





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_video_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import VivitImageProcessor

class       __A     (						unittest.TestCase    ):


				def __init__(		self   , UpperCAmelCase_   , UpperCAmelCase_=7   , UpperCAmelCase_=3   , UpperCAmelCase_=10   , UpperCAmelCase_=18   , UpperCAmelCase_=30   , UpperCAmelCase_=400   , UpperCAmelCase_=True   , UpperCAmelCase_=None   , UpperCAmelCase_=True   , UpperCAmelCase_=[0.5, 0.5, 0.5]   , UpperCAmelCase_=[0.5, 0.5, 0.5]   , UpperCAmelCase_=None   , ):
								lowerCamelCase	 =size if size is not None else {"""shortest_edge""": 18}
								lowerCamelCase	 =crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

								lowerCamelCase	 =parent
								lowerCamelCase	 =batch_size
								lowerCamelCase	 =num_channels
								lowerCamelCase	 =num_frames
								lowerCamelCase	 =image_size
								lowerCamelCase	 =min_resolution
								lowerCamelCase	 =max_resolution
								lowerCamelCase	 =do_resize
								lowerCamelCase	 =size
								lowerCamelCase	 =do_normalize
								lowerCamelCase	 =image_mean
								lowerCamelCase	 =image_std
								lowerCamelCase	 =crop_size


				def _snake_case       (		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,
								    "crop_size": self.crop_size,
								}




@require_torch
@require_vision
class       __A     (						a    ,					unittest.TestCase    ):
				__A      =				VivitImageProcessor if is_vision_available() else None


				def _snake_case       (		self							):
								lowerCamelCase	 =VivitImageProcessingTester(self							)


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


				def _snake_case       (		self							):
								lowerCamelCase	 =self.image_processing_class(**self.image_processor_dict							)
								self.assertTrue(hasattr(UpperCAmelCase_   , """image_mean"""							)							)
								self.assertTrue(hasattr(UpperCAmelCase_   , """image_std"""							)							)
								self.assertTrue(hasattr(UpperCAmelCase_   , """do_normalize"""							)							)
								self.assertTrue(hasattr(UpperCAmelCase_   , """do_resize"""							)							)
								self.assertTrue(hasattr(UpperCAmelCase_   , """do_center_crop"""							)							)
								self.assertTrue(hasattr(UpperCAmelCase_   , """size"""							)							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.image_processing_class.from_dict(self.image_processor_dict							)
								self.assertEqual(image_processor.size   , {"""shortest_edge""": 18}							)
								self.assertEqual(image_processor.crop_size   , {"""height""": 18, """width""": 18}							)

								lowerCamelCase	 =self.image_processing_class.from_dict(self.image_processor_dict   , size=42   , crop_size=84							)
								self.assertEqual(image_processor.size   , {"""shortest_edge""": 42}							)
								self.assertEqual(image_processor.crop_size   , {"""height""": 84, """width""": 84}							)


				def _snake_case       (		self							):
								# Initialize image_processing
								lowerCamelCase	 =self.image_processing_class(**self.image_processor_dict							)
								# create random PIL videos
								lowerCamelCase	 =prepare_video_inputs(self.image_processor_tester   , equal_resolution=UpperCAmelCase_							)
								for video in video_inputs:
												self.assertIsInstance(UpperCAmelCase_   , UpperCAmelCase_							)
												self.assertIsInstance(video[0]   , Image.Image							)

								# Test not batched input
								lowerCamelCase	 =image_processing(video_inputs[0]   , return_tensors="""pt"""							).pixel_values
								self.assertEqual(
								    encoded_videos.shape   , (
								        1,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   , )

								# Test batched
								lowerCamelCase	 =image_processing(UpperCAmelCase_   , return_tensors="""pt"""							).pixel_values
								self.assertEqual(
								    encoded_videos.shape   , (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   , )


				def _snake_case       (		self							):
								# Initialize image_processing
								lowerCamelCase	 =self.image_processing_class(**self.image_processor_dict							)
								# create random numpy tensors
								lowerCamelCase	 =prepare_video_inputs(self.image_processor_tester   , equal_resolution=UpperCAmelCase_   , numpify=UpperCAmelCase_							)
								for video in video_inputs:
												self.assertIsInstance(UpperCAmelCase_   , UpperCAmelCase_							)
												self.assertIsInstance(video[0]   , np.ndarray							)

								# Test not batched input
								lowerCamelCase	 =image_processing(video_inputs[0]   , return_tensors="""pt"""							).pixel_values
								self.assertEqual(
								    encoded_videos.shape   , (
								        1,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   , )

								# Test batched
								lowerCamelCase	 =image_processing(UpperCAmelCase_   , return_tensors="""pt"""							).pixel_values
								self.assertEqual(
								    encoded_videos.shape   , (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   , )


				def _snake_case       (		self							):
								# Initialize image_processing
								lowerCamelCase	 =self.image_processing_class(**self.image_processor_dict							)
								# create random PyTorch tensors
								lowerCamelCase	 =prepare_video_inputs(self.image_processor_tester   , equal_resolution=UpperCAmelCase_   , torchify=UpperCAmelCase_							)
								for video in video_inputs:
												self.assertIsInstance(UpperCAmelCase_   , UpperCAmelCase_							)
												self.assertIsInstance(video[0]   , torch.Tensor							)

								# Test not batched input
								lowerCamelCase	 =image_processing(video_inputs[0]   , return_tensors="""pt"""							).pixel_values
								self.assertEqual(
								    encoded_videos.shape   , (
								        1,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   , )

								# Test batched
								lowerCamelCase	 =image_processing(UpperCAmelCase_   , return_tensors="""pt"""							).pixel_values
								self.assertEqual(
								    encoded_videos.shape   , (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   , )












import argparse

import torch

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


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





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Any:
				lowerCamelCase	 =UniSpeechSatForSequenceClassification.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				lowerCamelCase	 =downstream_dict["""projector.weight"""]
				lowerCamelCase	 =downstream_dict["""projector.bias"""]
				lowerCamelCase	 =downstream_dict["""model.post_net.linear.weight"""]
				lowerCamelCase	 =downstream_dict["""model.post_net.linear.bias"""]
				return model





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  int:
				lowerCamelCase	 =UniSpeechSatForAudioFrameClassification.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				lowerCamelCase	 =downstream_dict["""model.linear.weight"""]
				lowerCamelCase	 =downstream_dict["""model.linear.bias"""]
				return model





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Dict:
				lowerCamelCase	 =UniSpeechSatForXVector.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				lowerCamelCase	 =downstream_dict["""connector.weight"""]
				lowerCamelCase	 =downstream_dict["""connector.bias"""]
				for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
								lowerCamelCase	 =downstream_dict[
								    F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
								]
								lowerCamelCase	 =downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

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





@torch.no_grad()
def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  str:
				lowerCamelCase	 =torch.load(_UpperCAmelCase			,      map_location="""cpu"""				)

				lowerCamelCase	 =checkpoint["""Downstream"""]

				lowerCamelCase	 =UniSpeechSatConfig.from_pretrained(_UpperCAmelCase				)
				lowerCamelCase	 =WavaVecaFeatureExtractor.from_pretrained(
				    _UpperCAmelCase			,      return_attention_mask=_UpperCAmelCase			,      do_normalize=_UpperCAmelCase				)

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

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

				hf_feature_extractor.save_pretrained(_UpperCAmelCase				)
				hf_model.save_pretrained(_UpperCAmelCase				)


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





