



import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


UpperCAmelCase							 =					logging.get_logger(__name__)

class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      Any         =					"""AutoTokenizer"""
							UpperCamelCase__				:      List[str]         =					["""tokenizer"""]

							UpperCamelCase__				:      Any         =					{
							    """semantic_prompt""": 1,
							    """coarse_prompt""": 2,
							    """fine_prompt""": 2,
							}


							def __init__(							self  ,  a_  ,  a_=None		):
											super().__init__(a_		)

											a__				      =		speaker_embeddings


							@classmethod
							def _a      (							cls  ,  a_  ,  a_="speaker_embeddings_path.json"  ,  **a_		):

											if speaker_embeddings_dict_path is not None:
															a__				      =		get_file_from_repo(
															    a_  ,  a_  ,  subfolder=kwargs.pop("""subfolder"""  ,  a_		)  ,  cache_dir=kwargs.pop("""cache_dir"""  ,  a_		)  ,  force_download=kwargs.pop("""force_download"""  ,  a_		)  ,  proxies=kwargs.pop("""proxies"""  ,  a_		)  ,  resume_download=kwargs.pop("""resume_download"""  ,  a_		)  ,  local_files_only=kwargs.pop("""local_files_only"""  ,  a_		)  ,  use_auth_token=kwargs.pop("""use_auth_token"""  ,  a_		)  ,  revision=kwargs.pop("""revision"""  ,  a_		)  ,  )
															if speaker_embeddings_path is None:
																			logger.warning(
																			    F'''`{os.path.join(a_  ,  a_		)}` does not exists
                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.'''		)
																			a__				      =		None
															else:
																			with open(a_		) as speaker_embeddings_json:
																							a__				      =		json.load(a_		)
											else:
															a__				      =		None

											a__				      =		AutoTokenizer.from_pretrained(a_  ,  **a_		)

											return cls(tokenizer=a_  ,  speaker_embeddings=a_		)


							def _a      (							self  ,  a_  ,  a_="speaker_embeddings_path.json"  ,  a_="speaker_embeddings"  ,  a_ = False  ,  **a_  ,  ):
											if self.speaker_embeddings is not None:
															os.makedirs(os.path.join(a_  ,  a_  ,  """v2"""		)  ,  exist_ok=a_		)

															a__				      =		{}

															a__				      =		save_directory

															for prompt_key in self.speaker_embeddings:
																			if prompt_key != "repo_or_path":
																							a__				      =		self._load_voice_preset(a_		)

																							a__				      =		{}
																							for key in self.speaker_embeddings[prompt_key]:
																											np.save(
																											    os.path.join(
																											        embeddings_dict["""repo_or_path"""]  ,  a_  ,  F'''{prompt_key}_{key}'''		)  ,  voice_preset[key]  ,  allow_pickle=a_  ,  )
																											a__				      =		os.path.join(a_  ,  F'''{prompt_key}_{key}.npy'''		)

																							a__				      =		tmp_dict

															with open(os.path.join(a_  ,  a_		)  ,  """w"""		) as fp:
																			json.dump(a_  ,  a_		)

											super().save_pretrained(a_  ,  a_  ,  **a_		)


							def _a      (							self  ,  a_ = None  ,  **a_		):
											a__				      =		self.speaker_embeddings[voice_preset]

											a__				      =		{}
											for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
															if key not in voice_preset_paths:
																			raise ValueError(
																			    F'''Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].'''		)

															a__				      =		get_file_from_repo(
															    self.speaker_embeddings.get("""repo_or_path"""  ,  """/"""		)  ,  voice_preset_paths[key]  ,  subfolder=kwargs.pop("""subfolder"""  ,  a_		)  ,  cache_dir=kwargs.pop("""cache_dir"""  ,  a_		)  ,  force_download=kwargs.pop("""force_download"""  ,  a_		)  ,  proxies=kwargs.pop("""proxies"""  ,  a_		)  ,  resume_download=kwargs.pop("""resume_download"""  ,  a_		)  ,  local_files_only=kwargs.pop("""local_files_only"""  ,  a_		)  ,  use_auth_token=kwargs.pop("""use_auth_token"""  ,  a_		)  ,  revision=kwargs.pop("""revision"""  ,  a_		)  ,  )
															if path is None:
																			raise ValueError(
																			    F'''`{os.path.join(self.speaker_embeddings.get("repo_or_path"  ,  "/"		)  ,  voice_preset_paths[key]		)}` does not exists
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings.'''		)

															a__				      =		np.load(a_		)

											return voice_preset_dict


							def _a      (							self  ,  a_ = None		):
											for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
															if key not in voice_preset:
																			raise ValueError(F'''Voice preset unrecognized, missing {key} as a key.'''		)

															if not isinstance(voice_preset[key]  ,  np.ndarray		):
																			raise ValueError(F'''{key} voice preset must be a {str(self.preset_shape[key]		)}D ndarray.'''		)

															if len(voice_preset[key].shape		) != self.preset_shape[key]:
																			raise ValueError(F'''{key} voice preset must be a {str(self.preset_shape[key]		)}D ndarray.'''		)







							def __call__(							self  ,  a_=None  ,  a_=None  ,  a_="pt"  ,  a_=256  ,  a_=False  ,  a_=True  ,  a_=False  ,  **a_  ,  ):
											if voice_preset is not None and not isinstance(a_  ,  a_		):
															if (
															    isinstance(a_  ,  a_		)
															    and self.speaker_embeddings is not None
															    and voice_preset in self.speaker_embeddings
															):
																			a__				      =		self._load_voice_preset(a_		)

															else:
																			if isinstance(a_  ,  a_		) and not voice_preset.endswith(""".npz"""		):
																							a__				      =		voice_preset + """.npz"""

																			a__				      =		np.load(a_		)

											if voice_preset is not None:
															self._validate_voice_preset_dict(a_  ,  **a_		)
															a__				      =		BatchFeature(data=a_  ,  tensor_type=a_		)

											a__				      =		self.tokenizer(
											    a_  ,  return_tensors=a_  ,  padding="""max_length"""  ,  max_length=a_  ,  return_attention_mask=a_  ,  return_token_type_ids=a_  ,  add_special_tokens=a_  ,  **a_  ,  )

											if voice_preset is not None:
															a__				      =		voice_preset

											return encoded_text











from __future__ import annotations

UpperCAmelCase							 =					8.988E9  # units = N * m^s * C^-2

def 			A_      (	__a     :				float ,		__a     :				float ,		__a     :				float ,		__a     :				float		):

				"""simple docstring"""
				a__				      =		abs(chargea * chargea		)

				if (force, chargea, chargea, distance).count(0		) != 1:
								raise ValueError("""One and only one argument must be 0"""		)
				if distance < 0:
								raise ValueError("""Distance cannot be negative"""		)
				if force == 0:
								a__				      =		COULOMBS_CONSTANT * charge_product / (distance**2)
								return {"force": force}
				elif chargea == 0:
								a__				      =		abs(__a		) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge1": chargea}
				elif chargea == 0:
								a__				      =		abs(__a		) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge2": chargea}
				elif distance == 0:
								a__				      =		(COULOMBS_CONSTANT * charge_product / abs(__a		)) ** 0.5
								return {"distance": distance}
				raise ValueError("""Exactly one argument must be 0"""		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

