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


_a:    Dict 							=			logging.get_logger(__name__)

class 						__UpperCamelCase    (					_lowercase						):
	SCREAMING_SNAKE_CASE__    				=     '''AutoTokenizer'''
	SCREAMING_SNAKE_CASE__    				=     ['''tokenizer''']

	SCREAMING_SNAKE_CASE__    				=     {
	    '''semantic_prompt''': 1,
	    '''coarse_prompt''': 2,
	    '''fine_prompt''': 2,
	}




	def __init__(		self	:						str		,	lowerCAmelCase	:						List[Any]		,	lowerCAmelCase	:						List[str]=None							):


		'''simple docstring'''







		super().__init__(A_							)

		UpperCAmelCase_       				=			speaker_embeddings




	@classmethod
	def       __A					(		cls	:						Optional[int]		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						Any="speaker_embeddings_path.json"		,	**lowerCAmelCase	:						Dict							):


		'''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	:						int		,	lowerCAmelCase	:						Optional[int]		,	lowerCAmelCase	:						Dict="speaker_embeddings_path.json"		,	lowerCAmelCase	:						Optional[int]="speaker_embeddings"		,	lowerCAmelCase	:						bool = False		,	**lowerCAmelCase	:						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	:						Any		,	lowerCAmelCase	:						str = None		,	**lowerCAmelCase	:						List[str]							):


		'''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	:						Union[str, Any]		,	lowerCAmelCase	:						Optional[dict] = None							):


		'''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	:						Any		,	lowerCAmelCase	:						Union[str, Any]=None		,	lowerCAmelCase	:						Optional[Any]=None		,	lowerCAmelCase	:						Optional[int]="pt"		,	lowerCAmelCase	:						Optional[int]=256		,	lowerCAmelCase	:						str=False		,	lowerCAmelCase	:						List[Any]=True		,	lowerCAmelCase	:						Tuple=False		,	**lowerCAmelCase	:						Union[str, 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       __lowerCAmelCase      (						A       ,     A    ):
	print(F"Vertex\tShortest Distance from vertex {src}"    )
	for i, d in enumerate(A    ):
		print(F"{i}\t\t{d}"    )



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	for j in range(A    ):
		UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			(graph[j][k] for k in ["src", "dst", "weight"])
		if distance[u] != float("inf"    ) and distance[u] + w < distance[v]:
			return True
	return False



def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			[float("inf"    )] * vertex_count
	UpperCAmelCase_       				=			0.0

	for _ in range(vertex_count - 1    ):
		for j in range(A    ):
			UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			(graph[j][k] for k in ["src", "dst", "weight"])

			if distance[u] != float("inf"    ) and distance[u] + w < distance[v]:
				UpperCAmelCase_       				=			distance[u] + w

	UpperCAmelCase_       				=			check_negative_cycle(A       ,     A       ,     A    )
	if negative_cycle_exists:
		raise Exception("Negative cycle found"    )

	return distance


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_a:    List[str] 							=			int(input("""Enter number of vertices: """).strip())
				_a:    str 							=			int(input("""Enter number of edges: """).strip())

				_a:    list[dict[str, int]] 							=			[{} for _ in range(E)]

				for i in range(E):
								print("""Edge """, i + 1)
								_a							,			_a							,			_a:    Any 							=			(
								    int(x)
								    for x in input("""Enter source, destination, weight: """).strip().split(""" """)
								)
								_a:    str 							=			{"""src""": src, """dst""": dest, """weight""": weight}

				_a:    List[Any] 							=			int(input("""\nEnter shortest path source:""").strip())
				_a:    Optional[int] 							=			bellman_ford(graph, V, E, source)
				print_distance(shortest_distance, 0)