"""simple docstring"""
import functools
from math import sqrt
from time import time







def 		__a  (      _SCREAMING_SNAKE_CASE						,	*_SCREAMING_SNAKE_CASE						,	**_SCREAMING_SNAKE_CASE  )      ->List[Any]:
  a__:	int     				=      time()
  a__:	Optional[int]     				=      func(*_lowercase						,	**_lowercase  )
  a__:	Tuple     				=      time()
  if int(end - start  ) > 0:
    print(F'{func.__name__} runtime: {(end - start):0.4f} s'  )
  else:
    print(F'{func.__name__} runtime: {(end - start) * 1000:0.4f} ms'  )
  return output







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->list[int]:
  if n < 0:
    raise Exception('n is negative'  )
  if n == 0:
    return [0]
  a__:	Optional[Any]     				=      [0, 1]
  for _ in range(n - 1  ):
    fib.append(fib[-1] + fib[-2]  )
  return fib







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->list[int]:

  def fib_recursive_term(_SCREAMING_SNAKE_CASE  ) -> int:
    if i < 0:
      raise Exception('n is negative'  )
    if i < 2:
      return i
    return fib_recursive_term(i - 1  ) + fib_recursive_term(i - 2  )

  if n < 0:
    raise Exception('n is negative'  )
  return [fib_recursive_term(_lowercase  ) for i in range(n + 1  )]







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->list[int]:

  @functools.cache
  def fib_recursive_term(_SCREAMING_SNAKE_CASE  ) -> int:
    if i < 0:
      raise Exception('n is negative'  )
    if i < 2:
      return i
    return fib_recursive_term(i - 1  ) + fib_recursive_term(i - 2  )

  if n < 0:
    raise Exception('n is negative'  )
  return [fib_recursive_term(_lowercase  ) for i in range(n + 1  )]







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->list[int]:
  if n < 0:
    raise Exception('n is negative'  )
  # Cache must be outside recursuive function
  # other it will reset every time it calls itself.
  a__:	dict[int, int]     				=      {0: 0, 1: 1, 2: 1}  # Prefilled cache

  def rec_fn_memoized(_SCREAMING_SNAKE_CASE  ) -> int:
    if num in cache:
      return cache[num]

    a__:	Dict     				=      rec_fn_memoized(num - 1  ) + rec_fn_memoized(num - 2  )
    a__:	Dict     				=      value
    return value

  return [rec_fn_memoized(_lowercase  ) for i in range(n + 1  )]







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->list[int]:
  if n < 0:
    raise Exception('n is negative'  )
  if n >= 1475:
    raise Exception('n is too large'  )
  a__:	str     				=      sqrt(5  )
  a__:	Union[str, Any]     				=      (1 + sqrt_a) / 2
  return [round(phi**i / sqrt_a  ) for i in range(n + 1  )]


if __name__ == "__main__":
 lowercase__   			=      30
 time_func(fib_iterative, num)
 time_func(fib_recursive, num)  # Around 3s runtime
 time_func(fib_recursive_cached, num)  # Around 0ms runtime
 time_func(fib_memoization, num)
 time_func(fib_binet, num)







'''simple docstring'''




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


a  :     Optional[Any]       =       {
    """agents""": ["""Agent""", """AzureOpenAiAgent""", """HfAgent""", """LocalAgent""", """OpenAiAgent"""],
    """base""": ["""PipelineTool""", """RemoteTool""", """Tool""", """launch_gradio_demo""", """load_tool"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a  :     Dict       =       ["""DocumentQuestionAnsweringTool"""]
							a  :     List[str]       =       ["""ImageCaptioningTool"""]
							a  :     Tuple       =       ["""ImageQuestionAnsweringTool"""]
							a  :     Tuple       =       ["""ImageSegmentationTool"""]
							a  :     List[str]       =       ["""SpeechToTextTool"""]
							a  :     Dict       =       ["""TextClassificationTool"""]
							a  :     Union[str, Any]       =       ["""TextQuestionAnsweringTool"""]
							a  :     List[str]       =       ["""TextSummarizationTool"""]
							a  :     Optional[Any]       =       ["""TextToSpeechTool"""]
							a  :     str       =       ["""TranslationTool"""]

if TYPE_CHECKING:
							from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
							from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .document_question_answering import DocumentQuestionAnsweringTool
														from .image_captioning import ImageCaptioningTool
														from .image_question_answering import ImageQuestionAnsweringTool
														from .image_segmentation import ImageSegmentationTool
														from .speech_to_text import SpeechToTextTool
														from .text_classification import TextClassificationTool
														from .text_question_answering import TextQuestionAnsweringTool
														from .text_summarization import TextSummarizationTool
														from .text_to_speech import TextToSpeechTool
														from .translation import TranslationTool
else:
							import sys

							a  :     Dict       =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

