


'''simple docstring'''




# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig





def 	a_					( lowerCamelCase	:     int	, lowerCamelCase	:     Optional[Any]	, lowerCamelCase	:     Union[str, Any]=0				):
   # Format the message.
   if name is None:
      lowerCAmelCase     =			None
   else:
      lowerCAmelCase     =			'.' * max(0	, spaces - 2				) + '# {:' + str(50 - spaces				) + 's}'
      lowerCAmelCase     =			fmt.format(lowerCamelCase				)

   # Print and recurse (if needed).
   if isinstance(lowerCamelCase	, lowerCamelCase				):
      if msg is not None:
         print(lowerCamelCase				)
      for k in val.keys():
         recursive_print(lowerCamelCase	, val[k]	, spaces + 2				)
   elif isinstance(lowerCamelCase	, torch.Tensor				):
      print(lowerCamelCase	, ':'	, val.size()				)
   else:
      print(lowerCamelCase	, ':'	, lowerCamelCase				)





def 	a_					( lowerCamelCase	:     Optional[int]	, lowerCamelCase	:     List[str]	, lowerCamelCase	:     List[Any]	, lowerCamelCase	:     Dict	, lowerCamelCase	:     Tuple				):
   # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
   # for compatibility with later versions of NVIDIA Megatron-LM.
   # The inverse operation is performed inside Megatron-LM to read checkpoints:
   # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
   # If param is the weight tensor of the self-attention block, the returned tensor
   # will have to be transposed one more time to be read by HuggingFace GPT2.
   lowerCAmelCase     =			param.size()
   if checkpoint_version == 1.0:
      # version 1.0 stores [num_heads * hidden_size * num_splits, :]
      lowerCAmelCase     =			(num_heads, hidden_size, num_splits) + input_shape[1:]
      lowerCAmelCase     =			param.view(*lowerCamelCase				)
      lowerCAmelCase     =			param.transpose(0	, 2				)
      lowerCAmelCase     =			param.transpose(1	, 2				).contiguous()
   elif checkpoint_version >= 2.0:
      # other versions store [num_heads * num_splits * hidden_size, :]
      lowerCAmelCase     =			(num_heads, num_splits, hidden_size) + input_shape[1:]
      lowerCAmelCase     =			param.view(*lowerCamelCase				)
      lowerCAmelCase     =			param.transpose(0	, 1				).contiguous()
   lowerCAmelCase     =			param.view(*lowerCamelCase				)
   return param





def 	a_					( lowerCamelCase	:     Optional[int]	, lowerCamelCase	:     Optional[int]	, lowerCamelCase	:     str				):
   # The converted output model.
   lowerCAmelCase     =			{}

   # old versions did not store training args
   lowerCAmelCase     =			input_state_dict.get('args'	, lowerCamelCase				)
   if ds_args is not None:
      # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
      # from pprint import pprint
      # pprint(vars(ds_args))

      lowerCAmelCase     =			ds_args.padded_vocab_size
      lowerCAmelCase     =			ds_args.max_position_embeddings
      lowerCAmelCase     =			ds_args.hidden_size
      lowerCAmelCase     =			ds_args.num_layers
      lowerCAmelCase     =			ds_args.num_attention_heads
      lowerCAmelCase     =			ds_args.ffn_hidden_size
      # pprint(config)

   # The number of heads.
   lowerCAmelCase     =			config.n_head
   # The hidden_size per head.
   lowerCAmelCase     =			config.n_embd // config.n_head
   # Megatron-LM checkpoint version
   if "checkpoint_version" in input_state_dict.keys():
      lowerCAmelCase     =			input_state_dict['checkpoint_version']
   else:
      lowerCAmelCase     =			0.0

   # The model.
   lowerCAmelCase     =			input_state_dict['model']
   # The language model.
   lowerCAmelCase     =			model['language_model']
   # The embeddings.
   lowerCAmelCase     =			lm['embedding']

   # The word embeddings.
   lowerCAmelCase     =			embeddings['word_embeddings']['weight']
   # Truncate the embedding table to vocab_size rows.
   lowerCAmelCase     =			word_embeddings[: config.vocab_size, :]
   lowerCAmelCase     =			word_embeddings

   # The position embeddings.
   lowerCAmelCase     =			embeddings['position_embeddings']['weight']
   # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
   lowerCAmelCase     =			pos_embeddings.size(0				)
   if n_positions != config.n_positions:
      raise ValueError(
          f'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match'''				)
   # Store the position embeddings.
   lowerCAmelCase     =			pos_embeddings

   # The transformer.
   lowerCAmelCase     =			lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

   # The regex to extract layer names.
   lowerCAmelCase     =			re.compile(R'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'				)

   # The simple map of names for "automated" rules.
   lowerCAmelCase     =			{
       'attention.dense': '.attn.c_proj.',
       'self_attention.dense': '.attn.c_proj.',
       'mlp.dense_h_to_4h': '.mlp.c_fc.',
       'mlp.dense_4h_to_h': '.mlp.c_proj.',
   }

   # Extract the layers.
   for key, val in transformer.items():
      # Match the name.
      lowerCAmelCase     =			layer_re.match(lowerCamelCase				)

      # Stop if that's not a layer
      if m is None:
         break

      # The index of the layer.
      lowerCAmelCase     =			int(m.group(1				)				)
      # The name of the operation.
      lowerCAmelCase     =			m.group(2				)
      # Is it a weight or a bias?
      lowerCAmelCase     =			m.group(3				)

      # The name of the layer.
      lowerCAmelCase     =			f'''transformer.h.{layer_idx}'''

      # For layernorm(s), simply store the layer norm.
      if op_name.endswith('layernorm'				):
         lowerCAmelCase     =			'ln_1' if op_name.startswith('input'				) else 'ln_2'
         lowerCAmelCase     =			val

      # Transpose the QKV matrix.
      elif (
          op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
      ) and weight_or_bias == "weight":
         # Insert a tensor of 1x1xDxD bias.
         lowerCAmelCase     =			torch.tril(torch.ones((n_positions, n_positions)	, dtype=torch.floataa				)				).view(
             1	, 1	, lowerCamelCase	, lowerCamelCase				)
         lowerCAmelCase     =			causal_mask

         # Insert a "dummy" tensor for masked_bias.
         lowerCAmelCase     =			torch.tensor(-1e4	, dtype=torch.floataa				)
         lowerCAmelCase     =			masked_bias

         lowerCAmelCase     =			fix_query_key_value_ordering(lowerCamelCase	, lowerCamelCase	, 3	, lowerCamelCase	, lowerCamelCase				)
         # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
         lowerCAmelCase     =			out_val.transpose(0	, 1				).contiguous()
         # Store.
         lowerCAmelCase     =			out_val

      # Transpose the bias.
      elif (
          op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
      ) and weight_or_bias == "bias":
         lowerCAmelCase     =			fix_query_key_value_ordering(lowerCamelCase	, lowerCamelCase	, 3	, lowerCamelCase	, lowerCamelCase				)
         # Store. No change of shape.
         lowerCAmelCase     =			out_val

      # Transpose the weights.
      elif weight_or_bias == "weight":
         lowerCAmelCase     =			megatron_to_transformers[op_name]
         lowerCAmelCase     =			val.transpose(0	, 1				)

      # Copy the bias.
      elif weight_or_bias == "bias":
         lowerCAmelCase     =			megatron_to_transformers[op_name]
         lowerCAmelCase     =			val

    # DEBUG.
   assert config.n_layer == layer_idx + 1

   # The final layernorm.
   lowerCAmelCase     =			transformer['final_layernorm.weight']
   lowerCAmelCase     =			transformer['final_layernorm.bias']

   # For LM head, transformers' wants the matrix to weight embeddings.
   lowerCAmelCase     =			word_embeddings

   # It should be done!
   return output_state_dict





def 	a_					( ):
   # Create the argument parser.
   lowerCAmelCase     =			argparse.ArgumentParser()
   parser.add_argument('--print-checkpoint-structure'	, action='store_true'				)
   parser.add_argument(
       'path_to_checkpoint'	, type=lowerCamelCase	, help='Path to the checkpoint file (.zip archive or direct .pt file)'	, )
   parser.add_argument(
       '--config_file'	, default=''	, type=lowerCamelCase	, help='An optional config json file describing the pre-trained model.'	, )
   lowerCAmelCase     =			parser.parse_args()

   # Extract the basename.
   lowerCAmelCase     =			os.path.dirname(args.path_to_checkpoint				)

   # Load the model.
   # the .zip is very optional, let's keep it for backward compatibility
   print(f'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}'''				)
   if args.path_to_checkpoint.endswith('.zip'				):
      with zipfile.ZipFile(args.path_to_checkpoint	, 'r'				) as checkpoint:
         with checkpoint.open('release/mp_rank_00/model_optim_rng.pt'				) as pytorch_dict:
            lowerCAmelCase     =			torch.load(lowerCamelCase	, map_location='cpu'				)
   else:
      lowerCAmelCase     =			torch.load(args.path_to_checkpoint	, map_location='cpu'				)

   lowerCAmelCase     =			input_state_dict.get('args'	, lowerCamelCase				)

   # Read the config, or default to the model released by NVIDIA.
   if args.config_file == "":
      if ds_args is not None:
         if ds_args.bias_gelu_fusion:
            lowerCAmelCase     =			'gelu_fast'
         elif ds_args.openai_gelu:
            lowerCAmelCase     =			'gelu_new'
         else:
            lowerCAmelCase     =			'gelu'
      else:
         # in the very early days this used to be "gelu_new"
         lowerCAmelCase     =			'gelu_new'

      # Spell out all parameters in case the defaults change.
      lowerCAmelCase     =			GPTaConfig(
          vocab_size=50257	, n_positions=1024	, n_embd=1024	, n_layer=24	, n_head=16	, n_inner=4096	, activation_function=lowerCamelCase	, resid_pdrop=0.1	, embd_pdrop=0.1	, attn_pdrop=0.1	, layer_norm_epsilon=1e-5	, initializer_range=0.02	, summary_type='cls_index'	, summary_use_proj=lowerCamelCase	, summary_activation=lowerCamelCase	, summary_proj_to_labels=lowerCamelCase	, summary_first_dropout=0.1	, scale_attn_weights=lowerCamelCase	, use_cache=lowerCamelCase	, bos_token_id=50256	, eos_token_id=50256	, )
   else:
      lowerCAmelCase     =			GPTaConfig.from_json_file(args.config_file				)

   lowerCAmelCase     =			['GPT2LMHeadModel']

   # Convert.
   print('Converting'				)
   lowerCAmelCase     =			convert_megatron_checkpoint(lowerCamelCase	, lowerCamelCase	, lowerCamelCase				)

   # Print the structure of converted state dict.
   if args.print_checkpoint_structure:
      recursive_print(lowerCamelCase	, lowerCamelCase				)

   # Add tokenizer class info to config
   # see https://github.com/huggingface/transformers/issues/13906)
   if ds_args is not None:
      lowerCAmelCase     =			ds_args.tokenizer_type
      if tokenizer_type == "GPT2BPETokenizer":
         lowerCAmelCase     =			'gpt2'
      elif tokenizer_type == "PretrainedFromHF":
         lowerCAmelCase     =			ds_args.tokenizer_name_or_path
      else:
         raise ValueError(f'''Unrecognized tokenizer_type {tokenizer_type}'''				)
   else:
      lowerCAmelCase     =			'gpt2'

   lowerCAmelCase     =			AutoTokenizer.from_pretrained(lowerCamelCase				)
   lowerCAmelCase     =			type(lowerCamelCase				).__name__
   lowerCAmelCase     =			tokenizer_class

   # Store the config to file.
   print('Saving config'				)
   config.save_pretrained(lowerCamelCase				)

   # Save tokenizer based on args
   print(f'''Adding {tokenizer_class} tokenizer files'''				)
   tokenizer.save_pretrained(lowerCamelCase				)

   # Store the state_dict to file.
   lowerCAmelCase     =			os.path.join(lowerCamelCase	, 'pytorch_model.bin'				)
   print(f'''Saving checkpoint to "{output_checkpoint_file}"'''				)
   torch.save(lowerCamelCase	, lowerCamelCase				)


####################################################################################################

if __name__ == "__main__":
       main()

####################################################################################################









"""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()
__UpperCamelCase						=      logging.get_logger(__name__)


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int  )   ->						List[str]:
						SCREAMING_SNAKE_CASE													=					ASTConfig()

						if "10-10" in model_name:
												pass
						elif "speech-commands" in model_name:
												SCREAMING_SNAKE_CASE													=					1_28
						elif "12-12" in model_name:
												SCREAMING_SNAKE_CASE													=					12
												SCREAMING_SNAKE_CASE													=					12
						elif "14-14" in model_name:
												SCREAMING_SNAKE_CASE													=					14
												SCREAMING_SNAKE_CASE													=					14
						elif "16-16" in model_name:
												SCREAMING_SNAKE_CASE													=					16
												SCREAMING_SNAKE_CASE													=					16
						else:
												raise ValueError('Model not supported'  )

						SCREAMING_SNAKE_CASE													=					'huggingface/label-files'
						if "speech-commands" in model_name:
												SCREAMING_SNAKE_CASE													=					35
												SCREAMING_SNAKE_CASE													=					'speech-commands-v2-id2label.json'
						else:
												SCREAMING_SNAKE_CASE													=					5_27
												SCREAMING_SNAKE_CASE													=					'audioset-id2label.json'

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

						return config


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

						return name


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Any ,			SCREAMING_SNAKE_CASE_		:				Dict  )   ->						Dict:
						for key in orig_state_dict.copy().keys():
												SCREAMING_SNAKE_CASE													=					orig_state_dict.pop(SCREAMING_SNAKE_CASE_  )

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

						return orig_state_dict


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int  )   ->						List[Any]:
						SCREAMING_SNAKE_CASE													=					[
						    '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     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				List[Any] ,			SCREAMING_SNAKE_CASE_		:				Union[str, Any]=False  )   ->						Optional[int]:
						SCREAMING_SNAKE_CASE													=					get_audio_spectrogram_transformer_config(SCREAMING_SNAKE_CASE_  )

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

						# load 🤗 model
						SCREAMING_SNAKE_CASE													=					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
						SCREAMING_SNAKE_CASE													=					-4.2_67_73_93 if 'speech-commands' not in model_name else -6.84_59_78
						SCREAMING_SNAKE_CASE													=					4.5_68_99_74 if 'speech-commands' not in model_name else 5.5_65_45_26
						SCREAMING_SNAKE_CASE													=					10_24 if 'speech-commands' not in model_name else 1_28
						SCREAMING_SNAKE_CASE													=					ASTFeatureExtractor(mean=SCREAMING_SNAKE_CASE_ ,			std=SCREAMING_SNAKE_CASE_ ,			max_length=SCREAMING_SNAKE_CASE_  )

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

												SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					torchaudio.load(SCREAMING_SNAKE_CASE_  )
												SCREAMING_SNAKE_CASE													=					waveform.squeeze().numpy()

						SCREAMING_SNAKE_CASE													=					feature_extractor(SCREAMING_SNAKE_CASE_ ,			sampling_rate=1_60_00 ,			return_tensors='pt'  )

						# forward pass
						SCREAMING_SNAKE_CASE													=					model(**SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					outputs.logits

						if model_name == "ast-finetuned-audioset-10-10-0.4593":
												SCREAMING_SNAKE_CASE													=					torch.tensor([-0.87_60, -7.00_42, -8.66_02]  )
						elif model_name == "ast-finetuned-audioset-10-10-0.450":
												SCREAMING_SNAKE_CASE													=					torch.tensor([-1.19_86, -7.09_03, -8.27_18]  )
						elif model_name == "ast-finetuned-audioset-10-10-0.448":
												SCREAMING_SNAKE_CASE													=					torch.tensor([-2.61_28, -8.00_80, -9.43_44]  )
						elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
												SCREAMING_SNAKE_CASE													=					torch.tensor([-1.50_80, -7.45_34, -8.89_17]  )
						elif model_name == "ast-finetuned-audioset-12-12-0.447":
												SCREAMING_SNAKE_CASE													=					torch.tensor([-0.50_50, -6.58_33, -8.08_43]  )
						elif model_name == "ast-finetuned-audioset-14-14-0.443":
												SCREAMING_SNAKE_CASE													=					torch.tensor([-0.38_26, -7.03_36, -8.24_13]  )
						elif model_name == "ast-finetuned-audioset-16-16-0.442":
												SCREAMING_SNAKE_CASE													=					torch.tensor([-1.21_13, -6.91_01, -8.34_70]  )
						elif model_name == "ast-finetuned-speech-commands-v2":
												SCREAMING_SNAKE_CASE													=					torch.tensor([6.15_89, -8.05_66, -8.79_84]  )
						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__":
	__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)


