




'''simple docstring'''





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


_SCREAMING_SNAKE_CASE							:   int      		= logging.get_logger(__name__)




class  _snake_case		(	lowercase_							):
			lowerCAmelCase_     :      Optional[int]						       =    "AutoTokenizer"
			lowerCAmelCase_     :      int						       =    ["tokenizer"]

			lowerCAmelCase_     :      List[Any]						       =    {
			    "semantic_prompt": 1,
			    "coarse_prompt": 2,
			    "fine_prompt": 2,
			}




			def __init__(					self				,							a__				,							a__=None							)				->				Optional[Any]:

										'''simple docstring'''




										super().__init__(a__							)

										snake_case_						=      speaker_embeddings




			@classmethod
			def    lowerCAmelCase__				(					cls				,							a__				,							a__="speaker_embeddings_path.json"				,							**a__							)				->				Any:

										'''simple docstring'''




										if speaker_embeddings_dict_path is not None:
																	snake_case_						=      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`.'							)
																								snake_case_						=      None
																	else:
																								with open(a__							) as speaker_embeddings_json:
																															snake_case_						=      json.load(a__							)
										else:
																	snake_case_						=      None

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

										return cls(tokenizer=a__				,							speaker_embeddings=a__							)




			def    lowerCAmelCase__				(					self				,							a__				,							a__="speaker_embeddings_path.json"				,							a__="speaker_embeddings"				,							a__ = False				,							**a__				,							)				->				Tuple:

										'''simple docstring'''




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

																	snake_case_						=      {}

																	snake_case_						=      save_directory

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

																															snake_case_						=      {}
																															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__				,							)
																																						snake_case_						=      os.path.join(a__				,							F'{prompt_key}_{key}.npy'							)

																															snake_case_						=      tmp_dict

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

										super().save_pretrained(a__				,							a__				,							**a__							)




			def    lowerCAmelCase__				(					self				,							a__ = None				,							**a__							)				->				List[str]:

										'''simple docstring'''




										snake_case_						=      self.speaker_embeddings[voice_preset]

										snake_case_						=      {}
										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}].'							)

																	snake_case_						=      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.'							)

																	snake_case_						=      np.load(a__							)

										return voice_preset_dict




			def    lowerCAmelCase__				(					self				,							a__ = None							)				->				Dict:

										'''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__=256				,							a__=False				,							a__=True				,							a__=False				,							**a__				,							)				->				List[str]:

										'''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
																	):
																								snake_case_						=      self._load_voice_preset(a__							)

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

																								snake_case_						=      np.load(a__							)

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

										snake_case_						=      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:
																	snake_case_						=      voice_preset

										return encoded_text












"""simple docstring"""
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
			from PIL import Image

			from ..image_utils import load_image

if is_tf_available():
			import tensorflow as tf

			from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
			from ..tf_utils import stable_softmax

if is_torch_available():
			from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowerCamelCase_					=      logging.get_logger(__name__)







@add_end_docstrings(__A     )
class 			UpperCamelCase_       (__A     ):





	def __init__(      self					: int	,	*lowerCAmelCase_					: Tuple	,	**lowerCAmelCase_					: List[str]						)						->			Optional[Any]:
				super().__init__(*lowerCAmelCase_	,	**lowerCAmelCase_						)
				requires_backends(self	,	"vision"						)
				self.check_model_type(
				    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
				    if self.framework == "tf"
				    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING						)





	def      _SCREAMING_SNAKE_CASE      (      self					: str	,	lowerCAmelCase_					: Optional[int]=None						)						->			List[Any]:
				UpperCAmelCase_     : str			     =					{}
				if top_k is not None:
							UpperCAmelCase_     : List[str]			     =					top_k
				return {}, {}, postprocess_params





	def __call__(      self					: str	,	lowerCAmelCase_					: Union[str, List[str], "Image.Image", List["Image.Image"]]	,	**lowerCAmelCase_					: Any						)						->			Tuple:
				return super().__call__(lowerCAmelCase_	,	**lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: str	,	lowerCAmelCase_					: str						)						->			Any:
				UpperCAmelCase_     : Tuple			     =					load_image(lowerCAmelCase_						)
				UpperCAmelCase_     : Dict			     =					self.image_processor(images=lowerCAmelCase_	,	return_tensors=self.framework						)
				return model_inputs





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]	,	lowerCAmelCase_					: Dict						)						->			str:
				UpperCAmelCase_     : Any			     =					self.model(**lowerCAmelCase_						)
				return model_outputs





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple	,	lowerCAmelCase_					: Union[str, Any]	,	lowerCAmelCase_					: Optional[int]=5						)						->			Any:
				if top_k > self.model.config.num_labels:
							UpperCAmelCase_     : int			     =					self.model.config.num_labels

				if self.framework == "pt":
							UpperCAmelCase_     : str			     =					model_outputs.logits.softmax(-1						)[0]
							UpperCAmelCase_     ,	UpperCAmelCase_     : Tuple			     =					probs.topk(lowerCAmelCase_						)
				elif self.framework == "tf":
							UpperCAmelCase_     : str			     =					stable_softmax(model_outputs.logits	,	axis=-1						)[0]
							UpperCAmelCase_     : Union[str, Any]			     =					tf.math.top_k(lowerCAmelCase_	,	k=lowerCAmelCase_						)
							UpperCAmelCase_     ,	UpperCAmelCase_     : List[Any]			     =					topk.values.numpy(), topk.indices.numpy()
				else:
							raise ValueError(f"""Unsupported framework: {self.framework}"""						)

				UpperCAmelCase_     : int			     =					scores.tolist()
				UpperCAmelCase_     : Optional[Any]			     =					ids.tolist()
				return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCAmelCase_	,	lowerCAmelCase_						)]



