCREAMING_SNAKE_CASE_      (      self       :List[str]						):
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	OrderedDict(
           [
               ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
           ]						)

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

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

      def 						SCREAMING_SNAKE_CASE_      (      self       :Union[str, Any]						,							_lowerCamelCase       :List[str]						,							_lowerCamelCase       :Union[str, Any] = -1						,							_lowerCamelCase       :str = -1						,							_lowerCamelCase       :Union[str, Any] = False						,							_lowerCamelCase       :str = 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
       __SCREAMING_SNAKE_CASE			:      str 							=	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
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	tokenizer.num_special_tokens_to_add(__UpperCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	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
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	[''' '''.join([tokenizer.unk_token]						) * seq_length] * batch_size
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	dict(tokenizer(__UpperCamelCase						,							return_tensors=__UpperCamelCase						)						)
       return common_inputs

      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[Any]						,							_lowerCamelCase       :Optional[int]						,							_lowerCamelCase       :Optional[int] = -1						,							_lowerCamelCase       :str = -1						,							_lowerCamelCase       :Any = False						,							_lowerCamelCase       :Optional[Any] = None						,							):
       __SCREAMING_SNAKE_CASE			:      Any 							=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           __UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase						)

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

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

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








"""simple docstring"""





def 		lowerCAmelCase_     (  lowercase_       :							int = 30		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      str 							=	exponent + 2
 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	(1 + 5**0.5) / 2
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5

 return int(lowercase_		)


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

