




import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

__lowerCAmelCase						:							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',
    'mask_emb': 'masked_spec_embed',
}




def      __magic_name__							(					A						:   Optional[int],    A						:   Any,    A						:   List[str],    A						:   List[str],    A						:   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      __magic_name__							(					A						:   Tuple,    A						:   Optional[Any]  ):






			'''simple docstring'''







			a       =     []
			a       =     fairseq_model.state_dict()

			a       =     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       =     mapped_key

												if key in name:
															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      __magic_name__							(					A						:   Tuple,    A						:   str,    A						:   str,    A						:   str,    A						:   Optional[int]  ):






			'''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  )




def      __magic_name__							(					A						:   Dict  ):






			'''simple docstring'''







			a       =     HubertConfig()
			a       =     model.config

			a       =     fs_config.activation_dropout
			a       =     False
			a       =     fs_config.attention_dropout
			a       =     False
			a       =     eval(fs_config.extractor_conv_feature_layers  )
			a       =     [x[0] for x in conv_layers]
			a       =     [x[1] for x in conv_layers]
			a       =     [x[2] for x in conv_layers]
			a       =     'gelu'
			a       =     'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
			a       =     False
			a       =     0.0
			a       =     0.0
			a       =     fs_config.activation_fn
			a       =     fs_config.dropout
			a       =     fs_config.encoder_embed_dim
			a       =     0.02
			a       =     fs_config.encoder_ffn_embed_dim
			a       =     1E-5
			a       =     0.0
			a       =     fs_config.encoder_attention_heads
			a       =     fs_config.conv_pos_groups
			a       =     fs_config.conv_pos
			a       =     len(A  )
			a       =     fs_config.encoder_layers

			return config




@torch.no_grad()
def      __magic_name__							(					A						:   Optional[Any],    A						:   Optional[int]=None  ):






			'''simple docstring'''







			a       =     distilhubert().model.model

			if config_path is not None:
						a       =     HubertConfig.from_pretrained(A  )
			else:
						a       =     convert_config(A  )
			a       =     model.eval()

			a       =     WavaVecaFeatureExtractor(
			    feature_size=1,    sampling_rate=16000,    padding_value=0,    do_normalize=A,    return_attention_mask=A,    )
			a       =     HubertModel(A  )

			recursively_load_weights(A,    A  )

			feature_extractor.save_pretrained(A  )
			hf_model.save_pretrained(A  )


if __name__ == "__main__":
		__lowerCAmelCase						:							Any			     =						argparse.ArgumentParser()
		parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
		__lowerCAmelCase						:							Union[str, Any]			     =						parser.parse_args()
		convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_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 		A						(  ):




			"""simple docstring"""


			UpperCAmelCase__		:Tuple 		=    Dataset.from_dict({'i': range(17							)}							)
			UpperCAmelCase__		:Optional[Any] 		=    len(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Dict 		=    3
			UpperCAmelCase__		:List[str] 		=    [
			    split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=SCREAMING_SNAKE_CASE     ,						world_size=SCREAMING_SNAKE_CASE							) for rank in range(SCREAMING_SNAKE_CASE							)
			]
			assert sum(len(SCREAMING_SNAKE_CASE							) 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 		A						(  ):




			"""simple docstring"""


			def gen():
						return ({"i": i} for i in range(17							))

			UpperCAmelCase__		:Union[str, Any] 		=    3
			UpperCAmelCase__		:Union[str, Any] 		=    IterableDataset.from_generator(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Union[str, Any] 		=    len(list(SCREAMING_SNAKE_CASE							)							)
			UpperCAmelCase__		:Dict 		=    [
			    split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=SCREAMING_SNAKE_CASE     ,						world_size=SCREAMING_SNAKE_CASE							) for rank in range(SCREAMING_SNAKE_CASE							)
			]
			assert sum(len(list(SCREAMING_SNAKE_CASE							)							) 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 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			def gen(SCREAMING_SNAKE_CASE							):
						for shard in shards:
									yield from ({"i": i, "shard": shard} for i in range(17							))

			UpperCAmelCase__		:int 		=    3
			UpperCAmelCase__		:Any 		=    shards_per_node * world_size
			UpperCAmelCase__		:int 		=    {'shards': [f"""shard_{shard_idx}.txt""" for shard_idx in range(SCREAMING_SNAKE_CASE							)]}
			UpperCAmelCase__		:Optional[Any] 		=    IterableDataset.from_generator(SCREAMING_SNAKE_CASE     ,						gen_kwargs=SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Tuple 		=    len(list(SCREAMING_SNAKE_CASE							)							)
			assert full_ds.n_shards == world_size * shards_per_node
			UpperCAmelCase__		:Optional[Any] 		=    [
			    split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=SCREAMING_SNAKE_CASE     ,						world_size=SCREAMING_SNAKE_CASE							) for rank in range(SCREAMING_SNAKE_CASE							)
			]
			assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
			assert sum(len(list(SCREAMING_SNAKE_CASE							)							) 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 		A						(  ):




			"""simple docstring"""


			def gen():
						return ({"i": i} for i in range(17							))

			UpperCAmelCase__		:Tuple 		=    2
			UpperCAmelCase__		:List[str] 		=    IterableDataset.from_generator(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:str 		=    len(list(SCREAMING_SNAKE_CASE							)							)

			UpperCAmelCase__		:List[str] 		=    split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=0     ,						world_size=SCREAMING_SNAKE_CASE							).shuffle(seed=42							)
			assert len(list(SCREAMING_SNAKE_CASE							)							) == 1 + full_size // world_size
			with pytest.raises(SCREAMING_SNAKE_CASE							):
						split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=0     ,						world_size=SCREAMING_SNAKE_CASE							).shuffle()

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




@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 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:Dict 		=    2
			UpperCAmelCase__		:str 		=    get_torch_dist_unique_port()
			UpperCAmelCase__		:Optional[int] 		=    Path(SCREAMING_SNAKE_CASE							).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
			UpperCAmelCase__		:Any 		=    f"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
			UpperCAmelCase__		:Optional[int] 		=    f"""
        --streaming={streaming}
    """.split()
			UpperCAmelCase__		:Optional[Any] 		=    [sys.executable] + distributed_args + args
			execute_subprocess_async(SCREAMING_SNAKE_CASE     ,						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 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:List[str] 		=    True
			UpperCAmelCase__		:Optional[Any] 		=    get_torch_dist_unique_port()
			UpperCAmelCase__		:Union[str, Any] 		=    Path(SCREAMING_SNAKE_CASE							).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
			UpperCAmelCase__		:Union[str, Any] 		=    f"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
			UpperCAmelCase__		:Union[str, Any] 		=    f"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
			UpperCAmelCase__		:List[Any] 		=    [sys.executable] + distributed_args + args
			execute_subprocess_async(SCREAMING_SNAKE_CASE     ,						env=os.environ.copy()							)





