




import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
 from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor

class  _lowerCamelCase						:

      """simple docstring"""

      def __init__(     self					: Optional[int] ,       snake_case					: Dict ,       snake_case					: Union[str, Any]=13 ,       snake_case					: List[str]=30 ,       snake_case					: str=2 ,       snake_case					: Tuple=3 ,       snake_case					: Dict=True ,       snake_case					: List[Any]=True ,       snake_case					: List[Any]=32 ,       snake_case					: str=5 ,       snake_case					: Optional[Any]=4 ,       snake_case					: Tuple=37 ,       snake_case					: List[Any]="gelu" ,       snake_case					: Union[str, Any]=0.1 ,       snake_case					: Tuple=0.1 ,       snake_case					: Any=10 ,       snake_case					: Tuple=0.02 ,       snake_case					: Union[str, Any]=None ,       snake_case					: Dict=2 ,       ):
          __UpperCamelCase  		=						parent
          __UpperCamelCase  		=						batch_size
          __UpperCamelCase  		=						image_size
          __UpperCamelCase  		=						patch_size
          __UpperCamelCase  		=						num_channels
          __UpperCamelCase  		=						is_training
          __UpperCamelCase  		=						use_labels
          __UpperCamelCase  		=						hidden_size
          __UpperCamelCase  		=						num_hidden_layers
          __UpperCamelCase  		=						num_attention_heads
          __UpperCamelCase  		=						intermediate_size
          __UpperCamelCase  		=						hidden_act
          __UpperCamelCase  		=						hidden_dropout_prob
          __UpperCamelCase  		=						attention_probs_dropout_prob
          __UpperCamelCase  		=						type_sequence_label_size
          __UpperCamelCase  		=						initializer_range
          __UpperCamelCase  		=						scope
          __UpperCamelCase  		=						encoder_stride

          # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
          __UpperCamelCase  		=						(image_size // patch_size) ** 2
          __UpperCamelCase  		=						num_patches + 1







      def       snake_case	(     self					: Optional[Any]				):
          __UpperCamelCase  		=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

          __UpperCamelCase  		=						None
          if self.use_labels:
              __UpperCamelCase  		=						ids_tensor([self.batch_size] ,       self.type_sequence_label_size				)

          __UpperCamelCase  		=						self.get_config()

          return config, pixel_values, labels







      def       snake_case	(     self					: Dict				):
          return ViTConfig(
              image_size=self.image_size ,       patch_size=self.patch_size ,       num_channels=self.num_channels ,       hidden_size=self.hidden_size ,       num_hidden_layers=self.num_hidden_layers ,       num_attention_heads=self.num_attention_heads ,       intermediate_size=self.intermediate_size ,       hidden_act=self.hidden_act ,       hidden_dropout_prob=self.hidden_dropout_prob ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob ,       is_decoder=snake_case ,       initializer_range=self.initializer_range ,       encoder_stride=self.encoder_stride ,       )







      def       snake_case	(     self					: Optional[int] ,       snake_case					: Union[str, Any] ,       snake_case					: str ,       snake_case					: Tuple				):
          __UpperCamelCase  		=						ViTModel(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case				)
          self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, self.seq_length, self.hidden_size)				)







      def       snake_case	(     self					: List[Any] ,       snake_case					: int ,       snake_case					: int ,       snake_case					: Any				):
          __UpperCamelCase  		=						ViTForMaskedImageModeling(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case				)
          self.parent.assertEqual(
              result.reconstruction.shape ,       (self.batch_size, self.num_channels, self.image_size, self.image_size)				)

          # test greyscale images
          __UpperCamelCase  		=						1
          __UpperCamelCase  		=						ViTForMaskedImageModeling(snake_case				)
          model.to(snake_case				)
          model.eval()

          __UpperCamelCase  		=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
          __UpperCamelCase  		=						model(snake_case				)
          self.parent.assertEqual(result.reconstruction.shape ,       (self.batch_size, 1, self.image_size, self.image_size)				)







      def       snake_case	(     self					: int ,       snake_case					: Optional[int] ,       snake_case					: List[str] ,       snake_case					: List[str]				):
          __UpperCamelCase  		=						self.type_sequence_label_size
          __UpperCamelCase  		=						ViTForImageClassification(snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case ,       labels=snake_case				)
          self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.type_sequence_label_size)				)

          # test greyscale images
          __UpperCamelCase  		=						1
          __UpperCamelCase  		=						ViTForImageClassification(snake_case				)
          model.to(snake_case				)
          model.eval()

          __UpperCamelCase  		=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
          __UpperCamelCase  		=						model(snake_case				)
          self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.type_sequence_label_size)				)







      def       snake_case	(     self					: Dict				):
          __UpperCamelCase  		=						self.prepare_config_and_inputs()
          (
              (
              __UpperCamelCase      
          )		,      (
              __UpperCamelCase      
          )		,      (
              __UpperCamelCase      
          )		,            
          )  		=						config_and_inputs
          __UpperCamelCase  		=						{'''pixel_values''': pixel_values}
          return config, inputs_dict







@require_torch
class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE							,			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: Optional[Any]    =					(
          (
              ViTModel,
              ViTForImageClassification,
              ViTForMaskedImageModeling,
          )
          if is_torch_available()
          else ()
      )
      lowerCAmelCase__			: str    =					(
          {"feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
          if is_torch_available()
          else {}
      )
      lowerCAmelCase__			: Any    =					True

      lowerCAmelCase__			: List[Any]    =					False
      lowerCAmelCase__			: Dict    =					False
      lowerCAmelCase__			: List[Any]    =					False







      def       snake_case	(     self					: Tuple				):
          __UpperCamelCase  		=						ViTModelTester(self				)
          __UpperCamelCase  		=						ConfigTester(self ,       config_class=snake_case ,       has_text_modality=snake_case ,       hidden_size=37				)







      def       snake_case	(     self					: List[Any]				):
          self.config_tester.run_common_tests()







      @unittest.skip(reason='''ViT does not use inputs_embeds'''				)
      def       snake_case	(     self					: Tuple				):
          pass







      def       snake_case	(     self					: str				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              __UpperCamelCase  		=						model_class(snake_case				)
              self.assertIsInstance(model.get_input_embeddings() ,       (nn.Module)				)
              __UpperCamelCase  		=						model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(snake_case ,       nn.Linear				)				)







      def       snake_case	(     self					: Dict				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              __UpperCamelCase  		=						model_class(snake_case				)
              __UpperCamelCase  		=						inspect.signature(model.forward				)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              __UpperCamelCase  		=						[*signature.parameters.keys()]

              __UpperCamelCase  		=						['''pixel_values''']
              self.assertListEqual(arg_names[:1] ,       snake_case				)







      def       snake_case	(     self					: int				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case				)







      def       snake_case	(     self					: Union[str, Any]				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*snake_case				)







      def       snake_case	(     self					: Tuple				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case				)







      @slow
      def       snake_case	(     self					: List[Any]				):
          for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCamelCase  		=						ViTModel.from_pretrained(snake_case				)
              self.assertIsNotNone(snake_case				)







def 					__SCREAMING_SNAKE_CASE   (  )       ->	Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase  		=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    return image


@require_torch
@require_vision
class  _lowerCamelCase						(			unittest.TestCase   ):

      """simple docstring"""

      @cached_property
      def       snake_case	(     self					: List[str]				):
          return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224'''				) if is_vision_available() else None







      @slow
      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						ViTForImageClassification.from_pretrained('''google/vit-base-patch16-224'''				).to(snake_case				)

          __UpperCamelCase  		=						self.default_image_processor
          __UpperCamelCase  		=						prepare_img()
          __UpperCamelCase  		=						image_processor(images=snake_case ,       return_tensors='''pt'''				).to(snake_case				)

          # forward pass
          with torch.no_grad():
              __UpperCamelCase  		=						model(**snake_case				)

          # verify the logits
          __UpperCamelCase  		=						torch.Size((1, 1000)				)
          self.assertEqual(outputs.logits.shape ,       snake_case				)

          __UpperCamelCase  		=						torch.tensor([-0.27_44, 0.82_15, -0.08_36]				).to(snake_case				)

          self.assertTrue(torch.allclose(outputs.logits[0, :3] ,       snake_case ,       atol=1E-4				)				)







      @slow
      def       snake_case	(     self					: List[Any]				):
          # ViT models have an `interpolate_pos_encoding` argument in their forward method,
          # allowing to interpolate the pre-trained position embeddings in order to use
          # the model on higher resolutions. The DINO model by Facebook AI leverages this
          # to visualize self-attention on higher resolution images.
          __UpperCamelCase  		=						ViTModel.from_pretrained('''facebook/dino-vits8'''				).to(snake_case				)

          __UpperCamelCase  		=						ViTImageProcessor.from_pretrained('''facebook/dino-vits8''' ,       size=480				)
          __UpperCamelCase  		=						prepare_img()
          __UpperCamelCase  		=						image_processor(images=snake_case ,       return_tensors='''pt'''				)
          __UpperCamelCase  		=						inputs.pixel_values.to(snake_case				)

          # forward pass
          with torch.no_grad():
              __UpperCamelCase  		=						model(snake_case ,       interpolate_pos_encoding=snake_case				)

          # verify the logits
          __UpperCamelCase  		=						torch.Size((1, 3601, 384)				)
          self.assertEqual(outputs.last_hidden_state.shape ,       snake_case				)

          __UpperCamelCase  		=						torch.tensor(
              [[4.23_40, 4.39_06, -6.66_92], [4.54_63, 1.89_28, -6.72_57], [4.44_29, 0.84_96, -5.85_85]]				).to(snake_case				)

          self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] ,       snake_case ,       atol=1E-4				)				)







      @slow
      @require_accelerate
      @require_torch_gpu
      def       snake_case	(     self					: List[str]				):
          __UpperCamelCase  		=						ViTModel.from_pretrained('''facebook/dino-vits8''' ,       torch_dtype=torch.floataa ,       device_map='''auto'''				)
          __UpperCamelCase  		=						self.default_image_processor

          __UpperCamelCase  		=						prepare_img()
          __UpperCamelCase  		=						image_processor(images=snake_case ,       return_tensors='''pt'''				)
          __UpperCamelCase  		=						inputs.pixel_values.to(snake_case				)

          # forward pass to make sure inference works in fp16
          with torch.no_grad():
              __UpperCamelCase  		=						model(snake_case				)








import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

a_   		=     {
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	List[str]:

    """simple docstring"""


    for attribute in key.split('''.'''      ):
        __UpperCamelCase  		=						getattr(lowercase_					,     lowercase_      )

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

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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	List[str]:

    """simple docstring"""


    __UpperCamelCase  		=						[]
    __UpperCamelCase  		=						fairseq_model.state_dict()

    __UpperCamelCase  		=						hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        __UpperCamelCase  		=						False
        if "conv_layers" in name:
            load_conv_layer(
                lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     hf_model.config.feat_extract_norm == '''group'''					,     )
            __UpperCamelCase  		=						True
        else:
            for key, mapped_key in MAPPING.items():
                __UpperCamelCase  		=						'''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]:
                    __UpperCamelCase  		=						True
                    if "*" in mapped_key:
                        __UpperCamelCase  		=						name.split(lowercase_      )[0].split('''.'''      )[-2]
                        __UpperCamelCase  		=						mapped_key.replace('''*'''					,     lowercase_      )
                    if "weight_g" in name:
                        __UpperCamelCase  		=						'''weight_g'''
                    elif "weight_v" in name:
                        __UpperCamelCase  		=						'''weight_v'''
                    elif "weight" in name:
                        __UpperCamelCase  		=						'''weight'''
                    elif "bias" in name:
                        __UpperCamelCase  		=						'''bias'''
                    else:
                        __UpperCamelCase  		=						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 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	Tuple:

    """simple docstring"""


    __UpperCamelCase  		=						full_name.split('''conv_layers.'''      )[-1]
    __UpperCamelCase  		=						name.split('''.'''      )
    __UpperCamelCase  		=						int(items[0]      )
    __UpperCamelCase  		=						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."
            )
            __UpperCamelCase  		=						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."
            )
            __UpperCamelCase  		=						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."
            )
            __UpperCamelCase  		=						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."
            )
            __UpperCamelCase  		=						value
            logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
    else:
        unused_weights.append(lowercase_      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	int:

    """simple docstring"""


    __UpperCamelCase  		=						SEWConfig()
    if is_finetuned:
        __UpperCamelCase  		=						model.wav_encoder.wav_model.cfg
    else:
        __UpperCamelCase  		=						model.cfg

    __UpperCamelCase  		=						fs_config.conv_bias
    __UpperCamelCase  		=						eval(fs_config.conv_feature_layers      )
    __UpperCamelCase  		=						[x[0] for x in conv_layers]
    __UpperCamelCase  		=						[x[1] for x in conv_layers]
    __UpperCamelCase  		=						[x[2] for x in conv_layers]
    __UpperCamelCase  		=						'''gelu'''
    __UpperCamelCase  		=						'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
    __UpperCamelCase  		=						0.0
    __UpperCamelCase  		=						fs_config.activation_fn.name
    __UpperCamelCase  		=						fs_config.encoder_embed_dim
    __UpperCamelCase  		=						0.02
    __UpperCamelCase  		=						fs_config.encoder_ffn_embed_dim
    __UpperCamelCase  		=						1E-5
    __UpperCamelCase  		=						fs_config.encoder_layerdrop
    __UpperCamelCase  		=						fs_config.encoder_attention_heads
    __UpperCamelCase  		=						fs_config.conv_pos_groups
    __UpperCamelCase  		=						fs_config.conv_pos
    __UpperCamelCase  		=						len(lowercase_      )
    __UpperCamelCase  		=						fs_config.encoder_layers
    __UpperCamelCase  		=						fs_config.squeeze_factor

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

    __UpperCamelCase  		=						'''Wav2Vec2FeatureExtractor'''
    __UpperCamelCase  		=						'''Wav2Vec2CTCTokenizer'''

    return config




@torch.no_grad()
def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_=None					,     lowercase_=None					,     lowercase_=True      )       ->	Any:

    """simple docstring"""


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

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

    __UpperCamelCase  		=						True if config.feat_extract_norm == '''layer''' else False
    __UpperCamelCase  		=						WavaVecaFeatureExtractor(
        feature_size=1					,     sampling_rate=1_60_00					,     padding_value=0					,     do_normalize=lowercase_					,     return_attention_mask=lowercase_					,     )

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

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            __UpperCamelCase  		=						target_dict.pad_index
            __UpperCamelCase  		=						target_dict.bos_index
            __UpperCamelCase  		=						target_dict.pad_index
            __UpperCamelCase  		=						target_dict.bos_index
            __UpperCamelCase  		=						target_dict.eos_index
            __UpperCamelCase  		=						len(target_dict.symbols      )
            __UpperCamelCase  		=						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_      )
            __UpperCamelCase  		=						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_					,     )
            __UpperCamelCase  		=						WavaVecaProcessor(feature_extractor=lowercase_					,     tokenizer=lowercase_      )
            processor.save_pretrained(lowercase_      )

        __UpperCamelCase  		=						SEWForCTC(lowercase_      )
    else:
        __UpperCamelCase  		=						SEWModel(lowercase_      )
        feature_extractor.save_pretrained(lowercase_      )

    recursively_load_weights(lowercase_					,     lowercase_					,     lowercase_      )

    hf_model.save_pretrained(lowercase_      )


if __name__ == "__main__":
 a_   		=     argparse.ArgumentParser()
 parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
 parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
 parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 parser.add_argument(
     "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
 )
 a_   		=     parser.parse_args()
 convert_sew_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
 )






