






"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin





@skip_mps
class   UpperCamelCase_ (       _UpperCAmelCase   ,					_UpperCAmelCase   ,					unittest.TestCase  ):
  _A       :					Optional[Any]				=	IFImgaImgSuperResolutionPipeline
  _A       :					str				=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
  _A       :					Dict				=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'original_image'}  )
  _A       :					int				=	PipelineTesterMixin.required_optional_params - {'latents'}



  def 		UpperCamelCase_				(      self				)			->			str:



         """simple docstring"""





         return self._get_superresolution_dummy_components()



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__=0				)			->			str:



         """simple docstring"""





         if str(lowercase__				).startswith("""mps"""				):
                lowercase_	    =			torch.manual_seed(lowercase__				)
         else:
                lowercase_	    =			torch.Generator(device=lowercase__				).manual_seed(lowercase__				)

         lowercase_	    =			floats_tensor((1, 3, 32, 32) ,		rng=random.Random(lowercase__				)				).to(lowercase__				)
         lowercase_	    =			floats_tensor((1, 3, 16, 16) ,		rng=random.Random(lowercase__				)				).to(lowercase__				)

         lowercase_	    =			{
             "prompt": "A painting of a squirrel eating a burger",
             "image": image,
             "original_image": original_image,
             "generator": generator,
             "num_inference_steps": 2,
             "output_type": "numpy",
         }

         return inputs



  @unittest.skipIf(
      torch_device != """cuda""" or not is_xformers_available() ,		reason="""XFormers attention is only available with CUDA and `xformers` installed""" ,		)
  def 		UpperCamelCase_				(      self				)			->			Union[str, Any]:



         """simple docstring"""





         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3				)



  def 		UpperCamelCase_				(      self				)			->			Union[str, Any]:



         """simple docstring"""





         self._test_save_load_optional_components()



  @unittest.skipIf(torch_device != """cuda""" ,		reason="""float16 requires CUDA"""				)
  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         super().test_save_load_floataa(expected_max_diff=1e-1				)



  def 		UpperCamelCase_				(      self				)			->			str:



         """simple docstring"""





         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2				)



  def 		UpperCamelCase_				(      self				)			->			List[Any]:



         """simple docstring"""





         self._test_save_load_local()



  def 		UpperCamelCase_				(      self				)			->			List[str]:



         """simple docstring"""





         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2 ,		)








"""simple docstring"""







import argparse

import torch

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


logging.set_verbosity_info()
lowerCAmelCase_	:    Any            =      logging.get_logger(__name__)






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			UniSpeechSatForSequenceClassification.from_pretrained(lowerCAmelCase	,      config=lowerCAmelCase		)
       UpperCAmelCase	    =			downstream_dict["""projector.weight"""]
       UpperCAmelCase	    =			downstream_dict["""projector.bias"""]
       UpperCAmelCase	    =			downstream_dict["""model.post_net.linear.weight"""]
       UpperCAmelCase	    =			downstream_dict["""model.post_net.linear.bias"""]
       return model






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCAmelCase	,      config=lowerCAmelCase		)
       UpperCAmelCase	    =			downstream_dict["""model.linear.weight"""]
       UpperCAmelCase	    =			downstream_dict["""model.linear.bias"""]
       return model






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			UniSpeechSatForXVector.from_pretrained(lowerCAmelCase	,      config=lowerCAmelCase		)
       UpperCAmelCase	    =			downstream_dict["""connector.weight"""]
       UpperCAmelCase	    =			downstream_dict["""connector.bias"""]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
              UpperCAmelCase	    =			downstream_dict[
                  F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
              ]
              UpperCAmelCase	    =			downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

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






@torch.no_grad()
def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			torch.load(lowerCAmelCase	,      map_location="""cpu"""		)

       UpperCAmelCase	    =			checkpoint["""Downstream"""]

       UpperCAmelCase	    =			UniSpeechSatConfig.from_pretrained(lowerCAmelCase		)
       UpperCAmelCase	    =			WavaVecaFeatureExtractor.from_pretrained(
           lowerCAmelCase	,      return_attention_mask=lowerCAmelCase	,      do_normalize=lowerCAmelCase		)

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

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

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


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