






'''simple docstring'''






import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__A     						= logging.get_logger(__name__)

__A     						= {
    '''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 							_SCREAMING_SNAKE_CASE	(					A	:							Optional[int]	,		A	:							Tuple	,		A	:							Union[str, Any]	,		A	:							Optional[Any]	,		A	:							List[str]	)     ->			int:







   """simple docstring"""
   for attribute in key.split('.'	):
      __snake_case					:   int  				=			getattr(__lowerCAmelCase	,		__lowerCAmelCase	)

   if weight_type is not None:
      __snake_case					:   List[Any]  				=			getattr(__lowerCAmelCase	,		__lowerCAmelCase	).shape
   else:
      __snake_case					:   str  				=			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					:   str  				=			value
   elif weight_type == "weight_g":
      __snake_case					:   List[Any]  				=			value
   elif weight_type == "weight_v":
      __snake_case					:   Tuple  				=			value
   elif weight_type == "bias":
      __snake_case					:   Union[str, Any]  				=			value
   else:
      __snake_case					:   Union[str, Any]  				=			value

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




def 							_SCREAMING_SNAKE_CASE	(					A	:							int	,		A	:							Optional[int]	)     ->			Union[str, Any]:







   """simple docstring"""
   __snake_case					:   Tuple  				=			[]
   __snake_case					:   int  				=			fairseq_model.state_dict()

   __snake_case					:   Optional[int]  				=			hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      __snake_case					:   Tuple  				=			False
      if "conv_layers" in name:
         load_conv_layer(
             __lowerCAmelCase	,		__lowerCAmelCase	,		__lowerCAmelCase	,		__lowerCAmelCase	,		hf_model.config.feat_extract_norm == 'group'	,		)
         __snake_case					:   str  				=			True
      else:
         for key, mapped_key in MAPPING.items():
            __snake_case					:   Any  				=			mapped_key

            if key in name:
               __snake_case					:   Dict  				=			True
               if "*" in mapped_key:
                  __snake_case					:   Tuple  				=			name.split(__lowerCAmelCase	)[0].split('.'	)[-2]
                  __snake_case					:   Optional[Any]  				=			mapped_key.replace('*'	,		__lowerCAmelCase	)
               if "weight_g" in name:
                  __snake_case					:   Dict  				=			"weight_g"
               elif "weight_v" in name:
                  __snake_case					:   List[Any]  				=			"weight_v"
               elif "weight" in name:
                  __snake_case					:   Union[str, Any]  				=			"weight"
               elif "bias" in name:
                  __snake_case					:   List[Any]  				=			"bias"
               else:
                  __snake_case					:   Tuple  				=			None
               set_recursively(__lowerCAmelCase	,		__lowerCAmelCase	,		__lowerCAmelCase	,		__lowerCAmelCase	,		__lowerCAmelCase	)
            continue
      if not is_used:
         unused_weights.append(__lowerCAmelCase	)

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




def 							_SCREAMING_SNAKE_CASE	(					A	:							List[str]	,		A	:							str	,		A	:							Optional[Any]	,		A	:							str	,		A	:							Optional[Any]	)     ->			List[Any]:







   """simple docstring"""
   __snake_case					:   Dict  				=			full_name.split('conv_layers.'	)[-1]
   __snake_case					:   str  				=			name.split('.'	)
   __snake_case					:   Any  				=			int(items[0]	)
   __snake_case					:   Union[str, Any]  				=			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					:   Optional[int]  				=			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					:   Union[str, Any]  				=			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					:   Dict  				=			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					:   List[str]  				=			value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
   else:
      unused_weights.append(__lowerCAmelCase	)




def 							_SCREAMING_SNAKE_CASE	(					A	:							Optional[int]	)     ->			Any:







   """simple docstring"""
   __snake_case					:   int  				=			HubertConfig()
   __snake_case					:   Any  				=			model.config

   __snake_case					:   Optional[Any]  				=			fs_config.activation_dropout
   __snake_case					:   str  				=			False
   __snake_case					:   Tuple  				=			fs_config.attention_dropout
   __snake_case					:   Dict  				=			False
   __snake_case					:   int  				=			eval(fs_config.extractor_conv_feature_layers	)
   __snake_case					:   Optional[Any]  				=			[x[0] for x in conv_layers]
   __snake_case					:   List[str]  				=			[x[1] for x in conv_layers]
   __snake_case					:   int  				=			[x[2] for x in conv_layers]
   __snake_case					:   str  				=			"gelu"
   __snake_case					:   Tuple  				=			"layer" if fs_config.extractor_mode == "layer_norm" else "group"
   __snake_case					:   str  				=			False
   __snake_case					:   Optional[int]  				=			0.0
   __snake_case					:   List[str]  				=			0.0
   __snake_case					:   List[Any]  				=			fs_config.activation_fn
   __snake_case					:   Optional[int]  				=			fs_config.dropout
   __snake_case					:   List[Any]  				=			fs_config.encoder_embed_dim
   __snake_case					:   Any  				=			0.02
   __snake_case					:   List[Any]  				=			fs_config.encoder_ffn_embed_dim
   __snake_case					:   Dict  				=			1e-5
   __snake_case					:   Optional[Any]  				=			0.0
   __snake_case					:   List[Any]  				=			fs_config.encoder_attention_heads
   __snake_case					:   Dict  				=			fs_config.conv_pos_groups
   __snake_case					:   Any  				=			fs_config.conv_pos
   __snake_case					:   int  				=			len(__lowerCAmelCase	)
   __snake_case					:   Dict  				=			fs_config.encoder_layers

   return config




@torch.no_grad()
def 							_SCREAMING_SNAKE_CASE	(					A	:							str	,		A	:							str=None	)     ->			str:







   """simple docstring"""
   __snake_case					:   List[Any]  				=			distilhubert().model.model

   if config_path is not None:
      __snake_case					:   Tuple  				=			HubertConfig.from_pretrained(__lowerCAmelCase	)
   else:
      __snake_case					:   List[Any]  				=			convert_config(__lowerCAmelCase	)
   __snake_case					:   Any  				=			model.eval()

   __snake_case					:   Optional[Any]  				=			WavaVecaFeatureExtractor(
       feature_size=1	,		sampling_rate=1_60_00	,		padding_value=0	,		do_normalize=__lowerCAmelCase	,		return_attention_mask=__lowerCAmelCase	,		)
   __snake_case					:   Tuple  				=			HubertModel(__lowerCAmelCase	)

   recursively_load_weights(__lowerCAmelCase	,		__lowerCAmelCase	)

   feature_extractor.save_pretrained(__lowerCAmelCase	)
   hf_model.save_pretrained(__lowerCAmelCase	)


if __name__ == "__main__":
      __A     						= 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''')
      __A     						= parser.parse_args()
      convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)



'''simple docstring'''


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 			__lowerCAmelCase			():
		_UpperCAmelCase		:							Optional[int]             =   Dataset.from_dict({"i": range(17 )} )
		_UpperCAmelCase		:							int             =   len(__lowerCAmelCase )
		_UpperCAmelCase		:							Dict             =   3
		_UpperCAmelCase		:							str             =   [
		    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 			__lowerCAmelCase			():
		def gen():
				return ({"i": i} for i in range(17 ))

		_UpperCAmelCase		:							Any             =   3
		_UpperCAmelCase		:							Any             =   IterableDataset.from_generator(__lowerCAmelCase )
		_UpperCAmelCase		:							str             =   len(list(__lowerCAmelCase ) )
		_UpperCAmelCase		:							Tuple             =   [
		    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 			__lowerCAmelCase			(__lowerCAmelCase ):
		def gen(__lowerCAmelCase ):
				for shard in shards:
						yield from ({"i": i, "shard": shard} for i in range(17 ))

		_UpperCAmelCase		:							Tuple             =   3
		_UpperCAmelCase		:							Tuple             =   shards_per_node * world_size
		_UpperCAmelCase		:							Optional[int]             =   {"shards": [F"""shard_{shard_idx}.txt""" for shard_idx in range(__lowerCAmelCase )]}
		_UpperCAmelCase		:							int             =   IterableDataset.from_generator(__lowerCAmelCase   ,			gen_kwargs=__lowerCAmelCase )
		_UpperCAmelCase		:							Any             =   len(list(__lowerCAmelCase ) )
		assert full_ds.n_shards == world_size * shards_per_node
		_UpperCAmelCase		:							Optional[Any]             =   [
		    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 			__lowerCAmelCase			():
		def gen():
				return ({"i": i} for i in range(17 ))

		_UpperCAmelCase		:							List[str]             =   2
		_UpperCAmelCase		:							int             =   IterableDataset.from_generator(__lowerCAmelCase )
		_UpperCAmelCase		:							Union[str, Any]             =   len(list(__lowerCAmelCase ) )

		_UpperCAmelCase		:							int             =   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()

		_UpperCAmelCase		:							Dict             =   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 			__lowerCAmelCase			(__lowerCAmelCase ):
		_UpperCAmelCase		:							Union[str, Any]             =   2
		_UpperCAmelCase		:							Dict             =   get_torch_dist_unique_port()
		_UpperCAmelCase		:							int             =   Path(__lowerCAmelCase ).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
		_UpperCAmelCase		:							List[Any]             =   F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
		_UpperCAmelCase		:							List[str]             =   F"""
        --streaming={streaming}
    """.split()
		_UpperCAmelCase		:							Tuple             =   [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 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase ):
		_UpperCAmelCase		:							Tuple             =   True
		_UpperCAmelCase		:							int             =   get_torch_dist_unique_port()
		_UpperCAmelCase		:							Optional[int]             =   Path(__lowerCAmelCase ).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
		_UpperCAmelCase		:							List[str]             =   F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
		_UpperCAmelCase		:							int             =   F"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
		_UpperCAmelCase		:							Optional[int]             =   [sys.executable] + distributed_args + args
		execute_subprocess_async(__lowerCAmelCase   ,			env=os.environ.copy() )



