
import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__     :    int      							=					logging.get_logger(__name__)




def 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		Dict	,						__lowerCAmelCase			:		Optional[Any]							)    ->     List[Any]:
				__lowerCamelCase					      =			WavLMForSequenceClassification.from_pretrained(__lowerCAmelCase	,						config=__lowerCAmelCase							)
				__lowerCamelCase					      =			downstream_dict['''projector.weight''']
				__lowerCamelCase					      =			downstream_dict['''projector.bias''']
				__lowerCamelCase					      =			downstream_dict['''model.post_net.linear.weight''']
				__lowerCamelCase					      =			downstream_dict['''model.post_net.linear.bias''']
				return model




def 				__magic_name__				(     __lowerCAmelCase			:		Optional[int]	,						__lowerCAmelCase			:		List[str]	,						__lowerCAmelCase			:		Dict							)    ->     int:
				__lowerCamelCase					      =			WavLMForAudioFrameClassification.from_pretrained(__lowerCAmelCase	,						config=__lowerCAmelCase							)
				__lowerCamelCase					      =			downstream_dict['''model.linear.weight''']
				__lowerCamelCase					      =			downstream_dict['''model.linear.bias''']
				return model




def 				__magic_name__				(     __lowerCAmelCase			:		Dict	,						__lowerCAmelCase			:		Any	,						__lowerCAmelCase			:		Tuple							)    ->     str:
				__lowerCamelCase					      =			WavLMForXVector.from_pretrained(__lowerCAmelCase	,						config=__lowerCAmelCase							)
				__lowerCamelCase					      =			downstream_dict['''connector.weight''']
				__lowerCamelCase					      =			downstream_dict['''connector.bias''']
				for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
								__lowerCamelCase					      =			downstream_dict[
								    f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
								]
								__lowerCamelCase					      =			downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

				__lowerCamelCase					      =			downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
				__lowerCamelCase					      =			downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
				__lowerCamelCase					      =			downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
				__lowerCamelCase					      =			downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
				__lowerCamelCase					      =			downstream_dict['''objective.W''']
				return model




@torch.no_grad()
def 				__magic_name__				(     __lowerCAmelCase			:		str	,						__lowerCAmelCase			:		Tuple	,						__lowerCAmelCase			:		Any	,						__lowerCAmelCase			:		str							)    ->     Union[str, Any]:
				__lowerCamelCase					      =			torch.load(__lowerCAmelCase	,						map_location='''cpu'''							)

				__lowerCamelCase					      =			checkpoint['''Downstream''']

				__lowerCamelCase					      =			WavLMConfig.from_pretrained(__lowerCAmelCase							)
				__lowerCamelCase					      =			WavaVecaFeatureExtractor.from_pretrained(
				    __lowerCAmelCase	,						return_attention_mask=__lowerCAmelCase	,						do_normalize=__lowerCAmelCase							)

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

				if hf_config.use_weighted_layer_sum:
								__lowerCamelCase					      =			checkpoint['''Featurizer''']['''weights''']

				hf_feature_extractor.save_pretrained(__lowerCAmelCase							)
				hf_model.save_pretrained(__lowerCAmelCase							)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__     :    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.")
					SCREAMING_SNAKE_CASE__     :    List[Any]      							=					parser.parse_args()
					convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch




def 				__magic_name__				(     )    ->     Union[str, Any]:
				__lowerCamelCase					      =			Dataset.from_dict({'''i''': range(17							)}							)
				__lowerCamelCase					      =			len(__lowerCAmelCase							)
				__lowerCamelCase					      =			3
				__lowerCamelCase					      =			[
				    split_dataset_by_node(__lowerCAmelCase	,						rank=__lowerCAmelCase	,						world_size=__lowerCAmelCase							) for rank in range(__lowerCAmelCase							)
				]
				assert sum(len(__lowerCAmelCase							) for ds in datasets_per_rank							) == full_size
				assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




def 				__magic_name__				(     )    ->     Optional[Any]:
				def gen():
								return ({"i": i} for i in range(17							))

				__lowerCamelCase					      =			3
				__lowerCamelCase					      =			IterableDataset.from_generator(__lowerCAmelCase							)
				__lowerCamelCase					      =			len(list(__lowerCAmelCase							)							)
				__lowerCamelCase					      =			[
				    split_dataset_by_node(__lowerCAmelCase	,						rank=__lowerCAmelCase	,						world_size=__lowerCAmelCase							) for rank in range(__lowerCAmelCase							)
				]
				assert sum(len(list(__lowerCAmelCase							)							) for ds in datasets_per_rank							) == full_size
				assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




@pytest.mark.parametrize('''shards_per_node'''	,						[1, 2, 3]							)
def 				__magic_name__				(     __lowerCAmelCase			:		Any							)    ->     Union[str, Any]:
				def gen(__lowerCAmelCase			:		Union[str, Any]							):
								for shard in shards:
												yield from ({"i": i, "shard": shard} for i in range(17							))

				__lowerCamelCase					      =			3
				__lowerCamelCase					      =			shards_per_node * world_size
				__lowerCamelCase					      =			{'''shards''': [f'''shard_{shard_idx}.txt''' for shard_idx in range(__lowerCAmelCase							)]}
				__lowerCamelCase					      =			IterableDataset.from_generator(__lowerCAmelCase	,						gen_kwargs=__lowerCAmelCase							)
				__lowerCamelCase					      =			len(list(__lowerCAmelCase							)							)
				assert full_ds.n_shards == world_size * shards_per_node
				__lowerCamelCase					      =			[
				    split_dataset_by_node(__lowerCAmelCase	,						rank=__lowerCAmelCase	,						world_size=__lowerCAmelCase							) for rank in range(__lowerCAmelCase							)
				]
				assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
				assert sum(len(list(__lowerCAmelCase							)							) for ds in datasets_per_rank							) == full_size
				assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




def 				__magic_name__				(     )    ->     Optional[int]:
				def gen():
								return ({"i": i} for i in range(17							))

				__lowerCamelCase					      =			2
				__lowerCamelCase					      =			IterableDataset.from_generator(__lowerCAmelCase							)
				__lowerCamelCase					      =			len(list(__lowerCAmelCase							)							)

				__lowerCamelCase					      =			split_dataset_by_node(__lowerCAmelCase	,						rank=0	,						world_size=__lowerCAmelCase							).shuffle(seed=42							)
				assert len(list(__lowerCAmelCase							)							) == 1 + full_size // world_size
				with pytest.raises(__lowerCAmelCase							):
								split_dataset_by_node(__lowerCAmelCase	,						rank=0	,						world_size=__lowerCAmelCase							).shuffle()

				__lowerCamelCase					      =			split_dataset_by_node(full_ds.shuffle(seed=42							)	,						rank=0	,						world_size=__lowerCAmelCase							)
				assert len(list(__lowerCAmelCase							)							) == 1 + full_size // world_size
				with pytest.raises(__lowerCAmelCase							):
								split_dataset_by_node(full_ds.shuffle()	,						rank=0	,						world_size=__lowerCAmelCase							)




@pytest.mark.parametrize('''streaming'''	,						[False, True]							)
@require_torch
@pytest.mark.skipif(os.name == '''nt'''	,						reason='''execute_subprocess_async doesn\'t support windows'''							)
@pytest.mark.integration
def 				__magic_name__				(     __lowerCAmelCase			:		List[Any]							)    ->     Optional[int]:
				__lowerCamelCase					      =			2
				__lowerCamelCase					      =			get_torch_dist_unique_port()
				__lowerCamelCase					      =			Path(__lowerCAmelCase							).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
				__lowerCamelCase					      =			f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
				__lowerCamelCase					      =			f'''
        --streaming={streaming}
    '''.split()
				__lowerCamelCase					      =			[sys.executable] + distributed_args + args
				execute_subprocess_async(__lowerCAmelCase	,						env=os.environ.copy()							)







@pytest.mark.parametrize(
    '''nproc_per_node, num_workers'''	,						[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]	,						)
@require_torch
@pytest.mark.skipif(os.name == '''nt'''	,						reason='''execute_subprocess_async doesn\'t support windows'''							)
@pytest.mark.integration
def 				__magic_name__				(     __lowerCAmelCase			:		Any	,						__lowerCAmelCase			:		Any							)    ->     int:
				__lowerCamelCase					      =			True
				__lowerCamelCase					      =			get_torch_dist_unique_port()
				__lowerCamelCase					      =			Path(__lowerCAmelCase							).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
				__lowerCamelCase					      =			f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
				__lowerCamelCase					      =			f'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
				__lowerCamelCase					      =			[sys.executable] + distributed_args + args
				execute_subprocess_async(__lowerCAmelCase	,						env=os.environ.copy()							)



