import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


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

__SCREAMING_SNAKE_CASE						:    List[Any]     = logging.get_logger(__name__)
def 					snake_case						(__lowercase					,			__lowercase						)       ->       np.array:

						'''simple docstring'''



						_snake_case						:     Any				=    F"""{sampling_rate}"""
						_snake_case						:     Tuple				=    "1"
						_snake_case						:     int				=    "f32le"
						_snake_case						:     Optional[Any]				=    [
						    "ffmpeg",
						    "-i",
						    "pipe:0",
						    "-ac",
						    ac,
						    "-ar",
						    ar,
						    "-f",
						    format_for_conversion,
						    "-hide_banner",
						    "-loglevel",
						    "quiet",
						    "pipe:1",
						]

						try:
												_snake_case						:     Dict				=    subprocess.Popen(__lowercase					,			stdin=subprocess.PIPE					,			stdout=subprocess.PIPE						)
						except FileNotFoundError:
												raise ValueError("ffmpeg was not found but is required to load audio files from filename"						)
						_snake_case						:     List[Any]				=    ffmpeg_process.communicate(__lowercase						)
						_snake_case						:     Tuple				=    output_stream[0]

						_snake_case						:     Any				=    np.frombuffer(__lowercase					,			np.floataa						)
						if audio.shape[0] == 0:
												raise ValueError("Malformed soundfile"						)
						return audio



@add_end_docstrings(__snake_case		)
class    lowercase_    (     __snake_case		):



							def __init__(						self      ,				*lowercase_      ,				**lowercase_				):
													# Default, might be overriden by the model.config.
													_snake_case						:     str				=    5
													super().__init__(*lowercase_      ,				**lowercase_				)

													if self.framework != "pt":
																			raise ValueError(f"""The {self.__class__} is only available in PyTorch."""				)

													self.check_model_type(lowercase_				)



							def __call__(						self      ,				lowercase_      ,				**lowercase_      ,				):
													return super().__call__(lowercase_      ,				**lowercase_				)



							def        UpperCamelCase       (						self      ,				lowercase_=None      ,				**lowercase_				):
													# No parameters on this pipeline right now
													_snake_case						:     str				=    {}
													if top_k is not None:
																			if top_k > self.model.config.num_labels:
																									_snake_case						:     Optional[int]				=    self.model.config.num_labels
																			_snake_case						:     int				=    top_k
													return {}, {}, postprocess_params



							def        UpperCamelCase       (						self      ,				lowercase_				):
													if isinstance(lowercase_      ,				lowercase_				):
																			if inputs.startswith("http://"				) or inputs.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
																									_snake_case						:     Tuple				=    requests.get(lowercase_				).content
																			else:
																									with open(lowercase_      ,				"rb"				) as f:
																															_snake_case						:     int				=    f.read()

													if isinstance(lowercase_      ,				lowercase_				):
																			_snake_case						:     Dict				=    ffmpeg_read(lowercase_      ,				self.feature_extractor.sampling_rate				)

													if isinstance(lowercase_      ,				lowercase_				):
																			# Accepting `"array"` which is the key defined in `datasets` for
																			# better integration
																			if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
																									raise ValueError(
																									    "When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a "
																									    "\"raw\" key containing the numpy array representing the audio and a \"sampling_rate\" key, "
																									    "containing the sampling_rate associated with that array"				)

																			_snake_case						:     str				=    inputs.pop("raw"      ,				lowercase_				)
																			if _inputs is None:
																									# Remove path which will not be used from `datasets`.
																									inputs.pop("path"      ,				lowercase_				)
																									_snake_case						:     Dict				=    inputs.pop("array"      ,				lowercase_				)
																			_snake_case						:     List[Any]				=    inputs.pop("sampling_rate"				)
																			_snake_case						:     Dict				=    _inputs
																			if in_sampling_rate != self.feature_extractor.sampling_rate:
																									import torch

																									if is_torchaudio_available():
																															from torchaudio import functional as F
																									else:
																															raise ImportError(
																															    "torchaudio is required to resample audio samples in AudioClassificationPipeline. "
																															    "The torchaudio package can be installed through: `pip install torchaudio`."				)

																									_snake_case						:     Optional[int]				=    F.resample(
																									    torch.from_numpy(lowercase_				)      ,				lowercase_      ,				self.feature_extractor.sampling_rate				).numpy()

													if not isinstance(lowercase_      ,				np.ndarray				):
																			raise ValueError("We expect a numpy ndarray as input"				)
													if len(inputs.shape				) != 1:
																			raise ValueError("We expect a single channel audio input for AudioClassificationPipeline"				)

													_snake_case						:     Union[str, Any]				=    self.feature_extractor(
													    lowercase_      ,				sampling_rate=self.feature_extractor.sampling_rate      ,				return_tensors="pt"				)
													return processed



							def        UpperCamelCase       (						self      ,				lowercase_				):
													_snake_case						:     List[str]				=    self.model(**lowercase_				)
													return model_outputs



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_=5				):
													_snake_case						:     Optional[Any]				=    model_outputs.logits[0].softmax(-1				)
													_snake_case				,_snake_case						:     Union[str, Any]				=    probs.topk(lowercase_				)

													_snake_case						:     int				=    scores.tolist()
													_snake_case						:     Any				=    ids.tolist()

													_snake_case						:     Union[str, Any]				=    [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowercase_      ,				lowercase_				)]

													return labels
import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE						:    Dict     = logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:    List[Any]     = ['UtteranceLevel']
@torch.no_grad()
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase						)       ->       Tuple:

						'''simple docstring'''



						_snake_case						:     Optional[int]				=    torch.load(__lowercase					,			map_location="cpu"						)
						if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
												raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}"""						)

						_snake_case						:     List[str]				=    checkpoint["Downstream"]

						_snake_case						:     List[str]				=    HubertConfig.from_pretrained(__lowercase						)
						_snake_case						:     Union[str, Any]				=    HubertForSequenceClassification.from_pretrained(__lowercase					,			config=__lowercase						)
						_snake_case						:     Union[str, Any]				=    WavaVecaFeatureExtractor.from_pretrained(
						    __lowercase					,			return_attention_mask=__lowercase					,			do_normalize=__lowercase						)

						if hf_congfig.use_weighted_layer_sum:
												_snake_case						:     Optional[int]				=    checkpoint["Featurizer"]["weights"]

						_snake_case						:     str				=    downstream_dict["projector.weight"]
						_snake_case						:     Tuple				=    downstream_dict["projector.bias"]
						_snake_case						:     List[str]				=    downstream_dict["model.post_net.linear.weight"]
						_snake_case						:     str				=    downstream_dict["model.post_net.linear.bias"]

						hf_feature_extractor.save_pretrained(__lowercase						)
						hf_model.save_pretrained(__lowercase						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:    Optional[int]     = argparse.ArgumentParser()
				parser.add_argument(
				    '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
				)
				parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
				parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
				parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
				__SCREAMING_SNAKE_CASE						:    str     = parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)