
import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler





class 							A__	(			unittest.TestCase):
      # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
      _UpperCAmelCase       :					List[Any]					 =    (ScoreSdeVeScheduler,)
      _UpperCAmelCase       :					Optional[int]					 =    ()
      @property
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			Union[str, Any]           =   4
       lowerCamelCase	:			Optional[int]           =   3
       lowerCamelCase	:			Any           =   8
       lowerCamelCase	:			str           =   8

       lowerCamelCase	:			Dict           =   torch.rand((batch_size, num_channels, height, width)   )

       return sample
      @property
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			Dict           =   4
       lowerCamelCase	:			Any           =   3
       lowerCamelCase	:			str           =   8
       lowerCamelCase	:			List[Any]           =   8

       lowerCamelCase	:			int           =   batch_size * num_channels * height * width
       lowerCamelCase	:			int           =   torch.arange(__magic_name__   )
       lowerCamelCase	:			Union[str, Any]           =   sample.reshape(__magic_name__					,						__magic_name__					,						__magic_name__					,						__magic_name__   )
       lowerCamelCase	:			Any           =   sample / num_elems
       lowerCamelCase	:			Tuple           =   sample.permute(3					,						0					,						1					,						2   )

       return sample
      def 		UpperCamelCase__							(   self   ):
       def model(__magic_name__					,						__magic_name__					,						*__magic_name__   ):
        return sample * t / (t + 1)

       return model
      def 		UpperCamelCase__							(   self					,						**__magic_name__   ):
       lowerCamelCase	:			List[str]           =   {
           """num_train_timesteps""": 2_0_0_0,
           """snr""": 0.15,
           """sigma_min""": 0.01,
           """sigma_max""": 1_3_4_8,
           """sampling_eps""": 1e-5,
       }

       config.update(**__magic_name__   )
       return config
      def 		UpperCamelCase__							(   self					,						__magic_name__=0					,						**__magic_name__   ):
       lowerCamelCase	:			Tuple           =   dict(self.forward_default_kwargs   )

       for scheduler_class in self.scheduler_classes:
        lowerCamelCase	:			List[Any]           =   self.dummy_sample
        lowerCamelCase	:			int           =   0.1 * sample

        lowerCamelCase	:			Optional[Any]           =   self.get_scheduler_config(**__magic_name__   )
        lowerCamelCase	:			List[Any]           =   scheduler_class(**__magic_name__   )

        with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(__magic_name__   )
         lowerCamelCase	:			Tuple           =   scheduler_class.from_pretrained(__magic_name__   )

        lowerCamelCase	:			Any           =   scheduler.step_pred(
            __magic_name__					,						__magic_name__					,						__magic_name__					,						generator=torch.manual_seed(0   )					,						**__magic_name__   ).prev_sample
        lowerCamelCase	:			Union[str, Any]           =   new_scheduler.step_pred(
            __magic_name__					,						__magic_name__					,						__magic_name__					,						generator=torch.manual_seed(0   )					,						**__magic_name__   ).prev_sample

        assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"

        lowerCamelCase	:			Optional[Any]           =   scheduler.step_correct(__magic_name__					,						__magic_name__					,						generator=torch.manual_seed(0   )					,						**__magic_name__   ).prev_sample
        lowerCamelCase	:			Any           =   new_scheduler.step_correct(
            __magic_name__					,						__magic_name__					,						generator=torch.manual_seed(0   )					,						**__magic_name__   ).prev_sample

        assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler correction are not identical"
      def 		UpperCamelCase__							(   self					,						__magic_name__=0					,						**__magic_name__   ):
       lowerCamelCase	:			List[str]           =   dict(self.forward_default_kwargs   )
       kwargs.update(__magic_name__   )

       for scheduler_class in self.scheduler_classes:
        lowerCamelCase	:			Optional[int]           =   self.dummy_sample
        lowerCamelCase	:			List[str]           =   0.1 * sample

        lowerCamelCase	:			Dict           =   self.get_scheduler_config()
        lowerCamelCase	:			Union[str, Any]           =   scheduler_class(**__magic_name__   )

        with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(__magic_name__   )
         lowerCamelCase	:			Tuple           =   scheduler_class.from_pretrained(__magic_name__   )

        lowerCamelCase	:			Optional[Any]           =   scheduler.step_pred(
            __magic_name__					,						__magic_name__					,						__magic_name__					,						generator=torch.manual_seed(0   )					,						**__magic_name__   ).prev_sample
        lowerCamelCase	:			str           =   new_scheduler.step_pred(
            __magic_name__					,						__magic_name__					,						__magic_name__					,						generator=torch.manual_seed(0   )					,						**__magic_name__   ).prev_sample

        assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"

        lowerCamelCase	:			Any           =   scheduler.step_correct(__magic_name__					,						__magic_name__					,						generator=torch.manual_seed(0   )					,						**__magic_name__   ).prev_sample
        lowerCamelCase	:			List[str]           =   new_scheduler.step_correct(
            __magic_name__					,						__magic_name__					,						generator=torch.manual_seed(0   )					,						**__magic_name__   ).prev_sample

        assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler correction are not identical"
      def 		UpperCamelCase__							(   self   ):
       for timesteps in [1_0, 1_0_0, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=__magic_name__   )
      def 		UpperCamelCase__							(   self   ):
       for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]					,						[1, 1_0_0, 1_0_0_0]   ):
        self.check_over_configs(sigma_min=__magic_name__					,						sigma_max=__magic_name__   )
      def 		UpperCamelCase__							(   self   ):
       for t in [0.1, 0.5, 0.75]:
        self.check_over_forward(time_step=__magic_name__   )
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			List[str]           =   dict(self.forward_default_kwargs   )

       lowerCamelCase	:			List[str]           =   self.scheduler_classes[0]
       lowerCamelCase	:			Union[str, Any]           =   self.get_scheduler_config()
       lowerCamelCase	:			List[str]           =   scheduler_class(**__magic_name__   )

       lowerCamelCase	:			Any           =   3

       lowerCamelCase	:			str           =   self.dummy_model()
       lowerCamelCase	:			Dict           =   self.dummy_sample_deter

       scheduler.set_sigmas(__magic_name__   )
       scheduler.set_timesteps(__magic_name__   )
       lowerCamelCase	:			str           =   torch.manual_seed(0   )

       for i, t in enumerate(scheduler.timesteps   ):
        lowerCamelCase	:			Union[str, Any]           =   scheduler.sigmas[i]

        for _ in range(scheduler.config.correct_steps   ):
         with torch.no_grad():
          lowerCamelCase	:			int           =   model(__magic_name__					,						__magic_name__   )
         lowerCamelCase	:			str           =   scheduler.step_correct(__magic_name__					,						__magic_name__					,						generator=__magic_name__					,						**__magic_name__   ).prev_sample

        with torch.no_grad():
         lowerCamelCase	:			Optional[Any]           =   model(__magic_name__					,						__magic_name__   )

        lowerCamelCase	:			str           =   scheduler.step_pred(__magic_name__					,						__magic_name__					,						__magic_name__					,						generator=__magic_name__					,						**__magic_name__   )
        lowerCamelCase	:			Tuple           =   output.prev_sample, output.prev_sample_mean

       lowerCamelCase	:			int           =   torch.sum(torch.abs(__magic_name__   )   )
       lowerCamelCase	:			Optional[Any]           =   torch.mean(torch.abs(__magic_name__   )   )

       assert np.isclose(result_sum.item()					,						1_4_3_7_2_7_5_8_5_2_8.0   )
       assert np.isclose(result_mean.item()					,						1_8_7_1_4_5_3_0.0   )






      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			Union[str, Any]           =   dict(self.forward_default_kwargs   )

       lowerCamelCase	:			Optional[Any]           =   kwargs.pop("""num_inference_steps"""					,						__magic_name__   )

       for scheduler_class in self.scheduler_classes:
        lowerCamelCase	:			Dict           =   self.get_scheduler_config()
        lowerCamelCase	:			Optional[int]           =   scheduler_class(**__magic_name__   )

        lowerCamelCase	:			str           =   self.dummy_sample
        lowerCamelCase	:			Tuple           =   0.1 * sample

        if num_inference_steps is not None and hasattr(__magic_name__					,						"""set_timesteps"""   ):
         scheduler.set_timesteps(__magic_name__   )
        elif num_inference_steps is not None and not hasattr(__magic_name__					,						"""set_timesteps"""   ):
         lowerCamelCase	:			List[Any]           =   num_inference_steps

        lowerCamelCase	:			str           =   scheduler.step_pred(__magic_name__					,						0					,						__magic_name__					,						generator=torch.manual_seed(0   )					,						**__magic_name__   ).prev_sample
        lowerCamelCase	:			Any           =   scheduler.step_pred(__magic_name__					,						1					,						__magic_name__					,						generator=torch.manual_seed(0   )					,						**__magic_name__   ).prev_sample

        self.assertEqual(output_a.shape					,						sample.shape   )
        self.assertEqual(output_a.shape					,						output_a.shape   )






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()
_lowerCamelCase           =logging.get_logger(__name__)

_lowerCamelCase           ={
    """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      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):
 for attribute in key.split("""."""							):
  lowerCamelCase	:			List[str]           =   getattr(lowerCamelCase,			lowerCamelCase							)

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

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





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):
 lowerCamelCase	:			Tuple           =   []
 lowerCamelCase	:			Union[str, Any]           =   fairseq_model.state_dict()

 lowerCamelCase	:			Optional[int]           =   hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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





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





def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 lowerCamelCase	:			List[Any]           =   SEWDConfig()
 if is_finetuned:
  lowerCamelCase	:			int           =   model.wav_encoder.wav_model.cfg
 else:
  lowerCamelCase	:			Optional[Any]           =   model.cfg

 lowerCamelCase	:			List[str]           =   fs_config.conv_bias
 lowerCamelCase	:			Tuple           =   eval(fs_config.conv_feature_layers							)
 lowerCamelCase	:			List[str]           =   [x[0] for x in conv_layers]
 lowerCamelCase	:			List[Any]           =   [x[1] for x in conv_layers]
 lowerCamelCase	:			int           =   [x[2] for x in conv_layers]
 lowerCamelCase	:			List[str]           =   """gelu"""
 lowerCamelCase	:			Union[str, Any]           =   """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
 lowerCamelCase	:			List[str]           =   0.0
 lowerCamelCase	:			Optional[int]           =   fs_config.activation_fn.name
 lowerCamelCase	:			Union[str, Any]           =   fs_config.encoder_embed_dim
 lowerCamelCase	:			Tuple           =   0.0_2
 lowerCamelCase	:			str           =   fs_config.encoder_ffn_embed_dim
 lowerCamelCase	:			Dict           =   1e-5
 lowerCamelCase	:			str           =   fs_config.encoder_layerdrop
 lowerCamelCase	:			Optional[Any]           =   fs_config.encoder_attention_heads
 lowerCamelCase	:			List[Any]           =   fs_config.conv_pos_groups
 lowerCamelCase	:			Optional[int]           =   fs_config.conv_pos
 lowerCamelCase	:			Dict           =   len(lowerCamelCase							)
 lowerCamelCase	:			List[Any]           =   fs_config.encoder_layers
 lowerCamelCase	:			Optional[int]           =   fs_config.squeeze_factor
 # DeBERTa-specific parameters:
 lowerCamelCase	:			Optional[int]           =   fs_config.max_position_embeddings
 lowerCamelCase	:			List[str]           =   fs_config.position_buckets
 lowerCamelCase	:			List[Any]           =   fs_config.share_att_key
 lowerCamelCase	:			Dict           =   fs_config.relative_attention
 lowerCamelCase	:			List[Any]           =   fs_config.position_biased_input
 lowerCamelCase	:			Dict           =   tuple(fs_config.pos_att_type.split("""|"""							)							)
 lowerCamelCase	:			List[str]           =   fs_config.norm_rel_ebd

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

 lowerCamelCase	:			Dict           =   """Wav2Vec2FeatureExtractor"""
 lowerCamelCase	:			int           =   """Wav2Vec2CTCTokenizer"""

 return config





@torch.no_grad()
def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase=None,			lowerCamelCase=None,			lowerCamelCase=True							):

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

 if config_path is not None:
  lowerCamelCase	:			List[str]           =   SEWDConfig.from_pretrained(lowerCamelCase							)
 else:
  lowerCamelCase	:			Optional[int]           =   convert_config(model[0],			lowerCamelCase							)
 lowerCamelCase	:			Dict           =   model[0].eval()

 lowerCamelCase	:			Dict           =   True if config.feat_extract_norm == """layer""" else False
 lowerCamelCase	:			List[str]           =   WavaVecaFeatureExtractor(
     feature_size=1,			sampling_rate=1_6000,			padding_value=0,			do_normalize=lowerCamelCase,			return_attention_mask=lowerCamelCase,			)

 if is_finetuned:
  if dict_path:
   lowerCamelCase	:			List[Any]           =   Dictionary.load(lowerCamelCase							)

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

  lowerCamelCase	:			Union[str, Any]           =   SEWDForCTC(lowerCamelCase							)
 else:
  lowerCamelCase	:			Dict           =   SEWDModel(lowerCamelCase							)
  feature_extractor.save_pretrained(lowerCamelCase							)

 recursively_load_weights(lowerCamelCase,			lowerCamelCase,			lowerCamelCase							)

 hf_model.save_pretrained(lowerCamelCase							)


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




