





from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray





def   lowerCamelCase__       (       UpperCamelCase__     :    float    ,      UpperCamelCase__     :    float    ,      UpperCamelCase__     :    bool = False  )     ->	list[float]:



     '''simple docstring'''




     if radian_mode:
          return [magnitude * cos(UpperCamelCase__  ), magnitude * sin(UpperCamelCase__  )]
     return [magnitude * cos(radians(UpperCamelCase__  )  ), magnitude * sin(radians(UpperCamelCase__  )  )]





def   lowerCamelCase__       (       UpperCamelCase__     :    NDArray[floataa]    ,      UpperCamelCase__     :    NDArray[floataa]    ,      UpperCamelCase__     :    float = 10**-1  )     ->	bool:



     '''simple docstring'''




     _snake_case													=			cross(UpperCamelCase__    ,      UpperCamelCase__  )
     _snake_case													=			sum(UpperCamelCase__  )
     return abs(UpperCamelCase__  ) < eps


if __name__ == "__main__":
     # Test to check if it works
     UpperCAmelCase_ 							=       array(
         [
             polar_force(718.4, 180 - 30),
             polar_force(879.54, 45),
             polar_force(100, -90),
         ]
     )

     UpperCAmelCase_ 							=       array([[0, 0], [0, 0], [0, 0]])

     assert in_static_equilibrium(forces, location)

     # Problem 1 in image_data/2D_problems.jpg
     UpperCAmelCase_ 							=       array(
         [
             polar_force(30 * 9.81, 15),
             polar_force(215, 180 - 45),
             polar_force(264, 90 - 30),
         ]
     )

     UpperCAmelCase_ 							=       array([[0, 0], [0, 0], [0, 0]])

     assert in_static_equilibrium(forces, location)

     # Problem in image_data/2D_problems_1.jpg
     UpperCAmelCase_ 							=       array([[0, -2000], [0, -1200], [0, 15600], [0, -12400]])

     UpperCAmelCase_ 							=       array([[0, 0], [6, 0], [10, 0], [12, 0]])

     assert in_static_equilibrium(forces, location)

     import doctest

     doctest.testmod()










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


UpperCAmelCase_ 							=       logging.get_logger(__name__)

UpperCAmelCase_ 							=       {
    """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      UpperCamelCase_						(					_lowerCamelCase   ):
					lowerCAmelCase_							=		'''gpt_neo'''
					lowerCAmelCase_							=		['''past_key_values''']
					lowerCAmelCase_							=		{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}




					def __init__(						self			,  lowerCAmelCase_=5_0257			,  lowerCAmelCase_=2048			,  lowerCAmelCase_=2048			,  lowerCAmelCase_=24			,  lowerCAmelCase_=[[["global", "local"], 12]]			,  lowerCAmelCase_=16			,  lowerCAmelCase_=None			,  lowerCAmelCase_=256			,  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_0256			,  lowerCAmelCase_=5_0256			,  **lowerCAmelCase_			,  )						->							Tuple:
										_snake_case													=			vocab_size
										_snake_case													=			max_position_embeddings
										_snake_case													=			hidden_size
										_snake_case													=			num_layers
										_snake_case													=			num_heads
										_snake_case													=			intermediate_size
										_snake_case													=			window_size
										_snake_case													=			activation_function
										_snake_case													=			resid_dropout
										_snake_case													=			embed_dropout
										_snake_case													=			attention_dropout
										_snake_case													=			classifier_dropout
										_snake_case													=			layer_norm_epsilon
										_snake_case													=			initializer_range
										_snake_case													=			use_cache

										_snake_case													=			bos_token_id
										_snake_case													=			eos_token_id

										_snake_case													=			attention_types
										_snake_case													=			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 		lowerCAmelCase     (						lowerCAmelCase_					)						->							Any:
										_snake_case													=			[]
										for item in attention_types:
															for _ in range(item[1]					):
																				attentions.extend(item[0]					)
										return attentions





def   lowerCamelCase__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    int    ,      UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Optional[int]  )     ->	Any:



					'''simple docstring'''




					import torch

					_snake_case													=			input.size()
					_snake_case													=			len(UpperCamelCase__  )
					_snake_case													=			shape[dimension]

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

					_snake_case													=			[slice(UpperCamelCase__  )] * rank
					_snake_case													=			indices
					_snake_case													=			input[s]

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

					return sliced.permute(UpperCamelCase__  )





def   lowerCamelCase__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Dict  )     ->	str:



					'''simple docstring'''




					import torch

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




class      UpperCamelCase_						(					_lowerCamelCase   ):




					@property
					def 		lowerCAmelCase     (						self					)						->							Mapping[str, Mapping[int, str]]:
										_snake_case													=			OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}					)
										if self.use_past:
															self.fill_with_past_key_values_(lowerCAmelCase_			,  direction='inputs'					)
															_snake_case													=			{0: 'batch', 1: 'past_sequence + sequence'}
										else:
															_snake_case													=			{0: 'batch', 1: 'sequence'}

										return common_inputs




					@property
					def 		lowerCAmelCase     (						self					)						->							int:
										return self._config.num_heads




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = -1			,  lowerCAmelCase_ = -1			,  lowerCAmelCase_ = False			,  lowerCAmelCase_ = None			,  )						->							Mapping[str, Any]:
										_snake_case													=			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()
										_snake_case													=			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

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

										_snake_case													=			common_inputs['attention_mask']
										if self.use_past:
															_snake_case													=			ordered_inputs['attention_mask'].dtype
															_snake_case													=			torch.cat(
															    [ordered_inputs['attention_mask'], torch.ones(lowerCAmelCase_			,  lowerCAmelCase_			,  dtype=lowerCAmelCase_					)]			,  dim=1					)

										return ordered_inputs






					@property
					def 		lowerCAmelCase     (						self					)						->							int:
										return 13



