





'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__lowerCAmelCase				:      List[str]						      =logging.get_logger(__name__)

__lowerCAmelCase				:      Union[str, 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",
    "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",
}
__lowerCAmelCase				:      Union[str, Any]						      =[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def 		UpperCamelCase							(				_lowerCamelCase   :   Tuple		,			_lowerCamelCase   :   List[str]		,			_lowerCamelCase   :   Optional[int]		,			_lowerCamelCase   :   List[Any]		,			_lowerCamelCase   :   List[Any]		,			_lowerCamelCase   :   Optional[Any]			):
    for attribute in key.split("."			):
        if is_finetuned:
            if attribute in ["quantizer", "project_q", "project_hid"]:
                # those layers are only relevant for pretraining and should be dropped
                return

            if attribute == "ctc_proj":
                # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
                A__							=     "lm_head"

        A__							=     getattr(_lowerCamelCase		,			_lowerCamelCase			)

    if weight_type is not None:
        A__							=     getattr(_lowerCamelCase		,			_lowerCamelCase			).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 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   Optional[int]		,			_lowerCamelCase   :   List[str]			):
    A__							=     []
    A__							=     fairseq_model.state_dict()

    A__							=     hf_model.unispeech.feature_extractor

    for name, value in fairseq_dict.items():
        A__							=     False
        if "conv_layers" in name:
            load_conv_layer(
                _lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase		,			hf_model.config.feat_extract_norm == "group"		,			)
            A__							=     True
        else:
            for key, mapped_key in MAPPING.items():
                A__							=     "unispeech." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                if key in name or key.split("w2v_model."			)[-1] == name.split("."			)[0]:
                    A__							=     True
                    if "*" in mapped_key:
                        A__							=     name.split(_lowerCamelCase			)[0].split("."			)[-2]
                        A__							=     mapped_key.replace("*"		,			_lowerCamelCase			)
                    if "weight_g" in name:
                        A__							=     "weight_g"
                    elif "weight_v" in name:
                        A__							=     "weight_v"
                    elif "bias" in name:
                        A__							=     "bias"
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        A__							=     "weight"
                    else:
                        A__							=     None
                    set_recursively(_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)
                continue
        if not is_used:
            unused_weights.append(_lowerCamelCase			)

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





def 		UpperCamelCase							(				_lowerCamelCase   :   Any		,			_lowerCamelCase   :   Dict		,			_lowerCamelCase   :   List[str]		,			_lowerCamelCase   :   Optional[Any]		,			_lowerCamelCase   :   Union[str, Any]			):
    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(_lowerCamelCase			)





@torch.no_grad()
def 		UpperCamelCase							(				_lowerCamelCase   :   Union[str, Any]		,			_lowerCamelCase   :   Any		,			_lowerCamelCase   :   int=None		,			_lowerCamelCase   :   Tuple=None		,			_lowerCamelCase   :   Optional[Any]=True			):
    if config_path is not None:
        A__							=     UniSpeechConfig.from_pretrained(_lowerCamelCase			)
    else:
        A__							=     UniSpeechConfig()

    if is_finetuned:
        if dict_path:
            A__							=     Dictionary.load_from_json(_lowerCamelCase			)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            A__							=     target_dict.pad_index
            A__							=     target_dict.bos_index
            A__							=     target_dict.eos_index
            A__							=     len(target_dict.symbols			)
            A__							=     os.path.join(_lowerCamelCase		,			"vocab.json"			)
            if not os.path.isdir(_lowerCamelCase			):
                logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_lowerCamelCase			)			)
                return
            os.makedirs(_lowerCamelCase		,			exist_ok=_lowerCamelCase			)
            A__							=     target_dict.indices

            # fairseq has the <pad> and <s> switched
            A__							=     42
            A__							=     43
            with open(_lowerCamelCase		,			"w"		,			encoding="utf-8"			) as vocab_handle:
                json.dump(_lowerCamelCase		,			_lowerCamelCase			)
            A__							=     WavaVecaPhonemeCTCTokenizer(
                _lowerCamelCase		,			unk_token=target_dict.unk_word		,			pad_token=target_dict.pad_word		,			bos_token=target_dict.bos_word		,			eos_token=target_dict.eos_word		,			word_delimiter_token="|"		,			do_lower_case=_lowerCamelCase		,			)
            A__							=     True if config.feat_extract_norm == "layer" else False
            A__							=     WavaVecaFeatureExtractor(
                feature_size=1		,			sampling_rate=1_60_00		,			padding_value=0		,			do_normalize=_lowerCamelCase		,			return_attention_mask=_lowerCamelCase		,			)
            A__							=     WavaVecaProcessor(feature_extractor=_lowerCamelCase		,			tokenizer=_lowerCamelCase			)
            processor.save_pretrained(_lowerCamelCase			)

        A__							=     UniSpeechForCTC(_lowerCamelCase			)
    else:
        A__							=     UniSpeechForPreTraining(_lowerCamelCase			)

    if is_finetuned:
        A__,      A__,      A__							=     fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]		,			arg_overrides={"data": "/".join(dict_path.split("/"			)[:-1]			), "w2v_path": checkpoint_path}			)
    else:
        A__,      A__,      A__							=     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			)

    A__							=     model[0].eval()

    recursively_load_weights(_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)

    hf_unispeech.save_pretrained(_lowerCamelCase			)


if __name__ == "__main__":
  __lowerCAmelCase				:      Union[str, 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("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
  parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  parser.add_argument(
      "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
  )
  __lowerCAmelCase				:      Optional[Any]						      =parser.parse_args()
  convert_unispeech_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )










'''simple docstring'''

import string
from math import logaa





def 		UpperCamelCase							(				_lowerCamelCase   :   str		,			_lowerCamelCase   :   str			):
    A__							=     document.translate(
        str.maketrans(""		,			""		,			string.punctuation			)			).replace("\n"		,			""			)
    A__							=     document_without_punctuation.split(" "			)  # word tokenization
    return len([word for word in tokenize_document if word.lower() == term.lower()]			)





def 		UpperCamelCase							(				_lowerCamelCase   :   str		,			_lowerCamelCase   :   str			):
    A__							=     corpus.lower().translate(
        str.maketrans(""		,			""		,			string.punctuation			)			)  # strip all punctuation and replace it with ''
    A__							=     corpus_without_punctuation.split("\n"			)
    A__							=     term.lower()
    return (len([doc for doc in docs if term in doc]			), len(_lowerCamelCase			))





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   Tuple=False			):
    if smoothing:
        if n == 0:
            raise ValueError("log10(0) is undefined."			)
        return round(1 + logaa(n / (1 + df)			)		,			3			)

    if df == 0:
        raise ZeroDivisionError("df must be > 0"			)
    elif n == 0:
        raise ValueError("log10(0) is undefined."			)
    return round(logaa(n / df			)		,			3			)





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int			):
    return round(tf * idf		,			3			)



