

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

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


SCREAMING_SNAKE_CASE		:Union[str, Any]				  =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:Dict				  =    {
    """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}






class 	__magic_name__				(       a_							):
     UpperCamelCase_  :Union[str, Any]								=			'mbart'
     UpperCamelCase_  :Any								=			['past_key_values']
     UpperCamelCase_  :List[str]								=			{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}






     def __init__(							self		,	_lowercase=50_265		,	_lowercase=1_024		,	_lowercase=12		,	_lowercase=4_096		,	_lowercase=16		,	_lowercase=12		,	_lowercase=4_096		,	_lowercase=16		,	_lowercase=0.0		,	_lowercase=0.0		,	_lowercase=True		,	_lowercase=True		,	_lowercase="gelu"		,	_lowercase=1_024		,	_lowercase=0.1		,	_lowercase=0.0		,	_lowercase=0.0		,	_lowercase=0.02		,	_lowercase=0.0		,	_lowercase=False		,	_lowercase=1		,	_lowercase=0		,	_lowercase=2		,	_lowercase=2		,	**_lowercase		,	)->    Tuple:
         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_      =	classifier_dropout
         UpperCamelCase_      =	use_cache
         UpperCamelCase_      =	encoder_layers
         UpperCamelCase_      =	scale_embedding  # scale factor will be sqrt(d_model) if True
         super().__init__(
             pad_token_id=_snake_case		,	bos_token_id=_snake_case		,	eos_token_id=_snake_case		,	is_encoder_decoder=_snake_case		,	forced_eos_token_id=_snake_case		,	**_snake_case		,	)






class 	__magic_name__				(       a_							):






     @property
     def   UpperCAmelCase_							(							self  )->    Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
             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_(_snake_case		,	direction="inputs"  )
         elif self.task == "causal-lm":
             # TODO: figure this case out.
             UpperCamelCase_      =	OrderedDict(
                 [
                     ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                     ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                 ]  )
             if self.use_past:
                 UpperCamelCase_      =	self.num_layers
                 for i in range(_snake_case  ):
                     UpperCamelCase_      =	{0: '''batch''', 2: '''past_sequence + sequence'''}
                     UpperCamelCase_      =	{0: '''batch''', 2: '''past_sequence + sequence'''}
         else:
             UpperCamelCase_      =	OrderedDict(
                 [
                     ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                     ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                     ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                     ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
                 ]  )

         return common_inputs






     @property
     def   UpperCAmelCase_							(							self  )->    Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
             UpperCamelCase_      =	super().outputs
         else:
             UpperCamelCase_      =	super(_snake_case		,	self  ).outputs
             if self.use_past:
                 UpperCamelCase_      =	self.num_layers
                 for i in range(_snake_case  ):
                     UpperCamelCase_      =	{0: '''batch''', 2: '''past_sequence + sequence'''}
                     UpperCamelCase_      =	{0: '''batch''', 2: '''past_sequence + sequence'''}
         return common_outputs






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = -1		,	_lowercase = -1		,	_lowercase = False		,	_lowercase = None		,	)->    Mapping[str, Any]:
         UpperCamelCase_      =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _snake_case		,	_snake_case		,	_snake_case		,	_snake_case		,	_snake_case  )

         # Generate decoder inputs
         UpperCamelCase_      =	seq_length if not self.use_past else 1
         UpperCamelCase_      =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _snake_case		,	_snake_case		,	_snake_case		,	_snake_case		,	_snake_case  )
         UpperCamelCase_      =	{F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
         UpperCamelCase_      =	dict(**_snake_case		,	**_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_      =	common_inputs['''input_ids'''].shape
             UpperCamelCase_      =	common_inputs['''decoder_input_ids'''].shape[1]
             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(_snake_case		,	_snake_case  )]		,	dim=1  )

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

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






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = -1		,	_lowercase = -1		,	_lowercase = False		,	_lowercase = None		,	)->    Mapping[str, Any]:
         UpperCamelCase_      =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _snake_case		,	_snake_case		,	_snake_case		,	_snake_case		,	_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_      =	common_inputs['''input_ids'''].shape
             # Not using the same length for past_key_values
             UpperCamelCase_      =	seqlen + 2
             UpperCamelCase_      =	self.num_layers
             UpperCamelCase_      =	self.num_attention_heads
             UpperCamelCase_      =	(
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )

             UpperCamelCase_      =	common_inputs['''attention_mask'''].dtype
             UpperCamelCase_      =	torch.cat(
                 [common_inputs["attention_mask"], torch.ones(_snake_case		,	_snake_case		,	dtype=_snake_case  )]		,	dim=1  )
             UpperCamelCase_      =	[
                 (torch.zeros(_snake_case  ), torch.zeros(_snake_case  )) for _ in range(_snake_case  )
             ]
         return common_inputs






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = -1		,	_lowercase = -1		,	_lowercase = False		,	_lowercase = None		,	)->    Mapping[str, Any]:
         # 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(
             _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_      =	tokenizer.num_special_tokens_to_add(_snake_case  )
         UpperCamelCase_      =	compute_effective_axis_dimension(
             _snake_case		,	fixed_dimension=OnnxConfig.default_fixed_sequence		,	num_token_to_add=_snake_case  )

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






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = -1		,	_lowercase = -1		,	_lowercase = False		,	_lowercase = None		,	)->    Mapping[str, Any]:
         if self.task in ["default", "seq2seq-lm"]:
             UpperCamelCase_      =	self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 _snake_case		,	batch_size=_snake_case		,	seq_length=_snake_case		,	is_pair=_snake_case		,	framework=_snake_case  )

         elif self.task == "causal-lm":
             UpperCamelCase_      =	self._generate_dummy_inputs_for_causal_lm(
                 _snake_case		,	batch_size=_snake_case		,	seq_length=_snake_case		,	is_pair=_snake_case		,	framework=_snake_case  )
         else:
             UpperCamelCase_      =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 _snake_case		,	batch_size=_snake_case		,	seq_length=_snake_case		,	is_pair=_snake_case		,	framework=_snake_case  )

         return common_inputs






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )->    Optional[int]:
         if self.task in ["default", "seq2seq-lm"]:
             UpperCamelCase_      =	super()._flatten_past_key_values_(_snake_case		,	_snake_case		,	_snake_case		,	_snake_case  )
         else:
             UpperCamelCase_      =	super(_snake_case		,	self  )._flatten_past_key_values_(
                 _snake_case		,	_snake_case		,	_snake_case		,	_snake_case  )



def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	str:



    """simple docstring"""






    if power < 0:
        return -1
    base %= mod
    UpperCamelCase_      =	1

    while power > 0:
        if power & 1:
            UpperCamelCase_      =	(result * base) % mod
        UpperCamelCase_      =	power >> 1
        UpperCamelCase_      =	(base * base) % mod

    return result




def  lowerCAmelCase(			)->	Tuple:



    """simple docstring"""






    print(modular_exponential(3 ,	2_0_0 ,	1_3 ) )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 main()
