




import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)

def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE=False				,_SCREAMING_SNAKE_CASE=False				,_SCREAMING_SNAKE_CASE="data2vec."					)			->				str:
       lowerCamelCase     :   str    				=      "backbone." if is_semantic else ""

       lowerCamelCase     :   int    				=      []
       for i in range(config.num_hidden_layers					):
              # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
              rename_keys.append(
                  (f'''{prefix}blocks.{i}.norm1.weight''', f'''{hf_prefix}encoder.layer.{i}.layernorm_before.weight''')					)
              rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''{hf_prefix}encoder.layer.{i}.layernorm_before.bias''')					)
              rename_keys.append(
                  (f'''{prefix}blocks.{i}.attn.proj.weight''', f'''{hf_prefix}encoder.layer.{i}.attention.output.dense.weight''')					)
              rename_keys.append(
                  (f'''{prefix}blocks.{i}.attn.proj.bias''', f'''{hf_prefix}encoder.layer.{i}.attention.output.dense.bias''')					)
              rename_keys.append(
                  (f'''{prefix}blocks.{i}.norm2.weight''', f'''{hf_prefix}encoder.layer.{i}.layernorm_after.weight''')					)
              rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''{hf_prefix}encoder.layer.{i}.layernorm_after.bias''')					)
              rename_keys.append(
                  (f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''{hf_prefix}encoder.layer.{i}.intermediate.dense.weight''')					)
              rename_keys.append(
                  (f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''{hf_prefix}encoder.layer.{i}.intermediate.dense.bias''')					)
              rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''{hf_prefix}encoder.layer.{i}.output.dense.weight''')					)
              rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''{hf_prefix}encoder.layer.{i}.output.dense.bias''')					)

       # projection layer + position embeddings
       rename_keys.extend(
           [
               (f'''{prefix}cls_token''', f'''{hf_prefix}embeddings.cls_token'''),
               (f'''{prefix}patch_embed.proj.weight''', f'''{hf_prefix}embeddings.patch_embeddings.projection.weight'''),
               (f'''{prefix}patch_embed.proj.bias''', f'''{hf_prefix}embeddings.patch_embeddings.projection.bias'''),
           ]					)

       if has_lm_head:
              # mask token + shared relative position bias + layernorm
              rename_keys.extend(
                  [
                      ("mask_token", f'''{hf_prefix}embeddings.mask_token'''),
                      (
                          "rel_pos_bias.relative_position_bias_table",
                          f'''{hf_prefix}encoder.relative_position_bias.relative_position_bias_table''',
                      ),
                      (
                          "rel_pos_bias.relative_position_index",
                          f'''{hf_prefix}encoder.relative_position_bias.relative_position_index''',
                      ),
                      ("norm.weight", "layernorm.weight"),
                      ("norm.bias", "layernorm.bias"),
                  ]					)
       elif is_semantic:
              # semantic segmentation classification heads
              rename_keys.extend(
                  [
                      ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
                      ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
                      ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
                      ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
                  ]					)
       else:
              # layernorm + classification head
              rename_keys.extend(
                  [
                      ("fc_norm.weight", f'''{hf_prefix}pooler.layernorm.weight'''),
                      ("fc_norm.bias", f'''{hf_prefix}pooler.layernorm.bias'''),
                      ("head.weight", "classifier.weight"),
                      ("head.bias", "classifier.bias"),
                  ]					)

       return rename_keys

def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE=False				,_SCREAMING_SNAKE_CASE=False				,_SCREAMING_SNAKE_CASE="data2vec_vision."					)			->				Optional[int]:
       for i in range(config.num_hidden_layers					):
              lowerCamelCase     :   Optional[Any]    				=      "backbone." if is_semantic else ""
              # queries, keys and values
              lowerCamelCase     :   List[str]    				=      state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''					)
              lowerCamelCase     :   List[Any]    				=      state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''					)
              lowerCamelCase     :   Dict    				=      state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''					)

              lowerCamelCase     :   List[Any]    				=      in_proj_weight[
                  : config.hidden_size, :
              ]
              lowerCamelCase     :   int    				=      q_bias
              lowerCamelCase     :   int    				=      in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              lowerCamelCase     :   Any    				=      in_proj_weight[
                  -config.hidden_size :, :
              ]
              lowerCamelCase     :   Optional[Any]    				=      v_bias

              # gamma_1 and gamma_2
              # we call them lambda because otherwise they are renamed when using .from_pretrained
              lowerCamelCase     :   Optional[Any]    				=      state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''					)
              lowerCamelCase     :   Tuple    				=      state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''					)

              lowerCamelCase     :   int    				=      gamma_a
              lowerCamelCase     :   Union[str, Any]    				=      gamma_a

              # relative_position bias table + index
              if not has_lm_head:
                     # each layer has its own relative position bias
                     lowerCamelCase     :   Optional[Any]    				=      state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_bias_table'''					)
                     lowerCamelCase     :   int    				=      state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_index'''					)

                     lowerCamelCase     :   Dict    				=      table
                     lowerCamelCase     :   int    				=      index

def   A   (   )			->				Dict:
       lowerCamelCase     :   Optional[int]    				=      argparse.ArgumentParser(
           "Convert Data2VecVision to HF for image classification and pretraining"				,add_help=_SCREAMING_SNAKE_CASE					)
       parser.add_argument("--hf_checkpoint_name"				,type=_SCREAMING_SNAKE_CASE					)
       parser.add_argument("--input_size"				,default=224				,type=_SCREAMING_SNAKE_CASE				,help="images input size"					)
       parser.add_argument("--beit_checkpoint"				,default=""				,help="beit checkpoint"					)

       return parser.parse_args()

def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)			->				int:
       def load_state_dict(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE=""				,_SCREAMING_SNAKE_CASE="relative_position_index"					):
              lowerCamelCase     :   Optional[Any]    				=      []
              lowerCamelCase     :   Tuple    				=      []
              lowerCamelCase     :   Any    				=      []
              # copy state_dict so _load_from_state_dict can modify it
              lowerCamelCase     :   List[str]    				=      getattr(_SCREAMING_SNAKE_CASE				,"_metadata"				,_SCREAMING_SNAKE_CASE					)
              lowerCamelCase     :   List[Any]    				=      state_dict.copy()
              if metadata is not None:
                     lowerCamelCase     :   Any    				=      metadata

              def load(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE=""					):
                     lowerCamelCase     :   Tuple    				=      {} if metadata is None else metadata.get(prefix[:-1]				,{}					)
                     module._load_from_state_dict(
                         _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)
                     for name, child in module._modules.items():
                            if child is not None:
                                   load(_SCREAMING_SNAKE_CASE				,prefix + name + "."					)

              load(_SCREAMING_SNAKE_CASE				,prefix=_SCREAMING_SNAKE_CASE					)

              lowerCamelCase     :   Optional[int]    				=      []
              lowerCamelCase     :   Tuple    				=      []
              for key in missing_keys:
                     lowerCamelCase     :   Any    				=      True
                     for ignore_key in ignore_missing.split("|"					):
                            if ignore_key in key:
                                   lowerCamelCase     :   List[Any]    				=      False
                                   break
                     if keep_flag:
                            warn_missing_keys.append(_SCREAMING_SNAKE_CASE					)
                     else:
                            ignore_missing_keys.append(_SCREAMING_SNAKE_CASE					)

              lowerCamelCase     :   Dict    				=      warn_missing_keys

              if len(_SCREAMING_SNAKE_CASE					) > 0:
                     print(
                         "Weights of {} not initialized from pretrained model: {}".format(
                             model.__class__.__name__				,_SCREAMING_SNAKE_CASE					)					)
              if len(_SCREAMING_SNAKE_CASE					) > 0:
                     print("Weights from pretrained model not used in {}: {}".format(model.__class__.__name__				,_SCREAMING_SNAKE_CASE					)					)
              if len(_SCREAMING_SNAKE_CASE					) > 0:
                     print(
                         "Ignored weights of {} not initialized from pretrained model: {}".format(
                             model.__class__.__name__				,_SCREAMING_SNAKE_CASE					)					)
              if len(_SCREAMING_SNAKE_CASE					) > 0:
                     print("\n".join(_SCREAMING_SNAKE_CASE					)					)

       lowerCamelCase     :   List[str]    				=      {
           "pretrained": False,
           "use_shared_rel_pos_bias": True,
           "use_abs_pos_emb": False,
           "init_values": 0.1,
       }

       if is_finetuned:
              model_kwargs.update(
                  {
                      "num_classes": 1000,
                      "use_mean_pooling": True,
                      "init_scale": 0.001,
                      "use_rel_pos_bias": True,
                  }					)

       lowerCamelCase     :   Tuple    				=      create_model(
           "beit_large_patch16_224" if is_large else "beit_base_patch16_224"				,**_SCREAMING_SNAKE_CASE				,)
       lowerCamelCase     :   Union[str, Any]    				=      model.patch_embed.patch_size
       lowerCamelCase     :   Tuple    				=      (args.input_size // patch_size[0], args.input_size // patch_size[1])
       lowerCamelCase     :   Dict    				=      torch.load(args.beit_checkpoint				,map_location="cpu"					)

       print(f'''Load ckpt from {args.beit_checkpoint}'''					)
       lowerCamelCase     :   Tuple    				=      None
       for model_key in ("model", "module"):
              if model_key in checkpoint:
                     lowerCamelCase     :   List[Any]    				=      checkpoint[model_key]
                     print(f'''Load state_dict by model_key = {model_key}'''					)
                     break

       lowerCamelCase     :   Dict    				=      list(checkpoint_model.keys()					)
       for key in all_keys:
              if "relative_position_index" in key:
                     checkpoint_model.pop(_SCREAMING_SNAKE_CASE					)

              if "relative_position_bias_table" in key:
                     lowerCamelCase     :   Any    				=      checkpoint_model[key]
                     lowerCamelCase				,   lowerCamelCase     :   Union[str, Any]    				=      rel_pos_bias.size()
                     lowerCamelCase				,   lowerCamelCase     :   Union[str, Any]    				=      model.state_dict()[key].size()
                     lowerCamelCase     :   str    				=      model.patch_embed.patch_shape
                     if dst_patch_shape[0] != dst_patch_shape[1]:
                            raise NotImplementedError()

       load_state_dict(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,prefix=""					)

       return model

def   A   (   )			->				Any:
       lowerCamelCase     :   Tuple    				=      get_args()

       lowerCamelCase     :   Dict    				=      "ft1k" in args.hf_checkpoint_name
       lowerCamelCase     :   List[str]    				=      "large" in args.hf_checkpoint_name

       if is_finetuned:
              # To convert Beit's data2vec_vision to HF you need to copy
              # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
              # into this folder.
              import modeling_finetune  # noqa: F401
       else:
              # To convert Beit's data2vec_vision to HF you need to copy
              # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
              # into this folder
              # IMPORTANT: Note that for now we've only converted the down-stream
              # model and not the full pretrained model. This means for the integration
              # test you need to add a `return x` after the following line:
              # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
              # to make the integration test pass.
              import modeling_cyclical  # noqa: F401

       # 1. Create model config
       lowerCamelCase     :   str    				=      DataaVecVisionConfig()
       if is_finetuned:
              lowerCamelCase     :   Optional[int]    				=      True
              lowerCamelCase     :   Dict    				=      False
              lowerCamelCase     :   int    				=      True
              lowerCamelCase     :   Dict    				=      1000

              lowerCamelCase     :   List[str]    				=      "huggingface/label-files"
              lowerCamelCase     :   Optional[int]    				=      "imagenet-1k-id2label.json"
              lowerCamelCase     :   List[Any]    				=      json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,repo_type="dataset"					)				,"r"					)					)
              lowerCamelCase     :   Optional[Any]    				=      {int(_SCREAMING_SNAKE_CASE					): v for k, v in idalabel.items()}
              lowerCamelCase     :   Optional[Any]    				=      idalabel
              lowerCamelCase     :   Dict    				=      {v: k for k, v in idalabel.items()}
       else:
              lowerCamelCase     :   Any    				=      False
              lowerCamelCase     :   Optional[int]    				=      True
              lowerCamelCase     :   Union[str, Any]    				=      False

       if is_large:
              lowerCamelCase     :   Any    				=      1024
              lowerCamelCase     :   int    				=      4096
              lowerCamelCase     :   Dict    				=      24
              lowerCamelCase     :   Dict    				=      16

       # 2. Load Beit model
       lowerCamelCase     :   List[Any]    				=      load_beit_model(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)
       orig_model.eval()

       # 3. Forward Beit model
       lowerCamelCase     :   int    				=      BeitImageProcessor(size=config.image_size				,do_center_crop=_SCREAMING_SNAKE_CASE					)
       lowerCamelCase     :   Optional[Any]    				=      Image.open("../../../../tests/fixtures/tests_samples/COCO/000000039769.png"					)
       lowerCamelCase     :   Optional[int]    				=      image_processor(images=_SCREAMING_SNAKE_CASE				,return_tensors="pt"					)
       lowerCamelCase     :   Union[str, Any]    				=      encoding["pixel_values"]

       lowerCamelCase     :   List[Any]    				=      (pixel_values,) if is_finetuned else (pixel_values, None)
       with torch.no_grad():
              lowerCamelCase     :   str    				=      orig_model(*_SCREAMING_SNAKE_CASE					)

       # 4. Load HF Data2VecVision model
       if is_finetuned:
              lowerCamelCase     :   Union[str, Any]    				=      DataaVecVisionForImageClassification(_SCREAMING_SNAKE_CASE					)
              hf_model.eval()
              lowerCamelCase     :   Dict    				=      False
              lowerCamelCase     :   Optional[Any]    				=      "data2vec_vision."
       else:
              lowerCamelCase     :   Any    				=      DataaVecVisionModel(_SCREAMING_SNAKE_CASE					)
              hf_model.eval()
              lowerCamelCase     :   int    				=      True
              lowerCamelCase     :   Any    				=      ""

       lowerCamelCase     :   Any    				=      create_rename_keys(_SCREAMING_SNAKE_CASE				,hf_prefix=_SCREAMING_SNAKE_CASE				,has_lm_head=_SCREAMING_SNAKE_CASE					)
       lowerCamelCase     :   List[Any]    				=      orig_model.state_dict()
       for src, dest in rename_keys:
              lowerCamelCase     :   List[Any]    				=      state_dict.pop(_SCREAMING_SNAKE_CASE					)
              lowerCamelCase     :   Any    				=      val

       read_in_q_k_v(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,hf_prefix=_SCREAMING_SNAKE_CASE				,has_lm_head=_SCREAMING_SNAKE_CASE					)
       lowerCamelCase				,   lowerCamelCase     :   str    				=      hf_model.load_state_dict(_SCREAMING_SNAKE_CASE				,strict=_SCREAMING_SNAKE_CASE					)
       print("HF missing"				,_SCREAMING_SNAKE_CASE					)
       print("HF unexpected_keys"				,_SCREAMING_SNAKE_CASE					)

       # 5. Forward HF Data2VecVision model
       with torch.no_grad():
              lowerCamelCase     :   Any    				=      hf_model(_SCREAMING_SNAKE_CASE					)

       lowerCamelCase     :   List[Any]    				=      hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

       # 6. Compare
       lowerCamelCase     :   Optional[Any]    				=      torch.max(torch.abs(hf_output - orig_model_output					)					).item()

       print(f'''max_absolute_diff = {max_absolute_diff}'''					)
       lowerCamelCase     :   Optional[Any]    				=      torch.allclose(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,atol=1e-3					)
       print("Do both models output the same tensors?"				,"🔥" if success else "💩"					)
       if not success:
              raise Exception("Something went wRoNg"					)

       # 7. Save
       print(f'''Saving to {args.hf_checkpoint_name}'''					)
       hf_model.save_pretrained(args.hf_checkpoint_name					)
       image_processor.save_pretrained(args.hf_checkpoint_name					)


if __name__ == "__main__":
       main()
       # Run the following to convert checkpoints
       #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
       #          --beit_checkpoint ./pretrained_base.pt \
       #          --hf_checkpoint_name "./data2vec-vision-base"
       #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
       #          --beit_checkpoint ./finetuned_base.pt \
       #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
       #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
       #          --beit_checkpoint ./pretrained_large.pt \
       #          --hf_checkpoint_name "./data2vec-vision-large"
       #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
       #          --beit_checkpoint ./finetuned_large.pt \
       #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"












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__ :   Union[str, Any]						=     logging.get_logger(__name__)

def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)			->				np.array:
       lowerCamelCase     :   Optional[Any]    				=      f'''{sampling_rate}'''
       lowerCamelCase     :   Optional[Any]    				=      "1"
       lowerCamelCase     :   Tuple    				=      "f32le"
       lowerCamelCase     :   Any    				=      [
           "ffmpeg",
           "-i",
           "pipe:0",
           "-ac",
           ac,
           "-ar",
           ar,
           "-f",
           format_for_conversion,
           "-hide_banner",
           "-loglevel",
           "quiet",
           "pipe:1",
       ]

       try:
              lowerCamelCase     :   Optional[int]    				=      subprocess.Popen(_SCREAMING_SNAKE_CASE				,stdin=subprocess.PIPE				,stdout=subprocess.PIPE					)
       except FileNotFoundError:
              raise ValueError("ffmpeg was not found but is required to load audio files from filename"					)
       lowerCamelCase     :   Optional[int]    				=      ffmpeg_process.communicate(_SCREAMING_SNAKE_CASE					)
       lowerCamelCase     :   Union[str, Any]    				=      output_stream[0]

       lowerCamelCase     :   Dict    				=      np.frombuffer(_SCREAMING_SNAKE_CASE				,np.floataa					)
       if audio.shape[0] == 0:
              raise ValueError("Malformed soundfile"					)
       return audio




@add_end_docstrings(lowerCAmelCase__  )
class 	UpperCamelCase__			(lowerCAmelCase__  ):

    '''simple docstring'''



    def __init__(					self  ,						*UpperCamelCase__  ,						**UpperCamelCase__  )  ->   Dict:
           # Default, might be overriden by the model.config.
           lowerCamelCase     :   Union[str, Any]    				=      5
           super().__init__(*UpperCamelCase__  ,						**UpperCamelCase__  )

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

           self.check_model_type(UpperCamelCase__  )







    def __call__(					self  ,						UpperCamelCase__  ,						**UpperCamelCase__  ,						)  ->   Optional[int]:
           return super().__call__(UpperCamelCase__  ,						**UpperCamelCase__  )







    def 					_lowercase  (					self  ,						UpperCamelCase__=None  ,						**UpperCamelCase__  )  ->   str:
           # No parameters on this pipeline right now
           lowerCamelCase     :   int    				=      {}
           if top_k is not None:
                  if top_k > self.model.config.num_labels:
                         lowerCamelCase     :   Any    				=      self.model.config.num_labels
                  lowerCamelCase     :   Any    				=      top_k
           return {}, {}, postprocess_params







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   Dict:
           if isinstance(UpperCamelCase__  ,						UpperCamelCase__  ):
                  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
                         lowerCamelCase     :   str    				=      requests.get(UpperCamelCase__  ).content
                  else:
                         with open(UpperCamelCase__  ,						"rb"  ) as f:
                                lowerCamelCase     :   int    				=      f.read()

           if isinstance(UpperCamelCase__  ,						UpperCamelCase__  ):
                  lowerCamelCase     :   str    				=      ffmpeg_read(UpperCamelCase__  ,						self.feature_extractor.sampling_rate  )

           if isinstance(UpperCamelCase__  ,						UpperCamelCase__  ):
                  # 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"  )

                  lowerCamelCase     :   Optional[int]    				=      inputs.pop("raw"  ,						UpperCamelCase__  )
                  if _inputs is None:
                         # Remove path which will not be used from `datasets`.
                         inputs.pop("path"  ,						UpperCamelCase__  )
                         lowerCamelCase     :   List[str]    				=      inputs.pop("array"  ,						UpperCamelCase__  )
                  lowerCamelCase     :   str    				=      inputs.pop("sampling_rate"  )
                  lowerCamelCase     :   Union[str, Any]    				=      _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`."  )

                         lowerCamelCase     :   Tuple    				=      F.resample(
                             torch.from_numpy(UpperCamelCase__  )  ,						UpperCamelCase__  ,						self.feature_extractor.sampling_rate  ).numpy()

           if not isinstance(UpperCamelCase__  ,						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"  )

           lowerCamelCase     :   Optional[int]    				=      self.feature_extractor(
               UpperCamelCase__  ,						sampling_rate=self.feature_extractor.sampling_rate  ,						return_tensors="pt"  )
           return processed







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   str:
           lowerCamelCase     :   Optional[Any]    				=      self.model(**UpperCamelCase__  )
           return model_outputs







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__=5  )  ->   Tuple:
           lowerCamelCase     :   Tuple    				=      model_outputs.logits[0].softmax(-1  )
           lowerCamelCase				,   lowerCamelCase     :   str    				=      probs.topk(UpperCamelCase__  )

           lowerCamelCase     :   str    				=      scores.tolist()
           lowerCamelCase     :   Optional[int]    				=      ids.tolist()

           lowerCamelCase     :   Optional[int]    				=      [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(UpperCamelCase__  ,						UpperCamelCase__  )]

           return labels






