



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_lowercase:					Any      =						logging.get_logger(__name__)

_lowercase:					List[Any]      =						{
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
    "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
    "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
    "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
    "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
    "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
    "output.dense": "encoder.encoder.layer.*.output.dense",
    "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
    "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
    "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}






def        a( A			:       str					,	A			:       Optional[Any]					,	A			:       Optional[int]					,	A			:       int					,	A			:       str			)						->			Optional[int]:
					"""simple docstring"""





					for attribute in key.split("."			):
										a   	=  getattr(A					,	A			)

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

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






def        a( A			:       int					,	A			:       str					,	A			:       Tuple			)						->			Tuple:
					"""simple docstring"""





					a   	=  []
					a   	=  fairseq_model.state_dict()

					a   	=  hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

					for name, value in fairseq_dict.items():
										a   	=  False
										if "conv_layers" in name:
															load_conv_layer(
															    A					,	A					,	A					,	A					,	hf_model.config.feat_extract_norm == "group"					,	)
															a   	=  True
										else:
															for key, mapped_key in MAPPING.items():
																				a   	=  "sew_d." + 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]:
																									a   	=  True
																									if "*" in mapped_key:
																														a   	=  name.split(A			)[0].split("."			)[-2]
																														if not layer_index.isnumeric():
																																			continue
																														a   	=  mapped_key.replace("*"					,	A			)
																									if "weight_g" in name:
																														a   	=  "weight_g"
																									elif "weight_v" in name:
																														a   	=  "weight_v"
																									elif "weight" in name:
																														a   	=  "weight"
																									elif "bias" in name:
																														a   	=  "bias"
																									else:
																														a   	=  None
																									set_recursively(A					,	A					,	A					,	A					,	A			)
																				continue
										if not is_used:
															unused_weights.append(A			)

					logger.warning(f'''Unused weights: {unused_weights}'''			)






def        a( A			:       str					,	A			:       List[str]					,	A			:       Tuple					,	A			:       int					,	A			:       List[Any]			)						->			List[str]:
					"""simple docstring"""





					a   	=  full_name.split("conv_layers."			)[-1]
					a   	=  name.split("."			)
					a   	=  int(items[0]			)
					a   	=  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.'''
															)
															a   	=  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.'''
															)
															a   	=  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."
															)
															a   	=  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.'''
															)
															a   	=  value
															logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''			)
					else:
										unused_weights.append(A			)






def        a( A			:       List[str]					,	A			:       Any			)						->			str:
					"""simple docstring"""





					a   	=  SEWDConfig()
					if is_finetuned:
										a   	=  model.wav_encoder.wav_model.cfg
					else:
										a   	=  model.cfg

					a   	=  fs_config.conv_bias
					a   	=  eval(fs_config.conv_feature_layers			)
					a   	=  [x[0] for x in conv_layers]
					a   	=  [x[1] for x in conv_layers]
					a   	=  [x[2] for x in conv_layers]
					a   	=  "gelu"
					a   	=  "layer" if fs_config.extractor_mode == "layer_norm" else "group"
					a   	=  0.0
					a   	=  fs_config.activation_fn.name
					a   	=  fs_config.encoder_embed_dim
					a   	=  0.02
					a   	=  fs_config.encoder_ffn_embed_dim
					a   	=  1e-5
					a   	=  fs_config.encoder_layerdrop
					a   	=  fs_config.encoder_attention_heads
					a   	=  fs_config.conv_pos_groups
					a   	=  fs_config.conv_pos
					a   	=  len(A			)
					a   	=  fs_config.encoder_layers
					a   	=  fs_config.squeeze_factor
					# DeBERTa-specific parameters:
					a   	=  fs_config.max_position_embeddings
					a   	=  fs_config.position_buckets
					a   	=  fs_config.share_att_key
					a   	=  fs_config.relative_attention
					a   	=  fs_config.position_biased_input
					a   	=  tuple(fs_config.pos_att_type.split("|"			)			)
					a   	=  fs_config.norm_rel_ebd

					# take care of any params that are overridden by the Wav2VecCtc model
					if is_finetuned:
										a   	=  model.cfg
										a   	=  fs_config.final_dropout
										a   	=  fs_config.layerdrop
					a   	=  fs_config.activation_dropout
					a   	=  fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
					a   	=  fs_config.attention_dropout
					a   	=  fs_config.dropout_input
					a   	=  fs_config.dropout
					a   	=  fs_config.mask_channel_length
					a   	=  fs_config.mask_channel_prob
					a   	=  fs_config.mask_length
					a   	=  fs_config.mask_prob

					a   	=  "Wav2Vec2FeatureExtractor"
					a   	=  "Wav2Vec2CTCTokenizer"

					return config






@torch.no_grad()
def        a( A			:       Optional[int]					,	A			:       Dict					,	A			:       List[str]=None					,	A			:       List[Any]=None					,	A			:       Optional[int]=True			)						->			Optional[Any]:
					"""simple docstring"""





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

					if config_path is not None:
										a   	=  SEWDConfig.from_pretrained(A			)
					else:
										a   	=  convert_config(model[0]					,	A			)
					a   	=  model[0].eval()

					a   	=  True if config.feat_extract_norm == "layer" else False
					a   	=  WavaVecaFeatureExtractor(
					    feature_size=1					,	sampling_rate=1_6000					,	padding_value=0					,	do_normalize=A					,	return_attention_mask=A					,	)

					if is_finetuned:
										if dict_path:
															a   	=  Dictionary.load(A			)

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															a   	=  target_dict.pad_index
															a   	=  target_dict.bos_index
															a   	=  target_dict.pad_index
															a   	=  target_dict.bos_index
															a   	=  target_dict.eos_index
															a   	=  len(target_dict.symbols			)
															a   	=  os.path.join(A					,	"vocab.json"			)
															if not os.path.isdir(A			):
																				logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(A			)			)
																				return
															os.makedirs(A					,	exist_ok=A			)
															with open(A					,	"w"					,	encoding="utf-8"			) as vocab_handle:
																				json.dump(target_dict.indices					,	A			)
															a   	=  WavaVecaCTCTokenizer(
															    A					,	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=A					,	)
															a   	=  WavaVecaProcessor(feature_extractor=A					,	tokenizer=A			)
															processor.save_pretrained(A			)

										a   	=  SEWDForCTC(A			)
					else:
										a   	=  SEWDModel(A			)
										feature_extractor.save_pretrained(A			)

					recursively_load_weights(A					,	A					,	A			)

					hf_model.save_pretrained(A			)


if __name__ == "__main__":
				_lowercase:					Any      =						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(
				    "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
				)
				_lowercase:					Union[str, Any]      =						parser.parse_args()
				convert_sew_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
				)





from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowercase:					int      =						logging.get_logger(__name__)



class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""
	__A		      =				["input_ids", "attention_mask"]




	def __init__(self						,			lowerCamelCase_="[PAD]"						,			lowerCamelCase_="[BOS]"						,			lowerCamelCase_="[EOS]"						,			lowerCamelCase_="[MASK]"						,			lowerCamelCase_="[CLS]"						,			lowerCamelCase_="[SEP]"						,			lowerCamelCase_=2048						,			**lowerCamelCase_						,			):
						"""simple docstring"""

						a   	=  AddedToken(lowerCamelCase_						,			lstrip=lowerCamelCase_						,			rstrip=lowerCamelCase_						) if isinstance(lowerCamelCase_						,			lowerCamelCase_						) else pad_token
						a   	=  AddedToken(lowerCamelCase_						,			lstrip=lowerCamelCase_						,			rstrip=lowerCamelCase_						) if isinstance(lowerCamelCase_						,			lowerCamelCase_						) else bos_token
						a   	=  AddedToken(lowerCamelCase_						,			lstrip=lowerCamelCase_						,			rstrip=lowerCamelCase_						) if isinstance(lowerCamelCase_						,			lowerCamelCase_						) else eos_token
						a   	=  AddedToken(lowerCamelCase_						,			lstrip=lowerCamelCase_						,			rstrip=lowerCamelCase_						) if isinstance(lowerCamelCase_						,			lowerCamelCase_						) else mask_token
						a   	=  AddedToken(lowerCamelCase_						,			lstrip=lowerCamelCase_						,			rstrip=lowerCamelCase_						) if isinstance(lowerCamelCase_						,			lowerCamelCase_						) else cls_token
						a   	=  AddedToken(lowerCamelCase_						,			lstrip=lowerCamelCase_						,			rstrip=lowerCamelCase_						) if isinstance(lowerCamelCase_						,			lowerCamelCase_						) else sep_token

						super().__init__(
						    pad_token=lowerCamelCase_						,			bos_token=lowerCamelCase_						,			eos_token=lowerCamelCase_						,			mask_token=lowerCamelCase_						,			cls_token=lowerCamelCase_						,			sep_token=lowerCamelCase_						,			model_max_length=lowerCamelCase_						,			**lowerCamelCase_						,			)

						a   	=  2**8  # utf is 8 bits

						# define special tokens dict
						a   	=  {
						    self.pad_token: 0,
						    self.bos_token: 1,
						    self.eos_token: 2,
						    self.mask_token: 3,
						    self.cls_token: 4,
						    self.sep_token: 5,
						}
						a   	=  len(self.special_tokens_encoder						)
						a   	=  {v: k for k, v in self.special_tokens_encoder.items()}




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  self.special_tokens_encoder.copy()
						vocab.update(self.added_tokens_encoder						)
						for i in range(self._utf_vocab_size						):
											a   	=  chr(lowerCamelCase_						)
											a   	=  i + len(self.special_tokens_encoder						)
						return vocab




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						return self._utf_vocab_size + self._num_special_tokens




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_ = None						,			lowerCamelCase_ = False						):
						"""simple docstring"""

						if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=lowerCamelCase_						,			token_ids_a=lowerCamelCase_						,			already_has_special_tokens=lowerCamelCase_						)

						# normal case: some special tokens
						if token_ids_a is None:
											return [1] + [0] * len(lowerCamelCase_						) + [1]
						return [1] + ([0] * len(lowerCamelCase_						)) + [1] + ([0] * len(lowerCamelCase_						)) + [1]




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_ = None						):
						"""simple docstring"""

						if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						else:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  [chr(lowerCamelCase_						) for i in text.encode("utf-8"						)]
						return tokens




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						if token in self.special_tokens_encoder:
											a   	=  self.special_tokens_encoder[token]
						elif token in self.added_tokens_encoder:
											a   	=  self.added_tokens_encoder[token]
						elif len(lowerCamelCase_						) != 1:
											a   	=  self.unk_token_id
						else:
											a   	=  ord(lowerCamelCase_						) + self._num_special_tokens
						return token_id




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						if index in self.special_tokens_decoder:
											a   	=  self.special_tokens_decoder[index]
						elif index in self.added_tokens_decoder:
											a   	=  self.added_tokens_decoder[index]
						else:
											a   	=  chr(index - self._num_special_tokens						)
						return token




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  B""
						for token in tokens:
											if token in self.special_tokens_decoder:
																a   	=  self.special_tokens_decoder[token].encode("utf-8"						)
											elif token in self.added_tokens_decoder:
																a   	=  self.special_tokens_decoder[token].encode("utf-8"						)
											elif token in self.special_tokens_encoder:
																a   	=  token.encode("utf-8"						)
											elif token in self.added_tokens_encoder:
																a   	=  token.encode("utf-8"						)
											else:
																a   	=  bytes([ord(lowerCamelCase_						)]						)
											bstring += tok_string
						a   	=  bstring.decode("utf-8"						,			errors="replace"						)
						return string




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_ = None						):
						"""simple docstring"""

						return ()
