





import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
    import torch
    from torch import nn

    from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
    from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import DPTImageProcessor

class     snake_case_		:






 '''simple docstring'''





 def __init__(  self,		A_,		A_=2,		A_=32,		A_=16,		A_=3,		A_=True,		A_=True,		A_=32,		A_=4,		A_=[0, 1, 2, 3],		A_=4,		A_=37,		A_="gelu",		A_=0.1,		A_=0.1,		A_=0.02,		A_=3,		A_=[1, 384, 24, 24],		A_=True,		A_=None,		)	->       Optional[int]:
    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__						=backbone_out_indices
    UpperCAmelCase__						=num_attention_heads
    UpperCAmelCase__						=intermediate_size
    UpperCAmelCase__						=hidden_act
    UpperCAmelCase__						=hidden_dropout_prob
    UpperCAmelCase__						=attention_probs_dropout_prob
    UpperCAmelCase__						=initializer_range
    UpperCAmelCase__						=num_labels
    UpperCAmelCase__						=backbone_featmap_shape
    UpperCAmelCase__						=scope
    UpperCAmelCase__						=is_hybrid
    # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
    UpperCAmelCase__						=(image_size // patch_size) ** 2
    UpperCAmelCase__						=num_patches + 1

 def  __UpperCAmelCase						(  self		)	->       str:
    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.image_size, self.image_size],		self.num_labels		)

    UpperCAmelCase__						=self.get_config()

    return config, pixel_values, labels

 def  __UpperCAmelCase						(  self		)	->       Dict:
    UpperCAmelCase__						={
        "global_padding": "same",
        "layer_type": "bottleneck",
        "depths": [3, 4, 9],
        "out_features": ["stage1", "stage2", "stage3"],
        "embedding_dynamic_padding": True,
        "hidden_sizes": [96, 192, 384, 768],
        "num_groups": 2,
    }

    return DPTConfig(
        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,		backbone_out_indices=self.backbone_out_indices,		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=A_,		initializer_range=self.initializer_range,		is_hybrid=self.is_hybrid,		backbone_config=A_,		backbone_featmap_shape=self.backbone_featmap_shape,		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_		)	->       Optional[Any]:
    UpperCAmelCase__						=DPTModel(config=A_		)
    model.to(A_		)
    model.eval()
    UpperCAmelCase__						=model(A_		)
    self.parent.assertEqual(result.last_hidden_state.shape,		(self.batch_size, self.seq_length, self.hidden_size)		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_		)	->       Union[str, Any]:
    UpperCAmelCase__						=self.num_labels
    UpperCAmelCase__						=DPTForDepthEstimation(A_		)
    model.to(A_		)
    model.eval()
    UpperCAmelCase__						=model(A_		)
    self.parent.assertEqual(result.predicted_depth.shape,		(self.batch_size, self.image_size, self.image_size)		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_		)	->       Optional[Any]:
    UpperCAmelCase__						=self.num_labels
    UpperCAmelCase__						=DPTForSemanticSegmentation(A_		)
    model.to(A_		)
    model.eval()
    UpperCAmelCase__						=model(A_,		labels=A_		)
    self.parent.assertEqual(
        result.logits.shape,		(self.batch_size, self.num_labels, self.image_size, self.image_size)		)






 def  __UpperCAmelCase						(  self		)	->       int:
    UpperCAmelCase__						=self.prepare_config_and_inputs()
    UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__						=config_and_inputs
    UpperCAmelCase__						={"pixel_values": pixel_values}
    return config, inputs_dict

@require_torch
class     snake_case_		(   a,		a,		unittest.TestCase							):






 '''simple docstring'''





 __UpperCamelCase       =      (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
 __UpperCamelCase       =      (
     {
         'depth-estimation': DPTForDepthEstimation,
         'feature-extraction': DPTModel,
         'image-segmentation': DPTForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 __UpperCamelCase       =      False
 __UpperCamelCase       =      False
 __UpperCamelCase       =      False

 def  __UpperCAmelCase						(  self		)	->       int:
    UpperCAmelCase__						=DPTModelTester(self		)
    UpperCAmelCase__						=ConfigTester(self,		config_class=A_,		has_text_modality=A_,		hidden_size=37		)

 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    self.config_tester.run_common_tests()

 @unittest.skip(reason="DPT does not use inputs_embeds"		)
 def  __UpperCAmelCase						(  self		)	->       List[Any]:
    pass

 def  __UpperCAmelCase						(  self		)	->       str:
    UpperCAmelCase__			,							UpperCAmelCase__						=self.model_tester.prepare_config_and_inputs_for_common()

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

 def  __UpperCAmelCase						(  self		)	->       Optional[int]:
    UpperCAmelCase__			,							UpperCAmelCase__						=self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       UpperCAmelCase__						=model_class(A_		)
       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],		A_		)

 def  __UpperCAmelCase						(  self		)	->       List[Any]:
    UpperCAmelCase__						=self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*A_		)

 def  __UpperCAmelCase						(  self		)	->       str:
    UpperCAmelCase__						=self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_depth_estimation(*A_		)

 def  __UpperCAmelCase						(  self		)	->       List[Any]:
    UpperCAmelCase__						=self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_semantic_segmentation(*A_		)

 def  __UpperCAmelCase						(  self		)	->       Any:
    for model_class in self.all_model_classes:
       if model_class.__name__ == "DPTForDepthEstimation":
          continue

       UpperCAmelCase__			,							UpperCAmelCase__						=self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase__						=True

       if model_class in get_values(A_		):
          continue

       UpperCAmelCase__						=model_class(A_		)
       model.to(A_		)
       model.train()
       UpperCAmelCase__						=self._prepare_for_class(A_,		A_,		return_labels=A_		)
       UpperCAmelCase__						=model(**A_		).loss
       loss.backward()

 def  __UpperCAmelCase						(  self		)	->       List[Any]:
    for model_class in self.all_model_classes:
       if model_class.__name__ == "DPTForDepthEstimation":
          continue

       UpperCAmelCase__			,							UpperCAmelCase__						=self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase__						=False
       UpperCAmelCase__						=True

       if model_class in get_values(A_		) or not model_class.supports_gradient_checkpointing:
          continue
       UpperCAmelCase__						=model_class(A_		)
       model.to(A_		)
       model.gradient_checkpointing_enable()
       model.train()
       UpperCAmelCase__						=self._prepare_for_class(A_,		A_,		return_labels=A_		)
       UpperCAmelCase__						=model(**A_		).loss
       loss.backward()

 def  __UpperCAmelCase						(  self		)	->       int:
    UpperCAmelCase__			,							UpperCAmelCase__						=self.model_tester.prepare_config_and_inputs_for_common()

    UpperCAmelCase__						=_config_zero_init(A_		)
    for model_class in self.all_model_classes:
       UpperCAmelCase__						=model_class(config=A_		)
       # Skip the check for the backbone
       UpperCAmelCase__						=[]
       for name, module in model.named_modules():
          if module.__class__.__name__ == "DPTViTHybridEmbeddings":
             UpperCAmelCase__						=[f"""{name}.{key}""" for key in module.state_dict().keys()]
             break

       for name, param in model.named_parameters():
          if param.requires_grad:
             if name in backbone_params:
                continue
             self.assertIn(
                 ((param.data.mean() * 1E9).round() / 1E9).item(),		[0.0, 1.0],		msg=f"""Parameter {name} of model {model_class} seems not properly initialized""",		)

 @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."		)
 def  __UpperCAmelCase						(  self		)	->       List[Any]:
    pass

 @slow
 def  __UpperCAmelCase						(  self		)	->       Optional[int]:
    for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
       UpperCAmelCase__						=DPTModel.from_pretrained(A_		)
       self.assertIsNotNone(A_		)






 def  __UpperCAmelCase						(  self		)	->       Any:
    # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
    UpperCAmelCase__			,							UpperCAmelCase__						=self.model_tester.prepare_config_and_inputs_for_common()
    UpperCAmelCase__						="add"
    with self.assertRaises(A_		):
       UpperCAmelCase__						=DPTForDepthEstimation(A_		)






def 				_UpperCAmelCase			(    ):




   '''simple docstring'''

   UpperCAmelCase__						=Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
   return image




@require_torch
@require_vision
@slow
class     snake_case_		(   unittest.TestCase							):






 '''simple docstring'''





 def  __UpperCAmelCase						(  self		)	->       Union[str, Any]:
    UpperCAmelCase__						=DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"		)
    UpperCAmelCase__						=DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"		).to(A_		)

    UpperCAmelCase__						=prepare_img()
    UpperCAmelCase__						=image_processor(images=A_,		return_tensors="pt"		).to(A_		)

    # forward pass
    with torch.no_grad():
       UpperCAmelCase__						=model(**A_		)
       UpperCAmelCase__						=outputs.predicted_depth

    # verify the predicted depth
    UpperCAmelCase__						=torch.Size((1, 384, 384)		)
    self.assertEqual(predicted_depth.shape,		A_		)

    UpperCAmelCase__						=torch.tensor(
        [[[5.64_37, 5.61_46, 5.65_11], [5.43_71, 5.56_49, 5.59_58], [5.52_15, 5.51_84, 5.52_93]]]		).to(A_		)

    self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100,		A_,		atol=1E-4		)		)










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()
UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	{
    '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 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A     ):




   '''simple docstring'''

   for attribute in key.split("."     ):
      UpperCAmelCase__						=getattr(A				,							A     )

   if weight_type is not None:
      UpperCAmelCase__						=getattr(A				,							A     ).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 				_UpperCAmelCase			(    A				,							A				,							A     ):




   '''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(
             A				,							A				,							A				,							A				,							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(A     )[0].split("."     )[-2]
                  UpperCAmelCase__						=mapped_key.replace("*"				,							A     )
               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(A				,							A				,							A				,							A				,							A     )
            continue
      if not is_used:
         unused_weights.append(A     )

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






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A     ):




   '''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(A     )






def 				_UpperCAmelCase			(    A				,							A     ):




   '''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(A     )
   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 				_UpperCAmelCase			(    A				,							A				,							A=None				,							A=None				,							A=True     ):




   '''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(A     )
   else:
      UpperCAmelCase__						=convert_config(model[0]				,							A     )
   UpperCAmelCase__						=model[0].eval()

   UpperCAmelCase__						=True if config.feat_extract_norm == "layer" else False
   UpperCAmelCase__						=WavaVecaFeatureExtractor(
       feature_size=1				,							sampling_rate=16000				,							padding_value=0				,							do_normalize=A				,							return_attention_mask=A				,							)

   if is_finetuned:
      if dict_path:
         UpperCAmelCase__						=Dictionary.load(A     )

         # 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(A				,							"vocab.json"     )
         if not os.path.isdir(A     ):
            logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(A     )     )
            return
         os.makedirs(A				,							exist_ok=A     )
         with open(A				,							"w"				,							encoding="utf-8"     ) as vocab_handle:
            json.dump(target_dict.indices				,							A     )
         UpperCAmelCase__						=WavaVecaCTCTokenizer(
             A				,							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=A				,							)
         UpperCAmelCase__						=WavaVecaProcessor(feature_extractor=A				,							tokenizer=A     )
         processor.save_pretrained(A     )

      UpperCAmelCase__						=SEWForCTC(A     )
   else:
      UpperCAmelCase__						=SEWModel(A     )
      feature_extractor.save_pretrained(A     )

   recursively_load_weights(A				,							A				,							A     )

   hf_model.save_pretrained(A     )


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



