
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()
SCREAMING_SNAKE_CASE							:int							 =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Dict							 =				{
    '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 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_		,   a_						)		->	Optional[Any]:







				"""simple docstring"""



				for attribute in key.split("."						):
								__A   			= getattr(a_		,   a_						)

				if weight_type is not None:
								__A   			= getattr(a_		,   a_						).shape
				else:
								__A   			= 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":
								__A   			= value
				elif weight_type == "weight_g":
								__A   			= value
				elif weight_type == "weight_v":
								__A   			= value
				elif weight_type == "bias":
								__A   			= value
				else:
								__A   			= value

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


def 						UpperCAmelCase			(	a_		,   a_		,   a_						)		->	Optional[Any]:







				"""simple docstring"""



				__A   			= []
				__A   			= fairseq_model.state_dict()

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

				for name, value in fairseq_dict.items():
								__A   			= False
								if "conv_layers" in name:
												load_conv_layer(
												    a_		,   a_		,   a_		,   a_		,   hf_model.config.feat_extract_norm == "group"		,   )
												__A   			= True
								else:
												for key, mapped_key in MAPPING.items():
																__A   			= "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):
																				__A   			= True
																				if "*" in mapped_key:
																								__A   			= name.split(a_						)[0].split("."						)[-2]
																								__A   			= mapped_key.replace("*"		,   a_						)
																				if "weight_g" in name:
																								__A   			= "weight_g"
																				elif "weight_v" in name:
																								__A   			= "weight_v"
																				elif "weight" in name:
																								__A   			= "weight"
																				elif "bias" in name:
																								__A   			= "bias"
																				else:
																								__A   			= None
																				set_recursively(a_		,   a_		,   a_		,   a_		,   a_						)
																continue
								if not is_used:
												unused_weights.append(a_						)

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


def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_		,   a_						)		->	str:







				"""simple docstring"""



				__A   			= full_name.split("conv_layers."						)[-1]
				__A   			= name.split("."						)
				__A   			= int(items[0]						)
				__A   			= 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.'''
												)
												__A   			= 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.'''
												)
												__A   			= 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."
												)
												__A   			= 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.'''
												)
												__A   			= value
												logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
				else:
								unused_weights.append(a_						)


@torch.no_grad()
def 						UpperCAmelCase			(	a_		,   a_		,   a_=None		,   a_=None		,   a_=True						)		->	Tuple:







				"""simple docstring"""



				if config_path is not None:
								__A   			= HubertConfig.from_pretrained(a_						)
				else:
								__A   			= HubertConfig()

				if is_finetuned:
								if dict_path:
												__A   			= Dictionary.load(a_						)

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												__A   			= target_dict.pad_index
												__A   			= target_dict.bos_index
												__A   			= target_dict.eos_index
												__A   			= len(target_dict.symbols						)
												__A   			= os.path.join(a_		,   "vocab.json"						)
												if not os.path.isdir(a_						):
																logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(a_						)						)
																return
												os.makedirs(a_		,   exist_ok=a_						)
												with open(a_		,   "w"		,   encoding="utf-8"						) as vocab_handle:
																json.dump(target_dict.indices		,   a_						)
												__A   			= WavaVecaCTCTokenizer(
												    a_		,   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=a_		,   )
												__A   			= True if config.feat_extract_norm == "layer" else False
												__A   			= WavaVecaFeatureExtractor(
												    feature_size=1		,   sampling_rate=1_6_0_0_0		,   padding_value=0		,   do_normalize=a_		,   return_attention_mask=a_		,   )
												__A   			= WavaVecaProcessor(feature_extractor=a_		,   tokenizer=a_						)
												processor.save_pretrained(a_						)

								__A   			= HubertForCTC(a_						)
				else:
								__A   			= HubertModel(a_						)

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

				__A   			= model[0].eval()

				recursively_load_weights(a_		,   a_		,   a_						)

				hf_wavavec.save_pretrained(a_						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Optional[Any]							 =				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'
					)
					SCREAMING_SNAKE_CASE							:List[Any]							 =				parser.parse_args()
					convert_hubert_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)


from typing import Dict, Optional

import numpy as np

import datasets


SCREAMING_SNAKE_CASE							:List[Any]							 =				'\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n'

SCREAMING_SNAKE_CASE							:List[str]							 =				'\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric("mean_iou")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), \'per_category_accuracy\': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}\n'

SCREAMING_SNAKE_CASE							:str							 =				'\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}'


def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_		,   a_ = None		,   a_ = False		,   )		->	Tuple:







				"""simple docstring"""



				if label_map is not None:
								for old_id, new_id in label_map.items():
												__A   			= new_id

    # turn into Numpy arrays
				__A   			= np.array(a_						)
				__A   			= np.array(a_						)

				if reduce_labels:
								__A   			= 2_5_5
								__A   			= label - 1
								__A   			= 2_5_5

				__A   			= label != ignore_index
				__A   			= np.not_equal(a_		,   a_						)
				__A   			= pred_label[mask]
				__A   			= np.array(a_						)[mask]

				__A   			= pred_label[pred_label == label]

				__A   			= np.histogram(a_		,   bins=a_		,   range=(0, num_labels - 1)						)[0]
				__A   			= np.histogram(a_		,   bins=a_		,   range=(0, num_labels - 1)						)[0]
				__A   			= np.histogram(a_		,   bins=a_		,   range=(0, num_labels - 1)						)[0]

				__A   			= area_pred_label + area_label - area_intersect

				return area_intersect, area_union, area_pred_label, area_label


def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_		,   a_ = None		,   a_ = False		,   )		->	Union[str, Any]:







				"""simple docstring"""



				__A   			= np.zeros((num_labels,)		,   dtype=np.floataa						)
				__A   			= np.zeros((num_labels,)		,   dtype=np.floataa						)
				__A   			= np.zeros((num_labels,)		,   dtype=np.floataa						)
				__A   			= np.zeros((num_labels,)		,   dtype=np.floataa						)
				for result, gt_seg_map in zip(a_		,   a_						):
								__A			,							__A			,							__A			,							__A   			= intersect_and_union(
								    a_		,   a_		,   a_		,   a_		,   a_		,   a_						)
								total_area_intersect += area_intersect
								total_area_union += area_union
								total_area_pred_label += area_pred_label
								total_area_label += area_label
				return total_area_intersect, total_area_union, total_area_pred_label, total_area_label


def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_		,   a_ = None		,   a_ = None		,   a_ = False		,   )		->	str:







				"""simple docstring"""



				__A			,							__A			,							__A			,							__A   			= total_intersect_and_union(
				    a_		,   a_		,   a_		,   a_		,   a_		,   a_						)

				# compute metrics
				__A   			= {}

				__A   			= total_area_intersect.sum() / total_area_label.sum()
				__A   			= total_area_intersect / total_area_union
				__A   			= total_area_intersect / total_area_label

				__A   			= np.nanmean(a_						)
				__A   			= np.nanmean(a_						)
				__A   			= all_acc
				__A   			= iou
				__A   			= acc

				if nan_to_num is not None:
								__A   			= {metric: np.nan_to_num(a_		,   nan=a_						) for metric, metric_value in metrics.items()}

				return metrics





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




	'''simple docstring'''





	def 				UpperCamelCase_  (       self					: List[Any] ):
					return datasets.MetricInfo(
					    description=_DESCRIPTION							,citation=_CITATION							,inputs_description=_KWARGS_DESCRIPTION							,features=datasets.Features(
					        # 1st Seq - height dim, 2nd - width dim
					        {
					            "predictions": datasets.Sequence(datasets.Sequence(datasets.Value("uint16" ) ) ),
					            "references": datasets.Sequence(datasets.Sequence(datasets.Value("uint16" ) ) ),
					        } )							,reference_urls=[
					        "https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"
					    ]							,)







	def 				UpperCamelCase_  (       self					: int							,A					: Optional[Any]							,A					: Optional[Any]							,A					: int							,A					: bool							,A					: Optional[int] = None							,A					: Optional[Dict[int, int]] = None							,A					: bool = False							,):
					__A   			= mean_iou(
					    results=A							,gt_seg_maps=A							,num_labels=A							,ignore_index=A							,nan_to_num=A							,label_map=A							,reduce_labels=A							,)
					return iou_result
