

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


A									=  logging.get_logger(__name__)

A									=  {
    '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 SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			__A    	=	"""gpt_neo"""
			__A    	=	["""past_key_values"""]
			__A    	=	{"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}


			def __init__(     self			,					__UpperCamelCase=5_02_57			,					__UpperCamelCase=20_48			,					__UpperCamelCase=20_48			,					__UpperCamelCase=24			,					__UpperCamelCase=[[["global", "local"], 12]]			,					__UpperCamelCase=16			,					__UpperCamelCase=None			,					__UpperCamelCase=2_56			,					__UpperCamelCase="gelu_new"			,					__UpperCamelCase=0.0			,					__UpperCamelCase=0.0			,					__UpperCamelCase=0.0			,					__UpperCamelCase=0.1			,					__UpperCamelCase=1E-5			,					__UpperCamelCase=0.02			,					__UpperCamelCase=True			,					__UpperCamelCase=5_02_56			,					__UpperCamelCase=5_02_56			,					**__UpperCamelCase			,					):

				"""simple docstring"""


				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(__UpperCamelCase				)

				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=__UpperCamelCase			,					eos_token_id=__UpperCamelCase			,					**__UpperCamelCase				)







			@staticmethod
			def       __lowerCAmelCase			(     __UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			[]
				for item in attention_types:
					for _ in range(item[1]				):
						attentions.extend(item[0]				)
				return attentions







def a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	import torch

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

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

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

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

	return sliced.permute(lowercase__						)





def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''
	import torch

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






class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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

				return common_inputs


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return self._config.num_heads


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase = -1			,					__UpperCamelCase = -1			,					__UpperCamelCase = False			,					__UpperCamelCase = None			,					):

				"""simple docstring"""


				snake_case_   		=			super(__UpperCamelCase			,					self				).generate_dummy_inputs(
				    __UpperCamelCase			,					batch_size=__UpperCamelCase			,					seq_length=__UpperCamelCase			,					is_pair=__UpperCamelCase			,					framework=__UpperCamelCase				)

				# 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(__UpperCamelCase				), torch.zeros(__UpperCamelCase				)) 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(__UpperCamelCase			,					__UpperCamelCase			,					dtype=__UpperCamelCase				)]			,					dim=1				)

				return ordered_inputs







			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return 13



class SCREAMING_SNAKE_CASE	:






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			val
				snake_case_   		=			None
				snake_case_   		=			None







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				if self.val:
					if val < self.val:
						if self.left is None:
							snake_case_   		=			Node(__UpperCamelCase				)
						else:
							self.left.insert(__UpperCamelCase				)
					elif val > self.val:
						if self.right is None:
							snake_case_   		=			Node(__UpperCamelCase				)
						else:
							self.right.insert(__UpperCamelCase				)
				else:
					snake_case_   		=			val







def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''
	# Recursive traversal
	if root:
		inorder(root.left      ,			lowercase__						)
		res.append(root.val						)
		inorder(root.right      ,			lowercase__						)





def a(lowercase__						):





	'''simple docstring'''
	# Build BST
	if len(lowercase__						) == 0:
		return arr
	snake_case_   		=			Node(arr[0]						)
	for i in range(1      ,			len(lowercase__						)						):
		root.insert(arr[i]						)
	# Traverse BST in order.
	snake_case_   		=			[]
	inorder(lowercase__      ,			lowercase__						)
	return res


if __name__ == "__main__":
		print(tree_sort([10, 1, 3, 2, 9, 14, 13]))
