

'''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()
SCREAMING_SNAKE_CASE							:				int     	=			logging.get_logger(__name__)

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




def 							_UpperCamelCase				(   lowerCAmelCase__:					str		,lowerCAmelCase__:					Optional[Any]		,lowerCAmelCase__:					Any		,lowerCAmelCase__:					Optional[int]		,lowerCAmelCase__:					Any		,lowerCAmelCase__:					List[str]   )      -> 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
																												SCREAMING_SNAKE_CASE_				=				'lm_head'

														SCREAMING_SNAKE_CASE_				=				getattr(lowerCAmelCase__		,lowerCAmelCase__   )

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

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




def 							_UpperCamelCase				(   lowerCAmelCase__:					List[Any]		,lowerCAmelCase__:					Tuple		,lowerCAmelCase__:					str   )      -> List[str]:
							SCREAMING_SNAKE_CASE_				=				[]
							SCREAMING_SNAKE_CASE_				=				fairseq_model.state_dict()

							SCREAMING_SNAKE_CASE_				=				hf_model.unispeech.feature_extractor

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

							if is_finetuned:
														if dict_path:
																					SCREAMING_SNAKE_CASE_				=				Dictionary.load_from_json(lowerCAmelCase__   )

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					SCREAMING_SNAKE_CASE_				=				target_dict.pad_index
																					SCREAMING_SNAKE_CASE_				=				target_dict.bos_index
																					SCREAMING_SNAKE_CASE_				=				target_dict.eos_index
																					SCREAMING_SNAKE_CASE_				=				len(target_dict.symbols   )
																					SCREAMING_SNAKE_CASE_				=				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__   )
																					SCREAMING_SNAKE_CASE_				=				target_dict.indices

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

														SCREAMING_SNAKE_CASE_				=				UniSpeechForCTC(lowerCAmelCase__   )
							else:
														SCREAMING_SNAKE_CASE_				=				UniSpeechForPreTraining(lowerCAmelCase__   )

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

							SCREAMING_SNAKE_CASE_				=				model[0].eval()

							recursively_load_weights(lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__   )

							hf_unispeech.save_pretrained(lowerCAmelCase__   )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE							:				int     	=			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[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'''

SCREAMING_SNAKE_CASE							:				int     	=			8.3_1_4_4_5_9_8




def 							_UpperCamelCase				(   lowerCAmelCase__:					float		,lowerCAmelCase__:					float   )      -> float:
							if temperature < 0:
														raise Exception('Temperature cannot be less than 0 K'   )
							if molar_mass <= 0:
														raise Exception('Molar mass cannot be less than or equal to 0 kg/mol'   )
							else:
														return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
							import doctest

							# run doctest
							doctest.testmod()

							# example
							SCREAMING_SNAKE_CASE							:				List[str]     	=			300
							SCREAMING_SNAKE_CASE							:				List[Any]     	=			28
							SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			rms_speed_of_molecule(temperature, molar_mass)
							print(f"Vrms of Nitrogen gas at 300 K is {vrms} m/s")





