






import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


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

a_		:						int             =    {
    '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.grep_linear': 'encoder.layers.*.attention.gru_rel_pos_linear',
    'self_attn.relative_attention_bias': 'encoder.layers.*.attention.rel_attn_embed',
    'self_attn.grep_a': 'encoder.layers.*.attention.gru_rel_pos_const',
    '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',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
a_		:						int             =    [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			for attribute in key.split('.'):
						SCREAMING_SNAKE_CASE						= getattr(snake_case__							,       snake_case__)

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

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






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= []
			SCREAMING_SNAKE_CASE						= fairseq_model.state_dict()

			SCREAMING_SNAKE_CASE						= hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						SCREAMING_SNAKE_CASE						= False
						if "conv_layers" in name:
									load_conv_layer(
									    snake_case__							,       snake_case__							,       snake_case__							,       snake_case__							,       hf_model.config.feat_extract_norm == 'group'							,       )
									SCREAMING_SNAKE_CASE						= True
						else:
									for key, mapped_key in MAPPING.items():
												if key in name or key.split('w2v_model.')[-1] == name.split('.')[0]:
															SCREAMING_SNAKE_CASE						= True
															if "*" in mapped_key:
																		SCREAMING_SNAKE_CASE						= name.split(snake_case__)[0].split('.')[-2]
																		SCREAMING_SNAKE_CASE						= mapped_key.replace('*'							,       snake_case__)
															if "weight_g" in name:
																		SCREAMING_SNAKE_CASE						= 'weight_g'
															elif "weight_v" in name:
																		SCREAMING_SNAKE_CASE						= 'weight_v'
															elif "bias" in name and "relative_attention_bias" not in name:
																		SCREAMING_SNAKE_CASE						= 'bias'
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		SCREAMING_SNAKE_CASE						= 'weight'
															else:
																		SCREAMING_SNAKE_CASE						= None

															set_recursively(snake_case__							,       snake_case__							,       snake_case__							,       snake_case__							,       snake_case__)
												continue
						if not is_used:
									unused_weights.append(snake_case__)

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






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






@torch.no_grad()
def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase=None):
			# load the pre-trained checkpoints
			SCREAMING_SNAKE_CASE						= torch.load(snake_case__)
			SCREAMING_SNAKE_CASE						= WavLMConfigOrig(checkpoint['cfg'])
			SCREAMING_SNAKE_CASE						= WavLMOrig(snake_case__)
			model.load_state_dict(checkpoint['model'])
			model.eval()

			if config_path is not None:
						SCREAMING_SNAKE_CASE						= WavLMConfig.from_pretrained(snake_case__)
			else:
						SCREAMING_SNAKE_CASE						= WavLMConfig()

			SCREAMING_SNAKE_CASE						= WavLMModel(snake_case__)

			recursively_load_weights(snake_case__							,       snake_case__)

			hf_wavlm.save_pretrained(snake_case__)


if __name__ == "__main__":
				a_		:						List[str]             =    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('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
				a_		:						Union[str, Any]             =    parser.parse_args()
				convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)












import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import MobileViTImageProcessor

class 		_snake_case  (  unittest.TestCase					):






			def __init__(       self		,					a		,					a=7		,					a=3		,					a=18		,					a=30		,					a=400		,					a=True		,					a=None		,					a=True		,					a=None		,					a=True		,					)      ->       Any:
						SCREAMING_SNAKE_CASE						= size if size is not None else {'shortest_edge': 20}
						SCREAMING_SNAKE_CASE						= crop_size if crop_size is not None else {'height': 18, 'width': 18}
						SCREAMING_SNAKE_CASE						= parent
						SCREAMING_SNAKE_CASE						= batch_size
						SCREAMING_SNAKE_CASE						= num_channels
						SCREAMING_SNAKE_CASE						= image_size
						SCREAMING_SNAKE_CASE						= min_resolution
						SCREAMING_SNAKE_CASE						= max_resolution
						SCREAMING_SNAKE_CASE						= do_resize
						SCREAMING_SNAKE_CASE						= size
						SCREAMING_SNAKE_CASE						= do_center_crop
						SCREAMING_SNAKE_CASE						= crop_size
						SCREAMING_SNAKE_CASE						= do_flip_channel_order







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_flip_channel_order": self.do_flip_channel_order,
						}



@require_torch
@require_vision
class 		_snake_case  (  A__   ,    unittest.TestCase					):
			_lowercase		:    Optional[int]			     =							MobileViTImageProcessor if is_vision_available() else None






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= MobileViTImageProcessingTester(self)






			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						return self.image_processor_tester.prepare_image_processor_dict()






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(a		,					'do_resize'))
						self.assertTrue(hasattr(a		,					'size'))
						self.assertTrue(hasattr(a		,					'do_center_crop'))
						self.assertTrue(hasattr(a		,					'center_crop'))
						self.assertTrue(hasattr(a		,					'do_flip_channel_order'))






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						SCREAMING_SNAKE_CASE						= self.image_processing_class.from_dict(self.image_processor_dict)
						self.assertEqual(image_processor.size		,					{'shortest_edge': 20})
						self.assertEqual(image_processor.crop_size		,					{'height': 18, 'width': 18})

						SCREAMING_SNAKE_CASE						= self.image_processing_class.from_dict(self.image_processor_dict		,					size=42		,					crop_size=84)
						self.assertEqual(image_processor.size		,					{'shortest_edge': 42})
						self.assertEqual(image_processor.crop_size		,					{'height': 84, 'width': 84})






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						pass






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					Image.Image)

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

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






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a		,					numpify=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					np.ndarray)

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

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







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a		,					torchify=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					torch.Tensor)

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

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




