





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


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import BlipImageProcessor





class 						_A( unittest.TestCase       ):


				"""simple docstring"""


				def __init__(      self					, _A					, _A=7					, _A=3					, _A=18					, _A=30					, _A=400					, _A=True					, _A=None					, _A=True					, _A=False					, _A=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]					, _A=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]					, _A=True					, ):
									__A     :						str          =    size if size is not None else {'height': 20, 'width': 20}
									__A     :						Dict          =    parent
									__A     :						Optional[Any]          =    batch_size
									__A     :						Tuple          =    num_channels
									__A     :						Optional[int]          =    image_size
									__A     :						Optional[Any]          =    min_resolution
									__A     :						Tuple          =    max_resolution
									__A     :						List[str]          =    do_resize
									__A     :						Any          =    size
									__A     :						Any          =    do_normalize
									__A     :						int          =    image_mean
									__A     :						Union[str, Any]          =    image_std
									__A     :						List[str]          =    do_pad
									__A     :						List[str]          =    do_convert_rgb



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





				def  UpperCAmelCase_		(      self					, _A=False					, _A=False					, _A=False					):
									assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

									if equal_resolution:
														__A     :						Tuple          =    []
														for i in range(self.batch_size					):
																			image_inputs.append(
																			    np.random.randint(
																			        255					, size=(self.num_channels, self.max_resolution, self.max_resolution)					, dtype=np.uinta					)					)
									else:
														__A     :						str          =    []
														for i in range(self.batch_size					):
																			__A   ,    __A     :						Any          =    np.random.choice(np.arange(self.min_resolution					, self.max_resolution					)					, 2					)
																			image_inputs.append(np.random.randint(255					, size=(self.num_channels, width, height)					, dtype=np.uinta					)					)

									if not numpify and not torchify:
														# PIL expects the channel dimension as last dimension
														__A     :						List[str]          =    [Image.fromarray(np.moveaxis(_A					, 0					, -1					)					) for x in image_inputs]

									if torchify:
														__A     :						Optional[Any]          =    [torch.from_numpy(_A					) for x in image_inputs]

									return image_inputs





@require_torch
@require_vision
class 						_A( snake_case__    ,			unittest.TestCase       ):


				"""simple docstring"""


				UpperCamelCase   :  Tuple    =     BlipImageProcessor if is_vision_available() else None



				def  UpperCAmelCase_		(      self					):
									__A     :						Optional[int]          =    BlipImageProcessingTester(self					)



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



				def  UpperCAmelCase_		(      self					):
									__A     :						int          =    self.image_processing_class(**self.image_processor_dict					)
									self.assertTrue(hasattr(_A					, 'do_resize'					)					)
									self.assertTrue(hasattr(_A					, 'size'					)					)
									self.assertTrue(hasattr(_A					, 'do_normalize'					)					)
									self.assertTrue(hasattr(_A					, 'image_mean'					)					)
									self.assertTrue(hasattr(_A					, 'image_std'					)					)
									self.assertTrue(hasattr(_A					, 'do_convert_rgb'					)					)



				def  UpperCAmelCase_		(      self					):
									pass



				def  UpperCAmelCase_		(      self					):
									# Initialize image_processor
									__A     :						str          =    self.image_processing_class(**self.image_processor_dict					)
									# create random PIL images
									__A     :						List[Any]          =    self.image_processor_tester.prepare_inputs(equal_resolution=_A					)
									for image in image_inputs:
														self.assertIsInstance(_A					, Image.Image					)

									# Test not batched input
									__A     :						Any          =    image_processor(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     :						Optional[int]          =    image_processor(_A					, 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  UpperCAmelCase_		(      self					):
									# Initialize image_processor
									__A     :						Optional[Any]          =    self.image_processing_class(**self.image_processor_dict					)
									# create random numpy tensors
									__A     :						str          =    self.image_processor_tester.prepare_inputs(equal_resolution=_A					, numpify=_A					)
									for image in image_inputs:
														self.assertIsInstance(_A					, np.ndarray					)

									# Test not batched input
									__A     :						List[str]          =    image_processor(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     :						str          =    image_processor(_A					, 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  UpperCAmelCase_		(      self					):
									# Initialize image_processor
									__A     :						Tuple          =    self.image_processing_class(**self.image_processor_dict					)
									# create random PyTorch tensors
									__A     :						Union[str, Any]          =    self.image_processor_tester.prepare_inputs(equal_resolution=_A					, torchify=_A					)
									for image in image_inputs:
														self.assertIsInstance(_A					, torch.Tensor					)

									# Test not batched input
									__A     :						str          =    image_processor(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     :						Optional[Any]          =    image_processor(_A					, 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'],
									    )					, )





@require_torch
@require_vision
class 						_A( snake_case__    ,			unittest.TestCase       ):


				"""simple docstring"""


				UpperCamelCase   :  str    =     BlipImageProcessor if is_vision_available() else None



				def  UpperCAmelCase_		(      self					):
									__A     :						int          =    BlipImageProcessingTester(self					, num_channels=4					)
									__A     :						Any          =    3



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



				def  UpperCAmelCase_		(      self					):
									__A     :						List[str]          =    self.image_processing_class(**self.image_processor_dict					)
									self.assertTrue(hasattr(_A					, 'do_resize'					)					)
									self.assertTrue(hasattr(_A					, 'size'					)					)
									self.assertTrue(hasattr(_A					, 'do_normalize'					)					)
									self.assertTrue(hasattr(_A					, 'image_mean'					)					)
									self.assertTrue(hasattr(_A					, 'image_std'					)					)
									self.assertTrue(hasattr(_A					, 'do_convert_rgb'					)					)



				def  UpperCAmelCase_		(      self					):
									pass





				def  UpperCAmelCase_		(      self					):
									# Initialize image_processor
									__A     :						int          =    self.image_processing_class(**self.image_processor_dict					)
									# create random PIL images
									__A     :						Union[str, Any]          =    self.image_processor_tester.prepare_inputs(equal_resolution=_A					)
									for image in image_inputs:
														self.assertIsInstance(_A					, Image.Image					)

									# Test not batched input
									__A     :						Optional[Any]          =    image_processor(image_inputs[0]					, return_tensors='pt'					).pixel_values
									self.assertEqual(
									    encoded_images.shape					, (
									        1,
									        self.expected_encoded_image_num_channels,
									        self.image_processor_tester.size['height'],
									        self.image_processor_tester.size['width'],
									    )					, )

									# Test batched
									__A     :						Dict          =    image_processor(_A					, return_tensors='pt'					).pixel_values
									self.assertEqual(
									    encoded_images.shape					, (
									        self.image_processor_tester.batch_size,
									        self.expected_encoded_image_num_channels,
									        self.image_processor_tester.size['height'],
									        self.image_processor_tester.size['width'],
									    )					, )











import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
UpperCAmelCase						:       Tuple        =					logging.get_logger(__name__)

UpperCAmelCase						:       List[Any]        =					['''UtteranceLevel''']







@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a ,						a  )				->				List[str]:
					__A     :						Any          =    torch.load(a ,						map_location='cpu'  )
					if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
										raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}"""  )

					__A     :						Dict          =    checkpoint['Downstream']

					__A     :						Optional[Any]          =    HubertConfig.from_pretrained(a  )
					__A     :						Tuple          =    HubertForSequenceClassification.from_pretrained(a ,						config=a  )
					__A     :						str          =    WavaVecaFeatureExtractor.from_pretrained(
					    a ,						return_attention_mask=a ,						do_normalize=a  )

					if hf_congfig.use_weighted_layer_sum:
										__A     :						Optional[Any]          =    checkpoint['Featurizer']['weights']

					__A     :						Dict          =    downstream_dict['projector.weight']
					__A     :						int          =    downstream_dict['projector.bias']
					__A     :						List[Any]          =    downstream_dict['model.post_net.linear.weight']
					__A     :						int          =    downstream_dict['model.post_net.linear.bias']

					hf_feature_extractor.save_pretrained(a  )
					hf_model.save_pretrained(a  )


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						:       Dict        =					parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




