
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


lowerCamelCase__	=							logging.get_logger(__name__)







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Any	=		"AutoTokenizer"
						UpperCamelCase_							: Union[str, Any]	=		["tokenizer"]

						UpperCamelCase_							: str	=		{
						    "semantic_prompt": 1,
						    "coarse_prompt": 2,
						    "fine_prompt": 2,
						}

						def __init__(			self       ,							a       ,							a=None				)					->      Dict:


								'''simple docstring'''

								super().__init__(a				)

								_UpperCamelCase						    =	speaker_embeddings

						@classmethod
						def  A_				(			cls       ,							a       ,							a="speaker_embeddings_path.json"       ,							**a				)					->      Union[str, Any]:


								'''simple docstring'''

								if speaker_embeddings_dict_path is not None:
										_UpperCamelCase						    =	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\n                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.'				)
												_UpperCamelCase						    =	None
										else:
												with open(a				) as speaker_embeddings_json:
														_UpperCamelCase						    =	json.load(a				)
								else:
										_UpperCamelCase						    =	None

								_UpperCamelCase						    =	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       ,							)					->      Optional[int]:


								'''simple docstring'''

								if self.speaker_embeddings is not None:
										os.makedirs(os.path.join(a       ,							a       ,							"""v2"""				)       ,							exist_ok=a				)

										_UpperCamelCase						    =	{}

										_UpperCamelCase						    =	save_directory

										for prompt_key in self.speaker_embeddings:
												if prompt_key != "repo_or_path":
														_UpperCamelCase						    =	self._load_voice_preset(a				)

														_UpperCamelCase						    =	{}
														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       ,							)
																_UpperCamelCase						    =	os.path.join(a       ,							F'{prompt_key}_{key}.npy'				)

														_UpperCamelCase						    =	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				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	self.speaker_embeddings[voice_preset]

								_UpperCamelCase						    =	{}
								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}].'				)

										_UpperCamelCase						    =	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\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings.'				)

										_UpperCamelCase						    =	np.load(a				)

								return voice_preset_dict

						def  A_				(			self       ,							a = None				)					->      Tuple:


								'''simple docstring'''

								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=2_56       ,							a=False       ,							a=True       ,							a=False       ,							**a       ,							)					->      Optional[Any]:


								'''simple docstring'''

								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
										):
												_UpperCamelCase						    =	self._load_voice_preset(a				)

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

												_UpperCamelCase						    =	np.load(a				)

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

								_UpperCamelCase						    =	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:
										_UpperCamelCase						    =	voice_preset

								return encoded_text



from __future__ import annotations


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  )		->				None:

		"""simple docstring"""






		_UpperCamelCase						    =	len(lowerCAmelCase )

		# If row is equal to the size of the board it means there are a queen in each row in
		# the current board (possible_board)
		if row == n:
				# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
				# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
				boards.append([""". """ * i + """Q """ + """. """ * (n - 1 - i) for i in possible_board] )
				return

		# We iterate each column in the row to find all possible results in each row
		for col in range(lowerCAmelCase ):
				# We apply that we learned previously. First we check that in the current board
				# (possible_board) there are not other same value because if there is it means
				# that there are a collision in vertical. Then we apply the two formulas we
				# learned before:
				#
				# 45º: y - x = b or 45: row - col = b
				# 135º: y + x = b or row + col = b.
				#
				# And we verify if the results of this two formulas not exist in their variables
				# respectively.  (diagonal_right_collisions, diagonal_left_collisions)
				#
				# If any or these are True it means there is a collision so we continue to the
				# next value in the for loop.
				if (
				    col in possible_board
				    or row - col in diagonal_right_collisions
				    or row + col in diagonal_left_collisions
				):
						continue

				# If it is False we call dfs function again and we update the inputs
				depth_first_search(
				    [*possible_board, col]						,  [*diagonal_right_collisions, row - col]						,  [*diagonal_left_collisions, row + col]						,  lowerCAmelCase						,  lowerCAmelCase						,  )


def 							__A(lowerCAmelCase )		->				None:

		"""simple docstring"""






		_UpperCamelCase						    =	[]
		depth_first_search([]						,  []						,  []						,  lowerCAmelCase						,  lowerCAmelCase )

		# Print all the boards
		for board in boards:
				for column in board:
						print(lowerCAmelCase )
				print("""""" )

		print(len(lowerCAmelCase )						,  """solutions were found.""" )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		n_queens_solution(4)



