






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 fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
__UpperCamelCase					:       str    					=      logging.get_logger(__name__)

__UpperCamelCase					:       List[str]    					=      {
    "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",
    "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": "lm_head",
    "mask_emb": "masked_spec_embed",
}
__UpperCamelCase					:       Union[str, Any]    					=      [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Union[str, Any]:
	for attribute in key.split("""."""    ):
		a										=							getattr(__lowerCamelCase							,			__lowerCamelCase    )

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

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





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase    )      ->       int:
	a										=							[]
	a										=							fairseq_model.state_dict()

	a										=							hf_model.feature_extractor
	a										=							hf_model.adapter

	for name, value in fairseq_dict.items():
		a										=							False
		if "conv_layers" in name:
			load_conv_layer(
			    __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			hf_model.config.feat_extract_norm == """group"""							,			)
			a										=							True
		elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]    ):
			load_adapter(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
			a										=							True
		else:
			for key, mapped_key in MAPPING.items():
				if key in name or key.split("""w2v_model."""    )[-1] == name.split("""."""    )[0]:
					a										=							True
					if "*" in mapped_key:
						a										=							name.split(__lowerCamelCase    )[0].split("""."""    )[-2]
						a										=							mapped_key.replace("""*"""							,			__lowerCamelCase    )
					if "weight_g" in name:
						a										=							"""weight_g"""
					elif "weight_v" in name:
						a										=							"""weight_v"""
					elif "bias" in name:
						a										=							"""bias"""
					elif "weight" in name:
						a										=							"""weight"""
					else:
						a										=							None
					set_recursively(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
				continue
		if not is_used:
			unused_weights.append(__lowerCamelCase    )

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





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Tuple:
	a										=							full_name.split("""conv_layers."""    )[-1]
	a										=							name.split("""."""    )
	a										=							int(items[0]    )
	a										=							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.'
			)
			a										=							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.'
			)
			a										=							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."
			)
			a										=							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.'
			)
			a										=							value
			logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'    )
	else:
		unused_weights.append(__lowerCamelCase    )





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Any:
	a										=							full_name.split("""adaptor."""    )[-1]
	a										=							name.split("""."""    )

	if items[1].isdigit():
		a										=							int(items[1]    )
	else:
		a										=							None

	if "adaptor" not in full_name:
		if "proj_ln" in full_name:
			# has to be layer norm
			if "bias" in name:
				assert (
				    value.shape == adapter.proj_layer_norm.bias.data.shape
				), f'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'
				a										=							value
				logger.info(f'Adapter proj layer norm bias was initialized from {full_name}.'    )
			if "weight" in name:
				assert (
				    value.shape == adapter.proj_layer_norm.weight.data.shape
				), f'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'
				a										=							value
		else:
			# has to be projection layer
			if "bias" in name:
				assert (
				    value.shape == adapter.proj.bias.data.shape
				), f'{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'
				a										=							value
				logger.info(f'Adapter proj layer bias was initialized from {full_name}.'    )
			if "weight" in name:
				assert (
				    value.shape == adapter.proj.weight.data.shape
				), f'{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'
				a										=							value
				logger.info(f'Adapter proj layer weight was initialized from {full_name}.'    )
	elif isinstance(__lowerCamelCase							,			__lowerCamelCase    ):
		if "bias" in name:
			assert (
			    value.shape == adapter.layers[layer_id].conv.bias.data.shape
			), f'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'
			a										=							value
			logger.info(f'Adapter layer {layer_id} bias was initialized from {full_name}.'    )
		elif "weight" in name:
			assert (
			    value.shape == adapter.layers[layer_id].conv.weight.data.shape
			), f'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'
			a										=							value
			logger.info(f'Adapter layer {layer_id} bias was initialized from {full_name}.'    )
	else:
		unused_weights.append(__lowerCamelCase    )





def 		__A		(	__lowerCamelCase    )      ->       Tuple:
	a				, a										=							emb.weight.shape
	a										=							nn.Linear(__lowerCamelCase							,			__lowerCamelCase							,			bias=__lowerCamelCase    )
	a										=							emb.weight.data
	return lin_layer





@torch.no_grad()
def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			)      ->       Optional[int]:
	a										=							WavaVecaConfig.from_pretrained(
	    __lowerCamelCase							,			add_adapter=__lowerCamelCase							,			adapter_stride=__lowerCamelCase							,			adapter_kernel_size=__lowerCamelCase							,			use_auth_token=__lowerCamelCase							,			output_hidden_size=__lowerCamelCase							,			)
	a										=							MBartConfig.from_pretrained(__lowerCamelCase    )

	# load model
	a				, a				, a										=							fairseq.checkpoint_utils.load_model_ensemble_and_task(
	    [checkpoint_path]							,			arg_overrides={
	        """config_yaml""": config_yaml_path,
	        """data""": """/""".join(dict_path.split("""/"""    )[:-1]    ),
	        """w2v_path""": checkpoint_path,
	        """load_pretrained_decoder_from""": None,
	    }							,			)
	a										=							model[0].eval()

	# load feature extractor
	a										=							WavaVecaFeatureExtractor.from_pretrained(__lowerCamelCase							,			use_auth_token=__lowerCamelCase    )

	# set weights for wav2vec2 encoder
	a										=							WavaVecaModel(__lowerCamelCase    )

	recursively_load_weights_wavaveca(model.encoder							,			__lowerCamelCase    )

	# load decoder weights
	a										=							MBartForCausalLM(__lowerCamelCase    )
	a				, a										=							hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()							,			strict=__lowerCamelCase    )
	logger.warning(f'The following keys are missing when loading the decoder weights: {missing_keys}'    )
	logger.warning(f'The following keys are unexpected when loading the decoder weights: {unexpected_keys}'    )

	a										=							SpeechEncoderDecoderModel(encoder=__lowerCamelCase							,			decoder=__lowerCamelCase    )
	a										=							False

	a										=							MBartaaTokenizer(__lowerCamelCase    )
	tokenizer.save_pretrained(__lowerCamelCase    )

	a										=							hf_wavavec.config.to_dict()
	a										=							tokenizer.pad_token_id
	a										=							tokenizer.bos_token_id
	a										=							tokenizer.eos_token_id
	a										=							"""mbart50"""
	a										=							"""wav2vec2"""

	a										=							tokenizer.eos_token_id
	a										=							25_0004
	a										=							tokenizer.eos_token_id

	a										=							SpeechEncoderDecoderConfig.from_dict(__lowerCamelCase    )

	hf_wavavec.save_pretrained(__lowerCamelCase    )
	feature_extractor.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
		__UpperCamelCase					:       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("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
		parser.add_argument("--config_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model")
		parser.add_argument(
		    "--encoder_config_path",
		    default="facebook/wav2vec2-xls-r-1b",
		    type=str,
		    help="Path to hf encoder wav2vec2 checkpoint config",
		)
		parser.add_argument(
		    "--decoder_config_path",
		    default="facebook/mbart-large-50-one-to-many-mmt",
		    type=str,
		    help="Path to hf decoder checkpoint config",
		)
		parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers")
		parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers")
		parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers")
		parser.add_argument("--encoder_output_dim", default=1_024, type=int, help="encoder output dim")
		parser.add_argument("--start_token_id", default=250_004, type=int, help="`decoder_start_token_id` of model config")

		__UpperCamelCase					:       int    					=      parser.parse_args()
		convert_wavaveca_checkpoint(
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.dict_path,
		    args.config_yaml_path,
		    encoder_config_path=args.encoder_config_path,
		    decoder_config_path=args.decoder_config_path,
		    add_adapter=args.add_adapter,
		    adapter_kernel_size=args.adapter_kernel_size,
		    adapter_stride=args.adapter_stride,
		    decoder_start_token_id=args.start_token_id,
		    encoder_output_dim=args.encoder_output_dim,
		)

