



'''simple docstring'''





import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


SCREAMING_SNAKE_CASE__      					=     logging.getLogger(__name__)




def        lowerCamelCase  (				):





    '''simple docstring'''


    lowercase__		       =						argparse.ArgumentParser(
        description="Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset."       )
    parser.add_argument(
        "--dataset_name"     ,type=_snake_case     ,default="wikitext"     ,help="Name of the training. Explore datasets at: hf.co/datasets."     ,)
    parser.add_argument(
        "--dataset_config"     ,type=_snake_case     ,default="wikitext-103-raw-v1"     ,help="Configuration name of the dataset."       )
    parser.add_argument(
        "--tokenizer_name_or_path"     ,type=_snake_case     ,default="sayakpaul/unigram-tokenizer-wikitext"     ,help="Tokenizer identifier. Can be a local filepath or a Hub identifier."     ,)
    parser.add_argument(
        "--shard_size"     ,type=_snake_case     ,default=1_000     ,help="Number of entries to go in a single shard."     ,)
    parser.add_argument("--split"     ,type=_snake_case     ,default="train"     ,choices=["train", "test", "validation"]       )
    parser.add_argument(
        "--limit"     ,default=_snake_case     ,type=_snake_case     ,help="Limit the number of shards (used for debugging)."     ,)
    parser.add_argument(
        "--max_length"     ,type=_snake_case     ,default=512     ,help="Maximum sequence length. For training on TPUs, it helps to have a maximum"
        " sequence length that is a multiple of 8."     ,)
    parser.add_argument(
        "--output_dir"     ,default="tf-tpu"     ,type=_snake_case     ,help="Output directory where the TFRecord shards will be saved. If the"
        " path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord"
        " shards will be directly saved to a Google Cloud Storage bucket."     ,)

    lowercase__		       =						parser.parse_args()
    return args




def        lowerCamelCase  (				_snake_case  :						Dict       ):





    '''simple docstring'''


    def fn(_snake_case  :						Optional[int]       ):
        return tokenizer(examples["text"]       )

    return fn




def        lowerCamelCase  (				_snake_case  :						List[str]       ):





    '''simple docstring'''


    lowercase__		       =						[]
    for i in range(len(tokenized_data["input_ids"]       )       ):
        lowercase__		       =						{
            "input_ids": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["input_ids"][i]       )       ),
            "attention_mask": tf.train.Feature(
                intaa_list=tf.train.IntaaList(value=tokenized_data["attention_mask"][i]       )       ),
        }
        lowercase__		       =						tf.train.Features(feature=_snake_case       )
        lowercase__		       =						tf.train.Example(features=_snake_case       )
        lowercase__		       =						example.SerializeToString()
        records.append(_snake_case       )
    return records




def        lowerCamelCase  (				_snake_case  :						Dict       ):





    '''simple docstring'''


    lowercase__		       =						datasets.load_dataset(args.dataset_name     ,args.dataset_config     ,split=args.split       )

    if args.limit is not None:
        lowercase__		       =						min(len(_snake_case       )     ,args.limit       )
        lowercase__		       =						dataset.select(range(_snake_case       )       )
        print(f'''Limiting the dataset to {args.limit} entries.'''       )

    lowercase__		       =						AutoTokenizer.from_pretrained(args.tokenizer_name_or_path       )

    # Handle output directory creation.
    # For serializing into a Google Cloud Storage Bucket, one needs to first
    # create a bucket.
    if "gs" not in args.output_dir:
        if not os.path.exists(args.output_dir       ):
            os.makedirs(args.output_dir       )
        lowercase__		       =						os.path.join(args.output_dir     ,args.split       )
        if not os.path.exists(_snake_case       ):
            os.makedirs(_snake_case       )
    else:
        lowercase__		       =						os.path.join(args.output_dir     ,args.split       )

    # Tokenize the whole dataset at once.
    lowercase__		       =						tokenize_function(_snake_case       )
    lowercase__		       =						dataset.map(_snake_case     ,batched=_snake_case     ,num_proc=4     ,remove_columns=["text"]       )

    # We need to concatenate all our texts together, and then split the result
    # into chunks of a fixed size, which we will call block_size. To do this, we
    # will use the map method again, with the option batched=True. When we use batched=True,
    # the function we pass to map() will be passed multiple inputs at once, allowing us
    # to group them into more or fewer examples than we had in the input.
    # This allows us to create our new fixed-length samples. The advantage of this
    # method is that we don't lose a whole lot of content from the dataset compared to the
    # case where we simply tokenize with a pre-defined max_length.

    def group_texts(_snake_case  :						List[str]       ):
        # Concatenate all texts.
        lowercase__		       =						{k: sum(examples[k]     ,[]       ) for k in examples.keys()}
        lowercase__		       =						len(concatenated_examples[list(examples.keys()       )[0]]       )
        # We drop the small remainder, though you could add padding instead if the model supports it
        # In this, as in all things, we advise you to follow your heart 🫀
        lowercase__		       =						(total_length // args.max_length) * args.max_length
        # Split by chunks of max_len.
        lowercase__		       =						{
            k: [t[i : i + args.max_length] for i in range(0     ,_snake_case     ,args.max_length       )]
            for k, t in concatenated_examples.items()
        }
        return result

    lowercase__		       =						dataset_tokenized.map(_snake_case     ,batched=_snake_case     ,batch_size=1_000     ,num_proc=4       )

    lowercase__		       =						0
    lowercase__		       =						0
    for shard in range(0     ,len(_snake_case       )     ,args.shard_size       ):
        lowercase__		       =						grouped_dataset[shard : shard + args.shard_size]
        lowercase__		       =						len(dataset_snapshot["input_ids"]       )
        lowercase__		       =						os.path.join(_snake_case     ,f'''dataset-{shard_count}-{records_containing}.tfrecord'''       )
        lowercase__		       =						get_serialized_examples(_snake_case       )

        with tf.io.TFRecordWriter(_snake_case       ) as out_file:
            for i in range(len(_snake_case       )       ):
                lowercase__		       =						serialized_examples[i]
                out_file.write(_snake_case       )
            print("Wrote file {} containing {} records".format(_snake_case     ,_snake_case       )       )

        shard_count += 1
        total_records += records_containing

    with open(f'''split-{args.split}-records-count.txt'''     ,"w"       ) as f:
        print(f'''Total {args.split} records: {total_records}'''     ,file=_snake_case       )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__      					=     parse_args()
 main(args)









'''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()
SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      					=     {
    "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",
}
SCREAMING_SNAKE_CASE__      					=     [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]




def        lowerCamelCase  (				_snake_case  :						int     ,_snake_case  :						Union[str, Any]     ,_snake_case  :						Any     ,_snake_case  :						Optional[int]     ,_snake_case  :						List[str]       ):





    '''simple docstring'''


    for attribute in key.split("."       ):
        lowercase__		       =						getattr(_snake_case     ,_snake_case       )

    if weight_type is not None:
        lowercase__		       =						getattr(_snake_case     ,_snake_case       ).shape
    else:
        lowercase__		       =						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":
        lowercase__		       =						value
    elif weight_type == "weight_g":
        lowercase__		       =						value
    elif weight_type == "weight_v":
        lowercase__		       =						value
    elif weight_type == "bias":
        lowercase__		       =						value
    else:
        lowercase__		       =						value

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




def        lowerCamelCase  (				_snake_case  :						Any     ,_snake_case  :						int       ):





    '''simple docstring'''


    lowercase__		       =						[]
    lowercase__		       =						fairseq_model.state_dict()

    lowercase__		       =						hf_model.feature_extractor

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

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

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




def        lowerCamelCase  (				_snake_case  :						str     ,_snake_case  :						int     ,_snake_case  :						Optional[Any]     ,_snake_case  :						Tuple     ,_snake_case  :						List[Any]       ):





    '''simple docstring'''


    lowercase__		       =						full_name.split("conv_layers."       )[-1]
    lowercase__		       =						name.split("."       )
    lowercase__		       =						int(items[0]       )
    lowercase__		       =						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.'''
            )
            lowercase__		       =						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.'''
            )
            lowercase__		       =						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."
            )
            lowercase__		       =						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.'''
            )
            lowercase__		       =						value
            logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
    else:
        unused_weights.append(_snake_case       )




@torch.no_grad()
def        lowerCamelCase  (				_snake_case  :						Optional[int]     ,_snake_case  :						Any     ,_snake_case  :						List[Any]=None       ):





    '''simple docstring'''


    lowercase__		       =						torch.load(_snake_case       )
    lowercase__		       =						WavLMConfigOrig(checkpoint["cfg"]       )
    lowercase__		       =						WavLMOrig(_snake_case       )
    model.load_state_dict(checkpoint["model"]       )
    model.eval()

    if config_path is not None:
        lowercase__		       =						WavLMConfig.from_pretrained(_snake_case       )
    else:
        lowercase__		       =						WavLMConfig()

    lowercase__		       =						WavLMModel(_snake_case       )

    recursively_load_weights(_snake_case     ,_snake_case       )

    hf_wavlm.save_pretrained(_snake_case       )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__      					=     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")
 SCREAMING_SNAKE_CASE__      					=     parser.parse_args()
 convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




