"""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   					=	logging.get_logger(__name__)

_A   					=	{
    '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   					=	[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   str:
				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(__UpperCAmelCase  ,					__UpperCAmelCase       )

				if weight_type is not None:
								SCREAMING_SNAKE_CASE__		  =				getattr(__UpperCAmelCase  ,					__UpperCAmelCase       ).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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   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(
												    __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					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(__UpperCAmelCase       )[0].split("."       )[-2]
																								SCREAMING_SNAKE_CASE__		  =				mapped_key.replace("*"  ,					__UpperCAmelCase       )
																				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(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )
																continue
								if not is_used:
												unused_weights.append(__UpperCAmelCase       )

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




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Any:
				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(__UpperCAmelCase       )




@torch.no_grad()
def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=True       )							->   Union[str, Any]:
				if config_path is not None:
								SCREAMING_SNAKE_CASE__		  =				UniSpeechConfig.from_pretrained(__UpperCAmelCase       )
				else:
								SCREAMING_SNAKE_CASE__		  =				UniSpeechConfig()

				if is_finetuned:
								if dict_path:
												SCREAMING_SNAKE_CASE__		  =				Dictionary.load_from_json(__UpperCAmelCase       )

												# 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(__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       )
												SCREAMING_SNAKE_CASE__		  =				target_dict.indices

												# fairseq has the <pad> and <s> switched
												SCREAMING_SNAKE_CASE__		  =				42
												SCREAMING_SNAKE_CASE__		  =				43
												with open(__UpperCAmelCase  ,					"w"  ,					encoding="utf-8"       ) as vocab_handle:
																json.dump(__UpperCAmelCase  ,					__UpperCAmelCase       )
												SCREAMING_SNAKE_CASE__		  =				WavaVecaPhonemeCTCTokenizer(
												    __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  ,					)
												SCREAMING_SNAKE_CASE__		  =				True if config.feat_extract_norm == "layer" else False
												SCREAMING_SNAKE_CASE__		  =				WavaVecaFeatureExtractor(
												    feature_size=1  ,					sampling_rate=16_000  ,					padding_value=0  ,					do_normalize=__UpperCAmelCase  ,					return_attention_mask=__UpperCAmelCase  ,					)
												SCREAMING_SNAKE_CASE__		  =				WavaVecaProcessor(feature_extractor=__UpperCAmelCase  ,					tokenizer=__UpperCAmelCase       )
												processor.save_pretrained(__UpperCAmelCase       )

								SCREAMING_SNAKE_CASE__		  =				UniSpeechForCTC(__UpperCAmelCase       )
				else:
								SCREAMING_SNAKE_CASE__		  =				UniSpeechForPreTraining(__UpperCAmelCase       )

				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(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )

				hf_unispeech.save_pretrained(__UpperCAmelCase       )


if __name__ == "__main__":
						_A   					=	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   					=	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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   int:

				if not isinstance(__UpperCAmelCase  ,					__UpperCAmelCase       ):
								raise ValueError("multiplicative_persistence() only accepts integral values"       )
				if num < 0:
								raise ValueError("multiplicative_persistence() does not accept negative values"       )

				SCREAMING_SNAKE_CASE__		  =				0
				SCREAMING_SNAKE_CASE__		  =				str(__UpperCAmelCase       )

				while len(__UpperCAmelCase       ) != 1:
								SCREAMING_SNAKE_CASE__		  =				[int(__UpperCAmelCase       ) for i in num_string]

								SCREAMING_SNAKE_CASE__		  =				1
								for i in range(0  ,					len(__UpperCAmelCase       )       ):
												total *= numbers[i]

								SCREAMING_SNAKE_CASE__		  =				str(__UpperCAmelCase       )

								steps += 1
				return steps




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   int:

				if not isinstance(__UpperCAmelCase  ,					__UpperCAmelCase       ):
								raise ValueError("additive_persistence() only accepts integral values"       )
				if num < 0:
								raise ValueError("additive_persistence() does not accept negative values"       )

				SCREAMING_SNAKE_CASE__		  =				0
				SCREAMING_SNAKE_CASE__		  =				str(__UpperCAmelCase       )

				while len(__UpperCAmelCase       ) != 1:
								SCREAMING_SNAKE_CASE__		  =				[int(__UpperCAmelCase       ) for i in num_string]

								SCREAMING_SNAKE_CASE__		  =				0
								for i in range(0  ,					len(__UpperCAmelCase       )       ):
												total += numbers[i]

								SCREAMING_SNAKE_CASE__		  =				str(__UpperCAmelCase       )

								steps += 1
				return steps


if __name__ == "__main__":
						import doctest

						doctest.testmod()




