





import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin







class 			__A       (      UpperCamelCase_  ):




      """simple docstring"""







      UpperCAmelCase__						=      """MCTCTFeatureExtractor"""
      UpperCAmelCase__						=      """AutoTokenizer"""


      def __init__(		self      ,							a__      ,							a__):



             """simple docstring"""





             super().__init__(a__      ,							a__)
             _lowerCamelCase							:     Tuple	=		self.feature_extractor
             _lowerCamelCase							:     Union[str, Any]	=		False


      def __call__(		self      ,							*a__      ,							**a__):



             """simple docstring"""





             if self._in_target_context_manager:
                    return self.current_processor(*a__      ,							**a__)

             if "raw_speech" in kwargs:
                    warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.''')
                    _lowerCamelCase							:     List[Any]	=		kwargs.pop('''raw_speech''')
             else:
                    _lowerCamelCase							:     List[str]	=		kwargs.pop('''audio'''      ,							a__)
             _lowerCamelCase							:     Union[str, Any]	=		kwargs.pop('''sampling_rate'''      ,							a__)
             _lowerCamelCase							:     List[str]	=		kwargs.pop('''text'''      ,							a__)
             if len(a__) > 0:
                    _lowerCamelCase							:     Optional[Any]	=		args[0]
                    _lowerCamelCase							:     Optional[int]	=		args[1:]

             if audio is None and text is None:
                    raise ValueError('''You need to specify either an `audio` or `text` input to process.''')

             if audio is not None:
                    _lowerCamelCase							:     Optional[Any]	=		self.feature_extractor(a__      ,							*a__      ,							sampling_rate=a__      ,							**a__)
             if text is not None:
                    _lowerCamelCase							:     Optional[Any]	=		self.tokenizer(a__      ,							**a__)

             if text is None:
                    return inputs
             elif audio is None:
                    return encodings
             else:
                    _lowerCamelCase							:     Optional[int]	=		encodings['''input_ids''']
                    return inputs


      def 				__snake_case			(		self      ,							*a__      ,							**a__):



             """simple docstring"""





             return self.tokenizer.batch_decode(*a__      ,							**a__)


      def 				__snake_case			(		self      ,							*a__      ,							**a__):



             """simple docstring"""





             if self._in_target_context_manager:
                    return self.current_processor.pad(*a__      ,							**a__)

             _lowerCamelCase							:     Optional[Any]	=		kwargs.pop('''input_features'''      ,							a__)
             _lowerCamelCase							:     int	=		kwargs.pop('''labels'''      ,							a__)
             if len(a__) > 0:
                    _lowerCamelCase							:     List[Any]	=		args[0]
                    _lowerCamelCase							:     Tuple	=		args[1:]

             if input_features is not None:
                    _lowerCamelCase							:     List[str]	=		self.feature_extractor.pad(a__      ,							*a__      ,							**a__)
             if labels is not None:
                    _lowerCamelCase							:     Tuple	=		self.tokenizer.pad(a__      ,							**a__)

             if labels is None:
                    return input_features
             elif input_features is None:
                    return labels
             else:
                    _lowerCamelCase							:     Union[str, Any]	=		labels['''input_ids''']
                    return input_features


      def 				__snake_case			(		self      ,							*a__      ,							**a__):



             """simple docstring"""





             return self.tokenizer.decode(*a__      ,							**a__)



      @contextmanager
      def 				__snake_case			(		self):



             """simple docstring"""





             warnings.warn(
                 '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
                 '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
                 '''your audio inputs, or in a separate call.''')
             _lowerCamelCase							:     Any	=		True
             _lowerCamelCase							:     int	=		self.tokenizer
             yield
             _lowerCamelCase							:     Optional[int]	=		self.feature_extractor
             _lowerCamelCase							:     Tuple	=		False








# 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    __UpperCAmelCase(      lowercase_="no" ,    lowercase_ = default_json_config_file ,    lowercase_ = False      ):
       _lowerCamelCase							:     List[str]	=		Path(lowercase_      )
       path.parent.mkdir(parents=lowercase_ ,    exist_ok=lowercase_      )
       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
       _lowerCamelCase							:     List[Any]	=		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}"""      )
       _lowerCamelCase							:     Optional[int]	=		{
           '''compute_environment''': '''LOCAL_MACHINE''',
           '''mixed_precision''': mixed_precision,
       }
       if torch.cuda.is_available():
              _lowerCamelCase							:     Any	=		torch.cuda.device_count()
              _lowerCamelCase							:     str	=		num_gpus
              _lowerCamelCase							:     Union[str, Any]	=		False
              if num_gpus > 1:
                     _lowerCamelCase							:     str	=		'''MULTI_GPU'''
              else:
                     _lowerCamelCase							:     List[str]	=		'''NO'''
       elif is_xpu_available() and use_xpu:
              _lowerCamelCase							:     Union[str, Any]	=		torch.xpu.device_count()
              _lowerCamelCase							:     int	=		num_xpus
              _lowerCamelCase							:     Dict	=		False
              if num_xpus > 1:
                     _lowerCamelCase							:     List[str]	=		'''MULTI_XPU'''
              else:
                     _lowerCamelCase							:     Any	=		'''NO'''
       elif is_npu_available():
              _lowerCamelCase							:     List[str]	=		torch.npu.device_count()
              _lowerCamelCase							:     Optional[int]	=		num_npus
              _lowerCamelCase							:     str	=		False
              if num_npus > 1:
                     _lowerCamelCase							:     List[Any]	=		'''MULTI_NPU'''
              else:
                     _lowerCamelCase							:     str	=		'''NO'''
       else:
              _lowerCamelCase							:     Tuple	=		0
              _lowerCamelCase							:     Optional[Any]	=		True
              _lowerCamelCase							:     List[str]	=		1
              _lowerCamelCase							:     Union[str, Any]	=		'''NO'''
       _lowerCamelCase							:     Any	=		ClusterConfig(**lowercase_      )
       config.to_json_file(lowercase_      )
       return path




def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     List[str]	=		parser.add_parser('''default''' ,    parents=lowercase_ ,    help=lowercase_ ,    formatter_class=lowercase_      )
       parser.add_argument(
           '''--config_file''' ,    default=lowercase_ ,    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=lowercase_ ,    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=lowercase_      )
       return parser




def    __UpperCAmelCase(      lowercase_      ):
       _lowerCamelCase							:     str	=		write_basic_config(args.mixed_precision ,    args.save_location      )
       if config_file:
              print(F"""accelerate configuration saved at {config_file}"""      )

