
'''simple docstring'''







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowercase					:							List[Any]   =      logging.get_logger(__name__)

lowercase					:							Any   =      {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A    ,       __A			) ->			int:
	for attribute in key.split('.'			):
		_snake_case     		=	getattr(__SCREAMING_SNAKE_CASE    ,       __SCREAMING_SNAKE_CASE			)

	if weight_type is not None:
		_snake_case     		=	getattr(__SCREAMING_SNAKE_CASE    ,       __SCREAMING_SNAKE_CASE			).shape
	else:
		_snake_case     		=	hf_pointer.shape

	assert hf_shape == value.shape, (
	    F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
	    F' {value.shape} for {full_name}'
	)

	if weight_type == "weight":
		_snake_case     		=	value
	elif weight_type == "weight_g":
		_snake_case     		=	value
	elif weight_type == "weight_v":
		_snake_case     		=	value
	elif weight_type == "bias":
		_snake_case     		=	value
	else:
		_snake_case     		=	value

	logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'			)







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A			) ->			Optional[int]:
	_snake_case     		=	[]
	_snake_case     		=	fairseq_model.state_dict()

	_snake_case     		=	hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		_snake_case     		=	False
		if "conv_layers" in name:
			load_conv_layer(
			    __SCREAMING_SNAKE_CASE    ,       __SCREAMING_SNAKE_CASE    ,       __SCREAMING_SNAKE_CASE    ,       __SCREAMING_SNAKE_CASE    ,       hf_model.config.feat_extract_norm == 'group'    ,       )
			_snake_case     		=	True
		else:
			for key, mapped_key in MAPPING.items():
				_snake_case     		=	'hubert.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

				if key in name or (key.split('w2v_model.'			)[-1] == name.split('.'			)[0] and not is_finetuned):
					_snake_case     		=	True
					if "*" in mapped_key:
						_snake_case     		=	name.split(__SCREAMING_SNAKE_CASE			)[0].split('.'			)[-2]
						_snake_case     		=	mapped_key.replace('*'    ,       __SCREAMING_SNAKE_CASE			)
					if "weight_g" in name:
						_snake_case     		=	'weight_g'
					elif "weight_v" in name:
						_snake_case     		=	'weight_v'
					elif "weight" in name:
						_snake_case     		=	'weight'
					elif "bias" in name:
						_snake_case     		=	'bias'
					else:
						_snake_case     		=	None
					set_recursively(__SCREAMING_SNAKE_CASE    ,       __SCREAMING_SNAKE_CASE    ,       __SCREAMING_SNAKE_CASE    ,       __SCREAMING_SNAKE_CASE    ,       __SCREAMING_SNAKE_CASE			)
				continue
		if not is_used:
			unused_weights.append(__SCREAMING_SNAKE_CASE			)

	logger.warning(F'Unused weights: {unused_weights}'			)







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A    ,       __A			) ->			Tuple:
	_snake_case     		=	full_name.split('conv_layers.'			)[-1]
	_snake_case     		=	name.split('.'			)
	_snake_case     		=	int(items[0]			)
	_snake_case     		=	int(items[1]			)

	if type_id == 0:
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
			    F'{full_name} has size {value.shape}, but'
			    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
			)
			_snake_case     		=	value
			logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'			)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
			    F'{full_name} has size {value.shape}, but'
			    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
			)
			_snake_case     		=	value
			logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'			)
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
			    F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
			    " found."
			)
			_snake_case     		=	value
			logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'			)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
			    F'{full_name} has size {value.shape}, but'
			    F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
			)
			_snake_case     		=	value
			logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'			)
	else:
		unused_weights.append(__SCREAMING_SNAKE_CASE			)







@torch.no_grad()
def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A=None    ,       __A=None    ,       __A=True			) ->			Dict:
	if config_path is not None:
		_snake_case     		=	HubertConfig.from_pretrained(__SCREAMING_SNAKE_CASE			)
	else:
		_snake_case     		=	HubertConfig()

	if is_finetuned:
		if dict_path:
			_snake_case     		=	Dictionary.load(__SCREAMING_SNAKE_CASE			)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			_snake_case     		=	target_dict.pad_index
			_snake_case     		=	target_dict.bos_index
			_snake_case     		=	target_dict.eos_index
			_snake_case     		=	len(target_dict.symbols			)
			_snake_case     		=	os.path.join(__SCREAMING_SNAKE_CASE    ,       'vocab.json'			)
			if not os.path.isdir(__SCREAMING_SNAKE_CASE			):
				logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__SCREAMING_SNAKE_CASE			)			)
				return
			os.makedirs(__SCREAMING_SNAKE_CASE    ,       exist_ok=__SCREAMING_SNAKE_CASE			)
			with open(__SCREAMING_SNAKE_CASE    ,       'w'    ,       encoding='utf-8'			) as vocab_handle:
				json.dump(target_dict.indices    ,       __SCREAMING_SNAKE_CASE			)
			_snake_case     		=	WavaVecaCTCTokenizer(
			    __SCREAMING_SNAKE_CASE    ,       unk_token=target_dict.unk_word    ,       pad_token=target_dict.pad_word    ,       bos_token=target_dict.bos_word    ,       eos_token=target_dict.eos_word    ,       word_delimiter_token='|'    ,       do_lower_case=__SCREAMING_SNAKE_CASE    ,       )
			_snake_case     		=	True if config.feat_extract_norm == 'layer' else False
			_snake_case     		=	WavaVecaFeatureExtractor(
			    feature_size=1    ,       sampling_rate=16_000    ,       padding_value=0    ,       do_normalize=__SCREAMING_SNAKE_CASE    ,       return_attention_mask=__SCREAMING_SNAKE_CASE    ,       )
			_snake_case     		=	WavaVecaProcessor(feature_extractor=__SCREAMING_SNAKE_CASE    ,       tokenizer=__SCREAMING_SNAKE_CASE			)
			processor.save_pretrained(__SCREAMING_SNAKE_CASE			)

		_snake_case     		=	HubertForCTC(__SCREAMING_SNAKE_CASE			)
	else:
		_snake_case     		=	HubertModel(__SCREAMING_SNAKE_CASE			)

	if is_finetuned:
		_snake_case		,				_snake_case		,				_snake_case     		=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]    ,       arg_overrides={'data': '/'.join(dict_path.split('/'			)[:-1]			)}			)
	else:
		_snake_case		,				_snake_case		,				_snake_case     		=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			)

	_snake_case     		=	model[0].eval()

	recursively_load_weights(__SCREAMING_SNAKE_CASE    ,       __SCREAMING_SNAKE_CASE    ,       __SCREAMING_SNAKE_CASE			)

	hf_wavavec.save_pretrained(__SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
					lowercase					:							Dict   =      argparse.ArgumentParser()
					parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
					parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
					parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
					parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
					parser.add_argument(
					    "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
					)
					lowercase					:							int   =      parser.parse_args()
					convert_hubert_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)



'''simple docstring'''







import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowercase					:							str   =      version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
					from nltk import word_tokenize


lowercase					:							int   =      "\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n"

lowercase					:							Any   =      "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"

lowercase					:							Optional[Any]   =      "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    'meteor': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric('meteor')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"


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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return datasets.MetricInfo(
								    description=_DESCRIPTION					,     citation=_CITATION					,     inputs_description=_KWARGS_DESCRIPTION					,     features=datasets.Features(
								        {
								            'predictions': datasets.Value('string'					,     id='sequence'						),
								            'references': datasets.Value('string'					,     id='sequence'						),
								        }						)					,     codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py']					,     reference_urls=[
								        'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
								        'https://en.wikipedia.org/wiki/METEOR',
								    ]					,     )


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								import nltk

								nltk.download('wordnet'						)
								if NLTK_VERSION >= version.Version('3.6.5'						):
									nltk.download('punkt'						)
								if NLTK_VERSION >= version.Version('3.6.6'						):
									nltk.download('omw-1.4'						)




							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_=0.9					,     lowerCAmelCase_=3					,     lowerCAmelCase_=0.5						):





								"""simple docstring"""






								if NLTK_VERSION >= version.Version('3.6.5'						):
									_snake_case     		=	[
									    meteor_score.single_meteor_score(
									        word_tokenize(lowerCAmelCase_						)					,     word_tokenize(lowerCAmelCase_						)					,     alpha=lowerCAmelCase_					,     beta=lowerCAmelCase_					,     gamma=lowerCAmelCase_						)
									    for ref, pred in zip(lowerCAmelCase_					,     lowerCAmelCase_						)
									]
								else:
									_snake_case     		=	[
									    meteor_score.single_meteor_score(lowerCAmelCase_					,     lowerCAmelCase_					,     alpha=lowerCAmelCase_					,     beta=lowerCAmelCase_					,     gamma=lowerCAmelCase_						)
									    for ref, pred in zip(lowerCAmelCase_					,     lowerCAmelCase_						)
									]

								return {"meteor": np.mean(lowerCAmelCase_						)}

