






import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


lowerCamelCase       :       Dict        =				'''bert-base-cased'''
lowerCamelCase       :       Optional[int]        =				'''google/pegasus-xsum'''
lowerCamelCase       :       List[Any]        =				[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
lowerCamelCase       :       Dict        =				['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
lowerCamelCase       :       Dict        =				'''patrickvonplaten/t5-tiny-random'''
lowerCamelCase       :       List[Any]        =				'''sshleifer/bart-tiny-random'''
lowerCamelCase       :       List[str]        =				'''sshleifer/tiny-mbart'''
lowerCamelCase       :       Tuple        =				'''sshleifer/tiny-marian-en-de'''
def 					snake_case_						(   lowerCAmelCase_				:  Path				,				lowerCAmelCase_				:  list					):
							__lowercase					:							int			=   """\n""".join(lowerCAmelCase_					)
							Path(lowerCAmelCase_					).open("""w"""					).writelines(lowerCAmelCase_					)
def 					snake_case_						(   lowerCAmelCase_				:  Dict					):
							for split in ["train", "val", "test"]:
														_dump_articles(os.path.join(lowerCAmelCase_				,				F"{split}.source"					)				,				lowerCAmelCase_					)
														_dump_articles(os.path.join(lowerCAmelCase_				,				F"{split}.target"					)				,				lowerCAmelCase_					)
							return tmp_dir



class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					@parameterized.expand(
					    [
					        MBART_TINY,
					        MARIAN_TINY,
					        T5_TINY,
					        BART_TINY,
					        PEGASUS_XSUM,
					    ]						,   )
					@slow
					def 					lowerCAmelCase (     self :		Union[str, Any]						,   __a :		Tuple							)	->     List[str]:


												"""simple docstring"""

												__lowercase					:							Tuple			=   AutoTokenizer.from_pretrained(__a							)
												__lowercase					:							Optional[int]			=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
												__lowercase					:							str			=   max(len(tokenizer.encode(__a							)							) for a in ARTICLES							)
												__lowercase					:							Union[str, Any]			=   max(len(tokenizer.encode(__a							)							) for a in SUMMARIES							)
												__lowercase					:							List[str]			=   4
												__lowercase					:							Optional[int]			=   8
												assert max_len_target > max_src_len  # Will be truncated
												assert max_len_source > max_src_len  # Will be truncated
												__lowercase	,	__lowercase					:							Any			=   """ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
												__lowercase					:							Optional[int]			=   SeqaSeqDataset(
												    __a						,   data_dir=__a						,   type_path="""train"""						,   max_source_length=__a						,   max_target_length=__a						,   src_lang=__a						,   tgt_lang=__a						,   )
												__lowercase					:							Any			=   DataLoader(__a						,   batch_size=2						,   collate_fn=train_dataset.collate_fn							)
												for batch in dataloader:
																			assert isinstance(__a						,   __a							)
																			assert batch["attention_mask"].shape == batch["input_ids"].shape
																			# show that articles were trimmed.
																			assert batch["input_ids"].shape[1] == max_src_len
																			# show that targets are the same len
																			assert batch["labels"].shape[1] == max_tgt_len
																			if tok_name != MBART_TINY:
																										continue
																			# check language codes in correct place
																			__lowercase					:							str			=   shift_tokens_right(batch["""labels"""]						,   tokenizer.pad_token_id							)
																			assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
																			assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
																			assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
																			assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

																			break  # No need to test every batch



					@parameterized.expand([BART_TINY, BERT_BASE_CASED]							)
					def 					lowerCAmelCase (     self :		Tuple						,   __a :		List[Any]							)	->     Tuple:


												"""simple docstring"""

												__lowercase					:							Any			=   AutoTokenizer.from_pretrained(__a							)
												__lowercase					:							List[str]			=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
												__lowercase					:							List[str]			=   max(len(tokenizer.encode(__a							)							) for a in ARTICLES							)
												__lowercase					:							Tuple			=   max(len(tokenizer.encode(__a							)							) for a in SUMMARIES							)
												__lowercase					:							Tuple			=   4
												__lowercase					:							Tuple			=   LegacySeqaSeqDataset(
												    __a						,   data_dir=__a						,   type_path="""train"""						,   max_source_length=20						,   max_target_length=__a						,   )
												__lowercase					:							Optional[Any]			=   DataLoader(__a						,   batch_size=2						,   collate_fn=train_dataset.collate_fn							)
												for batch in dataloader:
																			assert batch["attention_mask"].shape == batch["input_ids"].shape
																			# show that articles were trimmed.
																			assert batch["input_ids"].shape[1] == max_len_source
																			assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
																			# show that targets were truncated
																			assert batch["labels"].shape[1] == trunc_target  # Truncated
																			assert max_len_target > trunc_target  # Truncated
																			break  # No need to test every batch



					def 					lowerCAmelCase (     self :		Any							)	->     int:


												"""simple docstring"""

												__lowercase					:							str			=   AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""							)

												__lowercase					:							int			=   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)							)
												__lowercase					:							List[Any]			=   tmp_dir.joinpath("""train.source"""							).open().readlines()
												__lowercase					:							int			=   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)							)
												pack_data_dir(__a						,   __a						,   128						,   __a							)
												__lowercase					:							Tuple			=   {x.name for x in tmp_dir.iterdir()}
												__lowercase					:							int			=   {x.name for x in save_dir.iterdir()}
												__lowercase					:							int			=   save_dir.joinpath("""train.source"""							).open().readlines()
												# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
												# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
												assert len(__a							) < len(__a							)
												assert len(__a							) == 1
												assert len(packed_examples[0]							) == sum(len(__a							) for x in orig_examples							)
												assert orig_paths == new_paths



					@pytest.mark.skipif(not FAIRSEQ_AVAILABLE						,   reason="""This test requires fairseq"""							)
					def 					lowerCAmelCase (     self :		int							)	->     Dict:


												"""simple docstring"""

												if not FAIRSEQ_AVAILABLE:
																			return
												__lowercase	,	__lowercase	,	__lowercase					:							str			=   self._get_dataset(max_len=64							)
												__lowercase					:							Tuple			=   64
												__lowercase					:							int			=   ds.make_dynamic_sampler(__a						,   required_batch_size_multiple=__a							)
												__lowercase					:							int			=   [len(__a							) for x in batch_sampler]
												assert len(set(__a							)							) > 1  # it's not dynamic batch size if every batch is the same length
												assert sum(__a							) == len(__a							)  # no dropped or added examples
												__lowercase					:							Optional[int]			=   DataLoader(__a						,   batch_sampler=__a						,   collate_fn=ds.collate_fn						,   num_workers=2							)
												__lowercase					:							Tuple			=   []
												__lowercase					:							Optional[Any]			=   []
												for batch in data_loader:
																			__lowercase					:							Union[str, Any]			=   batch["""input_ids"""].shape
																			__lowercase					:							Union[str, Any]			=   src_shape[0]
																			assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
																			__lowercase					:							Any			=   np.product(batch["""input_ids"""].shape							)
																			num_src_per_batch.append(__a							)
																			if num_src_tokens > (max_tokens * 1.1):
																										failures.append(__a							)
												assert num_src_per_batch[0] == max(__a							)
												if failures:
																			raise AssertionError(F"too many tokens in {len(__a							)} batches"							)



					def 					lowerCAmelCase (     self :		Dict							)	->     Tuple:


												"""simple docstring"""

												__lowercase	,	__lowercase	,	__lowercase					:							str			=   self._get_dataset(max_len=512							)
												__lowercase					:							Tuple			=   2
												__lowercase					:							Dict			=   ds.make_sortish_sampler(__a						,   shuffle=__a							)

												__lowercase					:							Optional[Any]			=   DataLoader(__a						,   batch_size=__a						,   collate_fn=ds.collate_fn						,   num_workers=2							)
												__lowercase					:							str			=   DataLoader(__a						,   batch_size=__a						,   collate_fn=ds.collate_fn						,   num_workers=2						,   sampler=__a							)

												__lowercase					:							Any			=   tokenizer.pad_token_id

												def count_pad_tokens(__a :		Any						,   __a :		List[Any]="input_ids"							):
																			return [batch[k].eq(__a							).sum().item() for batch in data_loader]

												assert sum(count_pad_tokens(__a						,   k="""labels"""							)							) < sum(count_pad_tokens(__a						,   k="""labels"""							)							)
												assert sum(count_pad_tokens(__a							)							) < sum(count_pad_tokens(__a							)							)
												assert len(__a							) == len(__a							)



					def 					lowerCAmelCase (     self :		Tuple						,   __a :		Any=1000						,   __a :		Union[str, Any]=128							)	->     Optional[Any]:


												"""simple docstring"""

												if os.getenv("""USE_REAL_DATA"""						,   __a							):
																			__lowercase					:							List[str]			=   """examples/seq2seq/wmt_en_ro"""
																			__lowercase					:							Any			=   max_len * 2 * 64
																			if not Path(__a							).joinpath("""train.len"""							).exists():
																										save_len_file(__a						,   __a							)
												else:
																			__lowercase					:							Dict			=   """examples/seq2seq/test_data/wmt_en_ro"""
																			__lowercase					:							Dict			=   max_len * 4
																			save_len_file(__a						,   __a							)

												__lowercase					:							str			=   AutoTokenizer.from_pretrained(__a							)
												__lowercase					:							Tuple			=   SeqaSeqDataset(
												    __a						,   data_dir=__a						,   type_path="""train"""						,   max_source_length=__a						,   max_target_length=__a						,   n_obs=__a						,   )
												return ds, max_tokens, tokenizer



					def 					lowerCAmelCase (     self :		Tuple							)	->     str:


												"""simple docstring"""

												__lowercase	,	__lowercase	,	__lowercase					:							Dict			=   self._get_dataset()
												__lowercase					:							int			=   set(DistributedSortishSampler(__a						,   256						,   num_replicas=2						,   rank=0						,   add_extra_examples=__a							)							)
												__lowercase					:							Tuple			=   set(DistributedSortishSampler(__a						,   256						,   num_replicas=2						,   rank=1						,   add_extra_examples=__a							)							)
												assert idsa.intersection(__a							) == set()



					@parameterized.expand(
					    [
					        MBART_TINY,
					        MARIAN_TINY,
					        T5_TINY,
					        BART_TINY,
					        PEGASUS_XSUM,
					    ]						,   )
					def 					lowerCAmelCase (     self :		Optional[Any]						,   __a :		Dict							)	->     Tuple:


												"""simple docstring"""

												__lowercase					:							List[str]			=   AutoTokenizer.from_pretrained(__a						,   use_fast=__a							)
												if tok_name == MBART_TINY:
																			__lowercase					:							Tuple			=   SeqaSeqDataset(
																			    __a						,   data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)						,   type_path="""train"""						,   max_source_length=4						,   max_target_length=8						,   src_lang="""EN"""						,   tgt_lang="""FR"""						,   )
																			__lowercase					:							int			=   train_dataset.dataset_kwargs
																			assert "src_lang" in kwargs and "tgt_lang" in kwargs
												else:
																			__lowercase					:							Dict			=   SeqaSeqDataset(
																			    __a						,   data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)						,   type_path="""train"""						,   max_source_length=4						,   max_target_length=8						,   )
																			__lowercase					:							Tuple			=   train_dataset.dataset_kwargs
																			assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
																			assert len(__a							) == 1 if tok_name == BART_TINY else len(__a							) == 0







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()
lowerCamelCase       :       List[str]        =				logging.get_logger(__name__)
def 					snake_case_						(   lowerCAmelCase_				:  List[Any]					):
							__lowercase					:							Optional[Any]			=   ASTConfig()

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

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

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

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

							return name
def 					snake_case_						(   lowerCAmelCase_				:  Tuple				,				lowerCAmelCase_				:  List[str]					):
							for key in orig_state_dict.copy().keys():
														__lowercase					:							str			=   orig_state_dict.pop(lowerCAmelCase_					)

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

							return orig_state_dict
def 					snake_case_						(   lowerCAmelCase_				:  Optional[int]					):
							__lowercase					:							int			=   [
							    """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(lowerCAmelCase_				,				lowerCAmelCase_					)
@torch.no_grad()
def 					snake_case_						(   lowerCAmelCase_				:  Optional[Any]				,				lowerCAmelCase_				:  Dict				,				lowerCAmelCase_				:  int=False					):
							__lowercase					:							List[Any]			=   get_audio_spectrogram_transformer_config(lowerCAmelCase_					)

							__lowercase					:							Tuple			=   {
							    """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					:							str			=   model_name_to_url[model_name]
							__lowercase					:							Dict			=   torch.hub.load_state_dict_from_url(lowerCAmelCase_				,				map_location="""cpu"""					)
							# remove some keys
							remove_keys(lowerCAmelCase_					)
							# rename some keys
							__lowercase					:							Optional[Any]			=   convert_state_dict(lowerCAmelCase_				,				lowerCAmelCase_					)

							# load 🤗 model
							__lowercase					:							str			=   ASTForAudioClassification(lowerCAmelCase_					)
							model.eval()

							model.load_state_dict(lowerCAmelCase_					)

							# verify outputs on dummy input
							# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
							__lowercase					:							Tuple			=   -4.2_677_393 if """speech-commands""" not in model_name else -6.845_978
							__lowercase					:							Optional[int]			=   4.5_689_974 if """speech-commands""" not in model_name else 5.5_654_526
							__lowercase					:							Union[str, Any]			=   1024 if """speech-commands""" not in model_name else 128
							__lowercase					:							Any			=   ASTFeatureExtractor(mean=lowerCAmelCase_				,				std=lowerCAmelCase_				,				max_length=lowerCAmelCase_					)

							if "speech-commands" in model_name:
														__lowercase					:							Optional[int]			=   load_dataset("""speech_commands"""				,				"""v0.02"""				,				split="""validation"""					)
														__lowercase					:							Union[str, Any]			=   dataset[0]["""audio"""]["""array"""]
							else:
														__lowercase					:							List[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(lowerCAmelCase_					)
														__lowercase					:							Union[str, Any]			=   waveform.squeeze().numpy()

							__lowercase					:							int			=   feature_extractor(lowerCAmelCase_				,				sampling_rate=16000				,				return_tensors="""pt"""					)

							# forward pass
							__lowercase					:							Tuple			=   model(**lowerCAmelCase_					)
							__lowercase					:							int			=   outputs.logits

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

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

							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__":
	lowerCamelCase       :       int        =				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.'''
	)

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