

'''simple docstring'''



import unittest

from .lib import (
    Matrix,
    Vector,
    axpy,
    square_zero_matrix,
    unit_basis_vector,
    zero_vector,
)



class 				_snake_case					(   unittest.TestCase			):

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							Vector([1, 2, 3]	)
      self.assertEqual(x.component(0	)      , 1	)
      self.assertEqual(x.component(2	)      , 3	)
      lowerCAmelCase 						=							Vector()

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							Vector([0, 0, 0, 0, 0, 1]	)
      self.assertEqual(str(snake_case_	)      , '(0,0,0,0,0,1)'	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							Vector([1, 2, 3, 4]	)
      self.assertEqual(len(snake_case_	)      , 4	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							Vector([1, 2]	)
      lowerCAmelCase 						=							Vector([1, 2, 3, 4, 5]	)
      lowerCAmelCase 						=							Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]	)
      lowerCAmelCase 						=							Vector([1, -1, 1, -1, 2, -3, 4, -5]	)
      self.assertAlmostEqual(x.euclidean_length()      , 2.236      , 3	)
      self.assertAlmostEqual(y.euclidean_length()      , 7.416      , 3	)
      self.assertEqual(z.euclidean_length()      , 0	)
      self.assertAlmostEqual(w.euclidean_length()      , 7.616      , 3	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							Vector([1, 2, 3]	)
      lowerCAmelCase 						=							Vector([1, 1, 1]	)
      self.assertEqual((x + y).component(0	)      , 2	)
      self.assertEqual((x + y).component(1	)      , 3	)
      self.assertEqual((x + y).component(2	)      , 4	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							Vector([1, 2, 3]	)
      lowerCAmelCase 						=							Vector([1, 1, 1]	)
      self.assertEqual((x - y).component(0	)      , 0	)
      self.assertEqual((x - y).component(1	)      , 1	)
      self.assertEqual((x - y).component(2	)      , 2	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							Vector([1, 2, 3]	)
      lowerCAmelCase 						=							Vector([2, -1, 4]	)  # for test of dot product
      lowerCAmelCase 						=							Vector([1, -2, -1]	)
      self.assertEqual(str(x * 3.0	)      , '(3.0,6.0,9.0)'	)
      self.assertEqual((a * b)      , 0	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      self.assertEqual(str(zero_vector(10	)	).count('0'	)      , 10	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      self.assertEqual(str(unit_basis_vector(3      , 1	)	)      , '(0,1,0)'	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							Vector([1, 2, 3]	)
      lowerCAmelCase 						=							Vector([1, 0, 1]	)
      self.assertEqual(str(axpy(2      , snake_case_      , snake_case_	)	)      , '(3,4,7)'	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							Vector([1, 0, 0, 0, 0, 0]	)
      lowerCAmelCase 						=							x.copy()
      self.assertEqual(str(snake_case_	)      , str(snake_case_	)	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							Vector([1, 0, 0]	)
      x.change_component(0      , 0	)
      x.change_component(1      , 1	)
      self.assertEqual(str(snake_case_	)      , '(0,1,0)'	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]      , 3      , 3	)
      self.assertEqual('|1,2,3|\n|2,4,5|\n|6,7,8|\n'      , str(snake_case_	)	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]      , 3      , 3	)
      lowerCAmelCase 						=							[[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
      for x in range(a.height()	):
          for y in range(a.width()	):
              self.assertEqual(minors[x][y]      , a.minor(snake_case_      , snake_case_	)	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]      , 3      , 3	)
      lowerCAmelCase 						=							[[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
      for x in range(a.height()	):
          for y in range(a.width()	):
              self.assertEqual(cofactors[x][y]      , a.cofactor(snake_case_      , snake_case_	)	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]      , 3      , 3	)
      self.assertEqual(-5      , a.determinant()	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]      , 3      , 3	)
      lowerCAmelCase 						=							Vector([1, 2, 3]	)
      self.assertEqual('(14,32,50)'      , str(a * x	)	)
      self.assertEqual('|2,4,6|\n|8,10,12|\n|14,16,18|\n'      , str(a * 2	)	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]      , 3      , 3	)
      a.change_component(0      , 2      , 5	)
      self.assertEqual('|1,2,5|\n|2,4,5|\n|6,7,8|\n'      , str(snake_case_	)	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]      , 3      , 3	)
      self.assertEqual(7      , a.component(2      , 1	)      , 0.01	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]      , 3      , 3	)
      lowerCAmelCase 						=							Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]      , 3      , 3	)
      self.assertEqual('|2,4,10|\n|4,8,10|\n|12,14,18|\n'      , str(a + b	)	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]      , 3      , 3	)
      lowerCAmelCase 						=							Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]      , 3      , 3	)
      self.assertEqual('|0,0,-4|\n|0,0,0|\n|0,0,-2|\n'      , str(a - b	)	)


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      self.assertEqual(
          '|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n'      , str(square_zero_matrix(5	)	)      , )


if __name__ == "__main__":
     unittest.main()






"""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


_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    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Tuple					     =				'gpt_neo'
			_UpperCamelCase  :						Optional[Any]					     =				['past_key_values']
			_UpperCamelCase  :						int					     =				{'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}





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







										'''simple docstring'''


										A__       :     int								=			vocab_size
										A__       :     Optional[Any]								=			max_position_embeddings
										A__       :     int								=			hidden_size
										A__       :     List[Any]								=			num_layers
										A__       :     Any								=			num_heads
										A__       :     List[str]								=			intermediate_size
										A__       :     Dict								=			window_size
										A__       :     Optional[int]								=			activation_function
										A__       :     Optional[int]								=			resid_dropout
										A__       :     List[str]								=			embed_dropout
										A__       :     str								=			attention_dropout
										A__       :     List[str]								=			classifier_dropout
										A__       :     str								=			layer_norm_epsilon
										A__       :     str								=			initializer_range
										A__       :     Any								=			use_cache

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

										A__       :     Optional[Any]								=			attention_types
										A__       :     List[Any]								=			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 		lowerCamelCase					(			snake_case_							):







										'''simple docstring'''


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





def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							import torch

							A__       :     Union[str, Any]								=			input.size()
							A__       :     str								=			len(lowerCAmelCase							)
							A__       :     Union[str, Any]								=			shape[dimension]

							A__       :     Dict								=			torch.arange(0			,  lowerCAmelCase			,  lowerCAmelCase							)
							A__       :     Dict								=			torch.div(sizedim - size			,  lowerCAmelCase			,  rounding_mode="""floor"""							) + 1
							A__       :     str								=			torch.arange(lowerCAmelCase							) + low_indices[:min_length][:, None]

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

							A__       :     List[str]								=			list(range(0			,  rank + 1							)							)
							perm.append(perm.pop(dimension + 1							)							)

							return sliced.permute(lowerCAmelCase							)

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							import torch

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



class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Any								=			OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}							)
										if self.use_past:
																	self.fill_with_past_key_values_(snake_case_   ,      direction="""inputs"""							)
																	A__       :     str								=			{0: """batch""", 1: """past_sequence + sequence"""}
										else:
																	A__       :     Optional[Any]								=			{0: """batch""", 1: """sequence"""}

										return common_inputs





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return self._config.num_heads





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = -1   ,      snake_case_ = -1   ,      snake_case_ = False   ,      snake_case_ = None   ,      ):







										'''simple docstring'''


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

										A__       :     Tuple								=			common_inputs["""attention_mask"""]
										if self.use_past:
																	A__       :     List[Any]								=			ordered_inputs["""attention_mask"""].dtype
																	A__       :     Optional[Any]								=			torch.cat(
																	    [ordered_inputs["""attention_mask"""], torch.ones(snake_case_   ,      snake_case_   ,      dtype=snake_case_							)]   ,      dim=1							)

										return ordered_inputs





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return 13





