from collections.abc import Sequence
from queue import Queue






class 		a__						:

					def __init__(    self      :       int,_A      :       List[Any],_A      :       Optional[Any],_A      :       Optional[int],_A      :       int=None,_A      :       List[str]=None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    start
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    end
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    val
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    (start + end) // 2
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    left
							SCREAMING_SNAKE_CASE_  :		str				      =    right






					def __repr__(    self      :       Tuple    ):







							"""simple docstring"""






							return F'SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'







class 		a__						:

					def __init__(    self      :       Any,_A      :       Sequence,_A      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    collection
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    function
							if self.collection:
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    self._build_tree(0,len(_A    ) - 1    )

					def 				__UpperCamelCase   (    self      :       int,_A      :       Any,_A      :       List[Any]    ):







							"""simple docstring"""






							self._update_tree(self.root,_A,_A    )

					def 				__UpperCamelCase   (    self      :       str,_A      :       Any,_A      :       List[Any]    ):







							"""simple docstring"""






							return self._query_range(self.root,_A,_A    )

					def 				__UpperCamelCase   (    self      :       Any,_A      :       Optional[int],_A      :       int    ):







							"""simple docstring"""






							if start == end:
									return SegmentTreeNode(_A,_A,self.collection[start]    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    (start + end) // 2
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self._build_tree(_A,_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self._build_tree(mid + 1,_A    )
							return SegmentTreeNode(_A,_A,self.fn(left.val,right.val    ),_A,_A    )

					def 				__UpperCamelCase   (    self      :       int,_A      :       int,_A      :       Tuple,_A      :       Dict    ):







							"""simple docstring"""






							if node.start == i and node.end == i:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    val
									return
							if i <= node.mid:
									self._update_tree(node.left,_A,_A    )
							else:
									self._update_tree(node.right,_A,_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.fn(node.left.val,node.right.val    )

					def 				__UpperCamelCase   (    self      :       str,_A      :       List[str],_A      :       Optional[int],_A      :       Optional[Any]    ):







							"""simple docstring"""






							if node.start == i and node.end == j:
									return node.val

							if i <= node.mid:
									if j <= node.mid:
											# range in left child tree
											return self._query_range(node.left,_A,_A    )
									else:
											# range in left child tree and right child tree
											return self.fn(
											    self._query_range(node.left,_A,node.mid    ),self._query_range(node.right,node.mid + 1,_A    ),)
							else:
									# range in right child tree
									return self._query_range(node.right,_A,_A    )






					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							if self.root is not None:
									SCREAMING_SNAKE_CASE_  :		int				      =    Queue()
									queue.put(self.root    )
									while not queue.empty():
											SCREAMING_SNAKE_CASE_  :		Tuple				      =    queue.get()
											yield node

											if node.left is not None:
													queue.put(node.left    )

											if node.right is not None:
													queue.put(node.right    )


if __name__ == "__main__":
		import operator

		for fn in [operator.add, max, min]:
				print('''*''' * 50)
				__lowerCamelCase					:	int       =  SegmentTree([2, 1, 5, 3, 4], fn)
				for node in arr.traverse():
						print(node)
				print()

				arr.update(1, 5)
				for node in arr.traverse():
						print(node)
				print()

				print(arr.query_range(3, 4))  # 7
				print(arr.query_range(2, 2))  # 5
				print(arr.query_range(1, 3))  # 13
				print()

from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


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

__lowerCamelCase					:	Tuple       =  {
    '''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}






class 		a__						(					A__					):
					A	       =							'perceiver'

					def __init__(    self      :       List[Any],_A      :       Tuple=256,_A      :       str=1280,_A      :       List[Any]=768,_A      :       Union[str, Any]=1,_A      :       Union[str, Any]=26,_A      :       List[str]=8,_A      :       List[Any]=8,_A      :       List[Any]=None,_A      :       List[Any]=None,_A      :       Union[str, Any]="kv",_A      :       Any=1,_A      :       int=1,_A      :       Dict="gelu",_A      :       Any=0.1,_A      :       int=0.02,_A      :       int=1E-12,_A      :       Any=True,_A      :       Optional[Any]=262,_A      :       List[Any]=2048,_A      :       str=56,_A      :       Optional[int]=[368, 496],_A      :       Dict=16,_A      :       Tuple=1920,_A      :       List[Any]=16,_A      :       str=[1, 16, 224, 224],**_A      :       Optional[Any],):







							"""simple docstring"""






							super().__init__(**_A    )

							SCREAMING_SNAKE_CASE_  :		Dict				      =    num_latents
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    d_latents
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    d_model
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    num_blocks
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    num_self_attends_per_block
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    num_self_attention_heads
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    num_cross_attention_heads
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    qk_channels
							SCREAMING_SNAKE_CASE_  :		Any				      =    v_channels
							SCREAMING_SNAKE_CASE_  :		Any				      =    cross_attention_shape_for_attention
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self_attention_widening_factor
							SCREAMING_SNAKE_CASE_  :		Any				      =    cross_attention_widening_factor
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    hidden_act
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE_  :		Any				      =    initializer_range
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    layer_norm_eps
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    use_query_residual
							# masked language modeling attributes
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    vocab_size
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    max_position_embeddings
							# image classification attributes
							SCREAMING_SNAKE_CASE_  :		Dict				      =    image_size
							# flow attributes
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    train_size
							# multimodal autoencoding attributes
							SCREAMING_SNAKE_CASE_  :		str				      =    num_frames
							SCREAMING_SNAKE_CASE_  :		Any				      =    audio_samples_per_frame
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    samples_per_patch
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    output_shape







class 		a__						(					A__					):

					@property
					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							if self.task == "multiple-choice":
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    {0: "batch", 1: "choice", 2: "sequence"}
							else:
									SCREAMING_SNAKE_CASE_  :		str				      =    {0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("inputs", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]    )

					@property
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							return 1E-4






					def 				__UpperCamelCase   (    self      :       List[str],_A      :       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],_A      :       int = -1,_A      :       int = -1,_A      :       int = -1,_A      :       bool = False,_A      :       Optional[TensorType] = None,_A      :       int = 3,_A      :       int = 40,_A      :       int = 40,):







							"""simple docstring"""






							if isinstance(_A,_A    ):
									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    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
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    preprocessor.num_special_tokens_to_add(_A    )
									SCREAMING_SNAKE_CASE_  :		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
									SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    [" ".join(["a"]    ) * seq_length] * batch_size
									SCREAMING_SNAKE_CASE_  :		str				      =    dict(preprocessor(_A,return_tensors=_A    )    )
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    inputs.pop("input_ids"    )
									return inputs
							elif isinstance(_A,_A    ) and preprocessor.model_input_names[0] == "pixel_values":
									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									SCREAMING_SNAKE_CASE_  :		Any				      =    compute_effective_axis_dimension(_A,fixed_dimension=OnnxConfig.default_fixed_batch    )
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self._generate_dummy_images(_A,_A,_A,_A    )
									SCREAMING_SNAKE_CASE_  :		Any				      =    dict(preprocessor(images=_A,return_tensors=_A    )    )
									SCREAMING_SNAKE_CASE_  :		Any				      =    inputs.pop("pixel_values"    )
									return inputs
							else:
									raise ValueError(
									    "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."    )
