
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
					from ..ta.tokenization_ta import TaTokenizer
else:
					from ...utils.dummy_sentencepiece_objects import TaTokenizer

A     :	Optional[int]    = TaTokenizer

if is_tokenizers_available():
					from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
					from ...utils.dummy_tokenizers_objects import TaTokenizerFast

A     :	Dict    = TaTokenizerFast

A     :	Union[str, Any]    = {'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	str    = [
					    'MT5EncoderModel',
					    'MT5ForConditionalGeneration',
					    'MT5ForQuestionAnswering',
					    'MT5Model',
					    'MT5PreTrainedModel',
					    'MT5Stack',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Optional[int]    = ['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	int    = ['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']


if TYPE_CHECKING:
					from .configuration_mta import MTaConfig, MTaOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mta import (
										    MTaEncoderModel,
										    MTaForConditionalGeneration,
										    MTaForQuestionAnswering,
										    MTaModel,
										    MTaPreTrainedModel,
										    MTaStack,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
					import sys

					A     :	Any    = _LazyModule(
					    __name__,
					    globals()['__file__'],
					    _import_structure,
					    extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
					    module_spec=__spec__,
					)
"""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()
__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE    	={
    "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 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     Tuple     ,			__SCREAMING_SNAKE_CASE							:     List[str]     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     List[str]      ):
     for attribute in key.split('.'      ):
          lowercase_	:				Tuple													=	getattr(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )

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

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



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Tuple     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     Union[str, Any]      ):
     lowercase_	:				Optional[int]													=	[]
     lowercase_	:				Optional[int]													=	fairseq_model.state_dict()

     lowercase_	:				List[str]													=	hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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



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



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,			__SCREAMING_SNAKE_CASE							:     int      ):
     lowercase_	:				str													=	SEWConfig()
     if is_finetuned:
          lowercase_	:				List[Any]													=	model.wav_encoder.wav_model.cfg
     else:
          lowercase_	:				Tuple													=	model.cfg

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

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

     lowercase_	:				List[Any]													=	'Wav2Vec2FeatureExtractor'
     lowercase_	:				Union[str, Any]													=	'Wav2Vec2CTCTokenizer'

     return config



@torch.no_grad()
def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Dict     ,			__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,			__SCREAMING_SNAKE_CASE							:     int=None     ,			__SCREAMING_SNAKE_CASE							:     Any=None     ,			__SCREAMING_SNAKE_CASE							:     Tuple=True      ):

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

     if config_path is not None:
          lowercase_	:				List[str]													=	SEWConfig.from_pretrained(__SCREAMING_SNAKE_CASE      )
     else:
          lowercase_	:				Tuple													=	convert_config(model[0]     ,			__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Optional[Any]													=	model[0].eval()

     lowercase_	:				List[Any]													=	True if config.feat_extract_norm == 'layer' else False
     lowercase_	:				Optional[Any]													=	WavaVecaFeatureExtractor(
         feature_size=1     ,			sampling_rate=1_60_00     ,			padding_value=0     ,			do_normalize=__SCREAMING_SNAKE_CASE     ,			return_attention_mask=__SCREAMING_SNAKE_CASE     ,			)

     if is_finetuned:
          if dict_path:
               lowercase_	:				Dict													=	Dictionary.load(__SCREAMING_SNAKE_CASE      )

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

          lowercase_	:				List[str]													=	SEWForCTC(__SCREAMING_SNAKE_CASE      )
     else:
          lowercase_	:				Any													=	SEWModel(__SCREAMING_SNAKE_CASE      )
          feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE      )

     recursively_load_weights(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )

     hf_model.save_pretrained(__SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE    	=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    	=parser.parse_args()
       convert_sew_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
       )

