
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 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     "AutoTokenizer"
	snake_case_					    =     ["tokenizer"]

	snake_case_					    =     {
	    "semantic_prompt": 1,
	    "coarse_prompt": 2,
	    "fine_prompt": 2,
	}






	def __init__(       self					: Optional[Any]							,A					: Any							,A					: Union[str, Any]=None ):
					super().__init__(A )

					__A   			= speaker_embeddings






	@classmethod
	def 				UpperCamelCase_  (       cls					: Any							,A					: str							,A					: List[str]="speaker_embeddings_path.json"							,**A					: Optional[int] ):

					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 				UpperCamelCase_  (       self					: Optional[Any]							,A					: str							,A					: int="speaker_embeddings_path.json"							,A					: List[Any]="speaker_embeddings"							,A					: bool = False							,**A					: List[str]							,):
					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 				UpperCamelCase_  (       self					: Dict							,A					: str = None							,**A					: Optional[int] ):
					__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 				UpperCamelCase_  (       self					: List[str]							,A					: Optional[dict] = 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					: List[Any]							,A					: str=None							,A					: Dict=None							,A					: Dict="pt"							,A					: List[Any]=2_56							,A					: Optional[int]=False							,A					: Optional[Any]=True							,A					: Any=False							,**A					: Union[str, Any]							,):
					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


# Copyright 2021 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 packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


SCREAMING_SNAKE_CASE							:List[str]							 =				'pytorch_model.bin'
SCREAMING_SNAKE_CASE							:str							 =				'pytorch_model.bin.index.json'
SCREAMING_SNAKE_CASE							:Optional[int]							 =				'adapter_config.json'
SCREAMING_SNAKE_CASE							:Dict							 =				'adapter_model.bin'
SCREAMING_SNAKE_CASE							:Dict							 =				'adapter_model.safetensors'
SCREAMING_SNAKE_CASE							:str							 =				'tf_model.h5'
SCREAMING_SNAKE_CASE							:List[Any]							 =				'tf_model.h5.index.json'
SCREAMING_SNAKE_CASE							:str							 =				'model.ckpt'
SCREAMING_SNAKE_CASE							:List[Any]							 =				'flax_model.msgpack'
SCREAMING_SNAKE_CASE							:Optional[int]							 =				'flax_model.msgpack.index.json'
SCREAMING_SNAKE_CASE							:Tuple							 =				'model.safetensors'
SCREAMING_SNAKE_CASE							:List[Any]							 =				'model.safetensors.index.json'
SCREAMING_SNAKE_CASE							:str							 =				'config.json'
SCREAMING_SNAKE_CASE							:int							 =				'preprocessor_config.json'
SCREAMING_SNAKE_CASE							:Optional[Any]							 =				FEATURE_EXTRACTOR_NAME
SCREAMING_SNAKE_CASE							:Optional[int]							 =				'generation_config.json'
SCREAMING_SNAKE_CASE							:List[str]							 =				'modelcard.json'

SCREAMING_SNAKE_CASE							:Optional[int]							 =				'▁'
SCREAMING_SNAKE_CASE							:Optional[Any]							 =				SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

SCREAMING_SNAKE_CASE							:str							 =				[
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
SCREAMING_SNAKE_CASE							:Optional[Any]							 =				[[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
SCREAMING_SNAKE_CASE							:List[Any]							 =				[[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]


def 						UpperCAmelCase			(	a_						)		->	Dict:







				"""simple docstring"""



				if version.parse(a_						) < version.parse(a_						):
								if "dev" in min_version:
												__A   			= (
												    "This example requires a source install from HuggingFace Transformers (see "
												    "`https://huggingface.co/docs/transformers/installation#install-from-source`),"
												)
								else:
												__A   			= F'''This example requires a minimum version of {min_version},'''
								error_message += F''' but the version found is {__version__}.\n'''
								raise ImportError(
								    error_message
								    + "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
								    "versions of HuggingFace Transformers."						)
