


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


lowerCAmelCase           =		logging.get_logger(__name__)

lowerCAmelCase           =		{
    '''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       A    (   A_ ):
							UpperCamelCase_   :							Optional[int]       ='''gpt_neo'''
							UpperCamelCase_   :							List[Any]       =['''past_key_values''']
							UpperCamelCase_   :							Dict       ={'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}


							def __init__(self      ,				lowerCAmelCase=5_0_2_5_7      ,				lowerCAmelCase=2_0_4_8      ,				lowerCAmelCase=2_0_4_8      ,				lowerCAmelCase=2_4      ,				lowerCAmelCase=[[["global", "local"], 1_2]]      ,				lowerCAmelCase=1_6      ,				lowerCAmelCase=None      ,				lowerCAmelCase=2_5_6      ,				lowerCAmelCase="gelu_new"      ,				lowerCAmelCase=0.0      ,				lowerCAmelCase=0.0      ,				lowerCAmelCase=0.0      ,				lowerCAmelCase=0.1      ,				lowerCAmelCase=1E-5      ,				lowerCAmelCase=0.02      ,				lowerCAmelCase=True      ,				lowerCAmelCase=5_0_2_5_6      ,				lowerCAmelCase=5_0_2_5_6      ,				**lowerCAmelCase      ,				):
									__lowercase=			vocab_size
									__lowercase=			max_position_embeddings
									__lowercase=			hidden_size
									__lowercase=			num_layers
									__lowercase=			num_heads
									__lowercase=			intermediate_size
									__lowercase=			window_size
									__lowercase=			activation_function
									__lowercase=			resid_dropout
									__lowercase=			embed_dropout
									__lowercase=			attention_dropout
									__lowercase=			classifier_dropout
									__lowercase=			layer_norm_epsilon
									__lowercase=			initializer_range
									__lowercase=			use_cache

									__lowercase=			bos_token_id
									__lowercase=			eos_token_id

									__lowercase=			attention_types
									__lowercase=			self.expand_attention_types_params(lowerCAmelCase					)

									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=lowerCAmelCase      ,				eos_token_id=lowerCAmelCase      ,				**lowerCAmelCase					)







							@staticmethod
							def 			_A       (lowerCAmelCase					):
									__lowercase=			[]
									for item in attention_types:
											for _ in range(item[1]					):
													attentions.extend(item[0]					)
									return attentions







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__   , lowercase__	)						->	int:

		'''simple docstring'''







		import torch

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

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

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

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

		return sliced.permute(lowercase__	)







def        _lowerCamelCase(	lowercase__   , lowercase__	)						->	Optional[Any]:

		'''simple docstring'''







		import torch

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





class       A    (   A_ ):


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

									return common_inputs


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


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = -1      ,				lowerCAmelCase = -1      ,				lowerCAmelCase = False      ,				lowerCAmelCase = None      ,				):
									__lowercase=			super(lowerCAmelCase      ,				self					).generate_dummy_inputs(
									    lowerCAmelCase      ,				batch_size=lowerCAmelCase      ,				seq_length=lowerCAmelCase      ,				is_pair=lowerCAmelCase      ,				framework=lowerCAmelCase					)

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

									return ordered_inputs







							@property
							def 			_A       (self					):
									return 1_3










def        _lowerCamelCase(	lowercase__	)						->	int:

		'''simple docstring'''







		if collection == []:
				return []

		# get some information about the collection
		__lowercase=			len(lowercase__	)
		__lowercase=			max(lowercase__	)
		__lowercase=			min(lowercase__	)

		# create the counting array
		__lowercase=			coll_max + 1 - coll_min
		__lowercase=			[0] * counting_arr_length

		# count how much a number appears in the collection
		for number in collection:
				counting_arr[number - coll_min] += 1

		# sum each position with it's predecessors. now, counting_arr[i] tells
		# us how many elements <= i has in the collection
		for i in range(1   , lowercase__	):
				__lowercase=			counting_arr[i] + counting_arr[i - 1]

		# create the output collection
		__lowercase=			[0] * coll_len

		# place the elements in the output, respecting the original order (stable
		# sort) from end to begin, updating counting_arr
		for i in reversed(range(0   , lowercase__	)	):
				__lowercase=			collection[i]
				counting_arr[collection[i] - coll_min] -= 1

		return ordered







def        _lowerCamelCase(	lowercase__	)						->	List[str]:

		'''simple docstring'''







		return "".join([chr(lowercase__	) for i in counting_sort([ord(lowercase__	) for c in string]	)]	)


if __name__ == "__main__":
	# Test string sort
	assert counting_sort_string('''thisisthestring''') == "eghhiiinrsssttt"

	lowerCAmelCase           =		input('''Enter numbers separated by a comma:\n''').strip()
	lowerCAmelCase           =		[int(item) for item in user_input.split(''',''')]
	print(counting_sort(unsorted))






