


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


lowerCAmelCase           =		logging.get_logger(__name__)

lowerCAmelCase           =		{
    '''EleutherAI/gpt-j-6B''': '''https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json''',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}





class       A    (   A_ ):
							UpperCamelCase_   :							List[str]       ='''gptj'''
							UpperCamelCase_   :							List[str]       ={
							    '''max_position_embeddings''': '''n_positions''',
							    '''hidden_size''': '''n_embd''',
							    '''num_attention_heads''': '''n_head''',
							    '''num_hidden_layers''': '''n_layer''',
							}


							def __init__(self      ,				lowerCAmelCase=5_0_4_0_0      ,				lowerCAmelCase=2_0_4_8      ,				lowerCAmelCase=4_0_9_6      ,				lowerCAmelCase=2_8      ,				lowerCAmelCase=1_6      ,				lowerCAmelCase=6_4      ,				lowerCAmelCase=None      ,				lowerCAmelCase="gelu_new"      ,				lowerCAmelCase=0.0      ,				lowerCAmelCase=0.0      ,				lowerCAmelCase=0.0      ,				lowerCAmelCase=1E-5      ,				lowerCAmelCase=0.02      ,				lowerCAmelCase=True      ,				lowerCAmelCase=5_0_2_5_6      ,				lowerCAmelCase=5_0_2_5_6      ,				lowerCAmelCase=False      ,				**lowerCAmelCase      ,				):
									__lowercase=			vocab_size
									__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=lowerCAmelCase      ,				eos_token_id=lowerCAmelCase      ,				tie_word_embeddings=lowerCAmelCase      ,				**lowerCAmelCase					)





class       A    (   A_ ):


							def __init__(self      ,				lowerCAmelCase      ,				lowerCAmelCase = "default"      ,				lowerCAmelCase = None      ,				lowerCAmelCase = False      ,				):
									super().__init__(lowerCAmelCase      ,				task=lowerCAmelCase      ,				patching_specs=lowerCAmelCase      ,				use_past=lowerCAmelCase					)
									if not getattr(self._config      ,				'pad_token_id'      ,				lowerCAmelCase					):
											# TODO: how to do that better?
											__lowercase=			0


							@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.n_layer


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


							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))






