




"""simple docstring"""




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							:    Optional[int]  							=							logging.get_logger(__name__)

lowerCAmelCase							:    Optional[int]  							=							{
    """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 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''
				__UpperCamelCase    						=		"gpt_neo"
				__UpperCamelCase    						=		["past_key_values"]
				__UpperCamelCase    						=		{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}





				def __init__( self	,				_a=50_257	,				_a=2_048	,				_a=2_048	,				_a=24	,				_a=[[["global", "local"], 12]]	,				_a=16	,				_a=None	,				_a=256	,				_a="gelu_new"	,				_a=0.0	,				_a=0.0	,				_a=0.0	,				_a=0.1	,				_a=1e-5	,				_a=0.02	,				_a=True	,				_a=50_256	,				_a=50_256	,				**_a	,				):





							"""simple docstring"""
							lowerCamelCase       =      vocab_size
							lowerCamelCase       =      max_position_embeddings
							lowerCamelCase       =      hidden_size
							lowerCamelCase       =      num_layers
							lowerCamelCase       =      num_heads
							lowerCamelCase       =      intermediate_size
							lowerCamelCase       =      window_size
							lowerCamelCase       =      activation_function
							lowerCamelCase       =      resid_dropout
							lowerCamelCase       =      embed_dropout
							lowerCamelCase       =      attention_dropout
							lowerCamelCase       =      classifier_dropout
							lowerCamelCase       =      layer_norm_epsilon
							lowerCamelCase       =      initializer_range
							lowerCamelCase       =      use_cache

							lowerCamelCase       =      bos_token_id
							lowerCamelCase       =      eos_token_id

							lowerCamelCase       =      attention_types
							lowerCamelCase       =      self.expand_attention_types_params(_a				)

							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=_a	,				eos_token_id=_a	,				**_a				)





				@staticmethod
				def      _lowerCAmelCase       ( _a				):





							"""simple docstring"""
							lowerCamelCase       =      []
							for item in attention_types:
										for _ in range(item[1]				):
													attentions.extend(item[0]				)
							return attentions




def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__       )		->      int:
			import torch

			lowerCamelCase       =      input.size()
			lowerCamelCase       =      len(snake_case__       )
			lowerCamelCase       =      shape[dimension]

			lowerCamelCase       =      torch.arange(0 ,				snake_case__ ,				snake_case__       )
			lowerCamelCase       =      torch.div(sizedim - size ,				snake_case__ ,				rounding_mode="""floor"""       ) + 1
			lowerCamelCase       =      torch.arange(snake_case__       ) + low_indices[:min_length][:, None]

			lowerCamelCase       =      [slice(snake_case__       )] * rank
			lowerCamelCase       =      indices
			lowerCamelCase       =      input[s]

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

			return sliced.permute(snake_case__       )



def 					a__				(       snake_case__ ,				snake_case__       )		->      Optional[int]:
			import torch

			lowerCamelCase       =      torch.arange(1 ,				snake_case__       )
			lowerCamelCase       =      torch.remainder(snake_case__ ,				snake_case__       )
			lowerCamelCase       =      remainders == 0
			lowerCamelCase       =      candidates[divisor_indices]
			lowerCamelCase       =      torch.max(snake_case__       )
			return largest_divisor, torch.div(snake_case__ ,				snake_case__ ,				rounding_mode="""floor"""       )






class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''





				@property
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}				)
							if self.use_past:
										self.fill_with_past_key_values_(_a	,				direction="""inputs"""				)
										lowerCamelCase       =      {0: """batch""", 1: """past_sequence + sequence"""}
							else:
										lowerCamelCase       =      {0: """batch""", 1: """sequence"""}

							return common_inputs





				@property
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							return self._config.num_heads





				def      _lowerCAmelCase       ( self	,				_a	,				_a = -1	,				_a = -1	,				_a = False	,				_a = None	,				):





							"""simple docstring"""
							lowerCamelCase       =      super(_a	,				self				).generate_dummy_inputs(
							    _a	,				batch_size=_a	,				seq_length=_a	,				is_pair=_a	,				framework=_a				)

							# We need to order the input in the way they appears in the forward()
							lowerCamelCase       =      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

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

							lowerCamelCase       =      common_inputs["""attention_mask"""]
							if self.use_past:
										lowerCamelCase       =      ordered_inputs["""attention_mask"""].dtype
										lowerCamelCase       =      torch.cat(
										    [ordered_inputs["""attention_mask"""], torch.ones(_a	,				_a	,				dtype=_a				)]	,				dim=1				)

							return ordered_inputs





				@property
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							return 13









"""simple docstring"""




from collections.abc import Sequence



def 					a__				(       snake_case__ ,				snake_case__ = False       )		->      float:
			if not arr:
						return 0

			lowerCamelCase       =      0 if allow_empty_subarrays else float("""-inf"""       )
			lowerCamelCase       =      0.0
			for num in arr:
						lowerCamelCase       =      max(0 if allow_empty_subarrays else num ,				curr_sum + num       )
						lowerCamelCase       =      max(snake_case__ ,				snake_case__       )

			return max_sum


if __name__ == "__main__":
					from doctest import testmod

					testmod()

					lowerCAmelCase							:    Optional[int]  							=							[-2, 1, -3, 4, -1, 2, 1, -5, 4]
					print(F"""{max_subarray_sum(nums) = }""")



