





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


snake_case    :						Dict  =						logging.get_logger(__name__)

snake_case    :						Tuple  =						{
    '''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 							lowerCAmelCase__  (	__a						):
					__A     :    Optional[int]           =						'''gpt_neo'''
					__A     :    Tuple           =						['''past_key_values''']
					__A     :    str           =						{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}







					def __init__(       self      :     Dict			,		_A      :     List[str]=5_0257			,		_A      :     Dict=2048			,		_A      :     List[Any]=2048			,		_A      :     Dict=24			,		_A      :     Union[str, Any]=[[["global", "local"], 12]]			,		_A      :     List[str]=16			,		_A      :     List[Any]=None			,		_A      :     int=256			,		_A      :     Any="gelu_new"			,		_A      :     Union[str, Any]=0.0			,		_A      :     Union[str, Any]=0.0			,		_A      :     List[str]=0.0			,		_A      :     Any=0.1			,		_A      :     int=1e-5			,		_A      :     str=0.02			,		_A      :     int=True			,		_A      :     Union[str, Any]=5_0256			,		_A      :     Optional[int]=5_0256			,		**_A      :     List[Any]			,		):
											A__	:							Dict						    = vocab_size
											A__	:							List[str]						    = max_position_embeddings
											A__	:							Tuple						    = hidden_size
											A__	:							Optional[Any]						    = num_layers
											A__	:							int						    = num_heads
											A__	:							Dict						    = intermediate_size
											A__	:							Any						    = window_size
											A__	:							Any						    = activation_function
											A__	:							List[str]						    = resid_dropout
											A__	:							Any						    = embed_dropout
											A__	:							Dict						    = attention_dropout
											A__	:							Tuple						    = classifier_dropout
											A__	:							int						    = layer_norm_epsilon
											A__	:							Optional[int]						    = initializer_range
											A__	:							Dict						    = use_cache

											A__	:							List[Any]						    = bos_token_id
											A__	:							Union[str, Any]						    = eos_token_id

											A__	:							Dict						    = attention_types
											A__	:							Dict						    = 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  _lowercase			(       _A      :     Optional[int]):
											A__	:							List[Any]						    = []
											for item in attention_types:
																	for _ in range(item[1]):
																							attentions.extend(item[0])
											return attentions




def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		Dict:







						"""simple docstring"""

						import torch

						A__	:							List[str]						    = input.size()
						A__	:							Tuple						    = len(lowerCAmelCase__       )
						A__	:							int						    = shape[dimension]

						A__	:							Optional[Any]						    = torch.arange(0							,     lowerCAmelCase__							,     lowerCAmelCase__       )
						A__	:							Tuple						    = torch.div(sizedim - size							,     lowerCAmelCase__							,     rounding_mode="floor"       ) + 1
						A__	:							Optional[Any]						    = torch.arange(lowerCAmelCase__       ) + low_indices[:min_length][:, None]

						A__	:							Tuple						    = [slice(lowerCAmelCase__       )] * rank
						A__	:							List[Any]						    = indices
						A__	:							List[str]						    = input[s]

						A__	:							Dict						    = list(range(0							,     rank + 1       )       )
						perm.append(perm.pop(dimension + 1       )       )

						return sliced.permute(lowerCAmelCase__       )
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		Dict:







						"""simple docstring"""

						import torch

						A__	:							Tuple						    = torch.arange(1							,     lowerCAmelCase__       )
						A__	:							Tuple						    = torch.remainder(lowerCAmelCase__							,     lowerCAmelCase__       )
						A__	:							str						    = remainders == 0
						A__	:							List[Any]						    = candidates[divisor_indices]
						A__	:							Optional[Any]						    = torch.max(lowerCAmelCase__       )
						return largest_divisor, torch.div(lowerCAmelCase__							,     lowerCAmelCase__							,     rounding_mode="floor"       )




class 							lowerCAmelCase__  (	__a						):







					@property
					def  _lowercase			(       self      :     Tuple):
											A__	:							Tuple						    = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
											if self.use_past:
																	self.fill_with_past_key_values_(snake_case__			,		direction="inputs")
																	A__	:							Union[str, Any]						    = {0: "batch", 1: "past_sequence + sequence"}
											else:
																	A__	:							Optional[Any]						    = {0: "batch", 1: "sequence"}

											return common_inputs







					@property
					def  _lowercase			(       self      :     int):
											return self._config.num_heads







					def  _lowercase			(       self      :     str			,		_A      :     PreTrainedTokenizer			,		_A      :     int = -1			,		_A      :     int = -1			,		_A      :     bool = False			,		_A      :     Optional[TensorType] = None			,		):
											A__	:							Tuple						    = 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()
											A__	:							List[str]						    = 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

																							A__    ,       A__	:							List[Any]						    = common_inputs["input_ids"].shape
																							# Not using the same length for past_key_values
																							A__	:							Union[str, Any]						    = seqlen + 2
																							A__	:							Any						    = (
																							    batch,
																							    self.num_attention_heads,
																							    past_key_values_length,
																							    self._config.hidden_size // self.num_attention_heads,
																							)
																							A__	:							Optional[Any]						    = [
																							    (torch.zeros(snake_case__), torch.zeros(snake_case__)) for _ in range(self.num_layers)
																							]

											A__	:							int						    = common_inputs["attention_mask"]
											if self.use_past:
																	A__	:							List[str]						    = ordered_inputs["attention_mask"].dtype
																	A__	:							int						    = torch.cat(
																	    [ordered_inputs["attention_mask"], torch.ones(snake_case__			,		snake_case__			,		dtype=snake_case__)]			,		dim=1)

											return ordered_inputs







					@property
					def  _lowercase			(       self      :     Any):
											return 13




from collections import Counter
from timeit import timeit
def 	snake_case__       (					__lowercase = ""							,     )		->		bool:







						"""simple docstring"""

						return sum(c % 2 for c in Counter(input_str.replace(" "							,     ""       ).lower()       ).values()       ) < 2
def 	snake_case__       (					__lowercase = ""       )		->		bool:







						"""simple docstring"""

						if len(__lowercase       ) == 0:
												return True
						A__	:							Any						    = input_str.replace(" "							,     ""       ).lower()
						# character_freq_dict: Stores the frequency of every character in the input string
						A__	:							dict[str, int]						    = {}

						for character in lower_case_input_str:
												A__	:							Optional[int]						    = character_freq_dict.get(__lowercase							,     0       ) + 1
						A__	:							List[Any]						    = 0

						for character_count in character_freq_dict.values():
												if character_count % 2:
																		odd_char += 1
						if odd_char > 1:
												return False
						return True
def 	snake_case__       (					__lowercase = ""       )		->		None:







						"""simple docstring"""

						print("\nFor string = "							,     __lowercase							,     ":"       )
						print(
						    "> can_string_be_rearranged_as_palindrome_counter()"							,     "\tans ="							,     can_string_be_rearranged_as_palindrome_counter(__lowercase       )							,     "\ttime ="							,     timeit(
						        "z.can_string_be_rearranged_as_palindrome_counter(z.check_str)"							,     setup="import __main__ as z"							,     )							,     "seconds"							,     )
						print(
						    "> can_string_be_rearranged_as_palindrome()"							,     "\tans ="							,     can_string_be_rearranged_as_palindrome(__lowercase       )							,     "\ttime ="							,     timeit(
						        "z.can_string_be_rearranged_as_palindrome(z.check_str)"							,     setup="import __main__ as z"							,     )							,     "seconds"							,     )


if __name__ == "__main__":
		snake_case    :						Dict  =						input(
		    'Enter string to determine if it can be rearranged as a palindrome or not: '
		).strip()
		benchmark(check_str)
		snake_case    :						Dict  =						can_string_be_rearranged_as_palindrome_counter(check_str)
		print(f"""{check_str} can {'' if status else 'not '}be rearranged as a palindrome""")