'''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()
_a   :	Dict 							=   logging.get_logger(__name__)

_a   :	Optional[int] 							=   {
    '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',
}
_a   :	Optional[int] 							=   [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       int:





							"""simple docstring"""


							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
																												__UpperCAmelCase  :				Union[str, Any]       				=		"lm_head"

														__UpperCAmelCase  :				Optional[int]       				=		getattr(__lowerCAmelCase							,    __lowerCAmelCase						)

							if weight_type is not None:
														__UpperCAmelCase  :				Union[str, Any]       				=		getattr(__lowerCAmelCase							,    __lowerCAmelCase						).shape
							else:
														__UpperCAmelCase  :				str       				=		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  :				int       				=		value
							elif weight_type == "weight_g":
														__UpperCAmelCase  :				Tuple       				=		value
							elif weight_type == "weight_v":
														__UpperCAmelCase  :				Optional[int]       				=		value
							elif weight_type == "bias":
														__UpperCAmelCase  :				Optional[Any]       				=		value
							else:
														__UpperCAmelCase  :				Dict       				=		value

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

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       Union[str, Any]:





							"""simple docstring"""


							__UpperCAmelCase  :				Optional[int]       				=		[]
							__UpperCAmelCase  :				str       				=		fairseq_model.state_dict()

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

							for name, value in fairseq_dict.items():
														__UpperCAmelCase  :				Tuple       				=		False
														if "conv_layers" in name:
																					load_conv_layer(
																					    __lowerCAmelCase							,    __lowerCAmelCase							,    __lowerCAmelCase							,    __lowerCAmelCase							,    hf_model.config.feat_extract_norm == "group"							,    )
																					__UpperCAmelCase  :				Tuple       				=		True
														else:
																					for key, mapped_key in MAPPING.items():
																												__UpperCAmelCase  :				str       				=		"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]:
																																			__UpperCAmelCase  :				Union[str, Any]       				=		True
																																			if "*" in mapped_key:
																																										__UpperCAmelCase  :				Union[str, Any]       				=		name.split(__lowerCAmelCase						)[0].split("."						)[-2]
																																										__UpperCAmelCase  :				str       				=		mapped_key.replace("*"							,    __lowerCAmelCase						)
																																			if "weight_g" in name:
																																										__UpperCAmelCase  :				List[Any]       				=		"weight_g"
																																			elif "weight_v" in name:
																																										__UpperCAmelCase  :				List[Any]       				=		"weight_v"
																																			elif "bias" in name:
																																										__UpperCAmelCase  :				str       				=		"bias"
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										__UpperCAmelCase  :				Any       				=		"weight"
																																			else:
																																										__UpperCAmelCase  :				Any       				=		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 _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       Tuple:





							"""simple docstring"""


							__UpperCAmelCase  :				int       				=		full_name.split("conv_layers."						)[-1]
							__UpperCAmelCase  :				Tuple       				=		name.split("."						)
							__UpperCAmelCase  :				List[str]       				=		int(items[0]						)
							__UpperCAmelCase  :				List[str]       				=		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  :				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."""
																					)
																					__UpperCAmelCase  :				Optional[int]       				=		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  :				str       				=		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  :				int       				=		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 _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__=None							,    lowerCamelCase__=None							,    lowerCamelCase__=True						)					->       str:





							"""simple docstring"""


							if config_path is not None:
														__UpperCAmelCase  :				Optional[int]       				=		UniSpeechConfig.from_pretrained(__lowerCAmelCase						)
							else:
														__UpperCAmelCase  :				List[str]       				=		UniSpeechConfig()

							if is_finetuned:
														if dict_path:
																					__UpperCAmelCase  :				List[Any]       				=		Dictionary.load_from_json(__lowerCAmelCase						)

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

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

														__UpperCAmelCase  :				Optional[int]       				=		UniSpeechForCTC(__lowerCAmelCase						)
							else:
														__UpperCAmelCase  :				Dict       				=		UniSpeechForPreTraining(__lowerCAmelCase						)

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

							__UpperCAmelCase  :				Optional[int]       				=		model[0].eval()

							recursively_load_weights(__lowerCAmelCase							,    __lowerCAmelCase							,    __lowerCAmelCase						)

							hf_unispeech.save_pretrained(__lowerCAmelCase						)


if __name__ == "__main__":
			_a   :	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"
			)
			_a   :	Tuple 							=   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'''




def _lowercase	(      lowerCamelCase__ = 100						)					->       int:





       """simple docstring"""


       __UpperCAmelCase  :				Optional[Any]       				=		(n * (n + 1) // 2) ** 2
       __UpperCAmelCase  :				Any       				=		n * (n + 1) * (2 * n + 1) // 6
       return sum_cubes - sum_squares


if __name__ == "__main__":
   print(f"""{solution() = }""")






