


from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import numpy as np
  import tensorflow as tf

  from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
  from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import MobileViTImageProcessor







class  lowerCamelCase__  (			_lowercase):





     def 	__A   (self			)	->  Optional[int]:
      _lowercase				 =self.config_class(**self.inputs_dict			)
      self.parent.assertTrue(hasattr(__snake_case						,	'''hidden_sizes'''			)			)
      self.parent.assertTrue(hasattr(__snake_case						,	'''neck_hidden_sizes'''			)			)
      self.parent.assertTrue(hasattr(__snake_case						,	'''num_attention_heads'''			)			)







class  lowerCamelCase__  :





     def __init__(self						,	UpperCAmelCase						,	UpperCAmelCase=1_3						,	UpperCAmelCase=3_2						,	UpperCAmelCase=2						,	UpperCAmelCase=3						,	UpperCAmelCase=6_4_0						,	UpperCAmelCase=4						,	UpperCAmelCase="silu"						,	UpperCAmelCase=3						,	UpperCAmelCase=3_2						,	UpperCAmelCase=0.1						,	UpperCAmelCase=0.1						,	UpperCAmelCase=0.1						,	UpperCAmelCase=0.02						,	UpperCAmelCase=True						,	UpperCAmelCase=True						,	UpperCAmelCase=1_0						,	UpperCAmelCase=None						,	)	->  Optional[int]:
      _lowercase				 =parent
      _lowercase				 =batch_size
      _lowercase				 =image_size
      _lowercase				 =patch_size
      _lowercase				 =num_channels
      _lowercase				 =last_hidden_size
      _lowercase				 =num_attention_heads
      _lowercase				 =hidden_act
      _lowercase				 =conv_kernel_size
      _lowercase				 =output_stride
      _lowercase				 =hidden_dropout_prob
      _lowercase				 =attention_probs_dropout_prob
      _lowercase				 =classifier_dropout_prob
      _lowercase				 =use_labels
      _lowercase				 =is_training
      _lowercase				 =num_labels
      _lowercase				 =initializer_range
      _lowercase				 =scope





     def 	__A   (self			)	->  Tuple:
      _lowercase				 =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

      _lowercase				 =None
      _lowercase				 =None
      if self.use_labels:
       _lowercase				 =ids_tensor([self.batch_size]						,	self.num_labels			)
       _lowercase				 =ids_tensor([self.batch_size, self.image_size, self.image_size]						,	self.num_labels			)

      _lowercase				 =self.get_config()

      return config, pixel_values, labels, pixel_labels





     def 	__A   (self			)	->  List[Any]:
      return MobileViTConfig(
          image_size=self.image_size						,	patch_size=self.patch_size						,	num_channels=self.num_channels						,	num_attention_heads=self.num_attention_heads						,	hidden_act=self.hidden_act						,	conv_kernel_size=self.conv_kernel_size						,	output_stride=self.output_stride						,	hidden_dropout_prob=self.hidden_dropout_prob						,	attention_probs_dropout_prob=self.attention_probs_dropout_prob						,	classifier_dropout_prob=self.classifier_dropout_prob						,	initializer_range=self.initializer_range						,	)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Dict:
      _lowercase				 =TFMobileViTModel(config=__snake_case			)
      _lowercase				 =model(__snake_case						,	training=__snake_case			)
      _lowercase				 =self.image_size // self.output_stride
      self.parent.assertEqual(
          result.last_hidden_state.shape						,	(self.batch_size, self.last_hidden_size, expected_height, expected_width)			)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  List[Any]:
      _lowercase				 =self.num_labels
      _lowercase				 =TFMobileViTForImageClassification(__snake_case			)
      _lowercase				 =model(__snake_case						,	labels=__snake_case						,	training=__snake_case			)
      self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels)			)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  str:
      _lowercase				 =self.num_labels
      _lowercase				 =TFMobileViTForSemanticSegmentation(__snake_case			)
      _lowercase				 =self.image_size // self.output_stride

      _lowercase				 =model(__snake_case						,	training=__snake_case			)
      self.parent.assertEqual(
          result.logits.shape						,	(self.batch_size, self.num_labels, expected_height, expected_width)			)

      _lowercase				 =model(__snake_case						,	labels=__snake_case						,	training=__snake_case			)
      self.parent.assertEqual(
          result.logits.shape						,	(self.batch_size, self.num_labels, expected_height, expected_width)			)





     def 	__A   (self			)	->  List[Any]:
      _lowercase				 =self.prepare_config_and_inputs()
      _lowercase				 =config_and_inputs
      _lowercase				 ={'pixel_values': pixel_values}
      return config, inputs_dict







@require_tf
class  lowerCamelCase__  (			_lowercase  ,				_lowercase  ,				unittest.TestCase):
     SCREAMING_SNAKE_CASE__      =     (
         (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
         if is_tf_available()
         else ()
     )
     SCREAMING_SNAKE_CASE__      =     (
         {'''feature-extraction''': TFMobileViTModel, '''image-classification''': TFMobileViTForImageClassification}
         if is_tf_available()
         else {}
     )

     SCREAMING_SNAKE_CASE__      =     False
     SCREAMING_SNAKE_CASE__      =     False
     SCREAMING_SNAKE_CASE__      =     False
     SCREAMING_SNAKE_CASE__      =     False
     SCREAMING_SNAKE_CASE__      =     False





     def 	__A   (self			)	->  str:
      _lowercase				 =TFMobileViTModelTester(self			)
      _lowercase				 =TFMobileViTConfigTester(self						,	config_class=__snake_case						,	has_text_modality=__snake_case			)





     def 	__A   (self			)	->  Tuple:
      self.config_tester.run_common_tests()





     @unittest.skip(reason='''MobileViT does not use inputs_embeds'''			)
     def 	__A   (self			)	->  List[Any]:
      pass





     @unittest.skip(reason='''MobileViT does not support input and output embeddings'''			)
     def 	__A   (self			)	->  str:
      pass





     @unittest.skip(reason='''MobileViT does not output attentions'''			)
     def 	__A   (self			)	->  List[str]:
      pass





     def 	__A   (self			)	->  Optional[Any]:
      _lowercase				 =self.model_tester.prepare_config_and_inputs_for_common()

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

       _lowercase				 =['pixel_values']
       self.assertListEqual(arg_names[:1]						,	__snake_case			)





     def 	__A   (self			)	->  Any:
      _lowercase				 =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__snake_case			)





     def 	__A   (self			)	->  Union[str, Any]:
      def check_hidden_states_output(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			):
       _lowercase				 =model_class(__snake_case			)

       _lowercase				 =model(**self._prepare_for_class(__snake_case						,	__snake_case			)			)

       _lowercase				 =outputs.hidden_states

       _lowercase				 =5
       self.assertEqual(len(__snake_case			)						,	__snake_case			)

       # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
       # with the width and height being successively divided by 2.
       _lowercase				 =2
       for i in range(len(__snake_case			)			):
        self.assertListEqual(
            list(hidden_states[i].shape[-2:]			)						,	[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]						,	)
        divisor *= 2

       self.assertEqual(self.model_tester.output_stride						,	divisor // 2			)

      _lowercase				 =self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       _lowercase				 =True
       check_hidden_states_output(__snake_case						,	__snake_case						,	__snake_case			)

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       _lowercase				 =True

       check_hidden_states_output(__snake_case						,	__snake_case						,	__snake_case			)





     def 	__A   (self			)	->  Any:
      _lowercase				 =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__snake_case			)





     def 	__A   (self			)	->  str:
      _lowercase				 =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*__snake_case			)





     @unittest.skipIf(
         not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''			)			) == 0						,	reason='''TF does not support backprop for grouped convolutions on CPU.'''						,	)
     def 	__A   (self			)	->  str:
      super().test_dataset_conversion()





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase=2e-1						,	UpperCAmelCase=2e-1			)	->  List[Any]:
      self.assertTrue(np.allclose(__snake_case						,	__snake_case						,	atol=__snake_case						,	rtol=__snake_case			)			)





     @unittest.skipIf(
         not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''			)			) == 0						,	reason='''TF does not support backprop for grouped convolutions on CPU.'''						,	)
     @slow
     def 	__A   (self			)	->  Union[str, Any]:
      _lowercase				 =self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       # Since `TFMobileViTModel` cannot operate with the default `fit()` method.
       if model_class.__name__ != "TFMobileViTModel":
        _lowercase				 =model_class(__snake_case			)
        if getattr(__snake_case						,	'''hf_compute_loss'''						,	__snake_case			):
         super().test_keras_fit()





     def 	__A   (self			)	->  Optional[Any]:
      _lowercase				 =self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
       # set an ignore index to correctly test the masked loss used in
       # `TFMobileViTForSemanticSegmentation`.
       if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
        _lowercase				 =5

       _lowercase				 =model_class(__snake_case			)
       if getattr(__snake_case						,	'''hf_compute_loss'''						,	__snake_case			):
        # The number of elements in the loss should be the same as the number of elements in the label
        _lowercase				 =self._prepare_for_class(inputs_dict.copy()						,	__snake_case						,	return_labels=__snake_case			)
        _lowercase				 =prepared_for_class[
            sorted(prepared_for_class.keys() - inputs_dict.keys()						,	reverse=__snake_case			)[0]
        ]
        _lowercase				 =added_label.shape.as_list()[:1]

        # Test that model correctly compute the loss with kwargs
        _lowercase				 =self._prepare_for_class(inputs_dict.copy()						,	__snake_case						,	return_labels=__snake_case			)
        _lowercase				 ={'input_ids', 'pixel_values', 'input_features'}
        _lowercase				 =possible_input_names.intersection(set(__snake_case			)			).pop()
        _lowercase				 =prepared_for_class.pop(__snake_case			)

        _lowercase				 =model(__snake_case						,	**__snake_case			)[0]
        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]			)

        # Test that model correctly compute the loss when we mask some positions
        _lowercase				 =self._prepare_for_class(inputs_dict.copy()						,	__snake_case						,	return_labels=__snake_case			)
        _lowercase				 ={'input_ids', 'pixel_values', 'input_features'}
        _lowercase				 =possible_input_names.intersection(set(__snake_case			)			).pop()
        _lowercase				 =prepared_for_class.pop(__snake_case			)
        if "labels" in prepared_for_class:
         _lowercase				 =prepared_for_class['labels'].numpy()
         if len(labels.shape			) > 1 and labels.shape[1] != 1:
          # labels[0] = -100
          _lowercase				 =tf.convert_to_tensor(__snake_case			)
          _lowercase				 =model(__snake_case						,	**__snake_case			)[0]
          self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]			)
          self.assertTrue(not np.any(np.isnan(loss.numpy()			)			)			)

                # Test that model correctly compute the loss with a dict
        _lowercase				 =self._prepare_for_class(inputs_dict.copy()						,	__snake_case						,	return_labels=__snake_case			)
        _lowercase				 =model(__snake_case			)[0]
        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]			)

        # Test that model correctly compute the loss with a tuple
        _lowercase				 =self._prepare_for_class(inputs_dict.copy()						,	__snake_case						,	return_labels=__snake_case			)

        # Get keys that were added with the _prepare_for_class function
        _lowercase				 =prepared_for_class.keys() - inputs_dict.keys()
        _lowercase				 =inspect.signature(model.call			).parameters
        _lowercase				 =list(signature.keys()			)

        # Create a dictionary holding the location of the tensors in the tuple
        _lowercase				 ={0: input_name}
        for label_key in label_keys:
         _lowercase				 =signature_names.index(__snake_case			)
         _lowercase				 =label_key
        _lowercase				 =sorted(tuple_index_mapping.items()			)
        # Initialize a list with their default values, update the values and convert to a tuple
        _lowercase				 =[]

        for name in signature_names:
         if name != "kwargs":
          list_input.append(signature[name].default			)

        for index, value in sorted_tuple_index_mapping:
         _lowercase				 =prepared_for_class[value]

        _lowercase				 =tuple(__snake_case			)

        # Send to model
        _lowercase				 =model(tuple_input[:-1]			)[0]

        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]			)





     @slow
     def 	__A   (self			)	->  int:
      for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _lowercase				 =TFMobileViTModel.from_pretrained(__snake_case			)
       self.assertIsNotNone(__snake_case			)



def 							UpperCAmelCase_   (				)	->     str:







 """simple docstring"""


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







@require_tf
class  lowerCamelCase__  (			unittest.TestCase):





     @slow
     def 	__A   (self			)	->  Dict:
      _lowercase				 =TFMobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small'''			)

      _lowercase				 =MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small'''			)
      _lowercase				 =prepare_img()
      _lowercase				 =image_processor(images=__snake_case						,	return_tensors='''tf'''			)

      # forward pass
      _lowercase				 =model(**__snake_case						,	training=__snake_case			)

      # verify the logits
      _lowercase				 =tf.TensorShape((1, 1_0_0_0)			)
      self.assertEqual(outputs.logits.shape						,	__snake_case			)

      _lowercase				 =tf.constant([-1.9364, -1.2327, -0.4653]			)

      tf.debugging.assert_near(outputs.logits[0, :3]						,	__snake_case						,	atol=1e-4						,	rtol=1e-04			)





     @slow
     def 	__A   (self			)	->  List[Any]:
      # `from_pt` will be removed
      _lowercase				 =TFMobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''			)

      _lowercase				 =MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''			)

      _lowercase				 =prepare_img()
      _lowercase				 =image_processor(images=__snake_case						,	return_tensors='''tf'''			)

      # forward pass
      _lowercase				 =model(inputs.pixel_values						,	training=__snake_case			)
      _lowercase				 =outputs.logits

      # verify the logits
      _lowercase				 =tf.TensorShape((1, 2_1, 3_2, 3_2)			)
      self.assertEqual(logits.shape						,	__snake_case			)

      _lowercase				 =tf.constant(
          [
              [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
              [[-1_0.6_8_6_9, -1_0.3_2_5_0, -1_0.3_4_7_1], [-1_0.4_2_2_8, -9.9868, -9.7132], [-1_1.0_4_0_5, -1_1.0_2_2_1, -1_0.7_3_1_8]],
              [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
          ]			)

      tf.debugging.assert_near(logits[0, :3, :3, :3]						,	__snake_case						,	rtol=1e-4						,	atol=1e-4			)




import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

lowerCAmelCase__             =		{
    """post_extract_proj""": """feature_projection.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.layer_norm""": """encoder.layer_norm""",
    """mask_emb""": """masked_spec_embed""",
}






def        lowerCamelCase_  (				UpperCAmelCase_				:  Dict				,							UpperCAmelCase_				:  List[Any]				,							UpperCAmelCase_				:  Optional[int]				,							UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  List[Any]						)    ->					Any:




	'''simple docstring'''




	for attribute in key.split('.'						):
		_UpperCamelCase :		Any								=    getattr(UpperCAmelCase_				,							UpperCAmelCase_						)

	if weight_type is not None:
		_UpperCamelCase :		List[str]								=    getattr(UpperCAmelCase_				,							UpperCAmelCase_						).shape
	else:
		_UpperCamelCase :		Optional[int]								=    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 :		Tuple								=    value
	elif weight_type == "weight_g":
		_UpperCamelCase :		Optional[Any]								=    value
	elif weight_type == "weight_v":
		_UpperCamelCase :		Tuple								=    value
	elif weight_type == "bias":
		_UpperCamelCase :		Any								=    value
	else:
		_UpperCamelCase :		Tuple								=    value

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






def        lowerCamelCase_  (				UpperCAmelCase_				:  int				,							UpperCAmelCase_				:  Union[str, Any]						)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		Union[str, Any]								=    []
	_UpperCamelCase :		List[Any]								=    fairseq_model.state_dict()

	_UpperCamelCase :		int								=    hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		_UpperCamelCase :		Dict								=    False
		if "conv_layers" in name:
			load_conv_layer(
			    UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							hf_model.config.feat_extract_norm == 'group'				,							)
			_UpperCamelCase :		Union[str, Any]								=    True
		else:
			for key, mapped_key in MAPPING.items():
				_UpperCamelCase :		Tuple								=    mapped_key

				if key in name:
					_UpperCamelCase :		List[Any]								=    True
					if "*" in mapped_key:
						_UpperCamelCase :		List[Any]								=    name.split(UpperCAmelCase_						)[0].split('.'						)[-2]
						_UpperCamelCase :		Optional[Any]								=    mapped_key.replace('*'				,							UpperCAmelCase_						)
					if "weight_g" in name:
						_UpperCamelCase :		List[Any]								=    'weight_g'
					elif "weight_v" in name:
						_UpperCamelCase :		Tuple								=    'weight_v'
					elif "weight" in name:
						_UpperCamelCase :		Optional[Any]								=    'weight'
					elif "bias" in name:
						_UpperCamelCase :		List[Any]								=    'bias'
					else:
						_UpperCamelCase :		str								=    None
					set_recursively(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_						)
				continue
		if not is_used:
			unused_weights.append(UpperCAmelCase_						)

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






def        lowerCamelCase_  (				UpperCAmelCase_				:  Union[str, Any]				,							UpperCAmelCase_				:  List[Any]				,							UpperCAmelCase_				:  Optional[Any]				,							UpperCAmelCase_				:  int				,							UpperCAmelCase_				:  int						)    ->					Dict:




	'''simple docstring'''




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






def        lowerCamelCase_  (				UpperCAmelCase_				:  Tuple						)    ->					List[str]:




	'''simple docstring'''




	_UpperCamelCase :		Union[str, Any]								=    HubertConfig()
	_UpperCamelCase :		List[str]								=    model.config

	_UpperCamelCase :		Any								=    fs_config.activation_dropout
	_UpperCamelCase :		Tuple								=    False
	_UpperCamelCase :		Tuple								=    fs_config.attention_dropout
	_UpperCamelCase :		Dict								=    False
	_UpperCamelCase :		Optional[Any]								=    eval(fs_config.extractor_conv_feature_layers						)
	_UpperCamelCase :		Dict								=    [x[0] for x in conv_layers]
	_UpperCamelCase :		Any								=    [x[1] for x in conv_layers]
	_UpperCamelCase :		Any								=    [x[2] for x in conv_layers]
	_UpperCamelCase :		Dict								=    'gelu'
	_UpperCamelCase :		List[Any]								=    'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
	_UpperCamelCase :		List[Any]								=    False
	_UpperCamelCase :		List[Any]								=    0.0
	_UpperCamelCase :		Optional[int]								=    0.0
	_UpperCamelCase :		Optional[int]								=    fs_config.activation_fn
	_UpperCamelCase :		Optional[int]								=    fs_config.dropout
	_UpperCamelCase :		Union[str, Any]								=    fs_config.encoder_embed_dim
	_UpperCamelCase :		Tuple								=    0.0_2
	_UpperCamelCase :		int								=    fs_config.encoder_ffn_embed_dim
	_UpperCamelCase :		int								=    1e-5
	_UpperCamelCase :		Optional[Any]								=    0.0
	_UpperCamelCase :		List[str]								=    fs_config.encoder_attention_heads
	_UpperCamelCase :		List[str]								=    fs_config.conv_pos_groups
	_UpperCamelCase :		Union[str, Any]								=    fs_config.conv_pos
	_UpperCamelCase :		int								=    len(UpperCAmelCase_						)
	_UpperCamelCase :		Tuple								=    fs_config.encoder_layers

	return config






@torch.no_grad()
def        lowerCamelCase_  (				UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  Dict=None						)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		Tuple								=    distilhubert().model.model

	if config_path is not None:
		_UpperCamelCase :		Optional[Any]								=    HubertConfig.from_pretrained(UpperCAmelCase_						)
	else:
		_UpperCamelCase :		Optional[int]								=    convert_config(UpperCAmelCase_						)
	_UpperCamelCase :		List[Any]								=    model.eval()

	_UpperCamelCase :		List[Any]								=    WavaVecaFeatureExtractor(
	    feature_size=1				,							sampling_rate=1_6_0_0_0				,							padding_value=0				,							do_normalize=UpperCAmelCase_				,							return_attention_mask=UpperCAmelCase_				,							)
	_UpperCamelCase :		Optional[Any]								=    HubertModel(UpperCAmelCase_						)

	recursively_load_weights(UpperCAmelCase_				,							UpperCAmelCase_						)

	feature_extractor.save_pretrained(UpperCAmelCase_						)
	hf_model.save_pretrained(UpperCAmelCase_						)


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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							lowerCAmelCase__             =		parser.parse_args()
							convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)




