



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()
UpperCAmelCase_		 =					logging.get_logger(__name__)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Dict				)  ->     Optional[int]:
   _A      =       ASTConfig()

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

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

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

   return config

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

   return name

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Any	,   _snake_case	:List[str]				)  ->     List[Any]:
   for key in orig_state_dict.copy().keys():
      _A      =       orig_state_dict.pop(_snake_case				)

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

   return orig_state_dict

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[str]				)  ->     Dict:
   _A      =       [
       '''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(_snake_case	,   _snake_case				)

@torch.no_grad()
def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Tuple	,   _snake_case	:Dict	,   _snake_case	:List[Any]=False				)  ->     Optional[Any]:
   _A      =       get_audio_spectrogram_transformer_config(_snake_case				)

   _A      =       {
       '''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
   _A      =       model_name_to_url[model_name]
   _A      =       torch.hub.load_state_dict_from_url(_snake_case	,   map_location='''cpu'''				)
   # remove some keys
   remove_keys(_snake_case				)
   # rename some keys
   _A      =       convert_state_dict(_snake_case	,   _snake_case				)

   # load 🤗 model
   _A      =       ASTForAudioClassification(_snake_case				)
   model.eval()

   model.load_state_dict(_snake_case				)

   # verify outputs on dummy input
   # source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
   _A      =       -4.267_7393 if '''speech-commands''' not in model_name else -6.84_5978
   _A      =       4.568_9974 if '''speech-commands''' not in model_name else 5.565_4526
   _A      =       1_024 if '''speech-commands''' not in model_name else 128
   _A      =       ASTFeatureExtractor(mean=_snake_case	,   std=_snake_case	,   max_length=_snake_case				)

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

      _A  ,    _A      =       torchaudio.load(_snake_case				)
      _A      =       waveform.squeeze().numpy()

   _A      =       feature_extractor(_snake_case	,   sampling_rate=16_000	,   return_tensors='''pt'''				)

   # forward pass
   _A      =       model(**_snake_case				)
   _A      =       outputs.logits

   if model_name == "ast-finetuned-audioset-10-10-0.4593":
      _A      =       torch.tensor([-0.8760, -7.0042, -8.6602]				)
   elif model_name == "ast-finetuned-audioset-10-10-0.450":
      _A      =       torch.tensor([-1.1986, -7.0903, -8.2718]				)
   elif model_name == "ast-finetuned-audioset-10-10-0.448":
      _A      =       torch.tensor([-2.6128, -8.0080, -9.4344]				)
   elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
      _A      =       torch.tensor([-1.5080, -7.4534, -8.8917]				)
   elif model_name == "ast-finetuned-audioset-12-12-0.447":
      _A      =       torch.tensor([-0.5050, -6.5833, -8.0843]				)
   elif model_name == "ast-finetuned-audioset-14-14-0.443":
      _A      =       torch.tensor([-0.3826, -7.0336, -8.2413]				)
   elif model_name == "ast-finetuned-audioset-16-16-0.442":
      _A      =       torch.tensor([-1.2113, -6.9101, -8.3470]				)
   elif model_name == "ast-finetuned-speech-commands-v2":
      _A      =       torch.tensor([6.1589, -8.0566, -8.7984]				)
   else:
      raise ValueError('''Unknown model name'''				)
   if not torch.allclose(logits[0, :3]	,   _snake_case	,   atol=1E-4				):
      raise ValueError('''Logits don\'t match'''				)
   print('''Looks ok!'''				)

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

   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__":
   UpperCAmelCase_		 =					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."""
   )

   UpperCAmelCase_		 =					parser.parse_args()
   convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax.numpy as jnp

  from transformers.models.albert.modeling_flax_albert import (
      FlaxAlbertForMaskedLM,
      FlaxAlbertForMultipleChoice,
      FlaxAlbertForPreTraining,
      FlaxAlbertForQuestionAnswering,
      FlaxAlbertForSequenceClassification,
      FlaxAlbertForTokenClassification,
      FlaxAlbertModel,
  )




class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   def __init__(						self       :	Any		,	_lowercase       :	Optional[int]		,	_lowercase       :	Tuple=13		,	_lowercase       :	str=7		,	_lowercase       :	List[Any]=True		,	_lowercase       :	Optional[int]=True		,	_lowercase       :	str=True		,	_lowercase       :	Optional[int]=True		,	_lowercase       :	Dict=99		,	_lowercase       :	List[Any]=32		,	_lowercase       :	List[str]=5		,	_lowercase       :	str=4		,	_lowercase       :	int=37		,	_lowercase       :	List[str]="gelu"		,	_lowercase       :	Any=0.1		,	_lowercase       :	Optional[int]=0.1		,	_lowercase       :	Dict=512		,	_lowercase       :	int=16		,	_lowercase       :	Optional[Any]=2		,	_lowercase       :	Dict=0.02		,	_lowercase       :	List[Any]=4		,	):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			parent
    _UpperCamelCase:  int    						=			batch_size
    _UpperCamelCase:  List[str]    						=			seq_length
    _UpperCamelCase:  Optional[int]    						=			is_training
    _UpperCamelCase:  Optional[Any]    						=			use_attention_mask
    _UpperCamelCase:  Any    						=			use_token_type_ids
    _UpperCamelCase:  List[str]    						=			use_labels
    _UpperCamelCase:  Optional[int]    						=			vocab_size
    _UpperCamelCase:  List[str]    						=			hidden_size
    _UpperCamelCase:  Union[str, Any]    						=			num_hidden_layers
    _UpperCamelCase:  Any    						=			num_attention_heads
    _UpperCamelCase:  List[str]    						=			intermediate_size
    _UpperCamelCase:  Union[str, Any]    						=			hidden_act
    _UpperCamelCase:  Dict    						=			hidden_dropout_prob
    _UpperCamelCase:  List[str]    						=			attention_probs_dropout_prob
    _UpperCamelCase:  str    						=			max_position_embeddings
    _UpperCamelCase:  Dict    						=			type_vocab_size
    _UpperCamelCase:  Tuple    						=			type_sequence_label_size
    _UpperCamelCase:  List[Any]    						=			initializer_range
    _UpperCamelCase:  str    						=			num_choices



   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size				)

    _UpperCamelCase:  Any    						=			None
    if self.use_attention_mask:
     _UpperCamelCase:  List[str]    						=			random_attention_mask([self.batch_size, self.seq_length]				)

    _UpperCamelCase:  Optional[Any]    						=			None
    if self.use_token_type_ids:
     _UpperCamelCase:  Optional[Any]    						=			ids_tensor([self.batch_size, self.seq_length]		,	self.type_vocab_size				)

    _UpperCamelCase:  Tuple    						=			AlbertConfig(
        vocab_size=self.vocab_size		,	hidden_size=self.hidden_size		,	num_hidden_layers=self.num_hidden_layers		,	num_attention_heads=self.num_attention_heads		,	intermediate_size=self.intermediate_size		,	hidden_act=self.hidden_act		,	hidden_dropout_prob=self.hidden_dropout_prob		,	attention_probs_dropout_prob=self.attention_probs_dropout_prob		,	max_position_embeddings=self.max_position_embeddings		,	type_vocab_size=self.type_vocab_size		,	is_decoder=_lowercase		,	initializer_range=self.initializer_range		,	)

    return config, input_ids, token_type_ids, attention_mask



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			self.prepare_config_and_inputs()
    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  int    						=			config_and_inputs
    _UpperCamelCase:  int    						=			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
    return config, inputs_dict




@require_flax
class 			__magic_name__	(		__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     Any	      = (
       (
           FlaxAlbertModel,
           FlaxAlbertForPreTraining,
           FlaxAlbertForMaskedLM,
           FlaxAlbertForMultipleChoice,
           FlaxAlbertForQuestionAnswering,
           FlaxAlbertForSequenceClassification,
           FlaxAlbertForTokenClassification,
           FlaxAlbertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )



   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			FlaxAlbertModelTester(self				)



   @slow
   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    for model_class_name in self.all_model_classes:
     _UpperCamelCase:  int    						=			model_class_name.from_pretrained('''albert-base-v2'''				)
     _UpperCamelCase:  List[Any]    						=			model(np.ones((1, 1)				)				)
     self.assertIsNotNone(_lowercase				)




@require_flax
class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   @slow
   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			FlaxAlbertModel.from_pretrained('''albert-base-v2'''				)
    _UpperCamelCase:  List[Any]    						=			np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]				)
    _UpperCamelCase:  Optional[int]    						=			np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
    _UpperCamelCase:  Optional[int]    						=			model(_lowercase		,	attention_mask=_lowercase				)[0]
    _UpperCamelCase:  Tuple    						=			(1, 11, 768)
    self.assertEqual(output.shape		,	_lowercase				)
    _UpperCamelCase:  Tuple    						=			np.array(
        [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]				)

    self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]		,	_lowercase		,	atol=1E-4				)				)