


'''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


__snake_case				 =	logging.get_logger(__name__)

__snake_case				 =	{
    '''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      (				A__   ):

		"""simple docstring"""

		_a       					=							'marian'
		_a       					=							['past_key_values']
		_a       					=							{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




		def __init__(						self       ,  UpperCamelCase_=58101       ,  UpperCamelCase_=None       ,  UpperCamelCase_=1024       ,  UpperCamelCase_=12       ,  UpperCamelCase_=4096       ,  UpperCamelCase_=16       ,  UpperCamelCase_=12       ,  UpperCamelCase_=4096       ,  UpperCamelCase_=16       ,  UpperCamelCase_=0.0       ,  UpperCamelCase_=0.0       ,  UpperCamelCase_=True       ,  UpperCamelCase_=True       ,  UpperCamelCase_="gelu"       ,  UpperCamelCase_=1024       ,  UpperCamelCase_=0.1       ,  UpperCamelCase_=0.0       ,  UpperCamelCase_=0.0       ,  UpperCamelCase_=0.02       ,  UpperCamelCase_=58100       ,  UpperCamelCase_=False       ,  UpperCamelCase_=58100       ,  UpperCamelCase_=0       ,  UpperCamelCase_=0       ,  UpperCamelCase_=True       ,  **UpperCamelCase_       ,  ):

							'''simple docstring'''


							UpperCamelCase__  :Union[str, Any]				   =					vocab_size
							UpperCamelCase__  :Dict				   =					decoder_vocab_size or vocab_size
							UpperCamelCase__  :Any				   =					max_position_embeddings
							UpperCamelCase__  :str				   =					d_model
							UpperCamelCase__  :Optional[Any]				   =					encoder_ffn_dim
							UpperCamelCase__  :Tuple				   =					encoder_layers
							UpperCamelCase__  :Optional[int]				   =					encoder_attention_heads
							UpperCamelCase__  :Tuple				   =					decoder_ffn_dim
							UpperCamelCase__  :Union[str, Any]				   =					decoder_layers
							UpperCamelCase__  :Optional[Any]				   =					decoder_attention_heads
							UpperCamelCase__  :Any				   =					dropout
							UpperCamelCase__  :List[Any]				   =					attention_dropout
							UpperCamelCase__  :Tuple				   =					activation_dropout
							UpperCamelCase__  :List[str]				   =					activation_function
							UpperCamelCase__  :Optional[Any]				   =					init_std
							UpperCamelCase__  :str				   =					encoder_layerdrop
							UpperCamelCase__  :Any				   =					decoder_layerdrop
							UpperCamelCase__  :Optional[int]				   =					use_cache
							UpperCamelCase__  :int				   =					encoder_layers
							UpperCamelCase__  :int				   =					scale_embedding  # scale factor will be sqrt(d_model) if True
							UpperCamelCase__  :List[Any]				   =					share_encoder_decoder_embeddings
							super().__init__(
							    pad_token_id=UpperCamelCase_       ,  eos_token_id=UpperCamelCase_       ,  is_encoder_decoder=UpperCamelCase_       ,  decoder_start_token_id=UpperCamelCase_       ,  forced_eos_token_id=UpperCamelCase_       ,  **UpperCamelCase_       ,  )



class   lowercase      (				A__   ):

		"""simple docstring"""

		@property
		# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							if self.task in ["default", "seq2seq-lm"]:
												UpperCamelCase__  :Any				   =					OrderedDict(
												    [
												        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
												        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
												    ]				)

												if self.use_past:
																	UpperCamelCase__  :Tuple				   =					{0: '''batch'''}
																	UpperCamelCase__  :Tuple				   =					{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
												else:
																	UpperCamelCase__  :List[str]				   =					{0: '''batch''', 1: '''decoder_sequence'''}
																	UpperCamelCase__  :List[str]				   =					{0: '''batch''', 1: '''decoder_sequence'''}

												if self.use_past:
																	self.fill_with_past_key_values_(UpperCamelCase_       ,  direction='''inputs'''				)
							elif self.task == "causal-lm":
												# TODO: figure this case out.
												UpperCamelCase__  :int				   =					OrderedDict(
												    [
												        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
												        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
												    ]				)
												if self.use_past:
																	UpperCamelCase__   ,							UpperCamelCase__  :Optional[int]				   =					self.num_layers
																	for i in range(UpperCamelCase_				):
																						UpperCamelCase__  :int				   =					{0: '''batch''', 2: '''past_sequence + sequence'''}
																						UpperCamelCase__  :List[Any]				   =					{0: '''batch''', 2: '''past_sequence + sequence'''}
							else:
												UpperCamelCase__  :Any				   =					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        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							if self.task in ["default", "seq2seq-lm"]:
												UpperCamelCase__  :List[Any]				   =					super().outputs
							else:
												UpperCamelCase__  :int				   =					super(UpperCamelCase_       ,  self				).outputs
												if self.use_past:
																	UpperCamelCase__   ,							UpperCamelCase__  :Dict				   =					self.num_layers
																	for i in range(UpperCamelCase_				):
																						UpperCamelCase__  :Any				   =					{0: '''batch''', 2: '''past_sequence + sequence'''}
																						UpperCamelCase__  :Dict				   =					{0: '''batch''', 2: '''past_sequence + sequence'''}
							return common_outputs




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_ = -1       ,  UpperCamelCase_ = -1       ,  UpperCamelCase_ = False       ,  UpperCamelCase_ = None       ,  ):

							'''simple docstring'''


							UpperCamelCase__  :List[str]				   =					self._generate_dummy_inputs_for_encoder_and_decoder(
							    UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_				)

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

												UpperCamelCase__  :Any				   =					torch.cat(
												    [common_inputs['''decoder_attention_mask'''], torch.ones(UpperCamelCase_       ,  UpperCamelCase_				)]       ,  dim=1				)

												UpperCamelCase__  :int				   =					[]
												# If the number of encoder and decoder layers are present in the model configuration, both are considered
												UpperCamelCase__   ,							UpperCamelCase__  :int				   =					self.num_layers
												UpperCamelCase__  :Any				   =					min(UpperCamelCase_       ,  UpperCamelCase_				)
												UpperCamelCase__  :Tuple				   =					max(UpperCamelCase_       ,  UpperCamelCase_				) - min_num_layers
												UpperCamelCase__  :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.
												UpperCamelCase__  :Tuple				   =					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




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_ = -1       ,  UpperCamelCase_ = -1       ,  UpperCamelCase_ = False       ,  UpperCamelCase_ = None       ,  ):

							'''simple docstring'''


							UpperCamelCase__  :Dict				   =					self._generate_dummy_inputs_for_encoder_and_decoder(
							    UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  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
												UpperCamelCase__   ,							UpperCamelCase__  :List[str]				   =					common_inputs['''input_ids'''].shape
												# Not using the same length for past_key_values
												UpperCamelCase__  :Optional[Any]				   =					seqlen + 2
												UpperCamelCase__   ,							UpperCamelCase__  :Optional[Any]				   =					self.num_layers
												UpperCamelCase__   ,							UpperCamelCase__  :List[Any]				   =					self.num_attention_heads
												UpperCamelCase__  :List[Any]				   =					(
												    batch,
												    num_encoder_attention_heads,
												    past_key_values_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)

												UpperCamelCase__  :Any				   =					common_inputs['''attention_mask'''].dtype
												UpperCamelCase__  :Optional[int]				   =					torch.cat(
												    [common_inputs['''attention_mask'''], torch.ones(UpperCamelCase_       ,  UpperCamelCase_       ,  dtype=UpperCamelCase_				)]       ,  dim=1				)
												UpperCamelCase__  :Any				   =					[
												    (torch.zeros(UpperCamelCase_				), torch.zeros(UpperCamelCase_				)) for _ in range(UpperCamelCase_				)
												]
							return common_inputs




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_ = -1       ,  UpperCamelCase_ = -1       ,  UpperCamelCase_ = False       ,  UpperCamelCase_ = None       ,  ):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					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
							UpperCamelCase__  :Dict				   =					tokenizer.num_special_tokens_to_add(UpperCamelCase_				)
							UpperCamelCase__  :str				   =					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
							UpperCamelCase__  :List[str]				   =					[''' '''.join([tokenizer.unk_token]				) * seq_length] * batch_size
							UpperCamelCase__  :Union[str, Any]				   =					dict(tokenizer(UpperCamelCase_       ,  return_tensors=UpperCamelCase_				)				)
							return common_inputs




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_ = -1       ,  UpperCamelCase_ = -1       ,  UpperCamelCase_ = False       ,  UpperCamelCase_ = None       ,  ):

							'''simple docstring'''


							if self.task in ["default", "seq2seq-lm"]:
												UpperCamelCase__  :Dict				   =					self._generate_dummy_inputs_for_default_and_seqaseq_lm(
												    UpperCamelCase_       ,  batch_size=UpperCamelCase_       ,  seq_length=UpperCamelCase_       ,  is_pair=UpperCamelCase_       ,  framework=UpperCamelCase_				)

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

							return common_inputs




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_				):

							'''simple docstring'''


							if self.task in ["default", "seq2seq-lm"]:
												UpperCamelCase__  :Union[str, Any]				   =					super()._flatten_past_key_values_(UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_				)
							else:
												UpperCamelCase__  :Union[str, Any]				   =					super(UpperCamelCase_       ,  self				)._flatten_past_key_values_(
												    UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_				)




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return 1e-4



'''simple docstring'''
from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass
@dataclass
class   lowercase      :

		"""simple docstring"""

		_a       					=							42
		_a       					=							None
		_a       					=							None



__snake_case				 =	namedtuple('''CoinsDistribResult''', '''moves excess''')


def      a (     __a  )    ->      int:



					'''simple docstring'''






					if root is None:
										return 0

					# Validation
					def count_nodes(__a  ) -> int:
										if node is None:
															return 0

										return count_nodes(node.left  ) + count_nodes(node.right  ) + 1

					def count_coins(__a  ) -> int:
										if node is None:
															return 0

										return count_coins(node.left  ) + count_coins(node.right  ) + node.data

					if count_nodes(__a  ) != count_coins(__a  ):
										raise ValueError('''The nodes number should be same as the number of coins'''  )

					# Main calculation
					def get_distrib(__a  ) -> CoinsDistribResult:

										if node is None:
															return CoinsDistribResult(0					,   1  )

										UpperCamelCase__   ,							UpperCamelCase__  :Optional[Any]				   =					get_distrib(node.left  )
										UpperCamelCase__   ,							UpperCamelCase__  :Union[str, Any]				   =					get_distrib(node.right  )

										UpperCamelCase__  :Tuple				   =					1 - left_distrib_excess
										UpperCamelCase__  :Optional[int]				   =					1 - right_distrib_excess

										UpperCamelCase__  :int				   =					(
										    left_distrib_moves
										    + right_distrib_moves
										    + abs(__a  )
										    + abs(__a  )
										)
										UpperCamelCase__  :Optional[Any]				   =					node.data - coins_to_left - coins_to_right

										return CoinsDistribResult(__a					,   __a  )

					return get_distrib(__a  )[0]


if __name__ == "__main__":
		import doctest

		doctest.testmod()