


'''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


lowerCAmelCase						=					logging.get_logger(__name__)






class        lowerCAmelCase_  (     UpperCAmelCase	):
       __UpperCAmelCase					='AutoTokenizer'
       __UpperCAmelCase					=['tokenizer']

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






       def __init__( self			,     _UpperCamelCase			,     _UpperCamelCase=None   )->      Tuple:
            super().__init__(_UpperCamelCase   )

            _A				=							speaker_embeddings






       @classmethod
       def   UpperCamelCase						( cls			,     _UpperCamelCase			,     _UpperCamelCase="speaker_embeddings_path.json"			,     **_UpperCamelCase   )->      str:

            if speaker_embeddings_dict_path is not None:
                 _A				=							get_file_from_repo(
                     _UpperCamelCase			,     _UpperCamelCase			,     subfolder=kwargs.pop('subfolder'			,     _UpperCamelCase   )			,     cache_dir=kwargs.pop('cache_dir'			,     _UpperCamelCase   )			,     force_download=kwargs.pop('force_download'			,     _UpperCamelCase   )			,     proxies=kwargs.pop('proxies'			,     _UpperCamelCase   )			,     resume_download=kwargs.pop('resume_download'			,     _UpperCamelCase   )			,     local_files_only=kwargs.pop('local_files_only'			,     _UpperCamelCase   )			,     use_auth_token=kwargs.pop('use_auth_token'			,     _UpperCamelCase   )			,     revision=kwargs.pop('revision'			,     _UpperCamelCase   )			,     )
                 if speaker_embeddings_path is None:
                      logger.warning(
                          F'`{os.path.join(_UpperCamelCase			,     _UpperCamelCase   )}` 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`.'   )
                      _A				=							None
                 else:
                      with open(_UpperCamelCase   ) as speaker_embeddings_json:
                           _A				=							json.load(_UpperCamelCase   )
            else:
                 _A				=							None

            _A				=							AutoTokenizer.from_pretrained(_UpperCamelCase			,     **_UpperCamelCase   )

            return cls(tokenizer=_UpperCamelCase			,     speaker_embeddings=_UpperCamelCase   )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase="speaker_embeddings_path.json"			,     _UpperCamelCase="speaker_embeddings"			,     _UpperCamelCase = False			,     **_UpperCamelCase			,     )->      int:
            if self.speaker_embeddings is not None:
                 os.makedirs(os.path.join(_UpperCamelCase			,     _UpperCamelCase			,     'v2'   )			,     exist_ok=_UpperCamelCase   )

                 _A				=							{}

                 _A				=							save_directory

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

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

                           _A				=							tmp_dict

                 with open(os.path.join(_UpperCamelCase			,     _UpperCamelCase   )			,     'w'   ) as fp:
                      json.dump(_UpperCamelCase			,     _UpperCamelCase   )

            super().save_pretrained(_UpperCamelCase			,     _UpperCamelCase			,     **_UpperCamelCase   )






       def   UpperCamelCase						( self			,     _UpperCamelCase = None			,     **_UpperCamelCase   )->      Union[str, Any]:
            _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'			,     _UpperCamelCase   )			,     cache_dir=kwargs.pop('cache_dir'			,     _UpperCamelCase   )			,     force_download=kwargs.pop('force_download'			,     _UpperCamelCase   )			,     proxies=kwargs.pop('proxies'			,     _UpperCamelCase   )			,     resume_download=kwargs.pop('resume_download'			,     _UpperCamelCase   )			,     local_files_only=kwargs.pop('local_files_only'			,     _UpperCamelCase   )			,     use_auth_token=kwargs.pop('use_auth_token'			,     _UpperCamelCase   )			,     revision=kwargs.pop('revision'			,     _UpperCamelCase   )			,     )
                 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.'   )

                 _A				=							np.load(_UpperCamelCase   )

            return voice_preset_dict






       def   UpperCamelCase						( self			,     _UpperCamelCase = None   )->      List[Any]:
            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			,     _UpperCamelCase=None			,     _UpperCamelCase=None			,     _UpperCamelCase="pt"			,     _UpperCamelCase=256			,     _UpperCamelCase=False			,     _UpperCamelCase=True			,     _UpperCamelCase=False			,     **_UpperCamelCase			,     )->      Dict:
            if voice_preset is not None and not isinstance(_UpperCamelCase			,     _UpperCamelCase   ):
                 if (
                     isinstance(_UpperCamelCase			,     _UpperCamelCase   )
                     and self.speaker_embeddings is not None
                     and voice_preset in self.speaker_embeddings
                 ):
                      _A				=							self._load_voice_preset(_UpperCamelCase   )

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

                      _A				=							np.load(_UpperCamelCase   )

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

            _A				=							self.tokenizer(
                _UpperCamelCase			,     return_tensors=_UpperCamelCase			,     padding='max_length'			,     max_length=_UpperCamelCase			,     return_attention_mask=_UpperCamelCase			,     return_token_type_ids=_UpperCamelCase			,     add_special_tokens=_UpperCamelCase			,     **_UpperCamelCase			,     )

            if voice_preset is not None:
                 _A				=							voice_preset

            return encoded_text









'''simple docstring'''
# Copyright 2021 The HuggingFace 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 pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


lowerCAmelCase						=					"""Create a default config file for Accelerate with only a few flags set."""

def 					lowerCamelCase_ ( __UpperCamelCase					:       Union[str, Any]="no"							,    __UpperCamelCase					:       str = default_json_config_file							,    __UpperCamelCase					:       bool = False		)					->	Tuple:







     """simple docstring"""
     _A				=							Path(__UpperCamelCase		)
     path.parent.mkdir(parents=__UpperCamelCase							,    exist_ok=__UpperCamelCase		)
     if path.exists():
          print(
              F'Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'		)
          return False
     _A				=							mixed_precision.lower()
     if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
          raise ValueError(
              F'`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'		)
     _A				=							{
         'compute_environment': 'LOCAL_MACHINE',
         'mixed_precision': mixed_precision,
     }
     if torch.cuda.is_available():
          _A				=							torch.cuda.device_count()
          _A				=							num_gpus
          _A				=							False
          if num_gpus > 1:
               _A				=							'MULTI_GPU'
          else:
               _A				=							'NO'
     elif is_xpu_available() and use_xpu:
          _A				=							torch.xpu.device_count()
          _A				=							num_xpus
          _A				=							False
          if num_xpus > 1:
               _A				=							'MULTI_XPU'
          else:
               _A				=							'NO'
     elif is_npu_available():
          _A				=							torch.npu.device_count()
          _A				=							num_npus
          _A				=							False
          if num_npus > 1:
               _A				=							'MULTI_NPU'
          else:
               _A				=							'NO'
     else:
          _A				=							0
          _A				=							True
          _A				=							1
          _A				=							'NO'
     _A				=							ClusterConfig(**__UpperCamelCase		)
     config.to_json_file(__UpperCamelCase		)
     return path

def 					lowerCamelCase_ ( __UpperCamelCase					:       List[Any]							,    __UpperCamelCase					:       str		)					->	Any:







     """simple docstring"""
     _A				=							parser.add_parser('default'							,    parents=__UpperCamelCase							,    help=__UpperCamelCase							,    formatter_class=__UpperCamelCase		)
     parser.add_argument(
         '--config_file'							,    default=__UpperCamelCase							,    help=(
             'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
             'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
             'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
             'with \'huggingface\'.'
         )							,    dest='save_location'							,    )

     parser.add_argument(
         '--mixed_precision'							,    choices=['no', 'fp16', 'bf16']							,    type=__UpperCamelCase							,    help='Whether or not to use mixed precision training. '
         'Choose between FP16 and BF16 (bfloat16) training. '
         'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'							,    default='no'							,    )
     parser.set_defaults(func=__UpperCamelCase		)
     return parser

def 					lowerCamelCase_ ( __UpperCamelCase					:       Any		)					->	Union[str, Any]:







     """simple docstring"""
     _A				=							write_basic_config(args.mixed_precision							,    args.save_location		)
     if config_file:
          print(F'accelerate configuration saved at {config_file}'		)





