





'''simple docstring'''

import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
__lowerCamelCase : str											=			logging.get_logger(__name__)







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = WavaVecaForSequenceClassification.from_pretrained(lowerCAmelCase_		,							config=lowerCAmelCase_							)
				lowercase         = downstream_dict["projector.weight"]
				lowercase         = downstream_dict["projector.bias"]
				lowercase         = downstream_dict["model.post_net.linear.weight"]
				lowercase         = downstream_dict["model.post_net.linear.bias"]
				return model







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = WavaVecaForAudioFrameClassification.from_pretrained(lowerCAmelCase_		,							config=lowerCAmelCase_							)
				lowercase         = downstream_dict["model.linear.weight"]
				lowercase         = downstream_dict["model.linear.bias"]
				return model







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = WavaVecaForXVector.from_pretrained(lowerCAmelCase_		,							config=lowerCAmelCase_							)
				lowercase         = downstream_dict["connector.weight"]
				lowercase         = downstream_dict["connector.bias"]
				for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
								lowercase         = downstream_dict[
								    f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
								]
								lowercase         = downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

				lowercase         = downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
				lowercase         = downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
				lowercase         = downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
				lowercase         = downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
				lowercase         = downstream_dict["objective.W"]
				return model







@torch.no_grad()
def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = torch.load(lowerCAmelCase_		,							map_location="cpu"							)

				lowercase         = checkpoint["Downstream"]

				lowercase         = WavaVecaConfig.from_pretrained(lowerCAmelCase_							)
				lowercase         = WavaVecaFeatureExtractor.from_pretrained(
				    lowerCAmelCase_		,							return_attention_mask=lowerCAmelCase_		,							do_normalize=lowerCAmelCase_							)

				lowercase         = hf_config.architectures[0]
				if arch.endswith("ForSequenceClassification"							):
								lowercase         = convert_classification(lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							)
				elif arch.endswith("ForAudioFrameClassification"							):
								lowercase         = convert_diarization(lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							)
				elif arch.endswith("ForXVector"							):
								lowercase         = convert_xvector(lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							)
				else:
								raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}'							)

				if hf_config.use_weighted_layer_sum:
								lowercase         = checkpoint["Featurizer"]["weights"]

				hf_feature_extractor.save_pretrained(lowerCAmelCase_							)
				hf_model.save_pretrained(lowerCAmelCase_							)


if __name__ == "__main__":
			__lowerCamelCase : int											=			argparse.ArgumentParser()
			parser.add_argument(
			    "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
			)
			parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
			parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
			parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
			__lowerCamelCase : List[str]											=			parser.parse_args()
			convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)











'''simple docstring'''

import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


__lowerCamelCase : Any											=			{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}

class       UpperCAmelCase (						nn.Module  ):




		def __init__(self      :	Tuple							,			A__      :	Any				)					-> str:
						super().__init__()
						lowercase         = torchvision.models.resnetaaa(pretrained=A__				)
						lowercase         = list(model.children()				)[:-2]
						lowercase         = nn.Sequential(*A__				)
						lowercase         = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]				)





		def 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	Optional[Any]				)					-> Optional[int]:
						# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
						lowercase         = self.pool(self.model(A__				)				)
						lowercase         = torch.flatten(A__							,			start_dim=2				)
						lowercase         = out.transpose(1							,			2				).contiguous()
						return out  # BxNx2048




class       UpperCAmelCase (						_lowercase  ):




		def __init__(self      :	int							,			A__      :	Optional[int]							,			A__      :	Optional[Any]							,			A__      :	List[Any]							,			A__      :	Tuple							,			A__      :	Optional[int]				)					-> Union[str, Any]:
						lowercase         = [json.loads(A__				) for l in open(A__				)]
						lowercase         = os.path.dirname(A__				)
						lowercase         = tokenizer
						lowercase         = labels
						lowercase         = len(A__				)
						lowercase         = max_seq_length

						lowercase         = transforms




		def __len__(self      :	List[str]				)					-> Dict:
						return len(self.data				)




		def __getitem__(self      :	int							,			A__      :	Union[str, Any]				)					-> List[str]:
						lowercase         = torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]							,			add_special_tokens=A__				)				)
						lowercase   ,							lowercase   ,							lowercase         = sentence[0], sentence[1:-1], sentence[-1]
						lowercase         = sentence[: self.max_seq_length]

						lowercase         = torch.zeros(self.n_classes				)
						lowercase         = 1

						lowercase         = Image.open(os.path.join(self.data_dir							,			self.data[index]["img"]				)				).convert("RGB"				)
						lowercase         = self.transforms(A__				)

						return {
						    "image_start_token": start_token,
						    "image_end_token": end_token,
						    "sentence": sentence,
						    "image": image,
						    "label": label,
						}





		def 				UpperCAmelCase__   (self      :	str				)					-> str:
						lowercase         = Counter()
						for row in self.data:
										label_freqs.update(row["label"]				)
						return label_freqs







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = [len(row["sentence"]							) for row in batch]
				lowercase   ,							lowercase         = len(lowerCAmelCase_							), max(lowerCAmelCase_							)

				lowercase         = torch.zeros(lowerCAmelCase_		,							lowerCAmelCase_		,							dtype=torch.long							)
				lowercase         = torch.zeros(lowerCAmelCase_		,							lowerCAmelCase_		,							dtype=torch.long							)

				for i_batch, (input_row, length) in enumerate(zip(lowerCAmelCase_		,							lowerCAmelCase_							)							):
								lowercase         = input_row["sentence"]
								lowercase         = 1

				lowercase         = torch.stack([row["image"] for row in batch]							)
				lowercase         = torch.stack([row["label"] for row in batch]							)
				lowercase         = torch.stack([row["image_start_token"] for row in batch]							)
				lowercase         = torch.stack([row["image_end_token"] for row in batch]							)

				return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				return [
				    "Crime",
				    "Drama",
				    "Thriller",
				    "Action",
				    "Comedy",
				    "Romance",
				    "Documentary",
				    "Short",
				    "Mystery",
				    "History",
				    "Family",
				    "Adventure",
				    "Fantasy",
				    "Sci-Fi",
				    "Western",
				    "Horror",
				    "Sport",
				    "War",
				    "Music",
				    "Musical",
				    "Animation",
				    "Biography",
				    "Film-Noir",
				]







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				return transforms.Compose(
				    [
				        transforms.Resize(256							),
				        transforms.CenterCrop(224							),
				        transforms.ToTensor(),
				        transforms.Normalize(
				            mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]		,							std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]		,							),
				    ]							)




