




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

a_ 		=  logging.get_logger(__name__)




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						np.array:

    """simple docstring"""


    __UpperCamelCase			=    F"{sampling_rate}"
    __UpperCamelCase			=    '''1'''
    __UpperCamelCase			=    '''f32le'''
    __UpperCamelCase			=    [
        '''ffmpeg''',
        '''-i''',
        '''pipe:0''',
        '''-ac''',
        ac,
        '''-ar''',
        ar,
        '''-f''',
        format_for_conversion,
        '''-hide_banner''',
        '''-loglevel''',
        '''quiet''',
        '''pipe:1''',
    ]

    try:
        __UpperCamelCase			=    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'''   )
    __UpperCamelCase			=    ffmpeg_process.communicate(lowercase_   )
    __UpperCamelCase			=    output_stream[0]

    __UpperCamelCase			=    np.frombuffer(lowercase_       ,  np.floataa   )
    if audio.shape[0] == 0:
        raise ValueError('''Malformed soundfile'''   )
    return audio


@add_end_docstrings(_SCREAMING_SNAKE_CASE )
class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      def __init__(  self       :			int			,							*snake_case       :			Optional[Any]			,							**snake_case       :			Union[str, Any]					):
          # Default, might be overriden by the model.config.
          __UpperCamelCase			=    5
          super().__init__(*snake_case			,							**snake_case					)

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

          self.check_model_type(snake_case					)







      def __call__(  self       :			Dict			,							snake_case       :			Union[np.ndarray, bytes, str]			,							**snake_case       :			int			,							):
          return super().__call__(snake_case			,							**snake_case					)







      def    snake_case						(  self       :			str			,							snake_case       :			str=None			,							**snake_case       :			List[Any]					):
          # No parameters on this pipeline right now
          __UpperCamelCase			=    {}
          if top_k is not None:
              if top_k > self.model.config.num_labels:
                  __UpperCamelCase			=    self.model.config.num_labels
              __UpperCamelCase			=    top_k
          return {}, {}, postprocess_params







      def    snake_case						(  self       :			List[str]			,							snake_case       :			Union[str, Any]					):
          if isinstance(snake_case			,							snake_case					):
              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
                  __UpperCamelCase			=    requests.get(snake_case					).content
              else:
                  with open(snake_case			,							'''rb'''					) as f:
                      __UpperCamelCase			=    f.read()

          if isinstance(snake_case			,							snake_case					):
              __UpperCamelCase			=    ffmpeg_read(snake_case			,							self.feature_extractor.sampling_rate					)

          if isinstance(snake_case			,							snake_case					):
              # 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'''					)

              __UpperCamelCase			=    inputs.pop('''raw'''			,							snake_case					)
              if _inputs is None:
                  # Remove path which will not be used from `datasets`.
                  inputs.pop('''path'''			,							snake_case					)
                  __UpperCamelCase			=    inputs.pop('''array'''			,							snake_case					)
              __UpperCamelCase			=    inputs.pop('''sampling_rate'''					)
              __UpperCamelCase			=    _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`.'''					)

                  __UpperCamelCase			=    F.resample(
                      torch.from_numpy(snake_case					)			,							snake_case			,							self.feature_extractor.sampling_rate					).numpy()

          if not isinstance(snake_case			,							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'''					)

          __UpperCamelCase			=    self.feature_extractor(
              snake_case			,							sampling_rate=self.feature_extractor.sampling_rate			,							return_tensors='''pt'''					)
          return processed







      def    snake_case						(  self       :			int			,							snake_case       :			int					):
          __UpperCamelCase			=    self.model(**snake_case					)
          return model_outputs







      def    snake_case						(  self       :			Optional[int]			,							snake_case       :			Tuple			,							snake_case       :			Optional[Any]=5					):
          __UpperCamelCase			=    model_outputs.logits[0].softmax(-1					)
          __UpperCamelCase		,   __UpperCamelCase			=    probs.topk(snake_case					)

          __UpperCamelCase			=    scores.tolist()
          __UpperCamelCase			=    ids.tolist()

          __UpperCamelCase			=    [{'''score''': score, '''label''': self.model.config.idalabel[_id]} for score, _id in zip(snake_case			,							snake_case					)]

          return labels












import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
a_ 		=  logging.get_logger(__name__)

a_ 		=  {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "mask_emb": "masked_spec_embed",
}




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						Any:

    """simple docstring"""


    for attribute in key.split('''.'''   ):
        __UpperCamelCase			=    getattr(lowercase_       ,  lowercase_   )

    if weight_type is not None:
        __UpperCamelCase			=    getattr(lowercase_       ,  lowercase_   ).shape
    else:
        __UpperCamelCase			=    hf_pointer.shape

    assert hf_shape == value.shape, (
        F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
        F" {value.shape} for {full_name}"
    )

    if weight_type == "weight":
        __UpperCamelCase			=    value
    elif weight_type == "weight_g":
        __UpperCamelCase			=    value
    elif weight_type == "weight_v":
        __UpperCamelCase			=    value
    elif weight_type == "bias":
        __UpperCamelCase			=    value
    else:
        __UpperCamelCase			=    value

    logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."   )




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						int:

    """simple docstring"""


    __UpperCamelCase			=    []
    __UpperCamelCase			=    fairseq_model.state_dict()

    __UpperCamelCase			=    hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        __UpperCamelCase			=    False
        if "conv_layers" in name:
            load_conv_layer(
                lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  hf_model.config.feat_extract_norm == '''group'''       ,  )
            __UpperCamelCase			=    True
        else:
            for key, mapped_key in MAPPING.items():
                __UpperCamelCase			=    mapped_key

                if key in name:
                    __UpperCamelCase			=    True
                    if "*" in mapped_key:
                        __UpperCamelCase			=    name.split(lowercase_   )[0].split('''.'''   )[-2]
                        __UpperCamelCase			=    mapped_key.replace('''*'''       ,  lowercase_   )
                    if "weight_g" in name:
                        __UpperCamelCase			=    '''weight_g'''
                    elif "weight_v" in name:
                        __UpperCamelCase			=    '''weight_v'''
                    elif "weight" in name:
                        __UpperCamelCase			=    '''weight'''
                    elif "bias" in name:
                        __UpperCamelCase			=    '''bias'''
                    else:
                        __UpperCamelCase			=    None
                    set_recursively(lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )
                continue
        if not is_used:
            unused_weights.append(lowercase_   )

    logger.warning(F"Unused weights: {unused_weights}"   )




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						Optional[Any]:

    """simple docstring"""


    __UpperCamelCase			=    full_name.split('''conv_layers.'''   )[-1]
    __UpperCamelCase			=    name.split('''.'''   )
    __UpperCamelCase			=    int(items[0]   )
    __UpperCamelCase			=    int(items[1]   )

    if type_id == 0:
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                F"{full_name} has size {value.shape}, but"
                F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
            )
            __UpperCamelCase			=    value
            logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."   )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                F"{full_name} has size {value.shape}, but"
                F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
            )
            __UpperCamelCase			=    value
            logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."   )
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                F"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
                " found."
            )
            __UpperCamelCase			=    value
            logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."   )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                F"{full_name} has size {value.shape}, but"
                F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
            )
            __UpperCamelCase			=    value
            logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."   )
    else:
        unused_weights.append(lowercase_   )




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						List[Any]:

    """simple docstring"""


    __UpperCamelCase			=    HubertConfig()
    __UpperCamelCase			=    model.config

    __UpperCamelCase			=    fs_config.activation_dropout
    __UpperCamelCase			=    False
    __UpperCamelCase			=    fs_config.attention_dropout
    __UpperCamelCase			=    False
    __UpperCamelCase			=    eval(fs_config.extractor_conv_feature_layers   )
    __UpperCamelCase			=    [x[0] for x in conv_layers]
    __UpperCamelCase			=    [x[1] for x in conv_layers]
    __UpperCamelCase			=    [x[2] for x in conv_layers]
    __UpperCamelCase			=    '''gelu'''
    __UpperCamelCase			=    '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
    __UpperCamelCase			=    False
    __UpperCamelCase			=    0.0
    __UpperCamelCase			=    0.0
    __UpperCamelCase			=    fs_config.activation_fn
    __UpperCamelCase			=    fs_config.dropout
    __UpperCamelCase			=    fs_config.encoder_embed_dim
    __UpperCamelCase			=    0.02
    __UpperCamelCase			=    fs_config.encoder_ffn_embed_dim
    __UpperCamelCase			=    1E-5
    __UpperCamelCase			=    0.0
    __UpperCamelCase			=    fs_config.encoder_attention_heads
    __UpperCamelCase			=    fs_config.conv_pos_groups
    __UpperCamelCase			=    fs_config.conv_pos
    __UpperCamelCase			=    len(lowercase_   )
    __UpperCamelCase			=    fs_config.encoder_layers

    return config




@torch.no_grad()
def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_=None   )							->						Dict:

    """simple docstring"""


    __UpperCamelCase			=    distilhubert().model.model

    if config_path is not None:
        __UpperCamelCase			=    HubertConfig.from_pretrained(lowercase_   )
    else:
        __UpperCamelCase			=    convert_config(lowercase_   )
    __UpperCamelCase			=    model.eval()

    __UpperCamelCase			=    WavaVecaFeatureExtractor(
        feature_size=1       ,  sampling_rate=1_60_00       ,  padding_value=0       ,  do_normalize=lowercase_       ,  return_attention_mask=lowercase_       ,  )
    __UpperCamelCase			=    HubertModel(lowercase_   )

    recursively_load_weights(lowercase_       ,  lowercase_   )

    feature_extractor.save_pretrained(lowercase_   )
    hf_model.save_pretrained(lowercase_   )


if __name__ == "__main__":
 a_ 		=  argparse.ArgumentParser()
 parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 a_ 		=  parser.parse_args()
 convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)






