


'''simple docstring'''



from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]      ,    UpperCAmelCase_    :				int							)     ->		int:
     __lowerCamelCase   :	Union[str, Any]								=		[]
     for part_id in partition_order:
          __lowerCamelCase   :	List[str]								=		df.where(F'SPARK_PARTITION_ID() = {part_id}'							).collect()
          for row_idx, row in enumerate(UpperCAmelCase_							):
               expected_row_ids_and_row_dicts.append((F'{part_id}_{row_idx}', row.asDict())							)
     return expected_row_ids_and_row_dicts



@require_not_windows
@require_dill_gt_0_3_2
def 			UpperCAmelCase__					(	)     ->		Tuple:
     __lowerCamelCase   :	Optional[int]								=		pyspark.sql.SparkSession.builder.master('local[*]'							).appName('pyspark'							).getOrCreate()
     __lowerCamelCase   :	Any								=		spark.range(1_00							).repartition(1							)
     __lowerCamelCase   :	str								=		Spark(UpperCAmelCase_							)
     # The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
     # that each partition can hold 2 rows.
     spark_builder._repartition_df_if_needed(max_shard_size=16							)
     # Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
     assert spark_builder.df.rdd.getNumPartitions() == 50



@require_not_windows
@require_dill_gt_0_3_2
def 			UpperCAmelCase__					(	)     ->		Dict:
     __lowerCamelCase   :	Dict								=		pyspark.sql.SparkSession.builder.master('local[*]'							).appName('pyspark'							).getOrCreate()
     __lowerCamelCase   :	Union[str, Any]								=		spark.range(10							).repartition(2							)
     __lowerCamelCase   :	Union[str, Any]								=		[1, 0]
     __lowerCamelCase   :	Optional[int]								=		_generate_iterable_examples(UpperCAmelCase_      ,    UpperCAmelCase_							)  # Reverse the partitions.
     __lowerCamelCase   :	Union[str, Any]								=		_get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase_      ,    UpperCAmelCase_							)

     for i, (row_id, row_dict) in enumerate(generate_fn()							):
          __lowerCamelCase				,       __lowerCamelCase   :	int								=		expected_row_ids_and_row_dicts[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict



@require_not_windows
@require_dill_gt_0_3_2
def 			UpperCAmelCase__					(	)     ->		Dict:
     __lowerCamelCase   :	str								=		pyspark.sql.SparkSession.builder.master('local[*]'							).appName('pyspark'							).getOrCreate()
     __lowerCamelCase   :	int								=		spark.range(10							).repartition(1							)
     __lowerCamelCase   :	Optional[int]								=		SparkExamplesIterable(UpperCAmelCase_							)
     assert it.n_shards == 1
     for i, (row_id, row_dict) in enumerate(UpperCAmelCase_							):
          assert row_id == F'0_{i}'
          assert row_dict == {"id": i}



@require_not_windows
@require_dill_gt_0_3_2
def 			UpperCAmelCase__					(	)     ->		List[Any]:
     __lowerCamelCase   :	int								=		pyspark.sql.SparkSession.builder.master('local[*]'							).appName('pyspark'							).getOrCreate()
     __lowerCamelCase   :	List[Any]								=		spark.range(30							).repartition(3							)
     # Mock the generator so that shuffle reverses the partition indices.
     with patch('numpy.random.Generator'							) as generator_mock:
          __lowerCamelCase   :	int								=		lambda UpperCAmelCase_							: x.reverse()
          __lowerCamelCase   :	Dict								=		_get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase_      ,    [2, 1, 0]							)

          __lowerCamelCase   :	str								=		SparkExamplesIterable(UpperCAmelCase_							).shuffle_data_sources(UpperCAmelCase_							)
          assert shuffled_it.n_shards == 3
          for i, (row_id, row_dict) in enumerate(UpperCAmelCase_							):
               __lowerCamelCase				,       __lowerCamelCase   :	int								=		expected_row_ids_and_row_dicts[i]
               assert row_id == expected_row_id
               assert row_dict == expected_row_dict



@require_not_windows
@require_dill_gt_0_3_2
def 			UpperCAmelCase__					(	)     ->		str:
     __lowerCamelCase   :	int								=		pyspark.sql.SparkSession.builder.master('local[*]'							).appName('pyspark'							).getOrCreate()
     __lowerCamelCase   :	List[str]								=		spark.range(20							).repartition(4							)

     # Partitions 0 and 2
     __lowerCamelCase   :	int								=		SparkExamplesIterable(UpperCAmelCase_							).shard_data_sources(worker_id=0      ,    num_workers=2							)
     assert shard_it_a.n_shards == 2
     __lowerCamelCase   :	Any								=		_get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase_      ,    [0, 2]							)
     for i, (row_id, row_dict) in enumerate(UpperCAmelCase_							):
          __lowerCamelCase				,       __lowerCamelCase   :	Optional[Any]								=		expected_row_ids_and_row_dicts_a[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict

     # Partitions 1 and 3
     __lowerCamelCase   :	Union[str, Any]								=		SparkExamplesIterable(UpperCAmelCase_							).shard_data_sources(worker_id=1      ,    num_workers=2							)
     assert shard_it_a.n_shards == 2
     __lowerCamelCase   :	Union[str, Any]								=		_get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase_      ,    [1, 3]							)
     for i, (row_id, row_dict) in enumerate(UpperCAmelCase_							):
          __lowerCamelCase				,       __lowerCamelCase   :	Optional[Any]								=		expected_row_ids_and_row_dicts_a[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict



@require_not_windows
@require_dill_gt_0_3_2
def 			UpperCAmelCase__					(	)     ->		Optional[int]:
     __lowerCamelCase   :	Optional[Any]								=		pyspark.sql.SparkSession.builder.master('local[*]'							).appName('pyspark'							).getOrCreate()
     __lowerCamelCase   :	Optional[Any]								=		spark.range(1_00							).repartition(1							)
     __lowerCamelCase   :	Optional[Any]								=		Spark(UpperCAmelCase_							)
     # Choose a small max_shard_size for maximum partitioning.
     spark_builder._repartition_df_if_needed(max_shard_size=1							)
     # The new number of partitions should not be greater than the number of rows.
     assert spark_builder.df.rdd.getNumPartitions() == 1_00










'''simple docstring'''



import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
A__		:					Optional[int]												=  logging.get_logger(__name__)

A__		:					List[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.grep_linear""": """encoder.layers.*.attention.gru_rel_pos_linear""",
    """self_attn.relative_attention_bias""": """encoder.layers.*.attention.rel_attn_embed""",
    """self_attn.grep_a""": """encoder.layers.*.attention.gru_rel_pos_const""",
    """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""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """ctc_proj""",
    """mask_emb""": """masked_spec_embed""",
}
A__		:					Tuple												=  [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int      ,    UpperCAmelCase_    :				List[Any]      ,    UpperCAmelCase_    :				List[str]      ,    UpperCAmelCase_    :				List[Any]      ,    UpperCAmelCase_    :				Optional[int]							)     ->		Tuple:
     for attribute in key.split('.'							):
          __lowerCamelCase   :	List[Any]								=		getattr(UpperCAmelCase_      ,    UpperCAmelCase_							)

     if weight_type is not None:
          __lowerCamelCase   :	Any								=		getattr(UpperCAmelCase_      ,    UpperCAmelCase_							).shape
     else:
          __lowerCamelCase   :	Any								=		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":
          __lowerCamelCase   :	Tuple								=		value
     elif weight_type == "weight_g":
          __lowerCamelCase   :	Optional[int]								=		value
     elif weight_type == "weight_v":
          __lowerCamelCase   :	str								=		value
     elif weight_type == "bias":
          __lowerCamelCase   :	List[Any]								=		value
     else:
          __lowerCamelCase   :	List[str]								=		value

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



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				List[Any]      ,    UpperCAmelCase_    :				Union[str, Any]							)     ->		Optional[int]:
     __lowerCamelCase   :	Optional[Any]								=		[]
     __lowerCamelCase   :	Dict								=		fairseq_model.state_dict()

     __lowerCamelCase   :	List[str]								=		hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          __lowerCamelCase   :	Union[str, Any]								=		False
          if "conv_layers" in name:
               load_conv_layer(
                   UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    hf_model.config.feat_extract_norm == 'group'      ,    )
               __lowerCamelCase   :	str								=		True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split('w2v_model.'							)[-1] == name.split('.'							)[0]:
                         __lowerCamelCase   :	int								=		True
                         if "*" in mapped_key:
                              __lowerCamelCase   :	Optional[int]								=		name.split(UpperCAmelCase_							)[0].split('.'							)[-2]
                              __lowerCamelCase   :	List[str]								=		mapped_key.replace('*'      ,    UpperCAmelCase_							)
                         if "weight_g" in name:
                              __lowerCamelCase   :	Dict								=		'weight_g'
                         elif "weight_v" in name:
                              __lowerCamelCase   :	Any								=		'weight_v'
                         elif "bias" in name and "relative_attention_bias" not in name:
                              __lowerCamelCase   :	Any								=		'bias'
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              __lowerCamelCase   :	List[Any]								=		'weight'
                         else:
                              __lowerCamelCase   :	str								=		None

                         set_recursively(UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_							)
                    continue
          if not is_used:
               unused_weights.append(UpperCAmelCase_							)

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



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Any      ,    UpperCAmelCase_    :				List[Any]      ,    UpperCAmelCase_    :				Optional[int]      ,    UpperCAmelCase_    :				Optional[int]      ,    UpperCAmelCase_    :				List[Any]							)     ->		Tuple:
     __lowerCamelCase   :	List[str]								=		full_name.split('conv_layers.'							)[-1]
     __lowerCamelCase   :	List[Any]								=		name.split('.'							)
     __lowerCamelCase   :	Any								=		int(items[0]							)
     __lowerCamelCase   :	Optional[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.'
               )
               __lowerCamelCase   :	Union[str, Any]								=		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.'
               )
               __lowerCamelCase   :	Tuple								=		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."
               )
               __lowerCamelCase   :	str								=		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.'
               )
               __lowerCamelCase   :	List[Any]								=		value
               logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
     else:
          unused_weights.append(UpperCAmelCase_							)



@torch.no_grad()
def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int      ,    UpperCAmelCase_    :				Tuple      ,    UpperCAmelCase_    :				Optional[Any]=None							)     ->		Optional[int]:
     # load the pre-trained checkpoints
     __lowerCamelCase   :	Optional[Any]								=		torch.load(UpperCAmelCase_							)
     __lowerCamelCase   :	Optional[Any]								=		WavLMConfigOrig(checkpoint['cfg']							)
     __lowerCamelCase   :	Union[str, Any]								=		WavLMOrig(UpperCAmelCase_							)
     model.load_state_dict(checkpoint['model']							)
     model.eval()

     if config_path is not None:
          __lowerCamelCase   :	Optional[int]								=		WavLMConfig.from_pretrained(UpperCAmelCase_							)
     else:
          __lowerCamelCase   :	Any								=		WavLMConfig()

     __lowerCamelCase   :	Optional[int]								=		WavLMModel(UpperCAmelCase_							)

     recursively_load_weights(UpperCAmelCase_      ,    UpperCAmelCase_							)

     hf_wavlm.save_pretrained(UpperCAmelCase_							)


if __name__ == "__main__":
      A__		:					List[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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
      A__		:					List[str]												=  parser.parse_args()
      convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)






