






"""simple docstring"""






from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


SCREAMING_SNAKE_CASE						:						List[Any]					   =       logging.get_logger(__name__)





@add_end_docstrings(_lowerCamelCase       )
class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	def __init__(      self  ,					**__a						):


					"""simple docstring"""

					super().__init__(**__a						)

					if self.framework != "pt":
									raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''						)
					# No specific FOR_XXX available yet


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


					"""simple docstring"""

					return super().__call__(__a  ,					**__a						)


	def       _UpperCAmelCase						(      self  ,					**__a						):


					"""simple docstring"""

					A__							 =       {}
					if "candidate_labels" in kwargs:
									A__							 =       kwargs['candidate_labels']
					if "hypothesis_template" in kwargs:
									A__							 =       kwargs['hypothesis_template']

					return preprocess_params, {}, {}


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a=None  ,					__a="This is a sound of {}."						):


					"""simple docstring"""

					if isinstance(__a  ,					__a						):
									if audio.startswith('http://'						) or audio.startswith('https://'						):
													# We need to actually check for a real protocol, otherwise it's impossible to use a local file
													# like http_huggingface_co.png
													A__							 =       requests.get(__a						).content
									else:
													with open(__a  ,					'rb'						) as f:
																	A__							 =       f.read()

					if isinstance(__a  ,					__a						):
									A__							 =       ffmpeg_read(__a  ,					self.feature_extractor.sampling_rate						)

					if not isinstance(__a  ,					np.ndarray						):
									raise ValueError('We expect a numpy ndarray as input'						)
					if len(audio.shape						) != 1:
									raise ValueError('We expect a single channel audio input for ZeroShotAudioClassificationPipeline'						)

					A__							 =       self.feature_extractor(
					    [audio]  ,					sampling_rate=self.feature_extractor.sampling_rate  ,					return_tensors='pt'						)
					A__							 =       candidate_labels
					A__							 =       [hypothesis_template.format(__a						) for x in candidate_labels]
					A__							 =       self.tokenizer(__a  ,					return_tensors=self.framework  ,					padding=__a						)
					A__							 =       [text_inputs]
					return inputs


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       model_inputs.pop('candidate_labels'						)
					A__							 =       model_inputs.pop('text_inputs'						)
					if isinstance(text_inputs[0]  ,					__a						):
									A__							 =       text_inputs[0]
					else:
									# Batching case.
									A__							 =       text_inputs[0][0]

					A__							 =       self.model(**__a  ,					**__a						)

					A__							 =       {
					    'candidate_labels': candidate_labels,
					    'logits': outputs.logits_per_audio,
					}
					return model_outputs






	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       model_outputs.pop('candidate_labels'						)
					A__							 =       model_outputs['logits'][0]

					if self.framework == "pt":
									A__							 =       logits.softmax(dim=0						)
									A__							 =       probs.tolist()
					else:
									raise ValueError('`tf` framework not supported.'						)

					A__							 =       [
					    {'score': score, 'label': candidate_label}
					    for score, candidate_label in sorted(zip(__a  ,					__a						)  ,					key=lambda __a						: -x[0]						)
					]
					return result










"""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


SCREAMING_SNAKE_CASE						:						Dict					   =       '''Create a default config file for Accelerate with only a few flags set.'''



def  __lowerCamelCase					(					lowerCAmelCase__="no"       ,lowerCAmelCase__ = default_json_config_file       ,lowerCAmelCase__ = False				):
				A__							 =       Path(lowerCAmelCase__				)
				path.parent.mkdir(parents=lowerCAmelCase__       ,exist_ok=lowerCAmelCase__				)
				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(**lowerCAmelCase__				)
				config.to_json_file(lowerCAmelCase__				)
				return path



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       parser.add_parser('default'       ,parents=lowerCAmelCase__       ,help=lowerCAmelCase__       ,formatter_class=lowerCAmelCase__				)
				parser.add_argument(
				    '--config_file'       ,default=lowerCAmelCase__       ,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=lowerCAmelCase__       ,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=lowerCAmelCase__				)
				return parser







def  __lowerCamelCase					(					lowerCAmelCase__				):
				A__							 =       write_basic_config(args.mixed_precision       ,args.save_location				)
				if config_file:
								print(f'''accelerate configuration saved at {config_file}'''				)


