





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 	UpperCamelCase_							(  unittest.TestCase     ):


							def   _snake_case	(				self     :Any				,					__A     :str		) -> Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   3
									SCREAMING_SNAKE_CASE__								=   250

									SCREAMING_SNAKE_CASE__								=   ids_tensor((batch_size, length)				,					_A		)
									SCREAMING_SNAKE_CASE__								=   torch.ones((batch_size, length)				,					device=_A				,					dtype=torch.float		) / length
									return input_ids, scores


							def   _snake_case	(				self     :Any		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self._get_tensors(5		)

									SCREAMING_SNAKE_CASE__								=   StoppingCriteriaList(
									    [
									        MaxLengthCriteria(max_length=10		),
									        MaxTimeCriteria(max_time=0.1		),
									    ]		)

									self.assertFalse(criteria(_A				,					_A		)		)

									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self._get_tensors(9		)
									self.assertFalse(criteria(_A				,					_A		)		)

									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self._get_tensors(10		)
									self.assertTrue(criteria(_A				,					_A		)		)


							def   _snake_case	(				self     :List[str]		) -> Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   MaxLengthCriteria(max_length=10		)

									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self._get_tensors(5		)
									self.assertFalse(criteria(_A				,					_A		)		)

									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self._get_tensors(9		)
									self.assertFalse(criteria(_A				,					_A		)		)

									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self._get_tensors(10		)
									self.assertTrue(criteria(_A				,					_A		)		)


							def   _snake_case	(				self     :Optional[int]		) -> List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   MaxNewTokensCriteria(start_length=5				,					max_new_tokens=5		)

									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self._get_tensors(5		)
									self.assertFalse(criteria(_A				,					_A		)		)

									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self._get_tensors(9		)
									self.assertFalse(criteria(_A				,					_A		)		)

									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self._get_tensors(10		)
									self.assertTrue(criteria(_A				,					_A		)		)

									SCREAMING_SNAKE_CASE__								=   StoppingCriteriaList([criteria]		)
									self.assertEqual(criteria_list.max_length				,					10		)


							def   _snake_case	(				self     :Any		) -> int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self._get_tensors(5		)

									SCREAMING_SNAKE_CASE__								=   MaxTimeCriteria(max_time=0.1		)
									self.assertFalse(criteria(_A				,					_A		)		)

									SCREAMING_SNAKE_CASE__								=   MaxTimeCriteria(max_time=0.1				,					initial_timestamp=time.time() - 0.2		)
									self.assertTrue(criteria(_A				,					_A		)		)


							def   _snake_case	(				self     :Any		) -> List[Any]:



									"""simple docstring"""



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

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

									SCREAMING_SNAKE_CASE__								=   validate_stopping_criteria(StoppingCriteriaList()				,					11		)

									self.assertEqual(len(_A		)				,					1		)
from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm
def 					_snake_case	(  __snake_case  ,     __snake_case						):
					return math.sqrt(sum(pow(a - b  ,     2						) for a, b in zip(__snake_case  ,     __snake_case						)						)						)
def 					_snake_case	(  __snake_case  ,     __snake_case						):

					if dataset.ndim != value_array.ndim:
										_UpperCamelCase			     =     (
										    '''Wrong input data\'s dimensions... '''
										    f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
										)
										raise ValueError(__snake_case						)

					try:
										if dataset.shape[1] != value_array.shape[1]:
															_UpperCamelCase			     =     (
															    '''Wrong input data\'s shape... '''
															    f"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
															)
															raise ValueError(__snake_case						)
					except IndexError:
										if dataset.ndim != value_array.ndim:
															raise TypeError('''Wrong shape'''						)

					if dataset.dtype != value_array.dtype:
										_UpperCamelCase			     =     (
										    '''Input data have different datatype... '''
										    f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
										)
										raise TypeError(__snake_case						)

					_UpperCamelCase			     =     []

					for value in value_array:
										_UpperCamelCase			     =     euclidean(__snake_case  ,     dataset[0]						)
										_UpperCamelCase			     =     dataset[0].tolist()

										for dataset_value in dataset[1:]:
															_UpperCamelCase			     =     euclidean(__snake_case  ,     __snake_case						)

															if dist > temp_dist:
																				_UpperCamelCase			     =     temp_dist
																				_UpperCamelCase			     =     dataset_value.tolist()

										answer.append([vector, dist]						)

					return answer
def 					_snake_case	(  __snake_case  ,     __snake_case						):
					return np.dot(__snake_case  ,     __snake_case						) / (norm(__snake_case						) * norm(__snake_case						))


if __name__ == "__main__":
					import doctest

					doctest.testmod()
