

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 						lowercase     (					_lowercase				):


		"""simple docstring"""




		def 		A__       (					self):
			_UpperCamelCase :		int								=    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 						lowercase     :


		"""simple docstring"""




		def __init__(					self						,							__snake_case						,							__snake_case=13						,							__snake_case=32						,							__snake_case=2						,							__snake_case=3						,							__snake_case=6_40						,							__snake_case=4						,							__snake_case="silu"						,							__snake_case=3						,							__snake_case=32						,							__snake_case=0.1						,							__snake_case=0.1						,							__snake_case=0.1						,							__snake_case=0.0_2						,							__snake_case=True						,							__snake_case=True						,							__snake_case=10						,							__snake_case=None						,							):
			_UpperCamelCase :		List[str]								=    parent
			_UpperCamelCase :		List[Any]								=    batch_size
			_UpperCamelCase :		Dict								=    image_size
			_UpperCamelCase :		str								=    patch_size
			_UpperCamelCase :		Any								=    num_channels
			_UpperCamelCase :		str								=    last_hidden_size
			_UpperCamelCase :		Dict								=    num_attention_heads
			_UpperCamelCase :		Optional[Any]								=    hidden_act
			_UpperCamelCase :		Dict								=    conv_kernel_size
			_UpperCamelCase :		Any								=    output_stride
			_UpperCamelCase :		str								=    hidden_dropout_prob
			_UpperCamelCase :		Tuple								=    attention_probs_dropout_prob
			_UpperCamelCase :		Tuple								=    classifier_dropout_prob
			_UpperCamelCase :		Optional[Any]								=    use_labels
			_UpperCamelCase :		List[Any]								=    is_training
			_UpperCamelCase :		Any								=    num_labels
			_UpperCamelCase :		List[Any]								=    initializer_range
			_UpperCamelCase :		Optional[Any]								=    scope


		def 		A__       (					self):
			_UpperCamelCase :		List[Any]								=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

			_UpperCamelCase :		Dict								=    None
			_UpperCamelCase :		Dict								=    None
			if self.use_labels:
				_UpperCamelCase :		Any								=    ids_tensor([self.batch_size]						,							self.num_labels)
				_UpperCamelCase :		Optional[int]								=    ids_tensor([self.batch_size, self.image_size, self.image_size]						,							self.num_labels)

			_UpperCamelCase :		List[Any]								=    self.get_config()

			return config, pixel_values, labels, pixel_labels


		def 		A__       (					self):
			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						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case):
			_UpperCamelCase :		str								=    TFMobileViTModel(config=__snake_case)
			_UpperCamelCase :		Optional[Any]								=    model(__snake_case						,							training=__snake_case)
			_UpperCamelCase :		Dict								=    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						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case):
			_UpperCamelCase :		Dict								=    self.num_labels
			_UpperCamelCase :		List[str]								=    TFMobileViTForImageClassification(__snake_case)
			_UpperCamelCase :		Any								=    model(__snake_case						,							labels=__snake_case						,							training=__snake_case)
			self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.num_labels))


		def 		A__       (					self						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case):
			_UpperCamelCase :		Any								=    self.num_labels
			_UpperCamelCase :		int								=    TFMobileViTForSemanticSegmentation(__snake_case)
			_UpperCamelCase :		Tuple								=    self.image_size // self.output_stride

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

			_UpperCamelCase :		str								=    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):
			_UpperCamelCase :		int								=    self.prepare_config_and_inputs()
			_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase :		Dict								=    config_and_inputs
			_UpperCamelCase :		int								=    {'pixel_values': pixel_values}
			return config, inputs_dict







@require_tf
class 						lowercase     (					_lowercase     ,				_lowercase     ,				unittest.TestCase				):


		"""simple docstring"""




		a__       							=	(
		    (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
		    if is_tf_available()
		    else ()
		)
		a__       							=	(
		    {"feature-extraction": TFMobileViTModel, "image-classification": TFMobileViTForImageClassification}
		    if is_tf_available()
		    else {}
		)

		a__       							=	False
		a__       							=	False
		a__       							=	False
		a__       							=	False
		a__       							=	False


		def 		A__       (					self):
			_UpperCamelCase :		List[str]								=    TFMobileViTModelTester(self)
			_UpperCamelCase :		Any								=    TFMobileViTConfigTester(self						,							config_class=__snake_case						,							has_text_modality=__snake_case)


		def 		A__       (					self):
			self.config_tester.run_common_tests()


		@unittest.skip(reason='MobileViT does not use inputs_embeds')
		def 		A__       (					self):
			pass


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


		@unittest.skip(reason='MobileViT does not output attentions')
		def 		A__       (					self):
			pass


		def 		A__       (					self):
			_UpperCamelCase						,		_UpperCamelCase :		Union[str, Any]								=    self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				_UpperCamelCase :		List[str]								=    model_class(__snake_case)
				_UpperCamelCase :		Union[str, Any]								=    inspect.signature(model.call)
				# signature.parameters is an OrderedDict => so arg_names order is deterministic
				_UpperCamelCase :		Tuple								=    [*signature.parameters.keys()]

				_UpperCamelCase :		Optional[int]								=    ['pixel_values']
				self.assertListEqual(arg_names[:1]						,							__snake_case)


		def 		A__       (					self):
			_UpperCamelCase :		Tuple								=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*__snake_case)


		def 		A__       (					self):
			def check_hidden_states_output(__snake_case						,							__snake_case						,							__snake_case):
				_UpperCamelCase :		List[Any]								=    model_class(__snake_case)

				_UpperCamelCase :		Optional[Any]								=    model(**self._prepare_for_class(__snake_case						,							__snake_case))

				_UpperCamelCase :		Tuple								=    outputs.hidden_states

				_UpperCamelCase :		Any								=    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.
				_UpperCamelCase :		int								=    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)

			_UpperCamelCase						,		_UpperCamelCase :		Dict								=    self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				_UpperCamelCase :		Optional[Any]								=    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"]
				_UpperCamelCase :		Tuple								=    True

				check_hidden_states_output(__snake_case						,							__snake_case						,							__snake_case)


		def 		A__       (					self):
			_UpperCamelCase :		Tuple								=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*__snake_case)


		def 		A__       (					self):
			_UpperCamelCase :		Optional[Any]								=    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):
			super().test_dataset_conversion()


		def 		A__       (					self						,							__snake_case						,							__snake_case						,							__snake_case=2e-1						,							__snake_case=2e-1):
			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):
			_UpperCamelCase						,		_UpperCamelCase :		int								=    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":
					_UpperCamelCase :		Union[str, Any]								=    model_class(__snake_case)
					if getattr(__snake_case						,							'hf_compute_loss'						,							__snake_case):
						super().test_keras_fit()


		def 		A__       (					self):
			_UpperCamelCase						,		_UpperCamelCase :		List[str]								=    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":
					_UpperCamelCase :		List[str]								=    5

				_UpperCamelCase :		Dict								=    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
					_UpperCamelCase :		List[Any]								=    self._prepare_for_class(inputs_dict.copy()						,							__snake_case						,							return_labels=__snake_case)
					_UpperCamelCase :		Dict								=    prepared_for_class[
					    sorted(prepared_for_class.keys() - inputs_dict.keys()						,							reverse=__snake_case)[0]
					]
					_UpperCamelCase :		int								=    added_label.shape.as_list()[:1]

					# Test that model correctly compute the loss with kwargs
					_UpperCamelCase :		Optional[Any]								=    self._prepare_for_class(inputs_dict.copy()						,							__snake_case						,							return_labels=__snake_case)
					_UpperCamelCase :		Optional[Any]								=    {'input_ids', 'pixel_values', 'input_features'}
					_UpperCamelCase :		int								=    possible_input_names.intersection(set(__snake_case)).pop()
					_UpperCamelCase :		str								=    prepared_for_class.pop(__snake_case)

					_UpperCamelCase :		List[str]								=    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
					_UpperCamelCase :		Any								=    self._prepare_for_class(inputs_dict.copy()						,							__snake_case						,							return_labels=__snake_case)
					_UpperCamelCase :		int								=    {'input_ids', 'pixel_values', 'input_features'}
					_UpperCamelCase :		Any								=    possible_input_names.intersection(set(__snake_case)).pop()
					_UpperCamelCase :		Union[str, Any]								=    prepared_for_class.pop(__snake_case)
					if "labels" in prepared_for_class:
						_UpperCamelCase :		Dict								=    prepared_for_class['labels'].numpy()
						if len(labels.shape) > 1 and labels.shape[1] != 1:
							# labels[0] = -100
							_UpperCamelCase :		Optional[Any]								=    tf.convert_to_tensor(__snake_case)
							_UpperCamelCase :		str								=    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
					_UpperCamelCase :		List[str]								=    self._prepare_for_class(inputs_dict.copy()						,							__snake_case						,							return_labels=__snake_case)
					_UpperCamelCase :		List[str]								=    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
					_UpperCamelCase :		Union[str, Any]								=    self._prepare_for_class(inputs_dict.copy()						,							__snake_case						,							return_labels=__snake_case)

					# Get keys that were added with the _prepare_for_class function
					_UpperCamelCase :		str								=    prepared_for_class.keys() - inputs_dict.keys()
					_UpperCamelCase :		Optional[int]								=    inspect.signature(model.call).parameters
					_UpperCamelCase :		Any								=    list(signature.keys())

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

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

					for index, value in sorted_tuple_index_mapping:
						_UpperCamelCase :		Dict								=    prepared_for_class[value]

					_UpperCamelCase :		List[Any]								=    tuple(__snake_case)

					# Send to model
					_UpperCamelCase :		Optional[int]								=    model(tuple_input[:-1])[0]

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






		@slow
		def 		A__       (					self):
			for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				_UpperCamelCase :		str								=    TFMobileViTModel.from_pretrained(__snake_case)
				self.assertIsNotNone(__snake_case)






def        lowerCamelCase_  (				)    ->					str:




	'''simple docstring'''




	_UpperCamelCase :		Optional[Any]								=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
	return image







@require_tf
class 						lowercase     (					unittest.TestCase				):


		"""simple docstring"""




		@slow
		def 		A__       (					self):
			_UpperCamelCase :		str								=    TFMobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small')

			_UpperCamelCase :		List[Any]								=    MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small')
			_UpperCamelCase :		List[str]								=    prepare_img()
			_UpperCamelCase :		Tuple								=    image_processor(images=__snake_case						,							return_tensors='tf')

			# forward pass
			_UpperCamelCase :		Any								=    model(**__snake_case						,							training=__snake_case)

			# verify the logits
			_UpperCamelCase :		Tuple								=    tf.TensorShape((1, 10_00))
			self.assertEqual(outputs.logits.shape						,							__snake_case)

			_UpperCamelCase :		Union[str, Any]								=    tf.constant([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3])

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






		@slow
		def 		A__       (					self):
			# `from_pt` will be removed
			_UpperCamelCase :		int								=    TFMobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small')

			_UpperCamelCase :		Optional[int]								=    MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small')

			_UpperCamelCase :		Union[str, Any]								=    prepare_img()
			_UpperCamelCase :		List[str]								=    image_processor(images=__snake_case						,							return_tensors='tf')

			# forward pass
			_UpperCamelCase :		List[str]								=    model(inputs.pixel_values						,							training=__snake_case)
			_UpperCamelCase :		int								=    outputs.logits

			# verify the logits
			_UpperCamelCase :		Any								=    tf.TensorShape((1, 21, 32, 32))
			self.assertEqual(logits.shape						,							__snake_case)

			_UpperCamelCase :		Optional[Any]								=    tf.constant(
			    [
			        [[6.9_7_1_3, 6.9_7_8_6, 7.2_4_2_2], [7.2_8_9_3, 7.2_8_2_5, 7.4_4_4_6], [7.6_5_8_0, 7.8_7_9_7, 7.9_4_2_0]],
			        [[-1_0.6_8_6_9, -1_0.3_2_5_0, -1_0.3_4_7_1], [-1_0.4_2_2_8, -9.9_8_6_8, -9.7_1_3_2], [-1_1.0_4_0_5, -1_1.0_2_2_1, -1_0.7_3_1_8]],
			        [[-3.3_0_8_9, -2.8_5_3_9, -2.6_7_4_0], [-3.2_7_0_6, -2.5_6_2_1, -2.5_1_0_8], [-3.2_5_3_4, -2.6_6_1_5, -2.6_6_5_1]],
			    ])

			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)




