


from copy import deepcopy

class 				a		:




							"""simple docstring"""


							def __init__(							self       :  int    ,	lowerCamelCase       :  Tuple = None    ,	lowerCamelCase       :  Union[str, Any] = None				)	->    List[str]:

														if arr is None and size is not None:
																					__snake_case		:			str							 =   size
																					__snake_case		:			List[Any]							 =   [0] * size
														elif arr is not None:
																					self.init(__a				)
														else:
																					raise ValueError("Either arr or size must be specified"				)

							def     __snake_case					(							self       :  str    ,	lowerCamelCase       :  int				)	->    Tuple:
														__snake_case		:			Optional[Any]							 =   len(__a				)
														__snake_case		:			Dict							 =   deepcopy(__a				)
														for i in range(1    ,	self.size				):
																					__snake_case		:			Dict							 =   self.next_(__a				)
																					if j < self.size:
																												self.tree[j] += self.tree[i]

							def     __snake_case					(							self       :  str				)	->    Tuple:
														__snake_case		:			str							 =   self.tree[:]
														for i in range(self.size - 1    ,	0    ,	-1				):
																					__snake_case		:			Tuple							 =   self.next_(__a				)
																					if j < self.size:
																												arr[j] -= arr[i]
														return arr

							@staticmethod
							def     __snake_case					(							lowerCamelCase       :  int				)	->    Union[str, Any]:
														return index + (index & (-index))

							@staticmethod
							def     __snake_case					(							lowerCamelCase       :  str				)	->    Union[str, Any]:
														return index - (index & (-index))

							def     __snake_case					(							self       :  Dict    ,	lowerCamelCase       :  Any    ,	lowerCamelCase       :  Union[str, Any]				)	->    Optional[int]:
														if index == 0:
																					self.tree[0] += value
																					return
														while index < self.size:
																					self.tree[index] += value
																					__snake_case		:			Any							 =   self.next_(__a				)

							def     __snake_case					(							self       :  int    ,	lowerCamelCase       :  Union[str, Any]    ,	lowerCamelCase       :  str				)	->    str:
														self.add(__a    ,	value - self.get(__a				)				)

							def     __snake_case					(							self       :  Union[str, Any]    ,	lowerCamelCase       :  Any				)	->    List[str]:
														if right == 0:
																					return 0
														__snake_case		:			str							 =   self.tree[0]
														right -= 1  # make right inclusive
														while right > 0:
																					result += self.tree[right]
																					__snake_case		:			Union[str, Any]							 =   self.prev(__a				)
														return result

							def     __snake_case					(							self       :  str    ,	lowerCamelCase       :  Any    ,	lowerCamelCase       :  Tuple				)	->    Dict:
														return self.prefix(__a				) - self.prefix(__a				)

							def     __snake_case					(							self       :  List[Any]    ,	lowerCamelCase       :  int				)	->    Optional[Any]:
														return self.query(__a    ,	index + 1				)






							def     __snake_case					(							self       :  str    ,	lowerCamelCase       :  Tuple				)	->    int:
														value -= self.tree[0]
														if value < 0:
																					return -1

														__snake_case		:			Optional[Any]							 =   1  # Largest power of 2 <= size
														while j * 2 < self.size:
																					j *= 2

														__snake_case		:			int							 =   0

														while j > 0:
																					if i + j < self.size and self.tree[i + j] <= value:
																												value -= self.tree[i + j]
																												i += j
																					j //= 2
														return i


if __name__ == "__main__":
			import doctest

			doctest.testmod()





"""simple docstring"""


import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
	import torch
@is_pipeline_test
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							__UpperCAmelCase						:    Union[str, Any]				      =MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
							__UpperCAmelCase						:    Union[str, Any]				      =TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			TextaTextGenerationPipeline(model=__a    ,   tokenizer=__a	)
										return generator, ["Something to write", "Something else"]

							def 			snake_case				(     self    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			generator("Something there"	)
										self.assertEqual(__a    ,   [{"generated_text": ANY(__a	)}]	)
										# These are encoder decoder, they don't just append to incoming string
										self.assertFalse(outputs[0]["generated_text"].startswith("Something there"	)	)

										__lowerCAmelCase				    =			generator(["This is great !", "Something else"]    ,   num_return_sequences=2    ,   do_sample=__a	)
										self.assertEqual(
										    __a    ,   [
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										    ]    ,   )

										__lowerCAmelCase				    =			generator(
										    ["This is great !", "Something else"]    ,   num_return_sequences=2    ,   batch_size=2    ,   do_sample=__a	)
										self.assertEqual(
										    __a    ,   [
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										    ]    ,   )

										with self.assertRaises(__a	):
													generator(4	)

							@require_torch
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			pipeline("text2text-generation"    ,   model="patrickvonplaten/t5-tiny-random"    ,   framework="pt"	)
										# do_sample=False necessary for reproducibility
										__lowerCAmelCase				    =			generator("Something there"    ,   do_sample=__a	)
										self.assertEqual(__a    ,   [{"generated_text": ""}]	)

										__lowerCAmelCase				    =			3
										__lowerCAmelCase				    =			generator(
										    "Something there"    ,   num_return_sequences=__a    ,   num_beams=__a    ,   )
										__lowerCAmelCase				    =			[
										    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
										    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
										    {"generated_text": ""},
										]
										self.assertEqual(__a    ,   __a	)

										__lowerCAmelCase				    =			generator("This is a test"    ,   do_sample=__a    ,   num_return_sequences=2    ,   return_tensors=__a	)
										self.assertEqual(
										    __a    ,   [
										        {"generated_token_ids": ANY(torch.Tensor	)},
										        {"generated_token_ids": ANY(torch.Tensor	)},
										    ]    ,   )
										__lowerCAmelCase				    =			generator.model.config.eos_token_id
										__lowerCAmelCase				    =			"<pad>"
										__lowerCAmelCase				    =			generator(
										    ["This is a test", "This is a second test"]    ,   do_sample=__a    ,   num_return_sequences=2    ,   batch_size=2    ,   return_tensors=__a    ,   )
										self.assertEqual(
										    __a    ,   [
										        [
										            {"generated_token_ids": ANY(torch.Tensor	)},
										            {"generated_token_ids": ANY(torch.Tensor	)},
										        ],
										        [
										            {"generated_token_ids": ANY(torch.Tensor	)},
										            {"generated_token_ids": ANY(torch.Tensor	)},
										        ],
										    ]    ,   )



							@require_tf
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			pipeline("text2text-generation"    ,   model="patrickvonplaten/t5-tiny-random"    ,   framework="tf"	)
										# do_sample=False necessary for reproducibility
										__lowerCAmelCase				    =			generator("Something there"    ,   do_sample=__a	)
										self.assertEqual(__a    ,   [{"generated_text": ""}]	)




