
'''simple docstring'''





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


a__						:      Dict   				=				'''bert-base-cased'''
a__						:      Optional[int]   				=				'''google/pegasus-xsum'''
a__						:      List[Any]   				=				[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
a__						:      Dict   				=				['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
a__						:      Dict   				=				'''patrickvonplaten/t5-tiny-random'''
a__						:      List[Any]   				=				'''sshleifer/bart-tiny-random'''
a__						:      List[str]   				=				'''sshleifer/tiny-mbart'''
a__						:      Tuple   				=				'''sshleifer/tiny-marian-en-de'''





def 				snake_case							(						UpperCAmelCase					,     UpperCAmelCase		)->	List[Any]:







      """simple docstring"""
      __A						=	"""\n""".join(lowerCAmelCase_		)
      Path(lowerCAmelCase_		).open('w'		).writelines(lowerCAmelCase_		)





def 				snake_case							(						UpperCAmelCase		)->	Dict:







      """simple docstring"""
      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        UpperCamelCase__     (			__a):






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







       '''simple docstring'''



       __A						=	AutoTokenizer.from_pretrained(__a	)
       __A						=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
       __A						=	max(len(tokenizer.encode(__a	)	) for a in ARTICLES	)
       __A						=	max(len(tokenizer.encode(__a	)	) for a in SUMMARIES	)
       __A						=	4
       __A						=	8
       assert max_len_target > max_src_len  # Will be truncated
       assert max_len_source > max_src_len  # Will be truncated
       __A						=	"""ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
       __A						=	SeqaSeqDataset(
           __a		, data_dir=__a		, type_path='train'		, max_source_length=__a		, max_target_length=__a		, src_lang=__a		, tgt_lang=__a		, )
       __A						=	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
             __A						=	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 		lowercase_  (	self     :Tuple		, _A     :List[Any]	) ->	Tuple:







       '''simple docstring'''



       __A						=	AutoTokenizer.from_pretrained(__a	)
       __A						=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
       __A						=	max(len(tokenizer.encode(__a	)	) for a in ARTICLES	)
       __A						=	max(len(tokenizer.encode(__a	)	) for a in SUMMARIES	)
       __A						=	4
       __A						=	LegacySeqaSeqDataset(
           __a		, data_dir=__a		, type_path='train'		, max_source_length=20		, max_target_length=__a		, )
       __A						=	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 		lowercase_  (	self     :Any	) ->	int:







       '''simple docstring'''



       __A						=	AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'	)

       __A						=	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)	)
       __A						=	tmp_dir.joinpath('train.source'	).open().readlines()
       __A						=	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)	)
       pack_data_dir(__a		, __a		, 128		, __a	)
       __A						=	{x.name for x in tmp_dir.iterdir()}
       __A						=	{x.name for x in save_dir.iterdir()}
       __A						=	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 		lowercase_  (	self     :int	) ->	Dict:







       '''simple docstring'''



       if not FAIRSEQ_AVAILABLE:
             return
       __A						=	self._get_dataset(max_len=64	)
       __A						=	64
       __A						=	ds.make_dynamic_sampler(__a		, required_batch_size_multiple=__a	)
       __A						=	[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
       __A						=	DataLoader(__a		, batch_sampler=__a		, collate_fn=ds.collate_fn		, num_workers=2	)
       __A						=	[]
       __A						=	[]
       for batch in data_loader:
             __A						=	batch["""input_ids"""].shape
             __A						=	src_shape[0]
             assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
             __A						=	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 		lowercase_  (	self     :Dict	) ->	Tuple:







       '''simple docstring'''



       __A						=	self._get_dataset(max_len=512	)
       __A						=	2
       __A						=	ds.make_sortish_sampler(__a		, shuffle=__a	)

       __A						=	DataLoader(__a		, batch_size=__a		, collate_fn=ds.collate_fn		, num_workers=2	)
       __A						=	DataLoader(__a		, batch_size=__a		, collate_fn=ds.collate_fn		, num_workers=2		, sampler=__a	)

       __A						=	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 		lowercase_  (	self     :Tuple		, _A     :Any=1_000		, _A     :Union[str, Any]=128	) ->	Optional[Any]:







       '''simple docstring'''



       if os.getenv('USE_REAL_DATA'		, __a	):
             __A						=	"""examples/seq2seq/wmt_en_ro"""
             __A						=	max_len * 2 * 64
             if not Path(__a	).joinpath('train.len'	).exists():
                   save_len_file(__a		, __a	)
       else:
             __A						=	"""examples/seq2seq/test_data/wmt_en_ro"""
             __A						=	max_len * 4
             save_len_file(__a		, __a	)

       __A						=	AutoTokenizer.from_pretrained(__a	)
       __A						=	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 		lowercase_  (	self     :Tuple	) ->	str:







       '''simple docstring'''



       __A						=	self._get_dataset()
       __A						=	set(DistributedSortishSampler(__a		, 256		, num_replicas=2		, rank=0		, add_extra_examples=__a	)	)
       __A						=	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 		lowercase_  (	self     :Optional[Any]		, _A     :Dict	) ->	Tuple:







       '''simple docstring'''



       __A						=	AutoTokenizer.from_pretrained(__a		, use_fast=__a	)
       if tok_name == MBART_TINY:
             __A						=	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'		, )
             __A						=	train_dataset.dataset_kwargs
             assert "src_lang" in kwargs and "tgt_lang" in kwargs
       else:
             __A						=	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		, )
             __A						=	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)