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      __lowercase					(    _A    ):
						lowercase					 =     'gpt_neo'
						lowercase					 =     ['past_key_values']
						lowercase					 =     {'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}

						def __init__(	self				:      int							,							__lowerCamelCase				:      List[str]=5_02_57							,							__lowerCamelCase				:      Dict=20_48							,							__lowerCamelCase				:      Any=20_48							,							__lowerCamelCase				:      Optional[Any]=24							,							__lowerCamelCase				:      Optional[int]=[[["global", "local"], 12]]							,							__lowerCamelCase				:      Union[str, Any]=16							,							__lowerCamelCase				:      List[str]=None							,							__lowerCamelCase				:      Any=2_56							,							__lowerCamelCase				:      Optional[int]="gelu_new"							,							__lowerCamelCase				:      Any=0.0							,							__lowerCamelCase				:      Any=0.0							,							__lowerCamelCase				:      Optional[Any]=0.0							,							__lowerCamelCase				:      Any=0.1							,							__lowerCamelCase				:      Dict=1E-5							,							__lowerCamelCase				:      int=0.02							,							__lowerCamelCase				:      Optional[Any]=True							,							__lowerCamelCase				:      Dict=5_02_56							,							__lowerCamelCase				:      Dict=5_02_56							,							**__lowerCamelCase				:      str							,							)							->		Any:


									'''simple docstring'''

									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				:      Any   )							->		Optional[int]:


									'''simple docstring'''

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






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       Any:



			"""simple docstring"""







			import torch

			lowercase							=		input.size()
			lowercase							=		len(UpperCAmelCase       )
			lowercase							=		shape[dimension]

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

			lowercase							=		[slice(UpperCAmelCase       )] * rank
			lowercase							=		indices
			lowercase							=		input[s]

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

			return sliced.permute(UpperCAmelCase       )






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       Optional[Any]:



			"""simple docstring"""







			import torch

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






class      __lowercase					(    _A    ):

						@property
						def        __a		(	self				:      List[Any]   )							->		Mapping[str, Mapping[int, str]]:


									'''simple docstring'''

									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				:      Tuple   )							->		int:


									'''simple docstring'''

									return self._config.num_heads

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Optional[Any] = -1							,							__lowerCamelCase				:      Dict = -1							,							__lowerCamelCase				:      Union[str, Any] = False							,							__lowerCamelCase				:      str = None							,							)							->		Mapping[str, Any]:


									'''simple docstring'''

									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				:      Union[str, Any]   )							->		int:


									'''simple docstring'''

									return 13






import cmath
import math






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       complex:



			"""simple docstring"""







			lowercase							=		math.radians(UpperCAmelCase       )
			lowercase							=		math.radians(UpperCAmelCase       )

			# Convert voltage and current to rectangular form
			lowercase							=		cmath.rect(UpperCAmelCase,		UpperCAmelCase       )
			lowercase							=		cmath.rect(UpperCAmelCase,		UpperCAmelCase       )

			# Calculate apparent power
			return voltage_rect * current_rect


if __name__ == "__main__":
						import doctest

						doctest.testmod()





