





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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__SCREAMING_SNAKE_CASE							:    Dict          =logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE							:    Dict          ={
    '''Salesforce/codegen-350M-nl''': '''https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json''',
    '''Salesforce/codegen-350M-multi''': '''https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json''',
    '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json''',
    '''Salesforce/codegen-2B-nl''': '''https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-2B-multi''': '''https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-2B-mono''': '''https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-6B-nl''': '''https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-6B-multi''': '''https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-6B-mono''': '''https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-16B-nl''': '''https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-16B-multi''': '''https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-16B-mono''': '''https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json''',
}

class     A_		(			__a	):
			_A   :str  				=      '''codegen'''
			_A   :str  				=      {
			    '''max_position_embeddings''': '''n_positions''',
			    '''hidden_size''': '''n_embd''',
			    '''num_attention_heads''': '''n_head''',
			    '''num_hidden_layers''': '''n_layer''',
			}







			def __init__(				self  :							Optional[int]   ,      snake_case__  :							Dict=5_04_00   ,      snake_case__  :							str=20_48   ,      snake_case__  :							Any=20_48   ,      snake_case__  :							List[Any]=40_96   ,      snake_case__  :							Dict=28   ,      snake_case__  :							Optional[int]=16   ,      snake_case__  :							Any=64   ,      snake_case__  :							Tuple=None   ,      snake_case__  :							str="gelu_new"   ,      snake_case__  :							Tuple=0.0   ,      snake_case__  :							int=0.0   ,      snake_case__  :							Tuple=0.0   ,      snake_case__  :							Any=1E-5   ,      snake_case__  :							Union[str, Any]=0.02   ,      snake_case__  :							Union[str, Any]=True   ,      snake_case__  :							Any=5_02_56   ,      snake_case__  :							List[Any]=5_02_56   ,      snake_case__  :							Optional[int]=False   ,      **snake_case__  :							List[str]   ,      ):
							lowercase							=     vocab_size
							lowercase							=     n_ctx
							lowercase							=     n_positions
							lowercase							=     n_embd
							lowercase							=     n_layer
							lowercase							=     n_head
							lowercase							=     n_inner
							lowercase							=     rotary_dim
							lowercase							=     activation_function
							lowercase							=     resid_pdrop
							lowercase							=     embd_pdrop
							lowercase							=     attn_pdrop
							lowercase							=     layer_norm_epsilon
							lowercase							=     initializer_range
							lowercase							=     use_cache

							lowercase							=     bos_token_id
							lowercase							=     eos_token_id

							super().__init__(
							    bos_token_id=snake_case_   ,      eos_token_id=snake_case_   ,      tie_word_embeddings=snake_case_   ,      **snake_case_      )

class     A_		(			__a	):







			def __init__(				self  :							Optional[Any]   ,      snake_case__  :							PretrainedConfig   ,      snake_case__  :							str = "default"   ,      snake_case__  :							List[PatchingSpec] = None   ,      snake_case__  :							bool = False   ,      ):
							super().__init__(snake_case_   ,      task=snake_case_   ,      patching_specs=snake_case_   ,      use_past=snake_case_      )
							if not getattr(self._config   ,      """pad_token_id"""   ,      snake_case_      ):
											# TODO: how to do that better?
											lowercase							=     0







			@property
			def 						SCREAMING_SNAKE_CASE__	(				self  :							Optional[Any]      ):
							lowercase							=     OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}      )
							if self.use_past:
											self.fill_with_past_key_values_(snake_case_   ,      direction="""inputs"""      )
											lowercase							=     {0: """batch""", 1: """past_sequence + sequence"""}
							else:
											lowercase							=     {0: """batch""", 1: """sequence"""}

							return common_inputs







			@property
			def 						SCREAMING_SNAKE_CASE__	(				self  :							Optional[int]      ):
							return self._config.n_layer







			@property
			def 						SCREAMING_SNAKE_CASE__	(				self  :							List[Any]      ):
							return self._config.n_head







			def 						SCREAMING_SNAKE_CASE__	(				self  :							Optional[Any]   ,      snake_case__  :							PreTrainedTokenizer   ,      snake_case__  :							int = -1   ,      snake_case__  :							int = -1   ,      snake_case__  :							bool = False   ,      snake_case__  :							Optional[TensorType] = None   ,      ):
							lowercase							=     super(snake_case_   ,      self      ).generate_dummy_inputs(
							    snake_case_   ,      batch_size=snake_case_   ,      seq_length=snake_case_   ,      is_pair=snake_case_   ,      framework=snake_case_      )

							# We need to order the input in the way they appears in the forward()
							lowercase							=     OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}      )

							# Need to add the past_keys
							if self.use_past:
											if not is_torch_available():
															raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""      )
											else:
															import torch

															lowercase					,    lowercase							=     common_inputs["""input_ids"""].shape
															# Not using the same length for past_key_values
															lowercase							=     seqlen + 2
															lowercase							=     (
															    batch,
															    self.num_attention_heads,
															    past_key_values_length,
															    self._config.hidden_size // self.num_attention_heads,
															)
															lowercase							=     [
															    (torch.zeros(snake_case_      ), torch.zeros(snake_case_      )) for _ in range(self.num_layers      )
															]

							lowercase							=     common_inputs["""attention_mask"""]
							if self.use_past:
											lowercase							=     ordered_inputs["""attention_mask"""].dtype
											lowercase							=     torch.cat(
											    [ordered_inputs["""attention_mask"""], torch.ones(snake_case_   ,      snake_case_   ,      dtype=snake_case_      )]   ,      dim=1      )

							return ordered_inputs







			@property
			def 						SCREAMING_SNAKE_CASE__	(				self  :							int      ):
							return 13







from collections.abc import Sequence
from queue import Queue






class     __a			:



	def __init__(    self				:     str					,						snake_case_				:     List[str]					,						snake_case_				:     Tuple					,						snake_case_				:     Tuple					,						snake_case_				:     Optional[Any]=None					,						snake_case_				:     Dict=None)->				Optional[int]:
						__lowerCAmelCase						 =start
						__lowerCAmelCase						 =end
						__lowerCAmelCase						 =val
						__lowerCAmelCase						 =(start + end) // 2
						__lowerCAmelCase						 =left
						__lowerCAmelCase						 =right



	def __repr__(    self				:     List[Any])->				Dict:
						return F"""SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"""






class     __a			:



	def __init__(    self				:     List[str]					,						snake_case_				:     Sequence					,						snake_case_				:     int)->				str:
						__lowerCAmelCase						 =collection
						__lowerCAmelCase						 =function
						if self.collection:
											__lowerCAmelCase						 =self._build_tree(0					,						len(snake_case_) - 1)



	def 		UpperCamelCase				(    self				:     Dict					,						snake_case_				:     Tuple					,						snake_case_				:     Optional[int])->				Optional[int]:
						self._update_tree(self.root					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     Optional[Any]					,						snake_case_				:     Any					,						snake_case_				:     Union[str, Any])->				Union[str, Any]:
						return self._query_range(self.root					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     List[str]					,						snake_case_				:     List[str]					,						snake_case_				:     List[str])->				List[Any]:
						if start == end:
											return SegmentTreeNode(snake_case_					,						snake_case_					,						self.collection[start])
						__lowerCAmelCase						 =(start + end) // 2
						__lowerCAmelCase						 =self._build_tree(snake_case_					,						snake_case_)
						__lowerCAmelCase						 =self._build_tree(mid + 1					,						snake_case_)
						return SegmentTreeNode(snake_case_					,						snake_case_					,						self.fn(left.val					,						right.val)					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     Dict					,						snake_case_				:     Tuple					,						snake_case_				:     int					,						snake_case_				:     int)->				List[Any]:
						if node.start == i and node.end == i:
											__lowerCAmelCase						 =val
											return
						if i <= node.mid:
											self._update_tree(node.left					,						snake_case_					,						snake_case_)
						else:
											self._update_tree(node.right					,						snake_case_					,						snake_case_)
						__lowerCAmelCase						 =self.fn(node.left.val					,						node.right.val)



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     str					,						snake_case_				:     str					,						snake_case_				:     List[Any])->				Optional[int]:
						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					,						snake_case_					,						snake_case_)
											else:
																# range in left child tree and right child tree
																return self.fn(
																    self._query_range(node.left					,						snake_case_					,						node.mid)					,						self._query_range(node.right					,						node.mid + 1					,						snake_case_)					,						)
						else:
											# range in right child tree
											return self._query_range(node.right					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     List[str])->				Tuple:
						if self.root is not None:
											__lowerCAmelCase						 =Queue()
											queue.put(self.root)
											while not queue.empty():
																__lowerCAmelCase						 =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)
								lowercase_													=      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()




