
'''simple docstring'''







import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
_snake_case				:   int     =				logging.get_logger(__name__)



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Any	,			_SCREAMING_SNAKE_CASE		:   List[Any]	,			_SCREAMING_SNAKE_CASE		:   Optional[Any]  ):
						_SCREAMING_SNAKE_CASE				= UniSpeechSatForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE	,			config=_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= downstream_dict["projector.weight"]
						_SCREAMING_SNAKE_CASE				= downstream_dict["projector.bias"]
						_SCREAMING_SNAKE_CASE				= downstream_dict["model.post_net.linear.weight"]
						_SCREAMING_SNAKE_CASE				= downstream_dict["model.post_net.linear.bias"]
						return model



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[Any]	,			_SCREAMING_SNAKE_CASE		:   List[Any]	,			_SCREAMING_SNAKE_CASE		:   List[Any]  ):
						_SCREAMING_SNAKE_CASE				= UniSpeechSatForAudioFrameClassification.from_pretrained(_SCREAMING_SNAKE_CASE	,			config=_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= downstream_dict["model.linear.weight"]
						_SCREAMING_SNAKE_CASE				= downstream_dict["model.linear.bias"]
						return model



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

						_SCREAMING_SNAKE_CASE				= downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
						_SCREAMING_SNAKE_CASE				= downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
						_SCREAMING_SNAKE_CASE				= downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
						_SCREAMING_SNAKE_CASE				= downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
						_SCREAMING_SNAKE_CASE				= downstream_dict["objective.W"]
						return model



@torch.no_grad()
def 			_a		(	_SCREAMING_SNAKE_CASE		:   Union[str, Any]	,			_SCREAMING_SNAKE_CASE		:   Dict	,			_SCREAMING_SNAKE_CASE		:   Optional[int]	,			_SCREAMING_SNAKE_CASE		:   str  ):
						_SCREAMING_SNAKE_CASE				= torch.load(_SCREAMING_SNAKE_CASE	,			map_location="cpu"  )

						_SCREAMING_SNAKE_CASE				= checkpoint["Downstream"]

						_SCREAMING_SNAKE_CASE				= UniSpeechSatConfig.from_pretrained(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= WavaVecaFeatureExtractor.from_pretrained(
						    _SCREAMING_SNAKE_CASE	,			return_attention_mask=_SCREAMING_SNAKE_CASE	,			do_normalize=_SCREAMING_SNAKE_CASE  )

						_SCREAMING_SNAKE_CASE				= hf_config.architectures[0]
						if arch.endswith("ForSequenceClassification"  ):
												_SCREAMING_SNAKE_CASE				= convert_classification(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )
						elif arch.endswith("ForAudioFrameClassification"  ):
												_SCREAMING_SNAKE_CASE				= convert_diarization(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )
						elif arch.endswith("ForXVector"  ):
												_SCREAMING_SNAKE_CASE				= convert_xvector(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )
						else:
												raise NotImplementedError(F'S3PRL weights conversion is not supported for {arch}'  )

						if hf_config.use_weighted_layer_sum:
												_SCREAMING_SNAKE_CASE				= checkpoint["Featurizer"]["weights"]

						hf_feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE  )
						hf_model.save_pretrained(_SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
						_snake_case				:   List[str]     =				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.""")
						_snake_case				:   Any     =				parser.parse_args()
						convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

'''simple docstring'''







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 GLPNImageProcessor


class 			lowerCAmelCase (				unittest.TestCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase=7					,						UpperCamelCase=3					,						UpperCamelCase=18					,						UpperCamelCase=30					,						UpperCamelCase=400					,						UpperCamelCase=True					,						UpperCamelCase=32					,						UpperCamelCase=True					,						):
								_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_divisor
								_SCREAMING_SNAKE_CASE				= do_rescale




		def     lowercase       (				self      ):
								return {
								    "do_resize": self.do_resize,
								    "size_divisor": self.size_divisor,
								    "do_rescale": self.do_rescale,
								}


@require_torch
@require_vision
class 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					Dict       =					GLPNImageProcessor if is_vision_available() else None




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= GLPNImageProcessingTester(self      )




		@property
		def     lowercase       (				self      ):
								return self.image_processor_tester.prepare_image_processor_dict()




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								self.assertTrue(hasattr(UpperCamelCase					,						"do_resize"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"size_divisor"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"resample"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"do_rescale"      )      )




		def     lowercase       (				self      ):
								pass




		def     lowercase       (				self      ):
								# 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=UpperCamelCase      )
								for image in image_inputs:
														self.assertIsInstance(UpperCamelCase					,						Image.Image      )

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_SCREAMING_SNAKE_CASE				= image_processing(image_inputs[0]					,						return_tensors="pt"      ).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )




		def     lowercase       (				self      ):
								# 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=UpperCamelCase					,						numpify=UpperCamelCase      )
								for image in image_inputs:
														self.assertIsInstance(UpperCamelCase					,						np.ndarray      )

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_SCREAMING_SNAKE_CASE				= image_processing(image_inputs[0]					,						return_tensors="pt"      ).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )




		def     lowercase       (				self      ):
								# 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=UpperCamelCase					,						torchify=UpperCamelCase      )
								for image in image_inputs:
														self.assertIsInstance(UpperCamelCase					,						torch.Tensor      )

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_SCREAMING_SNAKE_CASE				= image_processing(image_inputs[0]					,						return_tensors="pt"      ).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )