

'''simple docstring'''

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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
A_       						=							logging.get_logger(__name__)

A_       						=							{
    "post_extract_proj": "feature_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.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_  ( snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case ):
		for attribute in key.split("." ):
				SCREAMING_SNAKE_CASE:str						      =					getattr(snake_case      ,       snake_case )

		if weight_type is not None:
				SCREAMING_SNAKE_CASE:Union[str, Any]						      =					getattr(snake_case      ,       snake_case ).shape
		else:
				SCREAMING_SNAKE_CASE:Optional[Any]						      =					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:str						      =					value
		elif weight_type == "weight_g":
				SCREAMING_SNAKE_CASE:List[Any]						      =					value
		elif weight_type == "weight_v":
				SCREAMING_SNAKE_CASE:List[str]						      =					value
		elif weight_type == "bias":
				SCREAMING_SNAKE_CASE:List[Any]						      =					value
		else:
				SCREAMING_SNAKE_CASE:Dict						      =					value

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





def    A_  ( snake_case      ,       snake_case      ,       snake_case ):
		SCREAMING_SNAKE_CASE:Dict						      =					[]
		SCREAMING_SNAKE_CASE:List[Any]						      =					fairseq_model.state_dict()

		SCREAMING_SNAKE_CASE:Dict						      =					hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

		for name, value in fairseq_dict.items():
				SCREAMING_SNAKE_CASE:Optional[Any]						      =					False
				if "conv_layers" in name:
						load_conv_layer(
						    snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       hf_model.config.feat_extract_norm == "group"      ,       )
						SCREAMING_SNAKE_CASE:List[str]						      =					True
				else:
						for key, mapped_key in MAPPING.items():
								SCREAMING_SNAKE_CASE:Dict						      =					"sew." + 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]:
										SCREAMING_SNAKE_CASE:Optional[int]						      =					True
										if "*" in mapped_key:
												SCREAMING_SNAKE_CASE:List[str]						      =					name.split(snake_case )[0].split("." )[-2]
												SCREAMING_SNAKE_CASE:Optional[int]						      =					mapped_key.replace("*"      ,       snake_case )
										if "weight_g" in name:
												SCREAMING_SNAKE_CASE:Union[str, Any]						      =					"weight_g"
										elif "weight_v" in name:
												SCREAMING_SNAKE_CASE:str						      =					"weight_v"
										elif "weight" in name:
												SCREAMING_SNAKE_CASE:Dict						      =					"weight"
										elif "bias" in name:
												SCREAMING_SNAKE_CASE:List[str]						      =					"bias"
										else:
												SCREAMING_SNAKE_CASE:Union[str, Any]						      =					None
										set_recursively(snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case )
								continue
				if not is_used:
						unused_weights.append(snake_case )

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





def    A_  ( snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case ):
		SCREAMING_SNAKE_CASE:Union[str, Any]						      =					full_name.split("conv_layers." )[-1]
		SCREAMING_SNAKE_CASE:List[Any]						      =					name.split("." )
		SCREAMING_SNAKE_CASE:Any						      =					int(items[0] )
		SCREAMING_SNAKE_CASE: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.'''
						)
						SCREAMING_SNAKE_CASE: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.'''
						)
						SCREAMING_SNAKE_CASE:Tuple						      =					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:Any						      =					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:Optional[Any]						      =					value
						logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
		else:
				unused_weights.append(snake_case )





def    A_  ( snake_case      ,       snake_case ):
		SCREAMING_SNAKE_CASE:Any						      =					SEWConfig()
		if is_finetuned:
				SCREAMING_SNAKE_CASE:List[Any]						      =					model.wav_encoder.wav_model.cfg
		else:
				SCREAMING_SNAKE_CASE:str						      =					model.cfg

		SCREAMING_SNAKE_CASE:Optional[int]						      =					fs_config.conv_bias
		SCREAMING_SNAKE_CASE:Tuple						      =					eval(fs_config.conv_feature_layers )
		SCREAMING_SNAKE_CASE:List[str]						      =					[x[0] for x in conv_layers]
		SCREAMING_SNAKE_CASE:List[Any]						      =					[x[1] for x in conv_layers]
		SCREAMING_SNAKE_CASE:Optional[int]						      =					[x[2] for x in conv_layers]
		SCREAMING_SNAKE_CASE:Optional[int]						      =					"gelu"
		SCREAMING_SNAKE_CASE:Optional[Any]						      =					"layer" if fs_config.extractor_mode == "layer_norm" else "group"
		SCREAMING_SNAKE_CASE:Union[str, Any]						      =					0.0
		SCREAMING_SNAKE_CASE:List[str]						      =					fs_config.activation_fn.name
		SCREAMING_SNAKE_CASE:Any						      =					fs_config.encoder_embed_dim
		SCREAMING_SNAKE_CASE:Optional[Any]						      =					0.02
		SCREAMING_SNAKE_CASE:Optional[int]						      =					fs_config.encoder_ffn_embed_dim
		SCREAMING_SNAKE_CASE:Dict						      =					1e-5
		SCREAMING_SNAKE_CASE:Any						      =					fs_config.encoder_layerdrop
		SCREAMING_SNAKE_CASE:Tuple						      =					fs_config.encoder_attention_heads
		SCREAMING_SNAKE_CASE:int						      =					fs_config.conv_pos_groups
		SCREAMING_SNAKE_CASE:Union[str, Any]						      =					fs_config.conv_pos
		SCREAMING_SNAKE_CASE:Tuple						      =					len(snake_case )
		SCREAMING_SNAKE_CASE:List[Any]						      =					fs_config.encoder_layers
		SCREAMING_SNAKE_CASE:Tuple						      =					fs_config.squeeze_factor

		# take care of any params that are overridden by the Wav2VecCtc model
		if is_finetuned:
				SCREAMING_SNAKE_CASE:int						      =					model.cfg
				SCREAMING_SNAKE_CASE:Optional[Any]						      =					fs_config.final_dropout
				SCREAMING_SNAKE_CASE:Any						      =					fs_config.layerdrop
		SCREAMING_SNAKE_CASE:List[Any]						      =					fs_config.activation_dropout
		SCREAMING_SNAKE_CASE:Union[str, Any]						      =					fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
		SCREAMING_SNAKE_CASE:Any						      =					fs_config.attention_dropout
		SCREAMING_SNAKE_CASE:str						      =					fs_config.dropout_input
		SCREAMING_SNAKE_CASE:Optional[int]						      =					fs_config.dropout
		SCREAMING_SNAKE_CASE:Any						      =					fs_config.mask_channel_length
		SCREAMING_SNAKE_CASE:int						      =					fs_config.mask_channel_prob
		SCREAMING_SNAKE_CASE:Optional[int]						      =					fs_config.mask_length
		SCREAMING_SNAKE_CASE:Tuple						      =					fs_config.mask_prob

		SCREAMING_SNAKE_CASE:Union[str, Any]						      =					"Wav2Vec2FeatureExtractor"
		SCREAMING_SNAKE_CASE:Dict						      =					"Wav2Vec2CTCTokenizer"

		return config





@torch.no_grad()
def    A_  ( snake_case      ,       snake_case      ,       snake_case=None      ,       snake_case=None      ,       snake_case=True ):

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

		if config_path is not None:
				SCREAMING_SNAKE_CASE:List[Any]						      =					SEWConfig.from_pretrained(snake_case )
		else:
				SCREAMING_SNAKE_CASE:Any						      =					convert_config(model[0]      ,       snake_case )
		SCREAMING_SNAKE_CASE:Optional[int]						      =					model[0].eval()

		SCREAMING_SNAKE_CASE:Optional[int]						      =					True if config.feat_extract_norm == "layer" else False
		SCREAMING_SNAKE_CASE:int						      =					WavaVecaFeatureExtractor(
		    feature_size=1      ,       sampling_rate=16000      ,       padding_value=0      ,       do_normalize=snake_case      ,       return_attention_mask=snake_case      ,       )

		if is_finetuned:
				if dict_path:
						SCREAMING_SNAKE_CASE:Optional[int]						      =					Dictionary.load(snake_case )

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

				SCREAMING_SNAKE_CASE:str						      =					SEWForCTC(snake_case )
		else:
				SCREAMING_SNAKE_CASE:Dict						      =					SEWModel(snake_case )
				feature_extractor.save_pretrained(snake_case )

		recursively_load_weights(snake_case      ,       snake_case      ,       snake_case )

		hf_model.save_pretrained(snake_case )


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(
				    "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
				)
				A_       						=							parser.parse_args()
				convert_sew_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
				)









'''simple docstring'''

def    A_  ( snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					word.split()

  def justify(snake_case      ,       snake_case      ,       snake_case ) -> str:
    SCREAMING_SNAKE_CASE:str						      =					max_width - width
    SCREAMING_SNAKE_CASE:Dict						      =					len(snake_case )
    if len(snake_case ) == 1:
      # if there is only word in line
      # just insert overall_spaces_count for the remainder of line
      return line[0] + " " * overall_spaces_count
    else:
      SCREAMING_SNAKE_CASE:Optional[int]						      =					words_count - 1
      # num_spaces_between_words_list[i] : tells you to insert
      # num_spaces_between_words_list[i] spaces
      # after word on line[i]
      SCREAMING_SNAKE_CASE:List[Any]						      =					spaces_to_insert_between_words * [
          overall_spaces_count // spaces_to_insert_between_words
      ]
      SCREAMING_SNAKE_CASE:List[Any]						      =					(
          overall_spaces_count % spaces_to_insert_between_words
      )
      # distribute spaces via round robin to the left words
      for i in range(snake_case ):
        num_spaces_between_words_list[i] += 1
      SCREAMING_SNAKE_CASE:str						      =					[]
      for i in range(snake_case ):
        # add the word
        aligned_words_list.append(line[i] )
        # add the spaces to insert
        aligned_words_list.append(num_spaces_between_words_list[i] * " " )
      # just add the last word to the sentence
      aligned_words_list.append(line[-1] )
      # join the aligned words list to form a justified line
      return "".join(snake_case )

  SCREAMING_SNAKE_CASE:Any						      =					[]
  SCREAMING_SNAKE_CASE:list[str]						      =					[]
  SCREAMING_SNAKE_CASE:List[str]						      =					0
  for word in words:
    if width + len(snake_case ) + len(snake_case ) <= max_width:
      # keep adding words until we can fill out max_width
      # width = sum of length of all words (without overall_spaces_count)
      # len(word) = length of current word
      # len(line) = number of overall_spaces_count to insert between words
      line.append(snake_case )
      width += len(snake_case )
    else:
      # justify the line and add it to result
      answer.append(justify(snake_case      ,       snake_case      ,       snake_case ) )
      # reset new line and new width
      SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					[word], len(snake_case )
  SCREAMING_SNAKE_CASE:Optional[int]						      =					max_width - width - len(snake_case )
  answer.append(" ".join(snake_case ) + (remaining_spaces + 1) * " " )
  return answer


if __name__ == "__main__":
    from doctest import testmod

    testmod()






