


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

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



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		Dict:
    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
                _lowercase :							Optional[int]   	=	'lm_head'

        _lowercase :							int   	=	getattr(lowerCamelCase_							,				lowerCamelCase_				)

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

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



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		Union[str, Any]:
    _lowercase :							List[Any]   	=	[]
    _lowercase :							str   	=	fairseq_model.state_dict()

    _lowercase :							List[str]   	=	hf_model.unispeech.feature_extractor

    for name, value in fairseq_dict.items():
        _lowercase :							str   	=	False
        if "conv_layers" in name:
            load_conv_layer(
                lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				hf_model.config.feat_extract_norm == 'group'							,				)
            _lowercase :							Optional[int]   	=	True
        else:
            for key, mapped_key in MAPPING.items():
                _lowercase :							Dict   	=	'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]:
                    _lowercase :							Union[str, Any]   	=	True
                    if "*" in mapped_key:
                        _lowercase :							Any   	=	name.split(lowerCamelCase_				)[0].split('.'				)[-2]
                        _lowercase :							str   	=	mapped_key.replace('*'							,				lowerCamelCase_				)
                    if "weight_g" in name:
                        _lowercase :							Tuple   	=	'weight_g'
                    elif "weight_v" in name:
                        _lowercase :							Union[str, Any]   	=	'weight_v'
                    elif "bias" in name:
                        _lowercase :							str   	=	'bias'
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        _lowercase :							str   	=	'weight'
                    else:
                        _lowercase :							Dict   	=	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_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		int:
    _lowercase :							Union[str, Any]   	=	full_name.split('conv_layers.'				)[-1]
    _lowercase :							Union[str, Any]   	=	name.split('.'				)
    _lowercase :							List[str]   	=	int(items[0]				)
    _lowercase :							Dict   	=	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 :							List[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.'''
            )
            _lowercase :							Optional[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."
            )
            _lowercase :							Any   	=	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 :							str   	=	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_							,				lowerCamelCase_							,				lowerCamelCase_=None							,				lowerCamelCase_=None							,				lowerCamelCase_=True				)					->		Tuple:
    if config_path is not None:
        _lowercase :							Tuple   	=	UniSpeechConfig.from_pretrained(lowerCamelCase_				)
    else:
        _lowercase :							Union[str, Any]   	=	UniSpeechConfig()

    if is_finetuned:
        if dict_path:
            _lowercase :							Any   	=	Dictionary.load_from_json(lowerCamelCase_				)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            _lowercase :							List[str]   	=	target_dict.pad_index
            _lowercase :							List[Any]   	=	target_dict.bos_index
            _lowercase :							int   	=	target_dict.eos_index
            _lowercase :							List[Any]   	=	len(target_dict.symbols				)
            _lowercase :							List[str]   	=	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_				)
            _lowercase :							Optional[Any]   	=	target_dict.indices

            # fairseq has the <pad> and <s> switched
            _lowercase :							str   	=	42
            _lowercase :							str   	=	43
            with open(lowerCamelCase_							,				'w'							,				encoding='utf-8'				) as vocab_handle:
                json.dump(lowerCamelCase_							,				lowerCamelCase_				)
            _lowercase :							Dict   	=	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_							,				)
            _lowercase :							Any   	=	True if config.feat_extract_norm == 'layer' else False
            _lowercase :							Any   	=	WavaVecaFeatureExtractor(
                feature_size=1							,				sampling_rate=1_6000							,				padding_value=0							,				do_normalize=lowerCamelCase_							,				return_attention_mask=lowerCamelCase_							,				)
            _lowercase :							Optional[Any]   	=	WavaVecaProcessor(feature_extractor=lowerCamelCase_							,				tokenizer=lowerCamelCase_				)
            processor.save_pretrained(lowerCamelCase_				)

        _lowercase :							Tuple   	=	UniSpeechForCTC(lowerCamelCase_				)
    else:
        _lowercase :							str   	=	UniSpeechForPreTraining(lowerCamelCase_				)

    if is_finetuned:
        _lowercase	,       _lowercase	,       _lowercase :							str   	=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]							,				arg_overrides={'data': '/'.join(dict_path.split('/'				)[:-1]				), 'w2v_path': checkpoint_path}				)
    else:
        _lowercase	,       _lowercase	,       _lowercase :							Union[str, Any]   	=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				)

    _lowercase :							int   	=	model[0].eval()

    recursively_load_weights(lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)

    hf_unispeech.save_pretrained(lowerCamelCase_				)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE :   List[str]							      =				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"
 )
 SCREAMING_SNAKE_CASE :   List[str]							      =				parser.parse_args()
 convert_unispeech_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )






from jiwer import compute_measures

import datasets


SCREAMING_SNAKE_CASE :   Tuple							      =				"\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

SCREAMING_SNAKE_CASE :   Optional[Any]							      =				"\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n"

SCREAMING_SNAKE_CASE :   Any							      =				"\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> wer = datasets.load_metric(\"wer\")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,				_KWARGS_DESCRIPTION     )
class _lowerCamelCase(		datasets.Metric     ):
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           return datasets.MetricInfo(
               description=_DESCRIPTION,   citation=_CITATION,   inputs_description=_KWARGS_DESCRIPTION,   features=datasets.Features(
                   {
                       'predictions': datasets.Value('string',   id='sequence'),
                       'references': datasets.Value('string',   id='sequence'),
                   }),   codebase_urls=['https://github.com/jitsi/jiwer/'],   reference_urls=[
                   'https://en.wikipedia.org/wiki/Word_error_rate',
               ],   )




       def     UpperCamelCase  (      self,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase=False) ->     Optional[Any]:




           """simple docstring"""

           if concatenate_texts:
               return compute_measures(lowerCamelCase,   lowerCamelCase)["wer"]
           else:
               _lowercase :							Dict   	=	0
               _lowercase :							Optional[int]   	=	0
               for prediction, reference in zip(lowerCamelCase,   lowerCamelCase):
                   _lowercase :							List[Any]   	=	compute_measures(lowerCamelCase,   lowerCamelCase)
                   incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                   total += measures["substitutions"] + measures["deletions"] + measures["hits"]
               return incorrect / total


