"""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 (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	:		Optional[int]   =   {
    '''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__    (	lowercase_     :	Tuple      , lowercase_     :	str      , lowercase_     :	Union[str, Any]      , lowercase_     :	Any      , lowercase_     :	Tuple						)  ->							Dict:
   for attribute in key.split('.'						):
      __lowerCamelCase    		=					getattr(lowercase_      , lowercase_						)

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

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



def       A__    (	lowercase_     :	Dict      , lowercase_     :	Union[str, Any]      , lowercase_     :	int						)  ->							Union[str, Any]:
   __lowerCamelCase    		=					[]
   __lowerCamelCase    		=					fairseq_model.state_dict()

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

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

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



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



def       A__    (	lowercase_     :	Dict      , lowercase_     :	int						)  ->							Optional[int]:
   __lowerCamelCase    		=					SEWDConfig()
   if is_finetuned:
      __lowerCamelCase    		=					model.wav_encoder.wav_model.cfg
   else:
      __lowerCamelCase    		=					model.cfg

   __lowerCamelCase    		=					fs_config.conv_bias
   __lowerCamelCase    		=					eval(fs_config.conv_feature_layers						)
   __lowerCamelCase    		=					[x[0] for x in conv_layers]
   __lowerCamelCase    		=					[x[1] for x in conv_layers]
   __lowerCamelCase    		=					[x[2] for x in conv_layers]
   __lowerCamelCase    		=					'gelu'
   __lowerCamelCase    		=					'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
   __lowerCamelCase    		=					0.0
   __lowerCamelCase    		=					fs_config.activation_fn.name
   __lowerCamelCase    		=					fs_config.encoder_embed_dim
   __lowerCamelCase    		=					0.0_2
   __lowerCamelCase    		=					fs_config.encoder_ffn_embed_dim
   __lowerCamelCase    		=					1e-5
   __lowerCamelCase    		=					fs_config.encoder_layerdrop
   __lowerCamelCase    		=					fs_config.encoder_attention_heads
   __lowerCamelCase    		=					fs_config.conv_pos_groups
   __lowerCamelCase    		=					fs_config.conv_pos
   __lowerCamelCase    		=					len(lowercase_						)
   __lowerCamelCase    		=					fs_config.encoder_layers
   __lowerCamelCase    		=					fs_config.squeeze_factor
   # DeBERTa-specific parameters:
   __lowerCamelCase    		=					fs_config.max_position_embeddings
   __lowerCamelCase    		=					fs_config.position_buckets
   __lowerCamelCase    		=					fs_config.share_att_key
   __lowerCamelCase    		=					fs_config.relative_attention
   __lowerCamelCase    		=					fs_config.position_biased_input
   __lowerCamelCase    		=					tuple(fs_config.pos_att_type.split('|'						)						)
   __lowerCamelCase    		=					fs_config.norm_rel_ebd

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

   __lowerCamelCase    		=					'Wav2Vec2FeatureExtractor'
   __lowerCamelCase    		=					'Wav2Vec2CTCTokenizer'

   return config



@torch.no_grad()
def       A__    (	lowercase_     :	Any      , lowercase_     :	Tuple      , lowercase_     :	Dict=None      , lowercase_     :	Any=None      , lowercase_     :	Optional[int]=True						)  ->							Optional[int]:

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

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

   __lowerCamelCase    		=					True if config.feat_extract_norm == 'layer' else False
   __lowerCamelCase    		=					WavaVecaFeatureExtractor(
       feature_size=1      , sampling_rate=16000      , padding_value=0      , do_normalize=lowercase_      , return_attention_mask=lowercase_      , )

   if is_finetuned:
      if dict_path:
         __lowerCamelCase    		=					Dictionary.load(lowercase_						)

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

      __lowerCamelCase    		=					SEWDForCTC(lowercase_						)
   else:
      __lowerCamelCase    		=					SEWDModel(lowercase_						)
      feature_extractor.save_pretrained(lowercase_						)

   recursively_load_weights(lowercase_      , lowercase_      , lowercase_						)

   hf_model.save_pretrained(lowercase_						)


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE	:		List[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'''
     )
     _SCREAMING_SNAKE_CASE	:		Optional[int]   =   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"""







import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


A     =      logging.getLogger(__name__)






@dataclass
class   UpperCAmelCase__		:
		lowerCAmelCase_							:						str							=  field(
		    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}  )
		lowerCAmelCase_							:						Optional[str]							=  field(
		    default=UpperCamelCase ,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}  )
		lowerCAmelCase_							:						Optional[str]							=  field(
		    default=UpperCamelCase ,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}  )
		lowerCAmelCase_							:						bool							=  field(default=UpperCamelCase ,metadata={"""help""": """Set this flag to use fast tokenization."""}  )
		# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
		# or just modify its tokenizer_config.json.
		lowerCAmelCase_							:						Optional[str]							=  field(
		    default=UpperCamelCase ,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} ,)



def 	lowerCAmelCase__      (  )			->				Optional[Any]:
							# See all possible arguments in src/transformers/training_args.py
							# or by passing the --help flag to this script.
							# We now keep distinct sets of args, for a cleaner separation of concerns.

							A    =       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )

							if len(sys.argv      ) == 2 and sys.argv[1].endswith('.json'      ):
														# If we pass only one argument to the script and it's the path to a json file,
														# let's parse it to get our arguments.
														A   ,	A   ,	A    =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
							else:
														A   ,	A   ,	A    =       parser.parse_args_into_dataclasses()

							if (
							    os.path.exists(training_args.output_dir      )
							    and os.listdir(training_args.output_dir      )
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
														    ' --overwrite_output_dir to overcome.'      )

							# Setup logging
							logging.basicConfig(
							    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'					,   datefmt='%m/%d/%Y %H:%M:%S'					,   level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN					,   )
							logger.warning(
							    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'					,   training_args.local_rank					,   training_args.device					,   training_args.n_gpu					,   bool(training_args.local_rank != -1      )					,   training_args.fpaa					,   )
							# Set the verbosity to info of the Transformers logger (on main process only):
							if is_main_process(training_args.local_rank      ):
														transformers.utils.logging.set_verbosity_info()
														transformers.utils.logging.enable_default_handler()
														transformers.utils.logging.enable_explicit_format()
							logger.info('Training/evaluation parameters %s'					,   lowerCamelCase__      )

							# Prepare Question-Answering task
							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.

							A    =       AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path					,   cache_dir=model_args.cache_dir					,   )
							A    =       AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					,   cache_dir=model_args.cache_dir					,   use_fast=lowerCamelCase__					,   )
							A    =       AutoModelForQuestionAnswering.from_pretrained(
							    model_args.model_name_or_path					,   from_tf=bool('.ckpt' in model_args.model_name_or_path      )					,   config=lowerCamelCase__					,   cache_dir=model_args.cache_dir					,   )

							# Get datasets
							A    =       hasattr(model.config					,   'lang2id'      )
							A    =       (
							    SquadDataset(
							        lowerCamelCase__					,   tokenizer=lowerCamelCase__					,   is_language_sensitive=lowerCamelCase__					,   cache_dir=model_args.cache_dir      )
							    if training_args.do_train
							    else None
							)
							A    =       (
							    SquadDataset(
							        lowerCamelCase__					,   tokenizer=lowerCamelCase__					,   mode='dev'					,   is_language_sensitive=lowerCamelCase__					,   cache_dir=model_args.cache_dir					,   )
							    if training_args.do_eval
							    else None
							)

							# Data collator
							A    =       DataCollatorWithPadding(lowerCamelCase__					,   pad_to_multiple_of=8      ) if training_args.fpaa else None

							# Initialize our Trainer
							A    =       Trainer(
							    model=lowerCamelCase__					,   args=lowerCamelCase__					,   train_dataset=lowerCamelCase__					,   eval_dataset=lowerCamelCase__					,   data_collator=lowerCamelCase__					,   )

							# Training
							if training_args.do_train:
														trainer.train(
														    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path      ) else None      )
														trainer.save_model()
														# For convenience, we also re-save the tokenizer to the same directory,
														# so that you can share your model easily on huggingface.co/models =)
														if trainer.is_world_master():
																					tokenizer.save_pretrained(training_args.output_dir      )

def 	lowerCAmelCase__      (  lowerCamelCase__      )			->				List[Any]:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
							main()



