


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


__magic_name__       					=      logging.get_logger(__name__)

__magic_name__       					=      {
    '''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    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""
						__UpperCAmelCase   :		Union[str, Any]  					=	'''mbart'''
						__UpperCAmelCase   :		str  					=	['''past_key_values''']
						__UpperCAmelCase   :		Optional[int]  					=	{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




						def __init__(   self				,							a_=5_0265				,							a_=1024				,							a_=12				,							a_=4096				,							a_=16				,							a_=12				,							a_=4096				,							a_=16				,							a_=0.0				,							a_=0.0				,							a_=True				,							a_=True				,							a_="gelu"				,							a_=1024				,							a_=0.1				,							a_=0.0				,							a_=0.0				,							a_=0.02				,							a_=0.0				,							a_=False				,							a_=1				,							a_=0				,							a_=2				,							a_=2				,							**a_				,							):
							lowerCamelCase_			:							List[Any]      =       vocab_size
							lowerCamelCase_			:							Any      =       max_position_embeddings
							lowerCamelCase_			:							Tuple      =       d_model
							lowerCamelCase_			:							Optional[int]      =       encoder_ffn_dim
							lowerCamelCase_			:							Any      =       encoder_layers
							lowerCamelCase_			:							str      =       encoder_attention_heads
							lowerCamelCase_			:							Optional[int]      =       decoder_ffn_dim
							lowerCamelCase_			:							List[str]      =       decoder_layers
							lowerCamelCase_			:							Tuple      =       decoder_attention_heads
							lowerCamelCase_			:							Tuple      =       dropout
							lowerCamelCase_			:							List[str]      =       attention_dropout
							lowerCamelCase_			:							str      =       activation_dropout
							lowerCamelCase_			:							Optional[int]      =       activation_function
							lowerCamelCase_			:							Optional[Any]      =       init_std
							lowerCamelCase_			:							Any      =       encoder_layerdrop
							lowerCamelCase_			:							Optional[Any]      =       decoder_layerdrop
							lowerCamelCase_			:							Dict      =       classifier_dropout
							lowerCamelCase_			:							str      =       use_cache
							lowerCamelCase_			:							Union[str, Any]      =       encoder_layers
							lowerCamelCase_			:							List[str]      =       scale_embedding  # scale factor will be sqrt(d_model) if True
							super().__init__(
							    pad_token_id=a_				,							bos_token_id=a_				,							eos_token_id=a_				,							is_encoder_decoder=a_				,							forced_eos_token_id=a_				,							**a_				,							)





class    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""




						@property
						def 					_UpperCamelCase			(   self     ):
							if self.task in ["default", "seq2seq-lm"]:
								lowerCamelCase_			:							int      =       OrderedDict(
								    [
								        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
								        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
								    ]     )

								if self.use_past:
									lowerCamelCase_			:							Union[str, Any]      =       {0: "batch"}
									lowerCamelCase_			:							Tuple      =       {0: "batch", 1: "past_decoder_sequence + sequence"}
								else:
									lowerCamelCase_			:							Optional[Any]      =       {0: "batch", 1: "decoder_sequence"}
									lowerCamelCase_			:							List[str]      =       {0: "batch", 1: "decoder_sequence"}

								if self.use_past:
									self.fill_with_past_key_values_(a_				,							direction="inputs"     )
							elif self.task == "causal-lm":
								# TODO: figure this case out.
								lowerCamelCase_			:							List[str]      =       OrderedDict(
								    [
								        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
								        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
								    ]     )
								if self.use_past:
									lowerCamelCase_   ,lowerCamelCase_			:							str      =       self.num_layers
									for i in range(a_     ):
										lowerCamelCase_			:							str      =       {0: "batch", 2: "past_sequence + sequence"}
										lowerCamelCase_			:							Tuple      =       {0: "batch", 2: "past_sequence + sequence"}
							else:
								lowerCamelCase_			:							List[str]      =       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     ):
							if self.task in ["default", "seq2seq-lm"]:
								lowerCamelCase_			:							Union[str, Any]      =       super().outputs
							else:
								lowerCamelCase_			:							Union[str, Any]      =       super(a_				,							self     ).outputs
								if self.use_past:
									lowerCamelCase_   ,lowerCamelCase_			:							Union[str, Any]      =       self.num_layers
									for i in range(a_     ):
										lowerCamelCase_			:							int      =       {0: "batch", 2: "past_sequence + sequence"}
										lowerCamelCase_			:							List[str]      =       {0: "batch", 2: "past_sequence + sequence"}
							return common_outputs




						def 					_UpperCamelCase			(   self				,							a_				,							a_ = -1				,							a_ = -1				,							a_ = False				,							a_ = None				,							):
							lowerCamelCase_			:							Any      =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    a_				,							a_				,							a_				,							a_				,							a_     )

							# Generate decoder inputs
							lowerCamelCase_			:							List[Any]      =       seq_length if not self.use_past else 1
							lowerCamelCase_			:							Optional[int]      =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    a_				,							a_				,							a_				,							a_				,							a_     )
							lowerCamelCase_			:							str      =       {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
							lowerCamelCase_			:							str      =       dict(**a_				,							**a_     )

							if self.use_past:
								if not is_torch_available():
									raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."     )
								else:
									import torch
								lowerCamelCase_   ,lowerCamelCase_			:							List[str]      =       common_inputs["input_ids"].shape
								lowerCamelCase_			:							Optional[Any]      =       common_inputs["decoder_input_ids"].shape[1]
								lowerCamelCase_   ,lowerCamelCase_			:							List[str]      =       self.num_attention_heads
								lowerCamelCase_			:							Tuple      =       (
								    batch,
								    num_encoder_attention_heads,
								    encoder_seq_length,
								    self._config.hidden_size // num_encoder_attention_heads,
								)
								lowerCamelCase_			:							Dict      =       decoder_seq_length + 3
								lowerCamelCase_			:							List[Any]      =       (
								    batch,
								    num_decoder_attention_heads,
								    decoder_past_length,
								    self._config.hidden_size // num_decoder_attention_heads,
								)

								lowerCamelCase_			:							Any      =       torch.cat(
								    [common_inputs["decoder_attention_mask"], torch.ones(a_				,							a_     )]				,							dim=1     )

								lowerCamelCase_			:							int      =       []
								# If the number of encoder and decoder layers are present in the model configuration, both are considered
								lowerCamelCase_   ,lowerCamelCase_			:							int      =       self.num_layers
								lowerCamelCase_			:							str      =       min(a_				,							a_     )
								lowerCamelCase_			:							int      =       max(a_				,							a_     ) - min_num_layers
								lowerCamelCase_			:							List[Any]      =       "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

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




						def 					_UpperCamelCase			(   self				,							a_				,							a_ = -1				,							a_ = -1				,							a_ = False				,							a_ = None				,							):
							lowerCamelCase_			:							Optional[Any]      =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    a_				,							a_				,							a_				,							a_				,							a_     )

							if self.use_past:
								if not is_torch_available():
									raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."     )
								else:
									import torch
								lowerCamelCase_   ,lowerCamelCase_			:							Tuple      =       common_inputs["input_ids"].shape
								# Not using the same length for past_key_values
								lowerCamelCase_			:							Dict      =       seqlen + 2
								lowerCamelCase_   ,lowerCamelCase_			:							int      =       self.num_layers
								lowerCamelCase_   ,lowerCamelCase_			:							int      =       self.num_attention_heads
								lowerCamelCase_			:							List[str]      =       (
								    batch,
								    num_encoder_attention_heads,
								    past_key_values_length,
								    self._config.hidden_size // num_encoder_attention_heads,
								)

								lowerCamelCase_			:							Any      =       common_inputs["attention_mask"].dtype
								lowerCamelCase_			:							int      =       torch.cat(
								    [common_inputs["attention_mask"], torch.ones(a_				,							a_				,							dtype=a_     )]				,							dim=1     )
								lowerCamelCase_			:							List[str]      =       [
								    (torch.zeros(a_     ), torch.zeros(a_     )) for _ in range(a_     )
								]
							return common_inputs




						def 					_UpperCamelCase			(   self				,							a_				,							a_ = -1				,							a_ = -1				,							a_ = False				,							a_ = 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
							lowerCamelCase_			:							Union[str, Any]      =       compute_effective_axis_dimension(
							    a_				,							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
							lowerCamelCase_			:							List[Any]      =       tokenizer.num_special_tokens_to_add(a_     )
							lowerCamelCase_			:							Any      =       compute_effective_axis_dimension(
							    a_				,							fixed_dimension=OnnxConfig.default_fixed_sequence				,							num_token_to_add=a_     )

							# Generate dummy inputs according to compute batch and sequence
							lowerCamelCase_			:							int      =       [" ".join([tokenizer.unk_token]     ) * seq_length] * batch_size
							lowerCamelCase_			:							Optional[Any]      =       dict(tokenizer(a_				,							return_tensors=a_     )     )
							return common_inputs




						def 					_UpperCamelCase			(   self				,							a_				,							a_ = -1				,							a_ = -1				,							a_ = False				,							a_ = None				,							):
							if self.task in ["default", "seq2seq-lm"]:
								lowerCamelCase_			:							List[Any]      =       self._generate_dummy_inputs_for_default_and_seqaseq_lm(
								    a_				,							batch_size=a_				,							seq_length=a_				,							is_pair=a_				,							framework=a_     )

							elif self.task == "causal-lm":
								lowerCamelCase_			:							List[str]      =       self._generate_dummy_inputs_for_causal_lm(
								    a_				,							batch_size=a_				,							seq_length=a_				,							is_pair=a_				,							framework=a_     )
							else:
								lowerCamelCase_			:							Any      =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    a_				,							batch_size=a_				,							seq_length=a_				,							is_pair=a_				,							framework=a_     )

							return common_inputs




						def 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_				,							a_     ):
							if self.task in ["default", "seq2seq-lm"]:
								lowerCamelCase_			:							Dict      =       super()._flatten_past_key_values_(a_				,							a_				,							a_				,							a_     )
							else:
								lowerCamelCase_			:							List[str]      =       super(a_				,							self     )._flatten_past_key_values_(
								    a_				,							a_				,							a_				,							a_     )





__magic_name__       					=      {
    '''0''': 1,
    '''1''': 1,
    '''2''': 2,
    '''3''': 6,
    '''4''': 2_4,
    '''5''': 1_2_0,
    '''6''': 7_2_0,
    '''7''': 5_0_4_0,
    '''8''': 4_0_3_2_0,
    '''9''': 3_6_2_8_8_0,
}

__magic_name__       					=      {1_4_5: 1_4_5}

__magic_name__       					=      {
    1_4_5: 0,
    1_6_9: 3,
    3_6_3_0_1: 3,
    1_4_5_4: 3,
    8_7_1: 2,
    4_5_3_6_1: 2,
    8_7_2: 2,
}







def     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


	if n in CACHE_SUM_DIGIT_FACTORIALS:
		return CACHE_SUM_DIGIT_FACTORIALS[n]
	lowerCamelCase_			:							List[str]      =       sum(DIGIT_FACTORIALS[let] for let in str(lowerCAmelCase_))
	lowerCamelCase_			:							List[str]      =       ret
	return ret







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_ = None):
	'''simple docstring'''


	lowerCamelCase_			:							List[str]      =       previous or set()
	if n in CHAIN_LENGTH_CACHE:
		return CHAIN_LENGTH_CACHE[n]
	lowerCamelCase_			:							str      =       sum_digit_factorials(lowerCAmelCase_)
	if next_number in previous:
		lowerCamelCase_			:							List[str]      =       0
		return 0
	else:
		previous.add(lowerCAmelCase_)
		lowerCamelCase_			:							List[Any]      =       1 + chain_length(lowerCAmelCase_      ,					lowerCAmelCase_)
		lowerCamelCase_			:							Optional[int]      =       ret
		return ret







def     __magic_name__  (      lowerCAmelCase_ = 60      ,					lowerCAmelCase_ = 100_0000):
	'''simple docstring'''


	return sum(1 for i in range(1      ,					lowerCAmelCase_) if chain_length(lowerCAmelCase_) == num_terms)


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

