

"""simple docstring"""






import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 (
						    MODEL_MAPPING,
						    BeitForImageClassification,
						    BeitForMaskedImageModeling,
						    BeitForSemanticSegmentation,
						    BeitModel,
						)
						from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						import PIL
						from PIL import Image

						from transformers import BeitImageProcessor






class      _UpperCAmelCase      :
			def __init__(		self   ,		_A   ,		_A=1_00   ,		_A=13   ,		_A=30   ,		_A=2   ,		_A=3   ,		_A=True   ,		_A=True   ,		_A=32   ,		_A=4   ,		_A=4   ,		_A=37   ,		_A="gelu"   ,		_A=0.1   ,		_A=0.1   ,		_A=10   ,		_A=0.02   ,		_A=3   ,		_A=None   ,		_A=[0, 1, 2, 3]   ,		)     ->     List[Any]:






				'''simple docstring'''







				_UpperCAmelCase     :				Union[str, Any]          =       parent
				_UpperCAmelCase     :				List[str]          =       1_00
				_UpperCAmelCase     :				str          =       batch_size
				_UpperCAmelCase     :				Optional[Any]          =       image_size
				_UpperCAmelCase     :				int          =       patch_size
				_UpperCAmelCase     :				Optional[int]          =       num_channels
				_UpperCAmelCase     :				int          =       is_training
				_UpperCAmelCase     :				Any          =       use_labels
				_UpperCAmelCase     :				str          =       hidden_size
				_UpperCAmelCase     :				Optional[Any]          =       num_hidden_layers
				_UpperCAmelCase     :				int          =       num_attention_heads
				_UpperCAmelCase     :				Optional[Any]          =       intermediate_size
				_UpperCAmelCase     :				int          =       hidden_act
				_UpperCAmelCase     :				List[str]          =       hidden_dropout_prob
				_UpperCAmelCase     :				List[Any]          =       attention_probs_dropout_prob
				_UpperCAmelCase     :				Any          =       type_sequence_label_size
				_UpperCAmelCase     :				List[Any]          =       initializer_range
				_UpperCAmelCase     :				str          =       scope
				_UpperCAmelCase     :				Union[str, Any]          =       out_indices
				_UpperCAmelCase     :				Any          =       num_labels

				# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
				_UpperCAmelCase     :				Any          =       (image_size // patch_size) ** 2
				_UpperCAmelCase     :				Dict          =       num_patches + 1
			def 				__snake_case			(		self  )     ->     Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase     :				Any          =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

				_UpperCAmelCase     :				Union[str, Any]          =       None
				_UpperCAmelCase     :				Optional[Any]          =       None
				if self.use_labels:
					_UpperCAmelCase     :				Union[str, Any]          =       ids_tensor([self.batch_size]   ,		self.type_sequence_label_size  )
					_UpperCAmelCase     :				Optional[int]          =       ids_tensor([self.batch_size, self.image_size, self.image_size]   ,		self.num_labels  )

				_UpperCAmelCase     :				Optional[Any]          =       self.get_config()

				return config, pixel_values, labels, pixel_labels
			def 				__snake_case			(		self  )     ->     List[str]:






				'''simple docstring'''







				return BeitConfig(
				    vocab_size=self.vocab_size   ,		image_size=self.image_size   ,		patch_size=self.patch_size   ,		num_channels=self.num_channels   ,		hidden_size=self.hidden_size   ,		num_hidden_layers=self.num_hidden_layers   ,		num_attention_heads=self.num_attention_heads   ,		intermediate_size=self.intermediate_size   ,		hidden_act=self.hidden_act   ,		hidden_dropout_prob=self.hidden_dropout_prob   ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,		is_decoder=_A   ,		initializer_range=self.initializer_range   ,		out_indices=self.out_indices   ,		)
			def 				__snake_case			(		self   ,		_A   ,		_A   ,		_A   ,		_A  )     ->     Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase     :				Optional[Any]          =       BeitModel(config=_A  )
				model.to(_A  )
				model.eval()
				_UpperCAmelCase     :				Optional[Any]          =       model(_A  )
				self.parent.assertEqual(result.last_hidden_state.shape   ,		(self.batch_size, self.seq_length, self.hidden_size)  )
			def 				__snake_case			(		self   ,		_A   ,		_A   ,		_A   ,		_A  )     ->     Tuple:






				'''simple docstring'''







				_UpperCAmelCase     :				Any          =       BeitForMaskedImageModeling(config=_A  )
				model.to(_A  )
				model.eval()
				_UpperCAmelCase     :				Any          =       model(_A  )
				self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.seq_length - 1, self.vocab_size)  )
			def 				__snake_case			(		self   ,		_A   ,		_A   ,		_A   ,		_A  )     ->     Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase     :				List[str]          =       self.type_sequence_label_size
				_UpperCAmelCase     :				List[Any]          =       BeitForImageClassification(_A  )
				model.to(_A  )
				model.eval()
				_UpperCAmelCase     :				int          =       model(_A   ,		labels=_A  )
				self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.type_sequence_label_size)  )

				# test greyscale images
				_UpperCAmelCase     :				Any          =       1
				_UpperCAmelCase     :				List[str]          =       BeitForImageClassification(_A  )
				model.to(_A  )
				model.eval()

				_UpperCAmelCase     :				int          =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
				_UpperCAmelCase     :				Optional[int]          =       model(_A   ,		labels=_A  )
				self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.type_sequence_label_size)  )
			def 				__snake_case			(		self   ,		_A   ,		_A   ,		_A   ,		_A  )     ->     str:






				'''simple docstring'''







				_UpperCAmelCase     :				int          =       self.num_labels
				_UpperCAmelCase     :				Optional[int]          =       BeitForSemanticSegmentation(_A  )
				model.to(_A  )
				model.eval()
				_UpperCAmelCase     :				Tuple          =       model(_A  )
				self.parent.assertEqual(
				    result.logits.shape   ,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)  )
				_UpperCAmelCase     :				Optional[int]          =       model(_A   ,		labels=_A  )
				self.parent.assertEqual(
				    result.logits.shape   ,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)  )
			def 				__snake_case			(		self  )     ->     Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase     :				Dict          =       self.prepare_config_and_inputs()
				_UpperCAmelCase     :				Optional[int]          =       config_and_inputs
				_UpperCAmelCase     :				List[Any]          =       {"pixel_values": pixel_values}
				return config, inputs_dict






@require_torch
class      _UpperCAmelCase      (		__a     ,  __a     ,  unittest.TestCase):
			__a			:							Optional[int]					       =				(
			    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)
			__a			:							Dict					       =				(
			    {
			        'feature-extraction': BeitModel,
			        'image-classification': BeitForImageClassification,
			        'image-segmentation': BeitForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			__a			:							int					       =				False
			__a			:							List[Any]					       =				False
			__a			:							Any					       =				False
			def 				__snake_case			(		self  )     ->     Optional[int]:






				'''simple docstring'''







				_UpperCAmelCase     :				Any          =       BeitModelTester(self  )
				_UpperCAmelCase     :				Dict          =       ConfigTester(self   ,		config_class=_A   ,		has_text_modality=_A   ,		hidden_size=37  )
			def 				__snake_case			(		self  )     ->     Tuple:






				'''simple docstring'''







				self.config_tester.run_common_tests()
			@unittest.skip(reason="""BEiT does not use inputs_embeds"""  )
			def 				__snake_case			(		self  )     ->     Union[str, Any]:






				'''simple docstring'''







				pass
			@require_torch_multi_gpu
			@unittest.skip(reason="""BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""  )
			def 				__snake_case			(		self  )     ->     Tuple:






				'''simple docstring'''







				pass
			def 				__snake_case			(		self  )     ->     Tuple:






				'''simple docstring'''







				_UpperCAmelCase     :				Union[str, Any]          =       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					_UpperCAmelCase     :				Optional[int]          =       model_class(_A  )
					self.assertIsInstance(model.get_input_embeddings()   ,		(nn.Module)  )
					_UpperCAmelCase     :				Union[str, Any]          =       model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(_A   ,		nn.Linear  )  )
			def 				__snake_case			(		self  )     ->     Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase     :				str          =       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					_UpperCAmelCase     :				List[str]          =       model_class(_A  )
					_UpperCAmelCase     :				Dict          =       inspect.signature(model.forward  )
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					_UpperCAmelCase     :				List[Any]          =       [*signature.parameters.keys()]

					_UpperCAmelCase     :				List[Any]          =       ["pixel_values"]
					self.assertListEqual(arg_names[:1]   ,		_A  )
			def 				__snake_case			(		self  )     ->     Tuple:






				'''simple docstring'''







				_UpperCAmelCase     :				Optional[Any]          =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_A  )
			def 				__snake_case			(		self  )     ->     int:






				'''simple docstring'''







				_UpperCAmelCase     :				str          =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*_A  )
			def 				__snake_case			(		self  )     ->     Dict:






				'''simple docstring'''







				_UpperCAmelCase     :				List[Any]          =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*_A  )
			def 				__snake_case			(		self  )     ->     Dict:






				'''simple docstring'''







				_UpperCAmelCase     :				List[str]          =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_semantic_segmentation(*_A  )
			def 				__snake_case			(		self  )     ->     Union[str, Any]:






				'''simple docstring'''







				if not self.model_tester.is_training:
					return

				_UpperCAmelCase     :				Any          =       self.model_tester.prepare_config_and_inputs_for_common()
				_UpperCAmelCase     :				List[Any]          =       True

				for model_class in self.all_model_classes:
					# we don't test BeitForMaskedImageModeling
					if model_class in [*get_values(_A  ), BeitForMaskedImageModeling]:
						continue

					_UpperCAmelCase     :				int          =       model_class(_A  )
					model.to(_A  )
					model.train()
					_UpperCAmelCase     :				int          =       self._prepare_for_class(_A   ,		_A   ,		return_labels=_A  )
					_UpperCAmelCase     :				int          =       model(**_A  ).loss
					loss.backward()
			def 				__snake_case			(		self  )     ->     Any:






				'''simple docstring'''







				_UpperCAmelCase     :				List[str]          =       self.model_tester.prepare_config_and_inputs_for_common()
				if not self.model_tester.is_training:
					return

				_UpperCAmelCase     :				int          =       False
				_UpperCAmelCase     :				int          =       True

				for model_class in self.all_model_classes:
					# we don't test BeitForMaskedImageModeling
					if (
					    model_class in [*get_values(_A  ), BeitForMaskedImageModeling]
					    or not model_class.supports_gradient_checkpointing
					):
						continue

					_UpperCAmelCase     :				int          =       model_class(_A  )
					model.gradient_checkpointing_enable()
					model.to(_A  )
					model.train()
					_UpperCAmelCase     :				Union[str, Any]          =       self._prepare_for_class(_A   ,		_A   ,		return_labels=_A  )
					_UpperCAmelCase     :				Optional[int]          =       model(**_A  ).loss
					loss.backward()
			def 				__snake_case			(		self  )     ->     Optional[int]:






				'''simple docstring'''







				_UpperCAmelCase     :				Union[str, Any]          =       self.model_tester.prepare_config_and_inputs_for_common()

				_UpperCAmelCase     :				Tuple          =       _config_zero_init(_A  )
				for model_class in self.all_model_classes:
					_UpperCAmelCase     :				Any          =       model_class(config=_A  )
					for name, param in model.named_parameters():
						# we skip lambda parameters as these require special initial values
						# determined by config.layer_scale_init_value
						if "lambda" in name:
							continue
						if 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'''   ,		)
			@slow
			def 				__snake_case			(		self  )     ->     Optional[Any]:






				'''simple docstring'''







				for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					_UpperCAmelCase     :				int          =       BeitModel.from_pretrained(_A  )
					self.assertIsNotNone(_A  )
def    UpperCamelCase       (      )					->      Optional[Any]:
	_UpperCAmelCase     :				List[Any]          =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
	return image






@require_torch
@require_vision
class      _UpperCAmelCase      (		unittest.TestCase):
			@cached_property
			def 				__snake_case			(		self  )     ->     Tuple:






				'''simple docstring'''







				return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224"""  ) if is_vision_available() else None
			@slow
			def 				__snake_case			(		self  )     ->     List[str]:






				'''simple docstring'''







				_UpperCAmelCase     :				str          =       BeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""  ).to(_A  )

				_UpperCAmelCase     :				Dict          =       self.default_image_processor
				_UpperCAmelCase     :				Union[str, Any]          =       prepare_img()
				_UpperCAmelCase     :				Tuple          =       image_processor(images=_A   ,		return_tensors="""pt"""  ).pixel_values.to(_A  )

				# prepare bool_masked_pos
				_UpperCAmelCase     :				List[str]          =       torch.ones((1, 1_96)   ,		dtype=torch.bool  ).to(_A  )

				# forward pass
				with torch.no_grad():
					_UpperCAmelCase     :				List[str]          =       model(pixel_values=_A   ,		bool_masked_pos=_A  )
				_UpperCAmelCase     :				int          =       outputs.logits

				# verify the logits
				_UpperCAmelCase     :				int          =       torch.Size((1, 1_96, 81_92)  )
				self.assertEqual(logits.shape   ,		_A  )

				_UpperCAmelCase     :				Tuple          =       torch.tensor(
				    [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]  ).to(_A  )

				self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]   ,		_A   ,		atol=1e-2  )  )
			@slow
			def 				__snake_case			(		self  )     ->     Tuple:






				'''simple docstring'''







				_UpperCAmelCase     :				Tuple          =       BeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""  ).to(_A  )

				_UpperCAmelCase     :				Union[str, Any]          =       self.default_image_processor
				_UpperCAmelCase     :				int          =       prepare_img()
				_UpperCAmelCase     :				Optional[int]          =       image_processor(images=_A   ,		return_tensors="""pt"""  ).to(_A  )

				# forward pass
				with torch.no_grad():
					_UpperCAmelCase     :				str          =       model(**_A  )
				_UpperCAmelCase     :				Any          =       outputs.logits

				# verify the logits
				_UpperCAmelCase     :				int          =       torch.Size((1, 10_00)  )
				self.assertEqual(logits.shape   ,		_A  )

				_UpperCAmelCase     :				str          =       torch.tensor([-1.2385, -1.0987, -1.0108]  ).to(_A  )

				self.assertTrue(torch.allclose(logits[0, :3]   ,		_A   ,		atol=1e-4  )  )

				_UpperCAmelCase     :				Optional[int]          =       2_81
				self.assertEqual(logits.argmax(-1  ).item()   ,		_A  )
			@slow
			def 				__snake_case			(		self  )     ->     Tuple:






				'''simple docstring'''







				_UpperCAmelCase     :				Dict          =       BeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k"""  ).to(
				    _A  )

				_UpperCAmelCase     :				Union[str, Any]          =       self.default_image_processor
				_UpperCAmelCase     :				str          =       prepare_img()
				_UpperCAmelCase     :				Any          =       image_processor(images=_A   ,		return_tensors="""pt"""  ).to(_A  )

				# forward pass
				with torch.no_grad():
					_UpperCAmelCase     :				Union[str, Any]          =       model(**_A  )
				_UpperCAmelCase     :				Tuple          =       outputs.logits

				# verify the logits
				_UpperCAmelCase     :				List[str]          =       torch.Size((1, 2_18_41)  )
				self.assertEqual(logits.shape   ,		_A  )

				_UpperCAmelCase     :				Any          =       torch.tensor([1.6881, -0.2787, 0.5901]  ).to(_A  )

				self.assertTrue(torch.allclose(logits[0, :3]   ,		_A   ,		atol=1e-4  )  )

				_UpperCAmelCase     :				Tuple          =       23_96
				self.assertEqual(logits.argmax(-1  ).item()   ,		_A  )
			@slow
			def 				__snake_case			(		self  )     ->     List[str]:






				'''simple docstring'''







				_UpperCAmelCase     :				List[Any]          =       BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640"""  )
				_UpperCAmelCase     :				str          =       model.to(_A  )

				_UpperCAmelCase     :				List[Any]          =       BeitImageProcessor(do_resize=_A   ,		size=6_40   ,		do_center_crop=_A  )

				_UpperCAmelCase     :				Tuple          =       load_dataset("""hf-internal-testing/fixtures_ade20k"""   ,		split="""test"""  )
				_UpperCAmelCase     :				Any          =       Image.open(ds[0]["""file"""]  )
				_UpperCAmelCase     :				Union[str, Any]          =       image_processor(images=_A   ,		return_tensors="""pt"""  ).to(_A  )

				# forward pass
				with torch.no_grad():
					_UpperCAmelCase     :				Union[str, Any]          =       model(**_A  )
				_UpperCAmelCase     :				Dict          =       outputs.logits

				# verify the logits
				_UpperCAmelCase     :				Any          =       torch.Size((1, 1_50, 1_60, 1_60)  )
				self.assertEqual(logits.shape   ,		_A  )

				_UpperCAmelCase     :				Tuple          =       version.parse(PIL.__version__  ) < version.parse("""9.0.0"""  )

				if is_pillow_less_than_a:
					_UpperCAmelCase     :				List[Any]          =       torch.tensor(
					    [
					        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
					        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
					        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
					    ]   ,		device=_A   ,		)
				else:
					_UpperCAmelCase     :				List[str]          =       torch.tensor(
					    [
					        [[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
					        [[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
					        [[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
					    ]   ,		device=_A   ,		)

				self.assertTrue(torch.allclose(logits[0, :3, :3, :3]   ,		_A   ,		atol=1e-4  )  )
			@slow
			def 				__snake_case			(		self  )     ->     List[Any]:






				'''simple docstring'''







				_UpperCAmelCase     :				Optional[Any]          =       BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640"""  )
				_UpperCAmelCase     :				List[Any]          =       model.to(_A  )

				_UpperCAmelCase     :				List[str]          =       BeitImageProcessor(do_resize=_A   ,		size=6_40   ,		do_center_crop=_A  )

				_UpperCAmelCase     :				Dict          =       load_dataset("""hf-internal-testing/fixtures_ade20k"""   ,		split="""test"""  )
				_UpperCAmelCase     :				List[Any]          =       Image.open(ds[0]["""file"""]  )
				_UpperCAmelCase     :				Dict          =       image_processor(images=_A   ,		return_tensors="""pt"""  ).to(_A  )

				# forward pass
				with torch.no_grad():
					_UpperCAmelCase     :				int          =       model(**_A  )

				_UpperCAmelCase     :				Optional[Any]          =       outputs.logits.detach().cpu()

				_UpperCAmelCase     :				int          =       image_processor.post_process_semantic_segmentation(outputs=_A   ,		target_sizes=[(5_00, 3_00)]  )
				_UpperCAmelCase     :				Tuple          =       torch.Size((5_00, 3_00)  )
				self.assertEqual(segmentation[0].shape   ,		_A  )

				_UpperCAmelCase     :				List[Any]          =       image_processor.post_process_semantic_segmentation(outputs=_A  )
				_UpperCAmelCase     :				List[Any]          =       torch.Size((1_60, 1_60)  )
				self.assertEqual(segmentation[0].shape   ,		_A  )













'''simple docstring'''
import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


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




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  WavaVecaForSequenceClassification.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     Optional[int]							     =  downstream_dict["projector.weight"]
   lowercase_						:     str							     =  downstream_dict["projector.bias"]
   lowercase_						:     int							     =  downstream_dict["model.post_net.linear.weight"]
   lowercase_						:     Optional[Any]							     =  downstream_dict["model.post_net.linear.bias"]
   return model




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Tuple							     =  WavaVecaForAudioFrameClassification.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     Any							     =  downstream_dict["model.linear.weight"]
   lowercase_						:     List[str]							     =  downstream_dict["model.linear.bias"]
   return model




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Any							     =  WavaVecaForXVector.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     str							     =  downstream_dict["connector.weight"]
   lowercase_						:     List[str]							     =  downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
      lowercase_						:     Union[str, Any]							     =  downstream_dict[
          F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
      ]
      lowercase_						:     Dict							     =  downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   lowercase_						:     List[Any]							     =  downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   lowercase_						:     Optional[Any]							     =  downstream_dict["objective.W"]
   return model




@torch.no_grad()
def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Tuple							     =  torch.load(_UpperCamelCase    ,		map_location="cpu"				)

   lowercase_						:     Dict							     =  checkpoint["Downstream"]

   lowercase_						:     Optional[Any]							     =  WavaVecaConfig.from_pretrained(_UpperCamelCase				)
   lowercase_						:     Dict							     =  WavaVecaFeatureExtractor.from_pretrained(
       _UpperCamelCase    ,		return_attention_mask=_UpperCamelCase    ,		do_normalize=_UpperCamelCase				)

   lowercase_						:     Dict							     =  hf_config.architectures[0]
   if arch.endswith("ForSequenceClassification"				):
      lowercase_						:     Any							     =  convert_classification(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)
   elif arch.endswith("ForAudioFrameClassification"				):
      lowercase_						:     Optional[int]							     =  convert_diarization(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)
   elif arch.endswith("ForXVector"				):
      lowercase_						:     List[Any]							     =  convert_xvector(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)
   else:
      raise NotImplementedError(F"""S3PRL weights conversion is not supported for {arch}"""				)

   if hf_config.use_weighted_layer_sum:
      lowercase_						:     List[str]							     =  checkpoint["Featurizer"]["weights"]

   hf_feature_extractor.save_pretrained(_UpperCamelCase				)
   hf_model.save_pretrained(_UpperCamelCase				)


if __name__ == "__main__":
 UpperCamelCase__			=      argparse.ArgumentParser()
 parser.add_argument(
     '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
 )
 parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
 parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
 parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
 UpperCamelCase__			=      parser.parse_args()
 convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




