import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Optional[Any]			  =		{}

    __UpperCamelCase     :str			  =		job['''started_at''']
    __UpperCamelCase     :Optional[int]			  =		job['''completed_at''']

    __UpperCamelCase     :int			  =		date_parser.parse(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :str			  =		date_parser.parse(SCREAMING_SNAKE_CASE	)

    __UpperCamelCase     :Any			  =		round((end_datetime - start_datetime).total_seconds() / 60.0	)

    __UpperCamelCase     :int			  =		start
    __UpperCamelCase     :Optional[Any]			  =		end
    __UpperCamelCase     :Tuple			  =		duration_in_min

    return job_info



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=None	):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		None
    if token is not None:
        __UpperCamelCase     :Optional[int]			  =		{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f"""Bearer {token}"""}

    __UpperCamelCase     :List[str]			  =		f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
    __UpperCamelCase     :Tuple			  =		requests.get(SCREAMING_SNAKE_CASE  ,			headers=SCREAMING_SNAKE_CASE	).json()
    __UpperCamelCase     :Union[str, Any]			  =		{}

    try:
        job_time.update({job['''name''']: extract_time_from_single_job(SCREAMING_SNAKE_CASE	) for job in result['''jobs''']}	)
        __UpperCamelCase     :Optional[Any]			  =		math.ceil((result['''total_count'''] - 100) / 100	)

        for i in range(SCREAMING_SNAKE_CASE	):
            __UpperCamelCase     :str			  =		requests.get(url + f"""&page={i + 2}"""  ,			headers=SCREAMING_SNAKE_CASE	).json()
            job_time.update({job['''name''']: extract_time_from_single_job(SCREAMING_SNAKE_CASE	) for job in result['''jobs''']}	)

        return job_time
    except Exception:
        print(f"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""	)

    return {}


if __name__ == "__main__":
    __lowercase							 =					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
    __lowercase							 =					parser.parse_args()

    __lowercase							 =					get_job_time(args.workflow_run_id)
    __lowercase							 =					dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))

    for k, v in job_time.items():
        print(F'{k}: {v["duration"]}')






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

__lowercase							 =					{
    '''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''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    for attribute in key.split('''.'''	):
        __UpperCamelCase     :str			  =		getattr(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

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

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		[]
    __UpperCamelCase     :int			  =		fairseq_model.state_dict()

    __UpperCamelCase     :List[Any]			  =		hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        __UpperCamelCase     :List[Any]			  =		False
        if "conv_layers" in name:
            load_conv_layer(
                SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			hf_model.config.feat_extract_norm == '''group'''  ,			)
            __UpperCamelCase     :List[str]			  =		True
        else:
            for key, mapped_key in MAPPING.items():
                __UpperCamelCase     :Dict			  =		'''hubert.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

                if key in name or (key.split('''w2v_model.'''	)[-1] == name.split('''.'''	)[0] and not is_finetuned):
                    __UpperCamelCase     :Optional[Any]			  =		True
                    if "*" in mapped_key:
                        __UpperCamelCase     :List[str]			  =		name.split(SCREAMING_SNAKE_CASE	)[0].split('''.'''	)[-2]
                        __UpperCamelCase     :Optional[int]			  =		mapped_key.replace('''*'''  ,			SCREAMING_SNAKE_CASE	)
                    if "weight_g" in name:
                        __UpperCamelCase     :int			  =		'''weight_g'''
                    elif "weight_v" in name:
                        __UpperCamelCase     :List[Any]			  =		'''weight_v'''
                    elif "weight" in name:
                        __UpperCamelCase     :Dict			  =		'''weight'''
                    elif "bias" in name:
                        __UpperCamelCase     :Dict			  =		'''bias'''
                    else:
                        __UpperCamelCase     :Dict			  =		None
                    set_recursively(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
                continue
        if not is_used:
            unused_weights.append(SCREAMING_SNAKE_CASE	)

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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



@torch.no_grad()
def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=None  ,			SCREAMING_SNAKE_CASE=None  ,			SCREAMING_SNAKE_CASE=True	):
    '''simple docstring'''







    if config_path is not None:
        __UpperCamelCase     :Tuple			  =		HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE	)
    else:
        __UpperCamelCase     :Optional[int]			  =		HubertConfig()

    if is_finetuned:
        if dict_path:
            __UpperCamelCase     :Optional[int]			  =		Dictionary.load(SCREAMING_SNAKE_CASE	)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            __UpperCamelCase     :Optional[int]			  =		target_dict.pad_index
            __UpperCamelCase     :Dict			  =		target_dict.bos_index
            __UpperCamelCase     :str			  =		target_dict.eos_index
            __UpperCamelCase     :Dict			  =		len(target_dict.symbols	)
            __UpperCamelCase     :List[Any]			  =		os.path.join(SCREAMING_SNAKE_CASE  ,			'''vocab.json'''	)
            if not os.path.isdir(SCREAMING_SNAKE_CASE	):
                logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(SCREAMING_SNAKE_CASE	)	)
                return
            os.makedirs(SCREAMING_SNAKE_CASE  ,			exist_ok=SCREAMING_SNAKE_CASE	)
            with open(SCREAMING_SNAKE_CASE  ,			'''w'''  ,			encoding='''utf-8'''	) as vocab_handle:
                json.dump(target_dict.indices  ,			SCREAMING_SNAKE_CASE	)
            __UpperCamelCase     :Optional[int]			  =		WavaVecaCTCTokenizer(
                SCREAMING_SNAKE_CASE  ,			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=SCREAMING_SNAKE_CASE  ,			)
            __UpperCamelCase     :Union[str, Any]			  =		True if config.feat_extract_norm == '''layer''' else False
            __UpperCamelCase     :Any			  =		WavaVecaFeatureExtractor(
                feature_size=1  ,			sampling_rate=16_000  ,			padding_value=0  ,			do_normalize=SCREAMING_SNAKE_CASE  ,			return_attention_mask=SCREAMING_SNAKE_CASE  ,			)
            __UpperCamelCase     :Any			  =		WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE  ,			tokenizer=SCREAMING_SNAKE_CASE	)
            processor.save_pretrained(SCREAMING_SNAKE_CASE	)

        __UpperCamelCase     :List[str]			  =		HubertForCTC(SCREAMING_SNAKE_CASE	)
    else:
        __UpperCamelCase     :str			  =		HubertModel(SCREAMING_SNAKE_CASE	)

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

    __UpperCamelCase     :Dict			  =		model[0].eval()

    recursively_load_weights(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
    __lowercase							 =					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'''
    )
    __lowercase							 =					parser.parse_args()
    convert_hubert_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
    )





