'''simple docstring'''
import argparse
import json
from pathlib import Path

import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download

from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__  :      List[str]        =					logging.get_logger(__name__)



def  __lowerCamelCase       (      _UpperCamelCase  :  Dict					):


      '''simple docstring'''


      UpperCAmelCase_            =  ASTConfig()

      if "10-10" in model_name:
            pass
      elif "speech-commands" in model_name:
            UpperCAmelCase_            =  128
      elif "12-12" in model_name:
            UpperCAmelCase_            =  12
            UpperCAmelCase_            =  12
      elif "14-14" in model_name:
            UpperCAmelCase_            =  14
            UpperCAmelCase_            =  14
      elif "16-16" in model_name:
            UpperCAmelCase_            =  16
            UpperCAmelCase_            =  16
      else:
            raise ValueError('''Model not supported'''					)

      UpperCAmelCase_            =  '''huggingface/label-files'''
      if "speech-commands" in model_name:
            UpperCAmelCase_            =  35
            UpperCAmelCase_            =  '''speech-commands-v2-id2label.json'''
      else:
            UpperCAmelCase_            =  527
            UpperCAmelCase_            =  '''audioset-id2label.json'''

      UpperCAmelCase_            =  json.load(open(hf_hub_download(a_							,    a_							,    repo_type='''dataset'''					)							,    '''r'''					)					)
      UpperCAmelCase_            =  {int(a_					): v for k, v in idalabel.items()}
      UpperCAmelCase_            =  idalabel
      UpperCAmelCase_            =  {v: k for k, v in idalabel.items()}

      return config



def  __lowerCamelCase       (      _UpperCamelCase  :  List[str]					):


      '''simple docstring'''


      if "module.v" in name:
            UpperCAmelCase_            =  name.replace('''module.v'''							,    '''audio_spectrogram_transformer'''					)
      if "cls_token" in name:
            UpperCAmelCase_            =  name.replace('''cls_token'''							,    '''embeddings.cls_token'''					)
      if "dist_token" in name:
            UpperCAmelCase_            =  name.replace('''dist_token'''							,    '''embeddings.distillation_token'''					)
      if "pos_embed" in name:
            UpperCAmelCase_            =  name.replace('''pos_embed'''							,    '''embeddings.position_embeddings'''					)
      if "patch_embed.proj" in name:
            UpperCAmelCase_            =  name.replace('''patch_embed.proj'''							,    '''embeddings.patch_embeddings.projection'''					)
      # transformer blocks
      if "blocks" in name:
            UpperCAmelCase_            =  name.replace('''blocks'''							,    '''encoder.layer'''					)
      if "attn.proj" in name:
            UpperCAmelCase_            =  name.replace('''attn.proj'''							,    '''attention.output.dense'''					)
      if "attn" in name:
            UpperCAmelCase_            =  name.replace('''attn'''							,    '''attention.self'''					)
      if "norm1" in name:
            UpperCAmelCase_            =  name.replace('''norm1'''							,    '''layernorm_before'''					)
      if "norm2" in name:
            UpperCAmelCase_            =  name.replace('''norm2'''							,    '''layernorm_after'''					)
      if "mlp.fc1" in name:
            UpperCAmelCase_            =  name.replace('''mlp.fc1'''							,    '''intermediate.dense'''					)
      if "mlp.fc2" in name:
            UpperCAmelCase_            =  name.replace('''mlp.fc2'''							,    '''output.dense'''					)
      # final layernorm
      if "audio_spectrogram_transformer.norm" in name:
            UpperCAmelCase_            =  name.replace('''audio_spectrogram_transformer.norm'''							,    '''audio_spectrogram_transformer.layernorm'''					)
      # classifier head
      if "module.mlp_head.0" in name:
            UpperCAmelCase_            =  name.replace('''module.mlp_head.0'''							,    '''classifier.layernorm'''					)
      if "module.mlp_head.1" in name:
            UpperCAmelCase_            =  name.replace('''module.mlp_head.1'''							,    '''classifier.dense'''					)

      return name



def  __lowerCamelCase       (      _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  List[Any]					):


      '''simple docstring'''


      for key in orig_state_dict.copy().keys():
            UpperCAmelCase_            =  orig_state_dict.pop(a_					)

            if "qkv" in key:
                  UpperCAmelCase_            =  key.split('''.'''					)
                  UpperCAmelCase_            =  int(key_split[3]					)
                  UpperCAmelCase_            =  config.hidden_size
                  if "weight" in key:
                        UpperCAmelCase_            =  val[:dim, :]
                        UpperCAmelCase_            =  val[dim : dim * 2, :]
                        UpperCAmelCase_            =  val[-dim:, :]
                  else:
                        UpperCAmelCase_            =  val[:dim]
                        UpperCAmelCase_            =  val[dim : dim * 2]
                        UpperCAmelCase_            =  val[-dim:]
            else:
                  UpperCAmelCase_            =  val

      return orig_state_dict



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      UpperCAmelCase_            =  [
          '''module.v.head.weight''',
          '''module.v.head.bias''',
          '''module.v.head_dist.weight''',
          '''module.v.head_dist.bias''',
      ]
      for k in ignore_keys:
            state_dict.pop(a_							,    a_					)



@torch.no_grad()
def  __lowerCamelCase       (      _UpperCamelCase  :  Optional[int]							,    _UpperCamelCase  :  str							,    _UpperCamelCase  :  Union[str, Any]=False					):


      '''simple docstring'''


      UpperCAmelCase_            =  get_audio_spectrogram_transformer_config(a_					)

      UpperCAmelCase_            =  {
          '''ast-finetuned-audioset-10-10-0.4593''': (
              '''https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1'''
          ),
          '''ast-finetuned-audioset-10-10-0.450''': (
              '''https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1'''
          ),
          '''ast-finetuned-audioset-10-10-0.448''': (
              '''https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1'''
          ),
          '''ast-finetuned-audioset-10-10-0.448-v2''': (
              '''https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1'''
          ),
          '''ast-finetuned-audioset-12-12-0.447''': (
              '''https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1'''
          ),
          '''ast-finetuned-audioset-14-14-0.443''': (
              '''https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1'''
          ),
          '''ast-finetuned-audioset-16-16-0.442''': (
              '''https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1'''
          ),
          '''ast-finetuned-speech-commands-v2''': (
              '''https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1'''
          ),
      }

      # load original state_dict
      UpperCAmelCase_            =  model_name_to_url[model_name]
      UpperCAmelCase_            =  torch.hub.load_state_dict_from_url(a_							,    map_location='''cpu'''					)
      # remove some keys
      remove_keys(a_					)
      # rename some keys
      UpperCAmelCase_            =  convert_state_dict(a_							,    a_					)

      # load 🤗 model
      UpperCAmelCase_            =  ASTForAudioClassification(a_					)
      model.eval()

      model.load_state_dict(a_					)

      # verify outputs on dummy input
      # source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
      UpperCAmelCase_            =  -4.2_677_393 if '''speech-commands''' not in model_name else -6.845_978
      UpperCAmelCase_            =  4.5_689_974 if '''speech-commands''' not in model_name else 5.5_654_526
      UpperCAmelCase_            =  1024 if '''speech-commands''' not in model_name else 128
      UpperCAmelCase_            =  ASTFeatureExtractor(mean=a_							,    std=a_							,    max_length=a_					)

      if "speech-commands" in model_name:
            UpperCAmelCase_            =  load_dataset('''speech_commands'''							,    '''v0.02'''							,    split='''validation'''					)
            UpperCAmelCase_            =  dataset[0]['''audio''']['''array''']
      else:
            UpperCAmelCase_            =  hf_hub_download(
                repo_id='''nielsr/audio-spectogram-transformer-checkpoint'''							,    filename='''sample_audio.flac'''							,    repo_type='''dataset'''							,    )

            UpperCAmelCase_            =  torchaudio.load(a_					)
            UpperCAmelCase_            =  waveform.squeeze().numpy()

      UpperCAmelCase_            =  feature_extractor(a_							,    sampling_rate=1_6000							,    return_tensors='''pt'''					)

      # forward pass
      UpperCAmelCase_            =  model(**a_					)
      UpperCAmelCase_            =  outputs.logits

      if model_name == "ast-finetuned-audioset-10-10-0.4593":
            UpperCAmelCase_            =  torch.tensor([-0.8_760, -7.0_042, -8.6_602]					)
      elif model_name == "ast-finetuned-audioset-10-10-0.450":
            UpperCAmelCase_            =  torch.tensor([-1.1_986, -7.0_903, -8.2_718]					)
      elif model_name == "ast-finetuned-audioset-10-10-0.448":
            UpperCAmelCase_            =  torch.tensor([-2.6_128, -8.0_080, -9.4_344]					)
      elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
            UpperCAmelCase_            =  torch.tensor([-1.5_080, -7.4_534, -8.8_917]					)
      elif model_name == "ast-finetuned-audioset-12-12-0.447":
            UpperCAmelCase_            =  torch.tensor([-0.5_050, -6.5_833, -8.0_843]					)
      elif model_name == "ast-finetuned-audioset-14-14-0.443":
            UpperCAmelCase_            =  torch.tensor([-0.3_826, -7.0_336, -8.2_413]					)
      elif model_name == "ast-finetuned-audioset-16-16-0.442":
            UpperCAmelCase_            =  torch.tensor([-1.2_113, -6.9_101, -8.3_470]					)
      elif model_name == "ast-finetuned-speech-commands-v2":
            UpperCAmelCase_            =  torch.tensor([6.1_589, -8.0_566, -8.7_984]					)
      else:
            raise ValueError('''Unknown model name'''					)
      if not torch.allclose(logits[0, :3]							,    a_							,    atol=1E-4					):
            raise ValueError('''Logits don\'t match'''					)
      print('''Looks ok!'''					)

      if pytorch_dump_folder_path is not None:
            Path(a_					).mkdir(exist_ok=a_					)
            print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""					)
            model.save_pretrained(a_					)
            print(F"""Saving feature extractor to {pytorch_dump_folder_path}"""					)
            feature_extractor.save_pretrained(a_					)

      if push_to_hub:
            print('''Pushing model and feature extractor to the hub...'''					)
            model.push_to_hub(F"""MIT/{model_name}"""					)
            feature_extractor.push_to_hub(F"""MIT/{model_name}"""					)


if __name__ == "__main__":
       lowercase__  :      List[str]        =					argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default="ast-finetuned-audioset-10-10-0.4593",
           type=str,
           help="Name of the Audio Spectrogram Transformer model you'd like to convert.",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
       )
       parser.add_argument(
           "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
       )

       lowercase__  :      Tuple        =					parser.parse_args()
       convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''
import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class lowerCamelCase							(	lowerCamelCase     ,		unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	XLMTokenizer
  lowerCAmelCase__										=	False






  def  lowerCAmelCase__ (      self			:       int  )				->Union[str, Any]:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        UpperCAmelCase_            =  [
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''w</w>''',
            '''r</w>''',
            '''t</w>''',
            '''lo''',
            '''low''',
            '''er</w>''',
            '''low</w>''',
            '''lowest</w>''',
            '''newer</w>''',
            '''wider</w>''',
            '''<unk>''',
        ]
        UpperCAmelCase_            =  dict(zip(UpperCAmelCase__  ,							range(len(UpperCAmelCase__  )  )  )  )
        UpperCAmelCase_            =  ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

        UpperCAmelCase_            =  os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['''vocab_file''']  )
        UpperCAmelCase_            =  os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['''merges_file''']  )
        with open(self.vocab_file  ,							'''w'''  ) as fp:
              fp.write(json.dumps(UpperCAmelCase__  )  )
        with open(self.merges_file  ,							'''w'''  ) as fp:
              fp.write('''\n'''.join(UpperCAmelCase__  )  )






  def  lowerCAmelCase__ (      self			:       Optional[int]  ,							UpperCAmelCase__			:       Any  )				->List[Any]:
        UpperCAmelCase_            =  '''lower newer'''
        UpperCAmelCase_            =  '''lower newer'''
        return input_text, output_text






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Tuple:
        UpperCAmelCase_            =  XLMTokenizer(self.vocab_file  ,							self.merges_file  )

        UpperCAmelCase_            =  '''lower'''
        UpperCAmelCase_            =  ['''low''', '''er</w>''']
        UpperCAmelCase_            =  tokenizer.tokenize(UpperCAmelCase__  )
        self.assertListEqual(UpperCAmelCase__  ,							UpperCAmelCase__  )

        UpperCAmelCase_            =  tokens + ['''<unk>''']
        UpperCAmelCase_            =  [14, 15, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__  )  ,							UpperCAmelCase__  )






  @slow
  def  lowerCAmelCase__ (      self			:       Any  )				->str:
        UpperCAmelCase_            =  XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''  )

        UpperCAmelCase_            =  tokenizer.encode('''sequence builders'''  ,							add_special_tokens=UpperCAmelCase__  )
        UpperCAmelCase_            =  tokenizer.encode('''multi-sequence build'''  ,							add_special_tokens=UpperCAmelCase__  )

        UpperCAmelCase_            =  tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__  )
        UpperCAmelCase_            =  tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__  ,							UpperCAmelCase__  )

        assert encoded_sentence == [0] + text + [1]
        assert encoded_pair == [0] + text + [1] + text_a + [1]





