"""simple docstring"""







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import ChineseCLIPImageProcessor







class        UpperCamelCase		(			unittest.TestCase  ):
   def __init__(			self     ,__UpperCamelCase     ,__UpperCamelCase=7     ,__UpperCamelCase=3     ,__UpperCamelCase=18     ,__UpperCamelCase=30     ,__UpperCamelCase=400     ,__UpperCamelCase=True     ,__UpperCamelCase=None     ,__UpperCamelCase=True     ,__UpperCamelCase=None     ,__UpperCamelCase=True     ,__UpperCamelCase=[0.4814_5466, 0.457_8275, 0.4082_1073]     ,__UpperCamelCase=[0.2686_2954, 0.2613_0258, 0.2757_7711]     ,__UpperCamelCase=True     ,)	->      int:


        '''simple docstring'''


        lowercase_	:				str													=	size if size is not None else {'height': 224, 'width': 224}
        lowercase_	:				Union[str, Any]													=	crop_size if crop_size is not None else {'height': 18, 'width': 18}
        lowercase_	:				Optional[int]													=	parent
        lowercase_	:				List[Any]													=	batch_size
        lowercase_	:				Union[str, Any]													=	num_channels
        lowercase_	:				Dict													=	image_size
        lowercase_	:				List[Any]													=	min_resolution
        lowercase_	:				Union[str, Any]													=	max_resolution
        lowercase_	:				int													=	do_resize
        lowercase_	:				str													=	size
        lowercase_	:				Union[str, Any]													=	do_center_crop
        lowercase_	:				int													=	crop_size
        lowercase_	:				Optional[int]													=	do_normalize
        lowercase_	:				str													=	image_mean
        lowercase_	:				Any													=	image_std
        lowercase_	:				List[Any]													=	do_convert_rgb
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
        }







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase=False     ,__UpperCamelCase=False     ,__UpperCamelCase=False			)	->      Union[str, Any]:


        '''simple docstring'''


        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
             lowercase_	:				int													=	[]
             for i in range(self.batch_size			):
                  image_inputs.append(
                      np.random.randint(
                          255     ,size=(self.num_channels, self.max_resolution, self.max_resolution)     ,dtype=np.uinta			)			)
        else:
             lowercase_	:				Tuple													=	[]
             for i in range(self.batch_size			):
                  lowercase_							,  lowercase_	:				Union[str, Any]													=	np.random.choice(np.arange(self.min_resolution     ,self.max_resolution			)     ,2			)
                  image_inputs.append(np.random.randint(255     ,size=(self.num_channels, width, height)     ,dtype=np.uinta			)			)

        if not numpify and not torchify:
             # PIL expects the channel dimension as last dimension
             lowercase_	:				Dict													=	[Image.fromarray(np.moveaxis(__UpperCamelCase     ,0     ,-1			)			) for x in image_inputs]

        if torchify:
             lowercase_	:				str													=	[torch.from_numpy(__UpperCamelCase			) for x in image_inputs]

        return image_inputs







@require_torch
@require_vision
class        UpperCamelCase		(			lowercase_						,      unittest.TestCase  ):
   lowercase							=  ChineseCLIPImageProcessor if is_vision_available() else None
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        lowercase_	:				Dict													=	ChineseCLIPImageProcessingTester(self     ,do_center_crop=__UpperCamelCase			)
   @property
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        return self.image_processor_tester.prepare_image_processor_dict()
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	self.image_processing_class(**self.image_processor_dict			)
        self.assertTrue(hasattr(__UpperCamelCase     ,'do_resize'			)			)
        self.assertTrue(hasattr(__UpperCamelCase     ,'size'			)			)
        self.assertTrue(hasattr(__UpperCamelCase     ,'do_center_crop'			)			)
        self.assertTrue(hasattr(__UpperCamelCase     ,'center_crop'			)			)
        self.assertTrue(hasattr(__UpperCamelCase     ,'do_normalize'			)			)
        self.assertTrue(hasattr(__UpperCamelCase     ,'image_mean'			)			)
        self.assertTrue(hasattr(__UpperCamelCase     ,'image_std'			)			)
        self.assertTrue(hasattr(__UpperCamelCase     ,'do_convert_rgb'			)			)
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				str													=	self.image_processing_class.from_dict(self.image_processor_dict			)
        self.assertEqual(image_processor.size     ,{'height': 224, 'width': 224}			)
        self.assertEqual(image_processor.crop_size     ,{'height': 18, 'width': 18}			)

        lowercase_	:				Union[str, Any]													=	self.image_processing_class.from_dict(self.image_processor_dict     ,size=42     ,crop_size=84			)
        self.assertEqual(image_processor.size     ,{'shortest_edge': 42}			)
        self.assertEqual(image_processor.crop_size     ,{'height': 84, 'width': 84}			)
   def 			_UpperCAmelCase   (			self			)	->      Dict:


        '''simple docstring'''


        pass
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				str													=	self.image_processing_class(**self.image_processor_dict			)
        # create random PIL images
        lowercase_	:				Union[str, Any]													=	self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase			)
        for image in image_inputs:
             self.assertIsInstance(__UpperCamelCase     ,Image.Image			)

        # Test not batched input
        lowercase_	:				str													=	image_processing(image_inputs[0]     ,return_tensors='pt'			).pixel_values
        self.assertEqual(
            encoded_images.shape     ,(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )     ,)

        # Test batched
        lowercase_	:				Union[str, Any]													=	image_processing(__UpperCamelCase     ,return_tensors='pt'			).pixel_values
        self.assertEqual(
            encoded_images.shape     ,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )     ,)
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	self.image_processing_class(**self.image_processor_dict			)
        # create random numpy tensors
        lowercase_	:				Tuple													=	self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase     ,numpify=__UpperCamelCase			)
        for image in image_inputs:
             self.assertIsInstance(__UpperCamelCase     ,np.ndarray			)

        # Test not batched input
        lowercase_	:				Optional[int]													=	image_processing(image_inputs[0]     ,return_tensors='pt'			).pixel_values
        self.assertEqual(
            encoded_images.shape     ,(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )     ,)

        # Test batched
        lowercase_	:				Dict													=	image_processing(__UpperCamelCase     ,return_tensors='pt'			).pixel_values
        self.assertEqual(
            encoded_images.shape     ,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )     ,)







   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	self.image_processing_class(**self.image_processor_dict			)
        # create random PyTorch tensors
        lowercase_	:				Optional[int]													=	self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase     ,torchify=__UpperCamelCase			)
        for image in image_inputs:
             self.assertIsInstance(__UpperCamelCase     ,torch.Tensor			)

        # Test not batched input
        lowercase_	:				Union[str, Any]													=	image_processing(image_inputs[0]     ,return_tensors='pt'			).pixel_values
        self.assertEqual(
            encoded_images.shape     ,(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )     ,)

        # Test batched
        lowercase_	:				Any													=	image_processing(__UpperCamelCase     ,return_tensors='pt'			).pixel_values
        self.assertEqual(
            encoded_images.shape     ,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )     ,)







@require_torch
@require_vision
class        UpperCamelCase		(			lowercase_						,      unittest.TestCase  ):
   lowercase							=  ChineseCLIPImageProcessor if is_vision_available() else None
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				int													=	ChineseCLIPImageProcessingTester(self     ,num_channels=4     ,do_center_crop=__UpperCamelCase			)
        lowercase_	:				Optional[int]													=	3
   @property
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        return self.image_processor_tester.prepare_image_processor_dict()
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        lowercase_	:				Any													=	self.image_processing_class(**self.image_processor_dict			)
        self.assertTrue(hasattr(__UpperCamelCase     ,'do_resize'			)			)
        self.assertTrue(hasattr(__UpperCamelCase     ,'size'			)			)
        self.assertTrue(hasattr(__UpperCamelCase     ,'do_center_crop'			)			)
        self.assertTrue(hasattr(__UpperCamelCase     ,'center_crop'			)			)
        self.assertTrue(hasattr(__UpperCamelCase     ,'do_normalize'			)			)
        self.assertTrue(hasattr(__UpperCamelCase     ,'image_mean'			)			)
        self.assertTrue(hasattr(__UpperCamelCase     ,'image_std'			)			)
        self.assertTrue(hasattr(__UpperCamelCase     ,'do_convert_rgb'			)			)
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        pass







   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	self.image_processing_class(**self.image_processor_dict			)
        # create random PIL images
        lowercase_	:				Tuple													=	self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase			)
        for image in image_inputs:
             self.assertIsInstance(__UpperCamelCase     ,Image.Image			)

        # Test not batched input
        lowercase_	:				List[Any]													=	image_processing(image_inputs[0]     ,return_tensors='pt'			).pixel_values
        self.assertEqual(
            encoded_images.shape     ,(
                1,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )     ,)

        # Test batched
        lowercase_	:				Optional[int]													=	image_processing(__UpperCamelCase     ,return_tensors='pt'			).pixel_values
        self.assertEqual(
            encoded_images.shape     ,(
                self.image_processor_tester.batch_size,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )     ,)


"""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
       )

