




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 ViTImageProcessor


class        UpperCAmelCase__				(							unittest.TestCase				):



     '''simple docstring'''
     def __init__(				self ,    _lowerCAmelCase ,    _lowerCAmelCase=7 ,    _lowerCAmelCase=3 ,    _lowerCAmelCase=18 ,    _lowerCAmelCase=30 ,    _lowerCAmelCase=400 ,    _lowerCAmelCase=True ,    _lowerCAmelCase=None ,    _lowerCAmelCase=True ,    _lowerCAmelCase=[0.5, 0.5, 0.5] ,    _lowerCAmelCase=[0.5, 0.5, 0.5] ,    ):
            a			   =size if size is not None else {"""height""": 18, """width""": 18}
            a			   =parent
            a			   =batch_size
            a			   =num_channels
            a			   =image_size
            a			   =min_resolution
            a			   =max_resolution
            a			   =do_resize
            a			   =size
            a			   =do_normalize
            a			   =image_mean
            a			   =image_std






     def        lowerCAmelCase__  (				self	):
            return {
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_normalize": self.do_normalize,
                "do_resize": self.do_resize,
                "size": self.size,
            }



@require_torch
@require_vision
class        UpperCAmelCase__				(							lowercase_      ,		unittest.TestCase				):



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					Dict     					=    ViTImageProcessor if is_vision_available() else None
     def        lowerCAmelCase__  (				self	):
            a			   =ViTImageProcessingTester(self	)
     @property
     def        lowerCAmelCase__  (				self	):
            return self.image_processor_tester.prepare_image_processor_dict()
     def        lowerCAmelCase__  (				self	):
            a			   =self.image_processing_class(**self.image_processor_dict	)
            self.assertTrue(hasattr(_lowerCAmelCase ,    """image_mean"""	)	)
            self.assertTrue(hasattr(_lowerCAmelCase ,    """image_std"""	)	)
            self.assertTrue(hasattr(_lowerCAmelCase ,    """do_normalize"""	)	)
            self.assertTrue(hasattr(_lowerCAmelCase ,    """do_resize"""	)	)
            self.assertTrue(hasattr(_lowerCAmelCase ,    """size"""	)	)
     def        lowerCAmelCase__  (				self	):
            a			   =self.image_processing_class.from_dict(self.image_processor_dict	)
            self.assertEqual(image_processor.size ,    {"""height""": 18, """width""": 18}	)

            a			   =self.image_processing_class.from_dict(self.image_processor_dict ,    size=42	)
            self.assertEqual(image_processor.size ,    {"""height""": 42, """width""": 42}	)
     def        lowerCAmelCase__  (				self	):
            pass
     def        lowerCAmelCase__  (				self	):
            # Initialize image_processing
            a			   =self.image_processing_class(**self.image_processor_dict	)
            # create random PIL images
            a			   =prepare_image_inputs(self.image_processor_tester ,    equal_resolution=_lowerCAmelCase	)
            for image in image_inputs:
                   self.assertIsInstance(_lowerCAmelCase ,    Image.Image	)

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

            # Test batched
            a			   =image_processing(_lowerCAmelCase ,    return_tensors="""pt"""	).pixel_values
            self.assertEqual(
                encoded_images.shape ,    (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""height"""],
                    self.image_processor_tester.size["""width"""],
                ) ,    )
     def        lowerCAmelCase__  (				self	):
            # Initialize image_processing
            a			   =self.image_processing_class(**self.image_processor_dict	)
            # create random numpy tensors
            a			   =prepare_image_inputs(self.image_processor_tester ,    equal_resolution=_lowerCAmelCase ,    numpify=_lowerCAmelCase	)
            for image in image_inputs:
                   self.assertIsInstance(_lowerCAmelCase ,    np.ndarray	)

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

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






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

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

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






"""simple docstring"""

import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


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




def    snake_case__						(							lowerCAmelCase :		str					,       lowerCAmelCase :		Optional[Any]					,       lowerCAmelCase :		Optional[int]		):
					__snake_case        =						WavLMForSequenceClassification.from_pretrained(lowerCAmelCase					,       config=lowerCAmelCase		)
					__snake_case        =						downstream_dict["""projector.weight"""]
					__snake_case        =						downstream_dict["""projector.bias"""]
					__snake_case        =						downstream_dict["""model.post_net.linear.weight"""]
					__snake_case        =						downstream_dict["""model.post_net.linear.bias"""]
					return model




def    snake_case__						(							lowerCAmelCase :		List[Any]					,       lowerCAmelCase :		Dict					,       lowerCAmelCase :		Dict		):
					__snake_case        =						WavLMForAudioFrameClassification.from_pretrained(lowerCAmelCase					,       config=lowerCAmelCase		)
					__snake_case        =						downstream_dict["""model.linear.weight"""]
					__snake_case        =						downstream_dict["""model.linear.bias"""]
					return model




def    snake_case__						(							lowerCAmelCase :		List[Any]					,       lowerCAmelCase :		Dict					,       lowerCAmelCase :		Dict		):
					__snake_case        =						WavLMForXVector.from_pretrained(lowerCAmelCase					,       config=lowerCAmelCase		)
					__snake_case        =						downstream_dict["""connector.weight"""]
					__snake_case        =						downstream_dict["""connector.bias"""]
					for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
										__snake_case        =						downstream_dict[
										    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
										]
										__snake_case        =						downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

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




@torch.no_grad()
def    snake_case__						(							lowerCAmelCase :		str					,       lowerCAmelCase :		Tuple					,       lowerCAmelCase :		List[str]					,       lowerCAmelCase :		Optional[int]		):
					__snake_case        =						torch.load(lowerCAmelCase					,       map_location="""cpu"""		)

					__snake_case        =						checkpoint["""Downstream"""]

					__snake_case        =						WavLMConfig.from_pretrained(lowerCAmelCase		)
					__snake_case        =						WavaVecaFeatureExtractor.from_pretrained(
					    lowerCAmelCase					,       return_attention_mask=lowerCAmelCase					,       do_normalize=lowerCAmelCase		)

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

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

					hf_feature_extractor.save_pretrained(lowerCAmelCase		)
					hf_model.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
		UpperCamelCase__			:     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.")
		UpperCamelCase__			:     Tuple			       =				parser.parse_args()
		convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)


