


'''simple docstring'''
import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
 import torch

 from transformers.generation import (
     MaxLengthCriteria,
     MaxNewTokensCriteria,
     MaxTimeCriteria,
     StoppingCriteriaList,
     validate_stopping_criteria,
 )

@require_torch
class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			3
  __A  :		Optional[int]       =			250

  __A  :		Optional[Any]       =			ids_tensor((batch_size, length)      ,      __lowercase						)
  __A  :		str       =			torch.ones((batch_size, length)      ,      device=__lowercase      ,      dtype=torch.float						) / length
  return input_ids, scores


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[Any]       =			self._get_tensors(5						)

  __A  :		Union[str, Any]       =			StoppingCriteriaList(
      [
          MaxLengthCriteria(max_length=10						),
          MaxTimeCriteria(max_time=0.1						),
      ]						)

  self.assertFalse(criteria(__lowercase      ,      __lowercase						)						)

  __A  :		Dict       =			self._get_tensors(9						)
  self.assertFalse(criteria(__lowercase      ,      __lowercase						)						)

  __A  :		int       =			self._get_tensors(10						)
  self.assertTrue(criteria(__lowercase      ,      __lowercase						)						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Dict       =			MaxLengthCriteria(max_length=10						)

  __A  :		Optional[int]       =			self._get_tensors(5						)
  self.assertFalse(criteria(__lowercase      ,      __lowercase						)						)

  __A  :		Dict       =			self._get_tensors(9						)
  self.assertFalse(criteria(__lowercase      ,      __lowercase						)						)

  __A  :		int       =			self._get_tensors(10						)
  self.assertTrue(criteria(__lowercase      ,      __lowercase						)						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[Any]       =			MaxNewTokensCriteria(start_length=5      ,      max_new_tokens=5						)

  __A  :		int       =			self._get_tensors(5						)
  self.assertFalse(criteria(__lowercase      ,      __lowercase						)						)

  __A  :		str       =			self._get_tensors(9						)
  self.assertFalse(criteria(__lowercase      ,      __lowercase						)						)

  __A  :		str       =			self._get_tensors(10						)
  self.assertTrue(criteria(__lowercase      ,      __lowercase						)						)

  __A  :		Optional[int]       =			StoppingCriteriaList([criteria]						)
  self.assertEqual(criteria_list.max_length      ,      10						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			self._get_tensors(5						)

  __A  :		Dict       =			MaxTimeCriteria(max_time=0.1						)
  self.assertFalse(criteria(__lowercase      ,      __lowercase						)						)

  __A  :		Union[str, Any]       =			MaxTimeCriteria(max_time=0.1      ,      initial_timestamp=time.time() - 0.2						)
  self.assertTrue(criteria(__lowercase      ,      __lowercase						)						)







 def  snake_case__   (      self						):

  """simple docstring"""







  validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10						)]						)      ,      10						)

  with self.assertWarns(__lowercase						):
   validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10						)]						)      ,      11						)

  __A  :		List[str]       =			validate_stopping_criteria(StoppingCriteriaList()      ,      11						)

  self.assertEqual(len(__lowercase						)      ,      1						)










'''simple docstring'''
import math

class 						_lowerCamelCase					:




 '''simple docstring'''


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		List[str]       =			0.0
  __A  :		Optional[int]       =			0.0
  for i in range(len(__lowercase						)						):
   da += math.pow((sample[i] - weights[0][i])      ,      2						)
   da += math.pow((sample[i] - weights[1][i])      ,      2						)
   return 0 if da > da else 1
  return 0







 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  for i in range(len(__lowercase						)						):
   weights[j][i] += alpha * (sample[i] - weights[j][i])
  return weights


def 			_lowercase       (				):
 # Training Examples ( m, n )
 __A  :		Union[str, Any]       =			[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

 # weight initialization ( n, C )
 __A  :		Tuple       =			[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

 # training
 __A  :		Optional[int]       =			SelfOrganizingMap()
 __A  :		int       =			3
 __A  :		Optional[Any]       =			0.5

 for _ in range(UpperCamelCase__			):
  for j in range(len(UpperCamelCase__			)			):
   # training sample
   __A  :		Dict       =			training_samples[j]

   # Compute the winning vector
   __A  :		List[Any]       =			self_organizing_map.get_winner(UpperCamelCase__,				UpperCamelCase__			)

   # Update the winning vector
   __A  :		Union[str, Any]       =			self_organizing_map.update(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

    # classify test sample
 __A  :		int       =			[0, 0, 0, 1]
 __A  :		List[Any]       =			self_organizing_map.get_winner(UpperCamelCase__,				UpperCamelCase__			)

 # results
 print(f"""Clusters that the test sample belongs to : {winner}"""			)
 print(f"""Weights that have been trained : {weights}"""			)


# running the main() function
if __name__ == "__main__":
 main()






