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


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

lowerCAmelCase__	:  List[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 					__snake_case    (     _lowerCamelCase      ):
					__lowerCamelCase			   =  """mbart"""
					__lowerCamelCase			   =  ["""past_key_values"""]
					__lowerCamelCase			   =  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




					def __init__(  self       ,			__UpperCamelCase=50265       ,			__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=0.0_2       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=False       ,			__UpperCamelCase=1       ,			__UpperCamelCase=0       ,			__UpperCamelCase=2       ,			__UpperCamelCase=2       ,			**__UpperCamelCase       ,			)	->    Dict:



									'''simple docstring'''







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





class 					__snake_case    (     _lowerCamelCase      ):




					@property
					def      __a     (  self						)	->    Mapping[str, Mapping[int, str]]:



									'''simple docstring'''







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

													if self.use_past:
																	snake_case__   :      Any   				= {0: 'batch'}
																	snake_case__   :      int   				= {0: 'batch', 1: 'past_decoder_sequence + sequence'}
													else:
																	snake_case__   :      Any   				= {0: 'batch', 1: 'decoder_sequence'}
																	snake_case__   :      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.
													snake_case__   :      int   				= OrderedDict(
													    [
													        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
													        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
													    ]						)
													if self.use_past:
																	snake_case__				,    snake_case__   :      Any   				= self.num_layers
																	for i in range(__UpperCamelCase						):
																					snake_case__   :      Any   				= {0: 'batch', 2: 'past_sequence + sequence'}
																					snake_case__   :      str   				= {0: 'batch', 2: 'past_sequence + sequence'}
									else:
													snake_case__   :      Tuple   				= 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						)	->    Mapping[str, Mapping[int, str]]:



									'''simple docstring'''







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




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = False       ,			__UpperCamelCase = None       ,			)	->    Mapping[str, Any]:



									'''simple docstring'''







									snake_case__   :      int   				= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)

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

													snake_case__   :      Dict   				= torch.cat(
													    [common_inputs['decoder_attention_mask'], torch.ones(__UpperCamelCase       ,			__UpperCamelCase						)]       ,			dim=1						)

													snake_case__   :      Optional[int]   				= []
													# If the number of encoder and decoder layers are present in the model configuration, both are considered
													snake_case__				,    snake_case__   :      Optional[int]   				= self.num_layers
													snake_case__   :      Optional[Any]   				= min(__UpperCamelCase       ,			__UpperCamelCase						)
													snake_case__   :      int   				= max(__UpperCamelCase       ,			__UpperCamelCase						) - min_num_layers
													snake_case__   :      List[str]   				= '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.
													snake_case__   :      Any   				= 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      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = False       ,			__UpperCamelCase = None       ,			)	->    Mapping[str, Any]:



									'''simple docstring'''







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

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




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = False       ,			__UpperCamelCase = None       ,			)	->    Mapping[str, Any]:



									'''simple docstring'''







									snake_case__   :      Tuple   				= 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
									snake_case__   :      Optional[Any]   				= tokenizer.num_special_tokens_to_add(__UpperCamelCase						)
									snake_case__   :      Union[str, Any]   				= 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
									snake_case__   :      Optional[int]   				= [' '.join([tokenizer.unk_token]						) * seq_length] * batch_size
									snake_case__   :      str   				= dict(tokenizer(__UpperCamelCase       ,			return_tensors=__UpperCamelCase						)						)
									return common_inputs




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = False       ,			__UpperCamelCase = None       ,			)	->    Mapping[str, Any]:



									'''simple docstring'''







									if self.task in ["default", "seq2seq-lm"]:
													snake_case__   :      Optional[Any]   				= self._generate_dummy_inputs_for_default_and_seqaseq_lm(
													    __UpperCamelCase       ,			batch_size=__UpperCamelCase       ,			seq_length=__UpperCamelCase       ,			is_pair=__UpperCamelCase       ,			framework=__UpperCamelCase						)

									elif self.task == "causal-lm":
													snake_case__   :      Dict   				= self._generate_dummy_inputs_for_causal_lm(
													    __UpperCamelCase       ,			batch_size=__UpperCamelCase       ,			seq_length=__UpperCamelCase       ,			is_pair=__UpperCamelCase       ,			framework=__UpperCamelCase						)
									else:
													snake_case__   :      Optional[int]   				= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    __UpperCamelCase       ,			batch_size=__UpperCamelCase       ,			seq_length=__UpperCamelCase       ,			is_pair=__UpperCamelCase       ,			framework=__UpperCamelCase						)

									return common_inputs




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Any:



									'''simple docstring'''







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



import math





class 					__snake_case    :




					def __init__(  self       ,			__UpperCamelCase						)	->    Dict:



									'''simple docstring'''







									snake_case__   :      Optional[int]   				= len(__UpperCamelCase						)
									snake_case__   :      Dict   				= [0] * (
									    4 * self.N
									)  # approximate the overall size of segment tree with array N
									self.build(1       ,			0       ,			self.N - 1						)




					def      __a     (  self       ,			__UpperCamelCase						)	->    Any:



									'''simple docstring'''







									return idx * 2




					def      __a     (  self       ,			__UpperCamelCase						)	->    Optional[int]:



									'''simple docstring'''







									return idx * 2 + 1




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    int:  # noqa: E741



									'''simple docstring'''







									if l == r:
													snake_case__   :      Dict   				= A[l]
									else:
													snake_case__   :      Optional[Any]   				= (l + r) // 2
													self.build(self.left(__UpperCamelCase						)       ,			__UpperCamelCase       ,			__UpperCamelCase						)
													self.build(self.right(__UpperCamelCase						)       ,			mid + 1       ,			__UpperCamelCase						)
													snake_case__   :      int   				= max(self.st[self.left(__UpperCamelCase						)]       ,			self.st[self.right(__UpperCamelCase						)]						)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    int:



									'''simple docstring'''







									return self.update_recursive(1       ,			0       ,			self.N - 1       ,			a - 1       ,			b - 1       ,			__UpperCamelCase						)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Tuple:  # noqa: E741



									'''simple docstring'''







									if r < a or l > b:
													return True
									if l == r:
													snake_case__   :      List[str]   				= val
													return True
									snake_case__   :      str   				= (l + r) // 2
									self.update_recursive(self.left(__UpperCamelCase						)       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)
									self.update_recursive(self.right(__UpperCamelCase						)       ,			mid + 1       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)
									snake_case__   :      Tuple   				= max(self.st[self.left(__UpperCamelCase						)]       ,			self.st[self.right(__UpperCamelCase						)]						)
									return True




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    List[str]:



									'''simple docstring'''







									return self.query_recursive(1       ,			0       ,			self.N - 1       ,			a - 1       ,			b - 1						)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Optional[int]:  # noqa: E741



									'''simple docstring'''







									if r < a or l > b:
													return -math.inf
									if l >= a and r <= b:
													return self.st[idx]
									snake_case__   :      str   				= (l + r) // 2
									snake_case__   :      Optional[Any]   				= self.query_recursive(self.left(__UpperCamelCase						)       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)
									snake_case__   :      List[str]   				= self.query_recursive(self.right(__UpperCamelCase						)       ,			mid + 1       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)
									return max(__UpperCamelCase       ,			__UpperCamelCase						)




					def      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__   :      Tuple   				= []
									for i in range(1       ,			N + 1						):
													show_list += [self.query(__UpperCamelCase       ,			__UpperCamelCase						)]
									print(__UpperCamelCase						)


if __name__ == "__main__":
				lowerCAmelCase__	:  Optional[int]						=  [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
				lowerCAmelCase__	:  Any						=  15
				lowerCAmelCase__	:  List[Any]						=  SegmentTree(A)
				print(segt.query(4, 6))
				print(segt.query(7, 11))
				print(segt.query(7, 12))
				segt.update(1, 3, 1_11)
				print(segt.query(1, 15))
				segt.update(7, 8, 2_35)
				segt.show_data()


