






import collections
import inspect
import unittest

from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
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 (
	    FocalNetBackbone,
	    FocalNetForImageClassification,
	    FocalNetForMaskedImageModeling,
	    FocalNetModel,
	)
	from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor

class     _SCREAMING_SNAKE_CASE       :





	def __init__(		self				:					Union[str, Any] ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					Optional[Any]=13 ,  __lowerCamelCase				:					List[Any]=32 ,  __lowerCamelCase				:					Tuple=2 ,  __lowerCamelCase				:					Optional[int]=3 ,  __lowerCamelCase				:					Optional[int]=16 ,  __lowerCamelCase				:					Optional[int]=[32, 64, 128] ,  __lowerCamelCase				:					int=[1, 2, 1] ,  __lowerCamelCase				:					int=[2, 2, 4] ,  __lowerCamelCase				:					Any=2 ,  __lowerCamelCase				:					Tuple=2.0 ,  __lowerCamelCase				:					Tuple=True ,  __lowerCamelCase				:					List[Any]=0.0 ,  __lowerCamelCase				:					Dict=0.0 ,  __lowerCamelCase				:					Any=0.1 ,  __lowerCamelCase				:					List[str]="gelu" ,  __lowerCamelCase				:					Tuple=False ,  __lowerCamelCase				:					str=True ,  __lowerCamelCase				:					List[Any]=0.02 ,  __lowerCamelCase				:					Optional[Any]=1E-5 ,  __lowerCamelCase				:					int=True ,  __lowerCamelCase				:					Optional[Any]=None ,  __lowerCamelCase				:					int=True ,  __lowerCamelCase				:					str=10 ,  __lowerCamelCase				:					Tuple=8 ,  __lowerCamelCase				:					List[Any]=["stage1", "stage2"] ,  __lowerCamelCase				:					Union[str, Any]=[1, 2] ,  ):
		UpperCamelCase     :str          =	parent
		UpperCamelCase     :Dict          =	batch_size
		UpperCamelCase     :str          =	image_size
		UpperCamelCase     :List[Any]          =	patch_size
		UpperCamelCase     :Union[str, Any]          =	num_channels
		UpperCamelCase     :str          =	embed_dim
		UpperCamelCase     :int          =	hidden_sizes
		UpperCamelCase     :Any          =	depths
		UpperCamelCase     :Union[str, Any]          =	num_heads
		UpperCamelCase     :List[str]          =	window_size
		UpperCamelCase     :int          =	mlp_ratio
		UpperCamelCase     :int          =	qkv_bias
		UpperCamelCase     :Optional[int]          =	hidden_dropout_prob
		UpperCamelCase     :Optional[int]          =	attention_probs_dropout_prob
		UpperCamelCase     :Tuple          =	drop_path_rate
		UpperCamelCase     :Optional[Any]          =	hidden_act
		UpperCamelCase     :List[Any]          =	use_absolute_embeddings
		UpperCamelCase     :List[Any]          =	patch_norm
		UpperCamelCase     :int          =	layer_norm_eps
		UpperCamelCase     :Union[str, Any]          =	initializer_range
		UpperCamelCase     :Union[str, Any]          =	is_training
		UpperCamelCase     :Tuple          =	scope
		UpperCamelCase     :int          =	use_labels
		UpperCamelCase     :Optional[int]          =	type_sequence_label_size
		UpperCamelCase     :Union[str, Any]          =	encoder_stride
		UpperCamelCase     :List[str]          =	out_features
		UpperCamelCase     :List[Any]          =	out_indices





	def 						_A (		self				:					List[str]	):
		UpperCamelCase     :int          =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

		UpperCamelCase     :Dict          =	self.get_config()

		return config, pixel_values, labels





	def 						_A (		self				:					Optional[int]	):
		return FocalNetConfig(
		    image_size=self.image_size ,  patch_size=self.patch_size ,  num_channels=self.num_channels ,  embed_dim=self.embed_dim ,  hidden_sizes=self.hidden_sizes ,  depths=self.depths ,  num_heads=self.num_heads ,  window_size=self.window_size ,  mlp_ratio=self.mlp_ratio ,  qkv_bias=self.qkv_bias ,  hidden_dropout_prob=self.hidden_dropout_prob ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob ,  drop_path_rate=self.drop_path_rate ,  hidden_act=self.hidden_act ,  use_absolute_embeddings=self.use_absolute_embeddings ,  path_norm=self.patch_norm ,  layer_norm_eps=self.layer_norm_eps ,  initializer_range=self.initializer_range ,  encoder_stride=self.encoder_stride ,  out_features=self.out_features ,  out_indices=self.out_indices ,  )





	def 						_A (		self				:					Dict ,  __lowerCamelCase				:					List[str] ,  __lowerCamelCase				:					Union[str, Any] ,  __lowerCamelCase				:					Optional[int]	):
		UpperCamelCase     :Union[str, Any]          =	FocalNetModel(config=__lowerCamelCase	)
		model.to(__lowerCamelCase	)
		model.eval()
		UpperCamelCase     :Optional[Any]          =	model(__lowerCamelCase	)

		UpperCamelCase     :Optional[Any]          =	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths	) - 1))
		UpperCamelCase     :Tuple          =	int(config.embed_dim * 2 ** (len(config.depths	) - 1)	)

		self.parent.assertEqual(result.last_hidden_state.shape ,  (self.batch_size, expected_seq_len, expected_dim)	)





	def 						_A (		self				:					Dict ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					List[str]	):
		UpperCamelCase     :List[str]          =	FocalNetBackbone(config=__lowerCamelCase	)
		model.to(__lowerCamelCase	)
		model.eval()
		UpperCamelCase     :List[str]          =	model(__lowerCamelCase	)

		# verify feature maps
		self.parent.assertEqual(len(result.feature_maps	) ,  len(config.out_features	)	)
		self.parent.assertListEqual(list(result.feature_maps[0].shape	) ,  [self.batch_size, self.image_size, 8, 8]	)

		# verify channels
		self.parent.assertEqual(len(model.channels	) ,  len(config.out_features	)	)
		self.parent.assertListEqual(model.channels ,  config.hidden_sizes[:-1]	)

		# verify backbone works with out_features=None
		UpperCamelCase     :List[Any]          =	None
		UpperCamelCase     :Dict          =	FocalNetBackbone(config=__lowerCamelCase	)
		model.to(__lowerCamelCase	)
		model.eval()
		UpperCamelCase     :Dict          =	model(__lowerCamelCase	)

		# verify feature maps
		self.parent.assertEqual(len(result.feature_maps	) ,  1	)
		self.parent.assertListEqual(list(result.feature_maps[0].shape	) ,  [self.batch_size, self.image_size * 2, 4, 4]	)

		# verify channels
		self.parent.assertEqual(len(model.channels	) ,  1	)
		self.parent.assertListEqual(model.channels ,  [config.hidden_sizes[-1]]	)





	def 						_A (		self				:					Optional[int] ,  __lowerCamelCase				:					Dict ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					int	):
		UpperCamelCase     :Tuple          =	FocalNetForMaskedImageModeling(config=__lowerCamelCase	)
		model.to(__lowerCamelCase	)
		model.eval()
		UpperCamelCase     :Optional[Any]          =	model(__lowerCamelCase	)
		self.parent.assertEqual(
		    result.reconstruction.shape ,  (self.batch_size, self.num_channels, self.image_size, self.image_size)	)

		# test greyscale images
		UpperCamelCase     :str          =	1
		UpperCamelCase     :List[str]          =	FocalNetForMaskedImageModeling(__lowerCamelCase	)
		model.to(__lowerCamelCase	)
		model.eval()

		UpperCamelCase     :str          =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
		UpperCamelCase     :Tuple          =	model(__lowerCamelCase	)
		self.parent.assertEqual(result.reconstruction.shape ,  (self.batch_size, 1, self.image_size, self.image_size)	)





	def 						_A (		self				:					Optional[Any] ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					Any ,  __lowerCamelCase				:					Optional[Any]	):
		UpperCamelCase     :Optional[int]          =	self.type_sequence_label_size
		UpperCamelCase     :Optional[Any]          =	FocalNetForImageClassification(__lowerCamelCase	)
		model.to(__lowerCamelCase	)
		model.eval()
		UpperCamelCase     :Tuple          =	model(__lowerCamelCase ,  labels=__lowerCamelCase	)
		self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.type_sequence_label_size)	)

		# test greyscale images
		UpperCamelCase     :List[str]          =	1
		UpperCamelCase     :Union[str, Any]          =	FocalNetForImageClassification(__lowerCamelCase	)
		model.to(__lowerCamelCase	)
		model.eval()

		UpperCamelCase     :Optional[int]          =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
		UpperCamelCase     :Union[str, Any]          =	model(__lowerCamelCase	)
		self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.type_sequence_label_size)	)





	def 						_A (		self				:					str	):
		UpperCamelCase     :Optional[Any]          =	self.prepare_config_and_inputs()

		UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :Tuple          =	config_and_inputs
		UpperCamelCase     :str          =	{"""pixel_values""": pixel_values}
		return config, inputs_dict

@require_torch
class     _SCREAMING_SNAKE_CASE       (			_a		,   _a		,   unittest.TestCase      ):
	snake_case__							:     str     	=  (
	    (
	        FocalNetModel,
	        FocalNetForImageClassification,
	        FocalNetForMaskedImageModeling,
	        FocalNetBackbone,
	    )
	    if is_torch_available()
	    else ()
	)
	snake_case__							:     List[str]     	=  (
	    {"""feature-extraction""": FocalNetModel, """image-classification""": FocalNetForImageClassification}
	    if is_torch_available()
	    else {}
	)
	snake_case__							:     Optional[int]     	=  False

	snake_case__							:     Tuple     	=  False
	snake_case__							:     Optional[Any]     	=  False
	snake_case__							:     Optional[int]     	=  False
	snake_case__							:     Union[str, Any]     	=  False





	def 						_A (		self				:					Optional[Any]	):
		UpperCamelCase     :Dict          =	FocalNetModelTester(self	)
		UpperCamelCase     :Optional[int]          =	ConfigTester(self ,  config_class=__lowerCamelCase ,  embed_dim=37 ,  has_text_modality=__lowerCamelCase	)





	def 						_A (		self				:					Union[str, Any]	):
		self.create_and_test_config_common_properties()
		self.config_tester.create_and_test_config_to_json_string()
		self.config_tester.create_and_test_config_to_json_file()
		self.config_tester.create_and_test_config_from_and_save_pretrained()
		self.config_tester.create_and_test_config_with_num_labels()
		self.config_tester.check_config_can_be_init_without_params()
		self.config_tester.check_config_arguments_init()





	def 						_A (		self				:					List[Any]	):
		return





	def 						_A (		self				:					Dict	):
		UpperCamelCase     :List[Any]          =	self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*__lowerCamelCase	)





	def 						_A (		self				:					Optional[int]	):
		UpperCamelCase     :List[str]          =	self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_backbone(*__lowerCamelCase	)





	def 						_A (		self				:					Any	):
		UpperCamelCase     :List[str]          =	self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCamelCase	)





	def 						_A (		self				:					Optional[int]	):
		UpperCamelCase     :List[str]          =	self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase	)





	@unittest.skip(reason="""FocalNet does not use inputs_embeds"""	)
	def 						_A (		self				:					Optional[int]	):
		pass





	@unittest.skip(reason="""FocalNet does not use feedforward chunking"""	)
	def 						_A (		self				:					Optional[Any]	):
		pass





	def 						_A (		self				:					List[str]	):
		UpperCamelCase     ,      UpperCamelCase     :Tuple          =	self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes[:-1]:
			UpperCamelCase     :Optional[Any]          =	model_class(__lowerCamelCase	)
			self.assertIsInstance(model.get_input_embeddings() ,  (nn.Module)	)
			UpperCamelCase     :Tuple          =	model.get_output_embeddings()
			self.assertTrue(x is None or isinstance(__lowerCamelCase ,  nn.Linear	)	)





	def 						_A (		self				:					int	):
		UpperCamelCase     ,      UpperCamelCase     :int          =	self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes[:-1]:
			UpperCamelCase     :Dict          =	model_class(__lowerCamelCase	)
			UpperCamelCase     :Optional[int]          =	inspect.signature(model.forward	)
			# signature.parameters is an OrderedDict => so arg_names order is deterministic
			UpperCamelCase     :Union[str, Any]          =	[*signature.parameters.keys()]

			UpperCamelCase     :str          =	["""pixel_values"""]
			self.assertListEqual(arg_names[:1] ,  __lowerCamelCase	)





	def 						_A (		self				:					Any ,  __lowerCamelCase				:					Dict ,  __lowerCamelCase				:					List[str] ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					Dict	):
		UpperCamelCase     :Optional[Any]          =	model_class(__lowerCamelCase	)
		model.to(__lowerCamelCase	)
		model.eval()

		with torch.no_grad():
			UpperCamelCase     :Optional[int]          =	model(**self._prepare_for_class(__lowerCamelCase ,  __lowerCamelCase	)	)

		UpperCamelCase     :Tuple          =	outputs.hidden_states

		UpperCamelCase     :Union[str, Any]          =	getattr(
		    self.model_tester ,  """expected_num_hidden_layers""" ,  len(self.model_tester.depths	) + 1	)
		self.assertEqual(len(__lowerCamelCase	) ,  __lowerCamelCase	)

		# FocalNet has a different seq_length
		UpperCamelCase     :Optional[Any]          =	(
		    config.patch_size
		    if isinstance(config.patch_size ,  collections.abc.Iterable	)
		    else (config.patch_size, config.patch_size)
		)

		UpperCamelCase     :Any          =	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

		self.assertListEqual(
		    list(hidden_states[0].shape[-2:]	) ,  [num_patches, self.model_tester.embed_dim] ,  )

		UpperCamelCase     :Union[str, Any]          =	outputs.reshaped_hidden_states
		self.assertEqual(len(__lowerCamelCase	) ,  __lowerCamelCase	)

		UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :List[Any]          =	reshaped_hidden_states[0].shape
		UpperCamelCase     :List[Any]          =	(
		    reshaped_hidden_states[0].view(__lowerCamelCase ,  __lowerCamelCase ,  height * width	).permute(0 ,  2 ,  1	)
		)
		self.assertListEqual(
		    list(reshaped_hidden_states.shape[-2:]	) ,  [num_patches, self.model_tester.embed_dim] ,  )





	def 						_A (		self				:					List[Any]	):
		UpperCamelCase     ,      UpperCamelCase     :Any          =	self.model_tester.prepare_config_and_inputs_for_common()

		UpperCamelCase     :Optional[Any]          =	(
		    self.model_tester.image_size
		    if isinstance(self.model_tester.image_size ,  collections.abc.Iterable	)
		    else (self.model_tester.image_size, self.model_tester.image_size)
		)

		for model_class in self.all_model_classes[:-1]:
			UpperCamelCase     :List[Any]          =	True
			self.check_hidden_states_output(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase	)

			# check that output_hidden_states also work using config
			del inputs_dict["output_hidden_states"]
			UpperCamelCase     :Optional[Any]          =	True

			self.check_hidden_states_output(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase	)





	def 						_A (		self				:					int	):
		UpperCamelCase     ,      UpperCamelCase     :Optional[Any]          =	self.model_tester.prepare_config_and_inputs_for_common()
		UpperCamelCase     :List[str]          =	3

		UpperCamelCase     :Optional[Any]          =	(
		    self.model_tester.image_size
		    if isinstance(self.model_tester.image_size ,  collections.abc.Iterable	)
		    else (self.model_tester.image_size, self.model_tester.image_size)
		)
		UpperCamelCase     :List[str]          =	(
		    config.patch_size
		    if isinstance(config.patch_size ,  collections.abc.Iterable	)
		    else (config.patch_size, config.patch_size)
		)

		UpperCamelCase     :Optional[Any]          =	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
		UpperCamelCase     :Dict          =	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

		for model_class in self.all_model_classes[:-1]:
			UpperCamelCase     :Optional[Any]          =	True
			self.check_hidden_states_output(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase ,  (padded_height, padded_width)	)

			# check that output_hidden_states also work using config
			del inputs_dict["output_hidden_states"]
			UpperCamelCase     :Dict          =	True
			self.check_hidden_states_output(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase ,  (padded_height, padded_width)	)





	@slow
	def 						_A (		self				:					Tuple	):
		for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			UpperCamelCase     :Union[str, Any]          =	FocalNetModel.from_pretrained(__lowerCamelCase	)
			self.assertIsNotNone(__lowerCamelCase	)





	def 						_A (		self				:					Optional[Any]	):
		UpperCamelCase     ,      UpperCamelCase     :Dict          =	self.model_tester.prepare_config_and_inputs_for_common()

		UpperCamelCase     :Dict          =	_config_zero_init(__lowerCamelCase	)
		for model_class in self.all_model_classes:
			UpperCamelCase     :Optional[Any]          =	model_class(config=__lowerCamelCase	)
			for name, param in model.named_parameters():
				if "embeddings" not in name and param.requires_grad:
					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""" ,  )

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





	@cached_property
	def 						_A (		self				:					List[str]	):
		# TODO update organization
		return AutoImageProcessor.from_pretrained("""microsoft/focalnet-tiny"""	) if is_vision_available() else None





	@slow
	def 						_A (		self				:					Optional[Any]	):
		UpperCamelCase     :Union[str, Any]          =	FocalNetForImageClassification.from_pretrained("""microsoft/focalnet-tiny"""	).to(__lowerCamelCase	)
		UpperCamelCase     :Optional[Any]          =	self.default_image_processor

		UpperCamelCase     :List[str]          =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
		UpperCamelCase     :int          =	image_processor(images=__lowerCamelCase ,  return_tensors="""pt"""	).to(__lowerCamelCase	)

		# forward pass
		with torch.no_grad():
			UpperCamelCase     :int          =	model(**__lowerCamelCase	)

		# verify the logits
		UpperCamelCase     :str          =	torch.Size((1, 1_000)	)
		self.assertEqual(outputs.logits.shape ,  __lowerCamelCase	)
		UpperCamelCase     :Tuple          =	torch.tensor([0.2166, -0.4368, 0.2191]	).to(__lowerCamelCase	)
		self.assertTrue(torch.allclose(outputs.logits[0, :3] ,  __lowerCamelCase ,  atol=1E-4	)	)
		self.assertTrue(outputs.logits.argmax(dim=-1	).item() ,  281	)

@require_torch
class     _SCREAMING_SNAKE_CASE       (			_a		,   unittest.TestCase      ):
	snake_case__							:     Optional[Any]     	=  (FocalNetBackbone,) if is_torch_available() else ()
	snake_case__							:     str     	=  FocalNetConfig

	snake_case__							:     int     	=  False





	def 						_A (		self				:					List[str]	):
		UpperCamelCase     :Optional[Any]          =	FocalNetModelTester(self	)








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()
UpperCAmelCase_ :						Optional[Any]      =      logging.get_logger(__name__)

UpperCAmelCase_ :						Optional[Any]      =      {
    '''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''',
}
UpperCAmelCase_ :						int      =      [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Dict	,			__magic_name__					:  Dict	,			__magic_name__					:  Union[str, Any]	,			__magic_name__					:  Tuple	,			__magic_name__					:  Optional[int]					)		->			Dict:






	"""simple docstring"""



	for attribute in key.split("""."""					):
		UpperCamelCase     :Dict          =	getattr(__magic_name__	,			__magic_name__					)

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

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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[Any]	,			__magic_name__					:  List[str]					)		->			Optional[Any]:






	"""simple docstring"""



	UpperCamelCase     :Union[str, Any]          =	[]
	UpperCamelCase     :Dict          =	fairseq_model.state_dict()

	UpperCamelCase     :int          =	hf_model.feature_extractor

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

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

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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Any	,			__magic_name__					:  str	,			__magic_name__					:  int	,			__magic_name__					:  int	,			__magic_name__					:  List[str]					)		->			Dict:






	"""simple docstring"""



	UpperCamelCase     :Dict          =	full_name.split("""conv_layers."""					)[-1]
	UpperCamelCase     :int          =	name.split("""."""					)
	UpperCamelCase     :str          =	int(items[0]					)
	UpperCamelCase     :str          =	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     :Tuple          =	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     :Dict          =	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     :Tuple          =	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     :Union[str, Any]          =	value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
	else:
		unused_weights.append(__magic_name__					)

@torch.no_grad()
def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[str]	,			__magic_name__					:  List[Any]	,			__magic_name__					:  str=None					)		->			int:






	"""simple docstring"""



	UpperCamelCase     :List[Any]          =	torch.load(__magic_name__					)
	UpperCamelCase     :List[Any]          =	WavLMConfigOrig(checkpoint["""cfg"""]					)
	UpperCamelCase     :int          =	WavLMOrig(__magic_name__					)
	model.load_state_dict(checkpoint["""model"""]					)
	model.eval()

	if config_path is not None:
		UpperCamelCase     :List[Any]          =	WavLMConfig.from_pretrained(__magic_name__					)
	else:
		UpperCamelCase     :Any          =	WavLMConfig()

	UpperCamelCase     :Dict          =	WavLMModel(__magic_name__					)

	recursively_load_weights(__magic_name__	,			__magic_name__					)

	hf_wavlm.save_pretrained(__magic_name__					)


if __name__ == "__main__":
	UpperCAmelCase_ :						Union[str, Any]      =      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''')
	UpperCAmelCase_ :						Optional[int]      =      parser.parse_args()
	convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
