



import argparse

import torch

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


logging.set_verbosity_info()
__snake_case    :		List[Any]  =logging.get_logger(__name__)






def      lowerCAmelCase__		(   lowerCamelCase_		:		List[Any]	,lowerCamelCase_		:		List[Any]	,lowerCamelCase_		:		Dict):
				lowerCAmelCase__					:    Dict												=					UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase_	,config=lowerCamelCase_)
				lowerCAmelCase__					:    Dict												=					downstream_dict['''projector.weight''']
				lowerCAmelCase__					:    Union[str, Any]												=					downstream_dict['''projector.bias''']
				lowerCAmelCase__					:    int												=					downstream_dict['''model.post_net.linear.weight''']
				lowerCAmelCase__					:    List[str]												=					downstream_dict['''model.post_net.linear.bias''']
				return model






def      lowerCAmelCase__		(   lowerCamelCase_		:		Optional[int]	,lowerCamelCase_		:		int	,lowerCamelCase_		:		Optional[Any]):
				lowerCAmelCase__					:    Any												=					UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase_	,config=lowerCamelCase_)
				lowerCAmelCase__					:    Dict												=					downstream_dict['''model.linear.weight''']
				lowerCAmelCase__					:    List[Any]												=					downstream_dict['''model.linear.bias''']
				return model






def      lowerCAmelCase__		(   lowerCamelCase_		:		Optional[Any]	,lowerCamelCase_		:		List[Any]	,lowerCamelCase_		:		Dict):
				lowerCAmelCase__					:    Union[str, Any]												=					UniSpeechSatForXVector.from_pretrained(lowerCamelCase_	,config=lowerCamelCase_)
				lowerCAmelCase__					:    List[Any]												=					downstream_dict['''connector.weight''']
				lowerCAmelCase__					:    Tuple												=					downstream_dict['''connector.bias''']
				for i, kernel_size in enumerate(hf_config.tdnn_kernel):
								lowerCAmelCase__					:    Dict												=					downstream_dict[
								    f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
								]
								lowerCAmelCase__					:    Optional[Any]												=					downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

				lowerCAmelCase__					:    List[str]												=					downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
				lowerCAmelCase__					:    List[Any]												=					downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
				lowerCAmelCase__					:    str												=					downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
				lowerCAmelCase__					:    Any												=					downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
				lowerCAmelCase__					:    Any												=					downstream_dict['''objective.W''']
				return model






@torch.no_grad()
def      lowerCAmelCase__		(   lowerCamelCase_		:		int	,lowerCamelCase_		:		List[str]	,lowerCamelCase_		:		str	,lowerCamelCase_		:		Union[str, Any]):
				lowerCAmelCase__					:    str												=					torch.load(lowerCamelCase_	,map_location='''cpu''')

				lowerCAmelCase__					:    List[Any]												=					checkpoint['''Downstream''']

				lowerCAmelCase__					:    str												=					UniSpeechSatConfig.from_pretrained(lowerCamelCase_)
				lowerCAmelCase__					:    Optional[int]												=					WavaVecaFeatureExtractor.from_pretrained(
				    lowerCamelCase_	,return_attention_mask=lowerCamelCase_	,do_normalize=lowerCamelCase_)

				lowerCAmelCase__					:    Optional[int]												=					hf_config.architectures[0]
				if arch.endswith('''ForSequenceClassification'''):
								lowerCAmelCase__					:    List[Any]												=					convert_classification(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_)
				elif arch.endswith('''ForAudioFrameClassification'''):
								lowerCAmelCase__					:    Any												=					convert_diarization(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_)
				elif arch.endswith('''ForXVector'''):
								lowerCAmelCase__					:    Optional[int]												=					convert_xvector(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_)
				else:
								raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}""")

				if hf_config.use_weighted_layer_sum:
								lowerCAmelCase__					:    List[str]												=					checkpoint['''Featurizer''']['''weights''']

				hf_feature_extractor.save_pretrained(lowerCamelCase_)
				hf_model.save_pretrained(lowerCamelCase_)


if __name__ == "__main__":
							__snake_case    :		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.')
							__snake_case    :		Dict  =parser.parse_args()
							convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class 	lowerCamelCase__      (							lowerCamelCase__				,    lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



		snake_case_     =IFInpaintingSuperResolutionPipeline
		snake_case_     =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
		snake_case_     =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""})
		snake_case_     =PipelineTesterMixin.required_optional_params - {"""latents"""}



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

						"""simple docstring"""






						return self._get_superresolution_dummy_components()



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=0			)					->      Dict:

						"""simple docstring"""






						if str(__lowerCamelCase			).startswith('''mps'''			):
										lowerCAmelCase__					:    Union[str, Any]												=					torch.manual_seed(__lowerCamelCase			)
						else:
										lowerCAmelCase__					:    int												=					torch.Generator(device=__lowerCamelCase			).manual_seed(__lowerCamelCase			)

						lowerCAmelCase__					:    str												=					floats_tensor((1, 3, 16, 16)		,rng=random.Random(__lowerCamelCase			)			).to(__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[Any]												=					floats_tensor((1, 3, 32, 32)		,rng=random.Random(__lowerCamelCase			)			).to(__lowerCamelCase			)
						lowerCAmelCase__					:    Union[str, Any]												=					floats_tensor((1, 3, 32, 32)		,rng=random.Random(__lowerCamelCase			)			).to(__lowerCamelCase			)

						lowerCAmelCase__					:    Dict												=					{
						    '''prompt''': '''A painting of a squirrel eating a burger''',
						    '''image''': image,
						    '''original_image''': original_image,
						    '''mask_image''': mask_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 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3			)



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						self._test_save_load_optional_components()



		@unittest.skipIf(torch_device != '''cuda'''		,reason='''float16 requires CUDA'''			)
		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						self._test_attention_slicing_forward_pass(expected_max_diff=1e-2			)



		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						self._test_save_load_local()



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






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

