"""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[Any] ,			_UpperCamelCase       :				Union[str, Any] ,			_UpperCamelCase       :				str ,			_UpperCamelCase       :				int ,			_UpperCamelCase       :				str					)					->				List[str]:





						"""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       :				str ,			_UpperCamelCase       :				Optional[Any] ,			_UpperCamelCase       :				List[str]					)					->				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       :				Dict ,			_UpperCamelCase       :				str ,			_UpperCamelCase       :				Optional[int] ,			_UpperCamelCase       :				str ,			_UpperCamelCase       :				str					)					->				Optional[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       :				List[Any] ,			_UpperCamelCase       :				Optional[Any] ,			_UpperCamelCase       :				Dict=None ,			_UpperCamelCase       :				Optional[int]=None ,			_UpperCamelCase       :				Any=True					)					->				Dict:





						"""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 math





def    lowerCAmelCase__    (			_UpperCamelCase       :				float ,			_UpperCamelCase       :				float					)					->				float:





						"""simple docstring"""






						if initial_intensity < 0:
												raise ValueError('The value of intensity cannot be negative'					)
												# handling of negative values of initial intensity
						if angle < 0 or angle > 3_6_0:
												raise ValueError('In Malus Law, the angle is in the range 0-360 degrees'					)
												# handling of values out of allowed range
						return initial_intensity * (math.cos(math.radians(_UpperCamelCase					)					) ** 2)


if __name__ == "__main__":
					import doctest

					doctest.testmod(name="malus_law")


