'''simple docstring'''




import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Optional[int]     				=					[
         'encoder.version',
         'decoder.version',
         'model.encoder.version',
         'model.decoder.version',
         'decoder.output_projection.weight',
         '_float_tensor',
         'encoder.embed_positions._float_tensor',
         'decoder.embed_positions._float_tensor',
     ]
     for k in ignore_keys:
          state_dict.pop(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Optional[Any]     				=					list(s_dict.keys()							)
     for key in keys:
          if "transformer_layers" in key:
               lowercase_					:       List[str]     				=					s_dict.pop(SCREAMING_SNAKE_CASE_							)
          elif "subsample" in key:
               lowercase_					:       List[Any]     				=					s_dict.pop(SCREAMING_SNAKE_CASE_							)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_				,lowercase_					:       Any     				=					emb.weight.shape
     lowercase_					:       Dict     				=					nn.Linear(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       bias=SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Any     				=					emb.weight.data
     return lin_layer

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Any     				=					torch.load(SCREAMING_SNAKE_CASE_  ,       map_location='cpu'							)
     lowercase_					:       List[Any]     				=					mam_aaa['args']
     lowercase_					:       List[Any]     				=					mam_aaa['model']
     lowercase_					:       Optional[int]     				=					state_dict['decoder.output_projection.weight']

     remove_ignore_keys_(SCREAMING_SNAKE_CASE_							)
     rename_keys(SCREAMING_SNAKE_CASE_							)

     lowercase_					:       Any     				=					state_dict['decoder.embed_tokens.weight'].shape[0]

     lowercase_					:       str     				=					args.share_decoder_input_output_embed

     lowercase_					:       Tuple     				=					[int(SCREAMING_SNAKE_CASE_							) for i in args.conv_kernel_sizes.split(','							)]
     lowercase_					:       Union[str, Any]     				=					SpeechaTextConfig(
         vocab_size=SCREAMING_SNAKE_CASE_  ,       max_source_positions=args.max_source_positions  ,       max_target_positions=args.max_target_positions  ,       encoder_layers=args.encoder_layers  ,       decoder_layers=args.decoder_layers  ,       encoder_attention_heads=args.encoder_attention_heads  ,       decoder_attention_heads=args.decoder_attention_heads  ,       encoder_ffn_dim=args.encoder_ffn_embed_dim  ,       decoder_ffn_dim=args.decoder_ffn_embed_dim  ,       d_model=args.encoder_embed_dim  ,       dropout=args.dropout  ,       attention_dropout=args.attention_dropout  ,       activation_dropout=args.activation_dropout  ,       activation_function='relu'  ,       num_conv_layers=len(SCREAMING_SNAKE_CASE_							)  ,       conv_channels=args.conv_channels  ,       conv_kernel_sizes=SCREAMING_SNAKE_CASE_  ,       input_feat_per_channel=args.input_feat_per_channel  ,       input_channels=args.input_channels  ,       tie_word_embeddings=SCREAMING_SNAKE_CASE_  ,       num_beams=5  ,       max_length=200  ,       use_cache=SCREAMING_SNAKE_CASE_  ,       decoder_start_token_id=2  ,       early_stopping=SCREAMING_SNAKE_CASE_  ,       )

     lowercase_					:       List[Any]     				=					SpeechaTextForConditionalGeneration(SCREAMING_SNAKE_CASE_							)
     lowercase_				,lowercase_					:       List[Any]     				=					model.model.load_state_dict(SCREAMING_SNAKE_CASE_  ,       strict=SCREAMING_SNAKE_CASE_							)
     if len(SCREAMING_SNAKE_CASE_							) > 0 and not set(SCREAMING_SNAKE_CASE_							) <= {
         "encoder.embed_positions.weights",
         "decoder.embed_positions.weights",
     }:
          raise ValueError(
              'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'
              f''' but all the following weights are missing {missing}'''							)

     if tie_embeds:
          lowercase_					:       int     				=					make_linear_from_emb(model.model.decoder.embed_tokens							)
     else:
          lowercase_					:       List[str]     				=					lm_head_weights

     model.save_pretrained(SCREAMING_SNAKE_CASE_							)


if __name__ == "__main__":
 _A								=   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
 parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 _A								=   parser.parse_args()
 convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)





'''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()
_A								=   logging.get_logger(__name__)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[Any]     				=					ASTConfig()

     if "10-10" in model_name:
          pass
     elif "speech-commands" in model_name:
          lowercase_					:       List[Any]     				=					128
     elif "12-12" in model_name:
          lowercase_					:       Tuple     				=					12
          lowercase_					:       List[Any]     				=					12
     elif "14-14" in model_name:
          lowercase_					:       List[str]     				=					14
          lowercase_					:       Optional[Any]     				=					14
     elif "16-16" in model_name:
          lowercase_					:       Union[str, Any]     				=					16
          lowercase_					:       List[str]     				=					16
     else:
          raise ValueError('Model not supported'							)

     lowercase_					:       Optional[Any]     				=					'huggingface/label-files'
     if "speech-commands" in model_name:
          lowercase_					:       List[str]     				=					35
          lowercase_					:       int     				=					'speech-commands-v2-id2label.json'
     else:
          lowercase_					:       Union[str, Any]     				=					527
          lowercase_					:       int     				=					'audioset-id2label.json'

     lowercase_					:       Union[str, Any]     				=					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       repo_type='dataset'							)  ,       'r'							)							)
     lowercase_					:       Union[str, Any]     				=					{int(SCREAMING_SNAKE_CASE_							): v for k, v in idalabel.items()}
     lowercase_					:       Optional[int]     				=					idalabel
     lowercase_					:       Optional[int]     				=					{v: k for k, v in idalabel.items()}

     return config

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

     return name

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     for key in orig_state_dict.copy().keys():
          lowercase_					:       List[str]     				=					orig_state_dict.pop(SCREAMING_SNAKE_CASE_							)

          if "qkv" in key:
               lowercase_					:       List[str]     				=					key.split('.'							)
               lowercase_					:       int     				=					int(key_split[3]							)
               lowercase_					:       Tuple     				=					config.hidden_size
               if "weight" in key:
                    lowercase_					:       Tuple     				=					val[:dim, :]
                    lowercase_					:       Union[str, Any]     				=					val[dim : dim * 2, :]
                    lowercase_					:       Optional[int]     				=					val[-dim:, :]
               else:
                    lowercase_					:       Optional[Any]     				=					val[:dim]
                    lowercase_					:       Any     				=					val[dim : dim * 2]
                    lowercase_					:       Tuple     				=					val[-dim:]
          else:
               lowercase_					:       Optional[Any]     				=					val

     return orig_state_dict

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[Any]     				=					[
         '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(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)

@torch.no_grad()
def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_=False							):
     lowercase_					:       Dict     				=					get_audio_spectrogram_transformer_config(SCREAMING_SNAKE_CASE_							)

     lowercase_					:       Optional[int]     				=					{
         '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
     lowercase_					:       Dict     				=					model_name_to_url[model_name]
     lowercase_					:       Optional[Any]     				=					torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_  ,       map_location='cpu'							)
     # remove some keys
     remove_keys(SCREAMING_SNAKE_CASE_							)
     # rename some keys
     lowercase_					:       str     				=					convert_state_dict(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)

     # load 🤗 model
     lowercase_					:       Optional[Any]     				=					ASTForAudioClassification(SCREAMING_SNAKE_CASE_							)
     model.eval()

     model.load_state_dict(SCREAMING_SNAKE_CASE_							)

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

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

          lowercase_				,lowercase_					:       Union[str, Any]     				=					torchaudio.load(SCREAMING_SNAKE_CASE_							)
          lowercase_					:       str     				=					waveform.squeeze().numpy()

     lowercase_					:       str     				=					feature_extractor(SCREAMING_SNAKE_CASE_  ,       sampling_rate=16_000  ,       return_tensors='pt'							)

     # forward pass
     lowercase_					:       Tuple     				=					model(**SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Tuple     				=					outputs.logits

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

     if pytorch_dump_folder_path is not None:
          Path(SCREAMING_SNAKE_CASE_							).mkdir(exist_ok=SCREAMING_SNAKE_CASE_							)
          print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''							)
          model.save_pretrained(SCREAMING_SNAKE_CASE_							)
          print(f'''Saving feature extractor to {pytorch_dump_folder_path}'''							)
          feature_extractor.save_pretrained(SCREAMING_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__":
 _A								=   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.'
 )

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




