






import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


lowerCamelCase       :       Union[str, Any]        =				'''\
@inproceedings{popovic-2015-chrf,
    title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W15-3049",
    doi = "10.18653/v1/W15-3049",
    pages = "392--395",
}
@inproceedings{popovic-2017-chrf,
    title = "chr{F}++: words helping character n-grams",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-4770",
    doi = "10.18653/v1/W17-4770",
    pages = "612--618",
}
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

lowerCamelCase       :       Tuple        =				'''\
ChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,
and ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation
that is already present in sacrebleu.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.
'''

lowerCamelCase       :       Tuple        =				'''
Produces ChrF(++) scores for hypotheses given reference translations.

Args:
    predictions (list of str): The predicted sentences.
    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.
    char_order (int): Character n-gram order. Defaults to `6`.
    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.
    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.
    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.
    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.
    eps_smoothing (bool): If `True`, applies epsilon smoothing similar
    to reference chrF++.py, NLTK and Moses implementations. If `False`,
    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.

Returns:
    \'score\' (float): The chrF (chrF++) score,
    \'char_order\' (int): The character n-gram order,
    \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,
    \'beta\' (int): Determine the importance of recall w.r.t precision

Examples:
    Example 1--a simple example of calculating chrF:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction, references=reference)
        >>> print(results)
        {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}

    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2)
        >>> print(results)
        {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}

    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2,
        ...                         lowercase=True)
        >>> print(results)
        {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}
'''

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,				_KWARGS_DESCRIPTION	)
class 						lowerCAmelCase   (       datasets.Metric	):

					'''simple docstring'''

					def 					lowerCAmelCase (     self :		List[Any]							)	->     List[str]:


												"""simple docstring"""

												if version.parse(scb.__version__							) < version.parse("""1.4.12"""							):
																			raise ImportWarning(
																			    """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
																			    """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""							)
												return datasets.MetricInfo(
												    description=_DESCRIPTION						,   citation=_CITATION						,   homepage="""https://github.com/mjpost/sacreBLEU#chrf--chrf"""						,   inputs_description=_KWARGS_DESCRIPTION						,   features=datasets.Features(
												        {
												            """predictions""": datasets.Value("""string"""						,   id="""sequence"""							),
												            """references""": datasets.Sequence(datasets.Value("""string"""						,   id="""sequence"""							)						,   id="""references"""							),
												        }							)						,   codebase_urls=["""https://github.com/mjpost/sacreBLEU#chrf--chrf"""]						,   reference_urls=[
												        """https://github.com/m-popovic/chrF""",
												    ]						,   )



					def 					lowerCAmelCase (     self :		Union[str, Any]						,   __a :		List[Any]						,   __a :		List[str]						,   __a :		int = CHRF.CHAR_ORDER						,   __a :		int = CHRF.WORD_ORDER						,   __a :		int = CHRF.BETA						,   __a :		bool = False						,   __a :		bool = False						,   __a :		bool = False						,   )	->     Any:


												"""simple docstring"""

												__lowercase					:							Optional[Any]			=   len(references[0]							)
												if any(len(__a							) != references_per_prediction for refs in references							):
																			raise ValueError("""Sacrebleu requires the same number of references for each prediction"""							)
												__lowercase					:							Optional[Any]			=   [[refs[i] for refs in references] for i in range(__a							)]

												__lowercase					:							Tuple			=   CHRF(__a						,   __a						,   __a						,   __a						,   __a						,   __a							)
												__lowercase					:							Tuple			=   sb_chrf.corpus_score(__a						,   __a							)

												return {
												    "score": output.score,
												    "char_order": output.char_order,
												    "word_order": output.word_order,
												    "beta": output.beta,
												}







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)