




'''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


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import SwinaSRImageProcessor
   from transformers.image_transforms import get_image_size


class      _a    (      unittest.TestCase					):
       def __init__(      self		:      int  ,							lowercase		:      Union[str, Any]  ,							lowercase		:      Any=7  ,							lowercase		:      List[str]=3  ,							lowercase		:      Optional[Any]=18  ,							lowercase		:      Union[str, Any]=30  ,							lowercase		:      str=400  ,							lowercase		:      Union[str, Any]=True  ,							lowercase		:      int=1 / 255  ,							lowercase		:      List[str]=True  ,							lowercase		:      Any=8  ,							):



           '''simple docstring'''

           UpperCAmelCase     			=						parent
           UpperCAmelCase     			=						batch_size
           UpperCAmelCase     			=						num_channels
           UpperCAmelCase     			=						image_size
           UpperCAmelCase     			=						min_resolution
           UpperCAmelCase     			=						max_resolution
           UpperCAmelCase     			=						do_rescale
           UpperCAmelCase     			=						rescale_factor
           UpperCAmelCase     			=						do_pad
           UpperCAmelCase     			=						pad_size
       def        A (      self		:      Union[str, Any]							):



           '''simple docstring'''

           return {
               "do_rescale": self.do_rescale,
               "rescale_factor": self.rescale_factor,
               "do_pad": self.do_pad,
               "pad_size": self.pad_size,
           }







       def        A (      self		:      Dict  ,							lowercase		:      List[Any]=False  ,							lowercase		:      List[str]=False  ,							lowercase		:      Dict=False							):



           '''simple docstring'''

           assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

           if equal_resolution:
               UpperCAmelCase     			=						[]
               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:
               UpperCAmelCase     			=						[]
               for i in range(self.batch_size							):
                   UpperCAmelCase			,	UpperCAmelCase     			=						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
               UpperCAmelCase     			=						[Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE  ,							0  ,							-1							)							) for x in image_inputs]

           if torchify:
               UpperCAmelCase     			=						[torch.from_numpy(_SCREAMING_SNAKE_CASE							) for x in image_inputs]

           return image_inputs






@require_torch
@require_vision
class      _a    (      __a				,				unittest.TestCase					):
       __a							:	List[str]  							=   SwinaSRImageProcessor if is_vision_available() else None
       def        A (      self		:      Any							):



           '''simple docstring'''

           UpperCAmelCase     			=						SwinaSRImageProcessingTester(self							)
       @property
       def        A (      self		:      str							):



           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()
       def        A (      self		:      List[Any]							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.image_processing_class(**self.image_processor_dict							)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,							'''do_rescale'''							)							)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,							'''rescale_factor'''							)							)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,							'''do_pad'''							)							)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,							'''pad_size'''							)							)
       def        A (      self		:      Optional[Any]							):



           '''simple docstring'''

           pass
       def        A (      self		:      Dict  ,							lowercase		:      int							):



           '''simple docstring'''

           UpperCAmelCase			,	UpperCAmelCase     			=						get_image_size(_SCREAMING_SNAKE_CASE							)
           UpperCAmelCase     			=						self.image_processor_tester.pad_size

           UpperCAmelCase     			=						(old_height // size + 1) * size - old_height
           UpperCAmelCase     			=						(old_width // size + 1) * size - old_width
           return old_height + pad_height, old_width + pad_width
       def        A (      self		:      List[Any]							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.image_processing_class(**self.image_processor_dict							)
           # create random PIL images
           UpperCAmelCase     			=						self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE							)
           for image in image_inputs:
               self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,							Image.Image							)

           # Test not batched input
           UpperCAmelCase     			=						image_processor(image_inputs[0]  ,							return_tensors='''pt'''							).pixel_values
           UpperCAmelCase			,	UpperCAmelCase     			=						self.calculate_expected_size(np.array(image_inputs[0]							)							)
           self.assertEqual(
               encoded_images.shape  ,							(
                   1,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )  ,							)
       def        A (      self		:      List[str]							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.image_processing_class(**self.image_processor_dict							)
           # create random numpy tensors
           UpperCAmelCase     			=						self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE  ,							numpify=_SCREAMING_SNAKE_CASE							)
           for image in image_inputs:
               self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,							np.ndarray							)

           # Test not batched input
           UpperCAmelCase     			=						image_processor(image_inputs[0]  ,							return_tensors='''pt'''							).pixel_values
           UpperCAmelCase			,	UpperCAmelCase     			=						self.calculate_expected_size(image_inputs[0]							)
           self.assertEqual(
               encoded_images.shape  ,							(
                   1,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )  ,							)







       def        A (      self		:      Union[str, Any]							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.image_processing_class(**self.image_processor_dict							)
           # create random PyTorch tensors
           UpperCAmelCase     			=						self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE  ,							torchify=_SCREAMING_SNAKE_CASE							)
           for image in image_inputs:
               self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,							torch.Tensor							)

           # Test not batched input
           UpperCAmelCase     			=						image_processor(image_inputs[0]  ,							return_tensors='''pt'''							).pixel_values
           UpperCAmelCase			,	UpperCAmelCase     			=						self.calculate_expected_size(image_inputs[0]							)
           self.assertEqual(
               encoded_images.shape  ,							(
                   1,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )  ,							)





"""simple docstring"""







import argparse

import torch

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


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



def       A__    (	lowercase_     :	List[str]      , lowercase_     :	Any      , lowercase_     :	Optional[int]						)  ->							Union[str, Any]:
   __lowerCamelCase    		=					WavLMForSequenceClassification.from_pretrained(lowercase_      , config=lowercase_						)
   __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       A__    (	lowercase_     :	Dict      , lowercase_     :	str      , lowercase_     :	int						)  ->							List[Any]:
   __lowerCamelCase    		=					WavLMForAudioFrameClassification.from_pretrained(lowercase_      , config=lowercase_						)
   __lowerCamelCase    		=					downstream_dict['model.linear.weight']
   __lowerCamelCase    		=					downstream_dict['model.linear.bias']
   return model



def       A__    (	lowercase_     :	List[str]      , lowercase_     :	Dict      , lowercase_     :	List[str]						)  ->							Tuple:
   __lowerCamelCase    		=					WavLMForXVector.from_pretrained(lowercase_      , config=lowercase_						)
   __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       A__    (	lowercase_     :	str      , lowercase_     :	Optional[int]      , lowercase_     :	List[str]      , lowercase_     :	Union[str, Any]						)  ->							Optional[Any]:
   __lowerCamelCase    		=					torch.load(lowercase_      , map_location='cpu'						)

   __lowerCamelCase    		=					checkpoint['Downstream']

   __lowerCamelCase    		=					WavLMConfig.from_pretrained(lowercase_						)
   __lowerCamelCase    		=					WavaVecaFeatureExtractor.from_pretrained(
       lowercase_      , return_attention_mask=lowercase_      , do_normalize=lowercase_						)

   __lowerCamelCase    		=					hf_config.architectures[0]
   if arch.endswith('ForSequenceClassification'						):
      __lowerCamelCase    		=					convert_classification(lowercase_      , lowercase_      , lowercase_						)
   elif arch.endswith('ForAudioFrameClassification'						):
      __lowerCamelCase    		=					convert_diarization(lowercase_      , lowercase_      , lowercase_						)
   elif arch.endswith('ForXVector'						):
      __lowerCamelCase    		=					convert_xvector(lowercase_      , lowercase_      , lowercase_						)
   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(lowercase_						)
   hf_model.save_pretrained(lowercase_						)


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE	:		Optional[Any]   =   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.''')
     _SCREAMING_SNAKE_CASE	:		Dict   =   parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






