

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

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


_UpperCAmelCase	:     Optional[Any]		       = logging.get_logger(__name__)

_UpperCAmelCase	:     Dict		       = {
    """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				List[Any]     =    '''gpt_neo'''
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    ['''past_key_values''']
				__SCREAMING_SNAKE_CASE      :				Tuple     =    {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}




				def __init__(      self   ,     snake_case=5_0257   ,     snake_case=2048   ,     snake_case=2048   ,     snake_case=24   ,     snake_case=[[["global", "local"], 12]]   ,     snake_case=16   ,     snake_case=None   ,     snake_case=256   ,     snake_case="gelu_new"   ,     snake_case=0.0   ,     snake_case=0.0   ,     snake_case=0.0   ,     snake_case=0.1   ,     snake_case=1e-5   ,     snake_case=0.02   ,     snake_case=True   ,     snake_case=5_0256   ,     snake_case=5_0256   ,     **snake_case   ,     ):
											snake_case_           =					vocab_size
											snake_case_           =					max_position_embeddings
											snake_case_           =					hidden_size
											snake_case_           =					num_layers
											snake_case_           =					num_heads
											snake_case_           =					intermediate_size
											snake_case_           =					window_size
											snake_case_           =					activation_function
											snake_case_           =					resid_dropout
											snake_case_           =					embed_dropout
											snake_case_           =					attention_dropout
											snake_case_           =					classifier_dropout
											snake_case_           =					layer_norm_epsilon
											snake_case_           =					initializer_range
											snake_case_           =					use_cache

											snake_case_           =					bos_token_id
											snake_case_           =					eos_token_id

											snake_case_           =					attention_types
											snake_case_           =					self.expand_attention_types_params(snake_case )

											if len(self.attention_layers ) != self.num_layers:
																		raise ValueError(
																		    'Configuration for convolutional module is incorrect. '
																		    'It is required that `len(config.attention_layers)` == `config.num_layers` '
																		    F'''but is `len(config.attention_layers) = {len(self.attention_layers )}`, '''
																		    F'''`config.num_layers = {self.num_layers}`. '''
																		    '`config.attention_layers` is prepared using `config.attention_types`. '
																		    'Please verify the value of `config.attention_types` argument.' )

											super().__init__(bos_token_id=snake_case   ,     eos_token_id=snake_case   ,     **snake_case )




				@staticmethod
				def 					a							(      snake_case ):
											snake_case_           =					[]
											for item in attention_types:
																		for _ in range(item[1] ):
																									attentions.extend(item[0] )
											return attentions





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							import torch

							snake_case_           =					input.size()
							snake_case_           =					len(UpperCamelCase__   )
							snake_case_           =					shape[dimension]

							snake_case_           =					torch.arange(0     ,							UpperCamelCase__     ,							UpperCamelCase__   )
							snake_case_           =					torch.div(sizedim - size     ,							UpperCamelCase__     ,							rounding_mode='floor'   ) + 1
							snake_case_           =					torch.arange(UpperCamelCase__   ) + low_indices[:min_length][:, None]

							snake_case_           =					[slice(UpperCamelCase__   )] * rank
							snake_case_           =					indices
							snake_case_           =					input[s]

							snake_case_           =					list(range(0     ,							rank + 1   )   )
							perm.append(perm.pop(dimension + 1   )   )

							return sliced.permute(UpperCamelCase__   )





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							import torch

							snake_case_           =					torch.arange(1     ,							UpperCamelCase__   )
							snake_case_           =					torch.remainder(UpperCamelCase__     ,							UpperCamelCase__   )
							snake_case_           =					remainders == 0
							snake_case_           =					candidates[divisor_indices]
							snake_case_           =					torch.max(UpperCamelCase__   )
							return largest_divisor, torch.div(UpperCamelCase__     ,							UpperCamelCase__     ,							rounding_mode='floor'   )







class 				lowercase       ( lowercase_    ):




				@property
				def 					a							(      self ):
											snake_case_           =					OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}} )
											if self.use_past:
																		self.fill_with_past_key_values_(snake_case   ,     direction='inputs' )
																		snake_case_           =					{0: 'batch', 1: 'past_sequence + sequence'}
											else:
																		snake_case_           =					{0: 'batch', 1: 'sequence'}

											return common_inputs




				@property
				def 					a							(      self ):
											return self._config.num_heads




				def 					a							(      self   ,     snake_case   ,     snake_case = -1   ,     snake_case = -1   ,     snake_case = False   ,     snake_case = None   ,     ):
											snake_case_           =					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()
											snake_case_           =					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

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

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

											return ordered_inputs




				@property
				def 					a							(      self ):
											return 13







from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any







@dataclass
class 				lowercase       :
				__SCREAMING_SNAKE_CASE      :				int
				__SCREAMING_SNAKE_CASE      :				Node | None     =    None
				__SCREAMING_SNAKE_CASE      :				Node | None     =    None





def       __lowerCamelCase			(				):







							'''simple docstring'''






							snake_case_           =					Node(1   )
							snake_case_           =					Node(2   )
							snake_case_           =					Node(3   )
							snake_case_           =					Node(4   )
							snake_case_           =					Node(5   )
							return tree





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							return [root.data, *preorder(root.left   ), *preorder(root.right   )] if root else []





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							return postorder(root.left   ) + postorder(root.right   ) + [root.data] if root else []





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							return [*inorder(root.left   ), root.data, *inorder(root.right   )] if root else []





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							return (max(height(root.left   )     ,							height(root.right   )   ) + 1) if root else 0





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					[]

							if root is None:
														return output

							snake_case_           =					deque([root]   )

							while process_queue:
														snake_case_           =					process_queue.popleft()
														output.append(node.data   )

														if node.left:
																					process_queue.append(node.left   )
														if node.right:
																					process_queue.append(node.right   )
							return output





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					[]

							def populate_output(UpperCamelCase__     ,							UpperCamelCase__   ) -> None:
														if not root:
																					return
														if level == 1:
																					output.append(root.data   )
														elif level > 1:
																					populate_output(root.left     ,							level - 1   )
																					populate_output(root.right     ,							level - 1   )

							populate_output(UpperCamelCase__     ,							UpperCamelCase__   )
							return output





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					[]

							def populate_output(UpperCamelCase__     ,							UpperCamelCase__   ) -> None:
														if root is None:
																					return
														if level == 1:
																					output.append(root.data   )
														elif level > 1:
																					populate_output(root.right     ,							level - 1   )
																					populate_output(root.left     ,							level - 1   )

							populate_output(UpperCamelCase__     ,							UpperCamelCase__   )
							return output





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							if root is None:
														return []

							snake_case_           =					[]

							snake_case_           =					0
							snake_case_           =					height(UpperCamelCase__   )

							for h in range(1     ,							height_tree + 1   ):
														if not flag:
																					output.append(get_nodes_from_left_to_right(UpperCamelCase__     ,							UpperCamelCase__   )   )
																					snake_case_           =					1
														else:
																					output.append(get_nodes_from_right_to_left(UpperCamelCase__     ,							UpperCamelCase__   )   )
																					snake_case_           =					0

							return output





def       __lowerCamelCase			(				):  # Main function for testing.







							'''simple docstring'''






							snake_case_           =					make_tree()
							print(F'''In-order Traversal: {inorder(UpperCamelCase__   )}'''   )
							print(F'''Pre-order Traversal: {preorder(UpperCamelCase__   )}'''   )
							print(F'''Post-order Traversal: {postorder(UpperCamelCase__   )}'''     ,							'\n'   )

							print(F'''Height of Tree: {height(UpperCamelCase__   )}'''     ,							'\n'   )

							print('Complete Level Order Traversal: '   )
							print(level_order(UpperCamelCase__   )     ,							'\n'   )

							print('Level-wise order Traversal: '   )

							for level in range(1     ,							height(UpperCamelCase__   ) + 1   ):
														print(F'''Level {level}:'''     ,							get_nodes_from_left_to_right(UpperCamelCase__     ,							level=UpperCamelCase__   )   )

							print('\nZigZag order Traversal: '   )
							print(zigzag(UpperCamelCase__   )   )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()




