




"""simple docstring"""

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


UpperCamelCase__ :    Any 					=							logging.get_logger(__name__)

UpperCamelCase__ :    Any 					=							{
    "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     A__      (      lowercase_     ):
	A_							:				List[str]						     =      """mbart"""
	A_							:				Optional[int]						     =      ["""past_key_values"""]
	A_							:				int						     =      {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




	def __init__(   self  :	Tuple      ,    lowercase  :	int=50_265      ,    lowercase  :	Union[str, Any]=1_024      ,    lowercase  :	Optional[Any]=12      ,    lowercase  :	List[str]=4_096      ,    lowercase  :	Union[str, Any]=16      ,    lowercase  :	Union[str, Any]=12      ,    lowercase  :	List[str]=4_096      ,    lowercase  :	List[Any]=16      ,    lowercase  :	str=0.0      ,    lowercase  :	str=0.0      ,    lowercase  :	List[Any]=True      ,    lowercase  :	List[str]=True      ,    lowercase  :	Dict="gelu"      ,    lowercase  :	Dict=1_024      ,    lowercase  :	Union[str, Any]=0.1      ,    lowercase  :	Optional[Any]=0.0      ,    lowercase  :	Tuple=0.0      ,    lowercase  :	Union[str, Any]=0.02      ,    lowercase  :	List[Any]=0.0      ,    lowercase  :	Any=False      ,    lowercase  :	List[str]=1      ,    lowercase  :	List[str]=0      ,    lowercase  :	Any=2      ,    lowercase  :	List[Any]=2      ,    **lowercase  :	Union[str, Any]      ,    )					-> Optional[int]:
						__snake_case          =	vocab_size
						__snake_case          =	max_position_embeddings
						__snake_case          =	d_model
						__snake_case          =	encoder_ffn_dim
						__snake_case          =	encoder_layers
						__snake_case          =	encoder_attention_heads
						__snake_case          =	decoder_ffn_dim
						__snake_case          =	decoder_layers
						__snake_case          =	decoder_attention_heads
						__snake_case          =	dropout
						__snake_case          =	attention_dropout
						__snake_case          =	activation_dropout
						__snake_case          =	activation_function
						__snake_case          =	init_std
						__snake_case          =	encoder_layerdrop
						__snake_case          =	decoder_layerdrop
						__snake_case          =	classifier_dropout
						__snake_case          =	use_cache
						__snake_case          =	encoder_layers
						__snake_case          =	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      ,    forced_eos_token_id=lowercase      ,    **lowercase      ,    )

class     A__      (      lowercase_     ):




	@property
	def 							a_		(   self  :	Any	)					-> Mapping[str, Mapping[int, str]]:
						if self.task in ["default", "seq2seq-lm"]:
											__snake_case          =	OrderedDict(
											    [
											        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
											        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
											    ]	)

											if self.use_past:
																__snake_case          =	{0: """batch"""}
																__snake_case          =	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
											else:
																__snake_case          =	{0: """batch""", 1: """decoder_sequence"""}
																__snake_case          =	{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.
											__snake_case          =	OrderedDict(
											    [
											        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
											        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
											    ]	)
											if self.use_past:
																__snake_case      ,      __snake_case          =	self.num_layers
																for i in range(lowercase	):
																					__snake_case          =	{0: """batch""", 2: """past_sequence + sequence"""}
																					__snake_case          =	{0: """batch""", 2: """past_sequence + sequence"""}
						else:
											__snake_case          =	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 							a_		(   self  :	Any	)					-> Mapping[str, Mapping[int, str]]:
						if self.task in ["default", "seq2seq-lm"]:
											__snake_case          =	super().outputs
						else:
											__snake_case          =	super(lowercase      ,    self	).outputs
											if self.use_past:
																__snake_case      ,      __snake_case          =	self.num_layers
																for i in range(lowercase	):
																					__snake_case          =	{0: """batch""", 2: """past_sequence + sequence"""}
																					__snake_case          =	{0: """batch""", 2: """past_sequence + sequence"""}
						return common_outputs




	def 							a_		(   self  :	List[Any]      ,    lowercase  :	PreTrainedTokenizer      ,    lowercase  :	int = -1      ,    lowercase  :	int = -1      ,    lowercase  :	bool = False      ,    lowercase  :	Optional[TensorType] = None      ,    )					-> Mapping[str, Any]:
						__snake_case          =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    lowercase      ,    lowercase      ,    lowercase      ,    lowercase      ,    lowercase	)

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

											__snake_case          =	torch.cat(
											    [common_inputs["""decoder_attention_mask"""], torch.ones(lowercase      ,    lowercase	)]      ,    dim=1	)

											__snake_case          =	[]
											# If the number of encoder and decoder layers are present in the model configuration, both are considered
											__snake_case      ,      __snake_case          =	self.num_layers
											__snake_case          =	min(lowercase      ,    lowercase	)
											__snake_case          =	max(lowercase      ,    lowercase	) - min_num_layers
											__snake_case          =	"""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.
											__snake_case          =	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 							a_		(   self  :	Tuple      ,    lowercase  :	PreTrainedTokenizer      ,    lowercase  :	int = -1      ,    lowercase  :	int = -1      ,    lowercase  :	bool = False      ,    lowercase  :	Optional[TensorType] = None      ,    )					-> Mapping[str, Any]:
						__snake_case          =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    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
											__snake_case      ,      __snake_case          =	common_inputs["""input_ids"""].shape
											# Not using the same length for past_key_values
											__snake_case          =	seqlen + 2
											__snake_case      ,      __snake_case          =	self.num_layers
											__snake_case      ,      __snake_case          =	self.num_attention_heads
											__snake_case          =	(
											    batch,
											    num_encoder_attention_heads,
											    past_key_values_length,
											    self._config.hidden_size // num_encoder_attention_heads,
											)

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




	def 							a_		(   self  :	str      ,    lowercase  :	PreTrainedTokenizer      ,    lowercase  :	int = -1      ,    lowercase  :	int = -1      ,    lowercase  :	bool = False      ,    lowercase  :	Optional[TensorType] = 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
						__snake_case          =	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
						__snake_case          =	tokenizer.num_special_tokens_to_add(lowercase	)
						__snake_case          =	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
						__snake_case          =	[""" """.join([tokenizer.unk_token]	) * seq_length] * batch_size
						__snake_case          =	dict(tokenizer(lowercase      ,    return_tensors=lowercase	)	)
						return common_inputs




	def 							a_		(   self  :	List[Any]      ,    lowercase  :	PreTrainedTokenizer      ,    lowercase  :	int = -1      ,    lowercase  :	int = -1      ,    lowercase  :	bool = False      ,    lowercase  :	Optional[TensorType] = None      ,    )					-> Mapping[str, Any]:
						if self.task in ["default", "seq2seq-lm"]:
											__snake_case          =	self._generate_dummy_inputs_for_default_and_seqaseq_lm(
											    lowercase      ,    batch_size=lowercase      ,    seq_length=lowercase      ,    is_pair=lowercase      ,    framework=lowercase	)

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

						return common_inputs




	def 							a_		(   self  :	str      ,    lowercase  :	Union[str, Any]      ,    lowercase  :	Optional[Any]      ,    lowercase  :	int      ,    lowercase  :	List[Any]	)					-> Dict:
						if self.task in ["default", "seq2seq-lm"]:
											__snake_case          =	super()._flatten_past_key_values_(lowercase      ,    lowercase      ,    lowercase      ,    lowercase	)
						else:
											__snake_case          =	super(lowercase      ,    self	)._flatten_past_key_values_(
											    lowercase      ,    lowercase      ,    lowercase      ,    lowercase	)








"""simple docstring"""

from itertools import combinations_with_replacement




def       snake_case__	(       lowerCAmelCase   :		int = 100		):
					__snake_case          =	[*list(range(1				,					21		)		), 25]
					__snake_case          =	[2 * x for x in range(1				,					21		)] + [50]
					__snake_case          =	[3 * x for x in range(1				,					21		)]
					__snake_case          =	singles + doubles + triples + [0]

					__snake_case          =	0
					__snake_case          =	42
					__snake_case          =	42
					__snake_case          =	42
					__snake_case          =	42

					for double in doubles:
										for throwa, throwa in combinations_with_replacement(lowerCAmelCase				,					2		):
															__snake_case          =	double + throwa + throwa
															if checkout_total < limit:
																				num_checkouts += 1

					return num_checkouts


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


