


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


a_ :Optional[int]  							=			logging.get_logger(__name__)

a_ :Union[str, Any]  							=			{
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}

class 							lowercase  (						_UpperCAmelCase			):
     lowerCamelCase			:			Union[str, Any]      = '''marian'''
     lowerCamelCase			:			Tuple      = ['''past_key_values''']
     lowerCamelCase			:			Optional[Any]      = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}







     def __init__(						self :						Optional[Any]   ,   _lowercase :						str=5_81_01   ,   _lowercase :						Union[str, Any]=None   ,   _lowercase :						Tuple=10_24   ,   _lowercase :						List[Any]=12   ,   _lowercase :						int=40_96   ,   _lowercase :						int=16   ,   _lowercase :						str=12   ,   _lowercase :						List[str]=40_96   ,   _lowercase :						Tuple=16   ,   _lowercase :						List[Any]=0.0   ,   _lowercase :						Any=0.0   ,   _lowercase :						List[Any]=True   ,   _lowercase :						Dict=True   ,   _lowercase :						Union[str, Any]="gelu"   ,   _lowercase :						int=10_24   ,   _lowercase :						Optional[Any]=0.1   ,   _lowercase :						List[Any]=0.0   ,   _lowercase :						Optional[int]=0.0   ,   _lowercase :						str=0.02   ,   _lowercase :						Tuple=5_81_00   ,   _lowercase :						int=False   ,   _lowercase :						Any=5_81_00   ,   _lowercase :						Tuple=0   ,   _lowercase :						Tuple=0   ,   _lowercase :						List[Any]=True   ,   **_lowercase :						int   ,   ):
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		vocab_size
           SCREAMING_SNAKE_CASE__       :  Dict						     =		decoder_vocab_size or vocab_size
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		max_position_embeddings
           SCREAMING_SNAKE_CASE__       :  int						     =		d_model
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		encoder_ffn_dim
           SCREAMING_SNAKE_CASE__       :  int						     =		encoder_layers
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		encoder_attention_heads
           SCREAMING_SNAKE_CASE__       :  Dict						     =		decoder_ffn_dim
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		decoder_layers
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		decoder_attention_heads
           SCREAMING_SNAKE_CASE__       :  int						     =		dropout
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		attention_dropout
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		activation_dropout
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		activation_function
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		init_std
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		encoder_layerdrop
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		decoder_layerdrop
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		use_cache
           SCREAMING_SNAKE_CASE__       :  str						     =		encoder_layers
           SCREAMING_SNAKE_CASE__       :  Any						     =		scale_embedding  # scale factor will be sqrt(d_model) if True
           SCREAMING_SNAKE_CASE__       :  str						     =		share_encoder_decoder_embeddings
           super().__init__(
               pad_token_id=_lowercase   ,   eos_token_id=_lowercase   ,   is_encoder_decoder=_lowercase   ,   decoder_start_token_id=_lowercase   ,   forced_eos_token_id=_lowercase   ,   **_lowercase   ,   )




class 							lowercase  (						_UpperCAmelCase			):







     @property
     # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
     def 							lowercase__       (						self :						Tuple      ):
           if self.task in ["default", "seq2seq-lm"]:
                 SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		OrderedDict(
                     [
                         ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                         ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ]      )

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

                 if self.use_past:
                       self.fill_with_past_key_values_(_lowercase   ,   direction='''inputs'''      )
           elif self.task == "causal-lm":
                 # TODO: figure this case out.
                 SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		OrderedDict(
                     [
                         ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                         ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ]      )
                 if self.use_past:
                       SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  int						     =		self.num_layers
                       for i in range(_lowercase      ):
                             SCREAMING_SNAKE_CASE__       :  int						     =		{0: '''batch''', 2: '''past_sequence + sequence'''}
                             SCREAMING_SNAKE_CASE__       :  Any						     =		{0: '''batch''', 2: '''past_sequence + sequence'''}
           else:
                 SCREAMING_SNAKE_CASE__       :  int						     =		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
     # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
     def 							lowercase__       (						self :						Tuple      ):
           if self.task in ["default", "seq2seq-lm"]:
                 SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		super().outputs
           else:
                 SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		super(_lowercase   ,   self      ).outputs
                 if self.use_past:
                       SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		self.num_layers
                       for i in range(_lowercase      ):
                             SCREAMING_SNAKE_CASE__       :  List[Any]						     =		{0: '''batch''', 2: '''past_sequence + sequence'''}
                             SCREAMING_SNAKE_CASE__       :  Any						     =		{0: '''batch''', 2: '''past_sequence + sequence'''}
           return common_outputs







     def 							lowercase__       (						self :						Optional[Any]   ,   _lowercase :						PreTrainedTokenizer   ,   _lowercase :						int = -1   ,   _lowercase :						int = -1   ,   _lowercase :						bool = False   ,   _lowercase :						Optional[TensorType] = None   ,   ):
           SCREAMING_SNAKE_CASE__       :  str						     =		self._generate_dummy_inputs_for_encoder_and_decoder(
               _lowercase   ,   _lowercase   ,   _lowercase   ,   _lowercase   ,   _lowercase      )

           # Generate decoder inputs
           SCREAMING_SNAKE_CASE__       :  str						     =		seq_length if not self.use_past else 1
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		self._generate_dummy_inputs_for_encoder_and_decoder(
               _lowercase   ,   _lowercase   ,   _lowercase   ,   _lowercase   ,   _lowercase      )
           SCREAMING_SNAKE_CASE__       :  Dict						     =		{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		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
                 SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  str						     =		common_inputs['''input_ids'''].shape
                 SCREAMING_SNAKE_CASE__       :  str						     =		common_inputs['''decoder_input_ids'''].shape[1]
                 SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Dict						     =		self.num_attention_heads
                 SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		(
                     batch,
                     num_encoder_attention_heads,
                     encoder_seq_length,
                     self._config.hidden_size // num_encoder_attention_heads,
                 )
                 SCREAMING_SNAKE_CASE__       :  Optional[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__       :  str						     =		torch.cat(
                     [common_inputs['''decoder_attention_mask'''], torch.ones(_lowercase   ,   _lowercase      )]   ,   dim=1      )

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







     def 							lowercase__       (						self :						int   ,   _lowercase :						PreTrainedTokenizer   ,   _lowercase :						int = -1   ,   _lowercase :						int = -1   ,   _lowercase :						bool = False   ,   _lowercase :						Optional[TensorType] = None   ,   ):
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		self._generate_dummy_inputs_for_encoder_and_decoder(
               _lowercase   ,   _lowercase   ,   _lowercase   ,   _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
                 SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  List[str]						     =		common_inputs['''input_ids'''].shape
                 # Not using the same length for past_key_values
                 SCREAMING_SNAKE_CASE__       :  Any						     =		seqlen + 2
                 SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		self.num_layers
                 SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  List[Any]						     =		self.num_attention_heads
                 SCREAMING_SNAKE_CASE__       :  str						     =		(
                     batch,
                     num_encoder_attention_heads,
                     past_key_values_length,
                     self._config.hidden_size // num_encoder_attention_heads,
                 )

                 SCREAMING_SNAKE_CASE__       :  Any						     =		common_inputs['''attention_mask'''].dtype
                 SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		torch.cat(
                     [common_inputs['''attention_mask'''], torch.ones(_lowercase   ,   _lowercase   ,   dtype=_lowercase      )]   ,   dim=1      )
                 SCREAMING_SNAKE_CASE__       :  List[str]						     =		[
                     (torch.zeros(_lowercase      ), torch.zeros(_lowercase      )) for _ in range(_lowercase      )
                 ]
           return common_inputs







     def 							lowercase__       (						self :						Optional[Any]   ,   _lowercase :						PreTrainedTokenizer   ,   _lowercase :						int = -1   ,   _lowercase :						int = -1   ,   _lowercase :						bool = False   ,   _lowercase :						Optional[TensorType] = 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(
               _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
           SCREAMING_SNAKE_CASE__       :  str						     =		tokenizer.num_special_tokens_to_add(_lowercase      )
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		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
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		[''' '''.join([tokenizer.unk_token]      ) * seq_length] * batch_size
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		dict(tokenizer(_lowercase   ,   return_tensors=_lowercase      )      )
           return common_inputs







     def 							lowercase__       (						self :						List[str]   ,   _lowercase :						PreTrainedTokenizer   ,   _lowercase :						int = -1   ,   _lowercase :						int = -1   ,   _lowercase :						bool = False   ,   _lowercase :						Optional[TensorType] = None   ,   ):
           if self.task in ["default", "seq2seq-lm"]:
                 SCREAMING_SNAKE_CASE__       :  Dict						     =		self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                     _lowercase   ,   batch_size=_lowercase   ,   seq_length=_lowercase   ,   is_pair=_lowercase   ,   framework=_lowercase      )

           else:
                 SCREAMING_SNAKE_CASE__       :  List[Any]						     =		self._generate_dummy_inputs_for_causal_lm(
                     _lowercase   ,   batch_size=_lowercase   ,   seq_length=_lowercase   ,   is_pair=_lowercase   ,   framework=_lowercase      )

           return common_inputs







     def 							lowercase__       (						self :						str   ,   _lowercase :						Union[str, Any]   ,   _lowercase :						Any   ,   _lowercase :						Optional[int]   ,   _lowercase :						Union[str, Any]      ):
           if self.task in ["default", "seq2seq-lm"]:
                 SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		super()._flatten_past_key_values_(_lowercase   ,   _lowercase   ,   _lowercase   ,   _lowercase      )
           else:
                 SCREAMING_SNAKE_CASE__       :  List[Any]						     =		super(_lowercase   ,   self      )._flatten_past_key_values_(
                     _lowercase   ,   _lowercase   ,   _lowercase   ,   _lowercase      )







     @property
     def 							lowercase__       (						self :						Dict      ):
           return 1E-4









UpperCAmelCase_							:					dict[tuple[int, int, int], int]		 =   {}
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int			,a__    :   int			,a__    :   int       )					->				int:

	# if we are absent twice, or late 3 consecutive days,
	# no further prize strings are possible
	if late == 3 or absent == 2:
		return 0

	# if we have no days left, and have not failed any other rules,
	# we have a prize string
	if days == 0:
		return 1

	# No easy solution, so now we need to do the recursive calculation

	# First, check if the combination is already in the cache, and
	# if yes, return the stored value from there since we already
	# know the number of possible prize strings from this point on
	__A :			List[Any]		    =			(days, absent, late)
	if key in cache:
		return cache[key]

	# now we calculate the three possible ways that can unfold from
	# this point on, depending on our attendance today

	# 1) if we are late (but not absent), the "absent" counter stays as
	# it is, but the "late" counter increases by one
	__A :			Dict		    =			_calculate(days - 1			,a__			,late + 1       )

	# 2) if we are absent, the "absent" counter increases by 1, and the
	# "late" counter resets to 0
	__A :			List[str]		    =			_calculate(days - 1			,absent + 1			,0       )

	# 3) if we are on time, this resets the "late" counter and keeps the
	# absent counter
	__A :			int		    =			_calculate(days - 1			,a__			,0       )

	__A :			Optional[int]		    =			state_late + state_absent + state_ontime

	__A :			Tuple		    =			prizestrings
	return prizestrings
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int = 30       )					->				int:
	return _calculate(a__			,absent=0			,late=0       )


if __name__ == "__main__":
			print(solution())





