
from .number_theory.prime_numbers import next_prime
class       A__  :





							def __init__(					self    :  Tuple	,					_UpperCAmelCase    :  int	,					_UpperCAmelCase    :  int | None = None	,					_UpperCAmelCase    :  float | None = None	,					)		->						None:



											"""simple docstring"""


											__lowercase								=   size_table
											__lowercase								=   [None] * self.size_table
											__lowercase								=   0.75 if lim_charge is None else lim_charge
											__lowercase								=   1 if charge_factor is None else charge_factor
											__lowercase								=   []
											__lowercase								=   {}





							def      a__					(					self    :  Optional[int]							)		->						Union[str, Any]:



											"""simple docstring"""


											return self._keys





							def      a__					(					self    :  Union[str, Any]							)		->						int:



											"""simple docstring"""


											return sum(1 for slot in self.values if slot is not None							) / (
											    self.size_table * self.charge_factor
											)





							def      a__					(					self    :  Tuple	,					_UpperCAmelCase    :  List[str]							)		->						List[str]:



											"""simple docstring"""


											return key % self.size_table





							def      a__					(					self    :  str	,					_UpperCAmelCase    :  List[str]							)		->						Dict:



											"""simple docstring"""


											print(f"""step {step_ord}"""							)
											print(list(range(len(self.values							)							)							)							)
											print(self.values							)





							def      a__					(					self    :  List[str]	,					_UpperCAmelCase    :  List[Any]							)		->						Tuple:



											"""simple docstring"""


											__lowercase								=   1
											__lowercase								=   values
											for value in values:
															self.insert_data(_UpperCAmelCase							)
															self._step_by_step(_UpperCAmelCase							)
															i += 1





							def      a__					(					self    :  int	,					_UpperCAmelCase    :  List[str]	,					_UpperCAmelCase    :  Union[str, Any]							)		->						Any:



											"""simple docstring"""


											__lowercase								=   data
											__lowercase								=   data





							def      a__					(					self    :  Dict	,					_UpperCAmelCase    :  str	,					_UpperCAmelCase    :  Any=None							)		->						Union[str, Any]:



											"""simple docstring"""


											__lowercase								=   self.hash_function(key + 1							)

											while self.values[new_key] is not None and self.values[new_key] != key:
															if self.values.count(_UpperCAmelCase							) > 0:
																			__lowercase								=   self.hash_function(new_key + 1							)
															else:
																			__lowercase								=   None
																			break

											return new_key





							def      a__					(					self    :  int							)		->						Optional[Any]:



											"""simple docstring"""


											__lowercase								=   [value for value in self.values if value is not None]
											__lowercase								=   next_prime(self.size_table	,					factor=2							)
											self._keys.clear()
											__lowercase								=   [None] * self.size_table  # hell's pointers D: don't DRY ;/
											for value in survivor_values:
															self.insert_data(_UpperCAmelCase							)





							def      a__					(					self    :  Union[str, Any]	,					_UpperCAmelCase    :  str							)		->						Optional[int]:



											"""simple docstring"""


											__lowercase								=   self.hash_function(_UpperCAmelCase							)

											if self.values[key] is None:
															self._set_value(_UpperCAmelCase	,					_UpperCAmelCase							)

											elif self.values[key] == data:
															pass

											else:
															__lowercase								=   self._collision_resolution(_UpperCAmelCase	,					_UpperCAmelCase							)
															if collision_resolution is not None:
																			self._set_value(_UpperCAmelCase	,					_UpperCAmelCase							)
															else:
																			self.rehashing()
																			self.insert_data(_UpperCAmelCase							)







# 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,
)


SCREAMING_SNAKE_CASE__				=							{
    """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:
							SCREAMING_SNAKE_CASE__				=							["""DocumentQuestionAnsweringTool"""]
							SCREAMING_SNAKE_CASE__				=							["""ImageCaptioningTool"""]
							SCREAMING_SNAKE_CASE__				=							["""ImageQuestionAnsweringTool"""]
							SCREAMING_SNAKE_CASE__				=							["""ImageSegmentationTool"""]
							SCREAMING_SNAKE_CASE__				=							["""SpeechToTextTool"""]
							SCREAMING_SNAKE_CASE__				=							["""TextClassificationTool"""]
							SCREAMING_SNAKE_CASE__				=							["""TextQuestionAnsweringTool"""]
							SCREAMING_SNAKE_CASE__				=							["""TextSummarizationTool"""]
							SCREAMING_SNAKE_CASE__				=							["""TextToSpeechTool"""]
							SCREAMING_SNAKE_CASE__				=							["""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

							SCREAMING_SNAKE_CASE__				=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





