"""simple docstring"""
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()
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_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__      (     _UpperCamelCase    :			Optional[int]	,     _UpperCamelCase    :			List[Any]	,     _UpperCamelCase    :			int	,     _UpperCamelCase    :			List[str]	,     _UpperCamelCase    :			Dict   )   ->			Tuple:





						"""simple docstring"""






						for attribute in key.split('.'   ):
												snake_case        =     getattr(_UpperCamelCase	,     _UpperCamelCase   )

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

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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[Any]	,     _UpperCamelCase    :			Union[str, Any]	,     _UpperCamelCase    :			Any   )   ->			List[str]:





						"""simple docstring"""






						snake_case        =     []
						snake_case        =     fairseq_model.state_dict()

						snake_case        =     hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												snake_case        =     False
												if "conv_layers" in name:
																		load_conv_layer(
																		    _UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase	,     hf_model.config.feat_extract_norm == 'group'	,     )
																		snake_case        =     True
												else:
																		for key, mapped_key in MAPPING.items():
																								snake_case        =     '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):
																														snake_case        =     True
																														if "*" in mapped_key:
																																				snake_case        =     name.split(_UpperCamelCase   )[0].split('.'   )[-2]
																																				snake_case        =     mapped_key.replace('*'	,     _UpperCamelCase   )
																														if "weight_g" in name:
																																				snake_case        =     'weight_g'
																														elif "weight_v" in name:
																																				snake_case        =     'weight_v'
																														elif "weight" in name:
																																				snake_case        =     'weight'
																														elif "bias" in name:
																																				snake_case        =     'bias'
																														else:
																																				snake_case        =     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 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[int]	,     _UpperCamelCase    :			List[Any]	,     _UpperCamelCase    :			Tuple	,     _UpperCamelCase    :			Tuple	,     _UpperCamelCase    :			Tuple   )   ->			Union[str, Any]:





						"""simple docstring"""






						snake_case        =     full_name.split('conv_layers.'   )[-1]
						snake_case        =     name.split('.'   )
						snake_case        =     int(items[0]   )
						snake_case        =     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."""
																		)
																		snake_case        =     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."""
																		)
																		snake_case        =     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."
																		)
																		snake_case        =     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."""
																		)
																		snake_case        =     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 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[Any]	,     _UpperCamelCase    :			str	,     _UpperCamelCase    :			str=None	,     _UpperCamelCase    :			Tuple=None	,     _UpperCamelCase    :			Union[str, Any]=True   )   ->			Optional[int]:





						"""simple docstring"""






						if config_path is not None:
												snake_case        =     HubertConfig.from_pretrained(_UpperCamelCase   )
						else:
												snake_case        =     HubertConfig()

						if is_finetuned:
												if dict_path:
																		snake_case        =     Dictionary.load(_UpperCamelCase   )

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		snake_case        =     target_dict.pad_index
																		snake_case        =     target_dict.bos_index
																		snake_case        =     target_dict.eos_index
																		snake_case        =     len(target_dict.symbols   )
																		snake_case        =     os.path.join(_UpperCamelCase	,     'vocab.json'   )
																		if not os.path.isdir(_UpperCamelCase   ):
																								logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(_UpperCamelCase   )   )
																								return
																		os.makedirs(_UpperCamelCase	,     exist_ok=_UpperCamelCase   )
																		with open(_UpperCamelCase	,     'w'	,     encoding='utf-8'   ) as vocab_handle:
																								json.dump(target_dict.indices	,     _UpperCamelCase   )
																		snake_case        =     WavaVecaCTCTokenizer(
																		    _UpperCamelCase	,     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=_UpperCamelCase	,     )
																		snake_case        =     True if config.feat_extract_norm == 'layer' else False
																		snake_case        =     WavaVecaFeatureExtractor(
																		    feature_size=1	,     sampling_rate=1_6_0_0_0	,     padding_value=0	,     do_normalize=_UpperCamelCase	,     return_attention_mask=_UpperCamelCase	,     )
																		snake_case        =     WavaVecaProcessor(feature_extractor=_UpperCamelCase	,     tokenizer=_UpperCamelCase   )
																		processor.save_pretrained(_UpperCamelCase   )

												snake_case        =     HubertForCTC(_UpperCamelCase   )
						else:
												snake_case        =     HubertModel(_UpperCamelCase   )

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

						snake_case        =     model[0].eval()

						recursively_load_weights(_UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase   )

						hf_wavavec.save_pretrained(_UpperCamelCase   )


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("--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__   =					parser.parse_args()
					convert_hubert_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)



"""simple docstring"""
import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Any   )   ->			int:





						"""simple docstring"""






						snake_case        =     {}

						snake_case        =     job['started_at']
						snake_case        =     job['completed_at']

						snake_case        =     date_parser.parse(_UpperCamelCase   )
						snake_case        =     date_parser.parse(_UpperCamelCase   )

						snake_case        =     round((end_datetime - start_datetime).total_seconds() / 60.0   )

						snake_case        =     start
						snake_case        =     end
						snake_case        =     duration_in_min

						return job_info





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Dict	,     _UpperCamelCase    :			Any=None   )   ->			Union[str, Any]:





						"""simple docstring"""






						snake_case        =     None
						if token is not None:
												snake_case        =     {'Accept': 'application/vnd.github+json', 'Authorization': f"""Bearer {token}"""}

						snake_case        =     f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
						snake_case        =     requests.get(_UpperCamelCase	,     headers=_UpperCamelCase   ).json()
						snake_case        =     {}

						try:
												job_time.update({job['name']: extract_time_from_single_job(_UpperCamelCase   ) for job in result['jobs']}   )
												snake_case        =     math.ceil((result['total_count'] - 1_0_0) / 1_0_0   )

												for i in range(_UpperCamelCase   ):
																		snake_case        =     requests.get(url + f"""&page={i + 2}"""	,     headers=_UpperCamelCase   ).json()
																		job_time.update({job['name']: extract_time_from_single_job(_UpperCamelCase   ) 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__":
					SCREAMING_SNAKE_CASE__   =					argparse.ArgumentParser()
					# Required parameters
					parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
					SCREAMING_SNAKE_CASE__   =					parser.parse_args()

					SCREAMING_SNAKE_CASE__   =					get_job_time(args.workflow_run_id)
					SCREAMING_SNAKE_CASE__   =					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']}""")


