


import argparse
import json
from pathlib import Path

import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download

from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging


logging.set_verbosity_info()
A			:						Optional[int]        =  logging.get_logger(__name__)






def 					UpperCamelCase   (      __magic_name__	:				Union[str, Any]	)  ->    Optional[int]:
		"""simple docstring"""




		lowercase__	     =						ASTConfig()

		if "10-10" in model_name:
				pass
		elif "speech-commands" in model_name:
				lowercase__	     =						128
		elif "12-12" in model_name:
				lowercase__	     =						12
				lowercase__	     =						12
		elif "14-14" in model_name:
				lowercase__	     =						14
				lowercase__	     =						14
		elif "16-16" in model_name:
				lowercase__	     =						16
				lowercase__	     =						16
		else:
				raise ValueError("""Model not supported"""	)

		lowercase__	     =						"""huggingface/label-files"""
		if "speech-commands" in model_name:
				lowercase__	     =						35
				lowercase__	     =						"""speech-commands-v2-id2label.json"""
		else:
				lowercase__	     =						527
				lowercase__	     =						"""audioset-id2label.json"""

		lowercase__	     =						json.load(open(hf_hub_download(__magic_name__       ,       __magic_name__       ,       repo_type="""dataset"""	)       ,       """r"""	)	)
		lowercase__	     =						{int(__magic_name__	): v for k, v in idalabel.items()}
		lowercase__	     =						idalabel
		lowercase__	     =						{v: k for k, v in idalabel.items()}

		return config






def 					UpperCamelCase   (      __magic_name__	:				List[str]	)  ->    Dict:
		"""simple docstring"""




		if "module.v" in name:
				lowercase__	     =						name.replace("""module.v"""       ,       """audio_spectrogram_transformer"""	)
		if "cls_token" in name:
				lowercase__	     =						name.replace("""cls_token"""       ,       """embeddings.cls_token"""	)
		if "dist_token" in name:
				lowercase__	     =						name.replace("""dist_token"""       ,       """embeddings.distillation_token"""	)
		if "pos_embed" in name:
				lowercase__	     =						name.replace("""pos_embed"""       ,       """embeddings.position_embeddings"""	)
		if "patch_embed.proj" in name:
				lowercase__	     =						name.replace("""patch_embed.proj"""       ,       """embeddings.patch_embeddings.projection"""	)
		# transformer blocks
		if "blocks" in name:
				lowercase__	     =						name.replace("""blocks"""       ,       """encoder.layer"""	)
		if "attn.proj" in name:
				lowercase__	     =						name.replace("""attn.proj"""       ,       """attention.output.dense"""	)
		if "attn" in name:
				lowercase__	     =						name.replace("""attn"""       ,       """attention.self"""	)
		if "norm1" in name:
				lowercase__	     =						name.replace("""norm1"""       ,       """layernorm_before"""	)
		if "norm2" in name:
				lowercase__	     =						name.replace("""norm2"""       ,       """layernorm_after"""	)
		if "mlp.fc1" in name:
				lowercase__	     =						name.replace("""mlp.fc1"""       ,       """intermediate.dense"""	)
		if "mlp.fc2" in name:
				lowercase__	     =						name.replace("""mlp.fc2"""       ,       """output.dense"""	)
		# final layernorm
		if "audio_spectrogram_transformer.norm" in name:
				lowercase__	     =						name.replace("""audio_spectrogram_transformer.norm"""       ,       """audio_spectrogram_transformer.layernorm"""	)
		# classifier head
		if "module.mlp_head.0" in name:
				lowercase__	     =						name.replace("""module.mlp_head.0"""       ,       """classifier.layernorm"""	)
		if "module.mlp_head.1" in name:
				lowercase__	     =						name.replace("""module.mlp_head.1"""       ,       """classifier.dense"""	)

		return name






def 					UpperCamelCase   (      __magic_name__	:				List[str]       ,       __magic_name__	:				Any	)  ->    int:
		"""simple docstring"""




		for key in orig_state_dict.copy().keys():
				lowercase__	     =						orig_state_dict.pop(__magic_name__	)

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

		return orig_state_dict






def 					UpperCamelCase   (      __magic_name__	:				Union[str, Any]	)  ->    Tuple:
		"""simple docstring"""




		lowercase__	     =						[
		    """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(__magic_name__       ,       __magic_name__	)






@torch.no_grad()
def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				Any       ,       __magic_name__	:				Optional[Any]=False	)  ->    List[str]:
		"""simple docstring"""




		lowercase__	     =						get_audio_spectrogram_transformer_config(__magic_name__	)

		lowercase__	     =						{
		    """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__	     =						model_name_to_url[model_name]
		lowercase__	     =						torch.hub.load_state_dict_from_url(__magic_name__       ,       map_location="""cpu"""	)
		# remove some keys
		remove_keys(__magic_name__	)
		# rename some keys
		lowercase__	     =						convert_state_dict(__magic_name__       ,       __magic_name__	)

		# load 🤗 model
		lowercase__	     =						ASTForAudioClassification(__magic_name__	)
		model.eval()

		model.load_state_dict(__magic_name__	)

		# verify outputs on dummy input
		# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
		lowercase__	     =						-4.2_6_7_7_3_9_3 if """speech-commands""" not in model_name else -6.8_4_5_9_7_8
		lowercase__	     =						4.5_6_8_9_9_7_4 if """speech-commands""" not in model_name else 5.5_6_5_4_5_2_6
		lowercase__	     =						1024 if """speech-commands""" not in model_name else 128
		lowercase__	     =						ASTFeatureExtractor(mean=__magic_name__       ,       std=__magic_name__       ,       max_length=__magic_name__	)

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

				lowercase__     ,      lowercase__	     =						torchaudio.load(__magic_name__	)
				lowercase__	     =						waveform.squeeze().numpy()

		lowercase__	     =						feature_extractor(__magic_name__       ,       sampling_rate=1_6000       ,       return_tensors="""pt"""	)

		# forward pass
		lowercase__	     =						model(**__magic_name__	)
		lowercase__	     =						outputs.logits

		if model_name == "ast-finetuned-audioset-10-10-0.4593":
				lowercase__	     =						torch.tensor([-0.8_7_6_0, -7.0_0_4_2, -8.6_6_0_2]	)
		elif model_name == "ast-finetuned-audioset-10-10-0.450":
				lowercase__	     =						torch.tensor([-1.1_9_8_6, -7.0_9_0_3, -8.2_7_1_8]	)
		elif model_name == "ast-finetuned-audioset-10-10-0.448":
				lowercase__	     =						torch.tensor([-2.6_1_2_8, -8.0_0_8_0, -9.4_3_4_4]	)
		elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
				lowercase__	     =						torch.tensor([-1.5_0_8_0, -7.4_5_3_4, -8.8_9_1_7]	)
		elif model_name == "ast-finetuned-audioset-12-12-0.447":
				lowercase__	     =						torch.tensor([-0.5_0_5_0, -6.5_8_3_3, -8.0_8_4_3]	)
		elif model_name == "ast-finetuned-audioset-14-14-0.443":
				lowercase__	     =						torch.tensor([-0.3_8_2_6, -7.0_3_3_6, -8.2_4_1_3]	)
		elif model_name == "ast-finetuned-audioset-16-16-0.442":
				lowercase__	     =						torch.tensor([-1.2_1_1_3, -6.9_1_0_1, -8.3_4_7_0]	)
		elif model_name == "ast-finetuned-speech-commands-v2":
				lowercase__	     =						torch.tensor([6.1_5_8_9, -8.0_5_6_6, -8.7_9_8_4]	)
		else:
				raise ValueError("""Unknown model name"""	)
		if not torch.allclose(logits[0, :3]       ,       __magic_name__       ,       atol=1E-4	):
				raise ValueError("""Logits don't match"""	)
		print("""Looks ok!"""	)

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

		if push_to_hub:
				print("""Pushing model and feature extractor to the hub..."""	)
				model.push_to_hub(f'''MIT/{model_name}'''	)
				feature_extractor.push_to_hub(f'''MIT/{model_name}'''	)


if __name__ == "__main__":
	A			:						Optional[Any]        =  argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--model_name',
	    default='ast-finetuned-audioset-10-10-0.4593',
	    type=str,
	    help='Name of the Audio Spectrogram Transformer model you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)
	parser.add_argument(
	    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
	)

	A			:						List[Any]        =  parser.parse_args()
	convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





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

import datasets


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

A			:						List[str]        =  '\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n'

A			:						Any        =  '\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    \'score\' (float): The chrF (chrF++) score,\n    \'char_order\' (int): The character n-gram order,\n    \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    \'beta\' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> 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."]\n        >>> 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."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> 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."]\n        >>> 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."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> 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."]\n        >>> 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."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n'



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,    _KWARGS_DESCRIPTION				)
class 				A       (			datasets.Metric				):
					'''simple docstring'''






					def     lowerCamelCase__	(self       :		Dict						)		->  Union[str, Any]:




							"""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       :		Optional[int] ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		int = CHRF.CHAR_ORDER ,   _UpperCAmelCase       :		int = CHRF.WORD_ORDER ,   _UpperCAmelCase       :		int = CHRF.BETA ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		bool = False ,   )		->  int:




							"""simple docstring"""


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

							lowercase__	     =						CHRF(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)
							lowercase__	     =						sb_chrf.corpus_score(_UpperCAmelCase ,   _UpperCAmelCase						)

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

