



import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

A__  :  str       = ['''UtteranceLevel''']


@torch.no_grad()
def       UpperCamelCase(  __UpperCamelCase							:		Dict					,__UpperCamelCase							:		Optional[int]					,__UpperCamelCase							:		Tuple					,__UpperCamelCase							:		int			):
		lowerCAmelCase_      :					Optional[Any]     =			torch.load(__UpperCamelCase					,map_location='''cpu'''			)
		if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
				raise NotImplementedError(f"""The supported s3prl models are {SUPPORTED_MODELS}"""			)

		lowerCAmelCase_      :					int     =			checkpoint['''Downstream''']

		lowerCAmelCase_      :					List[str]     =			HubertConfig.from_pretrained(__UpperCamelCase			)
		lowerCAmelCase_      :					Union[str, Any]     =			HubertForSequenceClassification.from_pretrained(__UpperCamelCase					,config=__UpperCamelCase			)
		lowerCAmelCase_      :					Any     =			WavaVecaFeatureExtractor.from_pretrained(
		    __UpperCamelCase					,return_attention_mask=__UpperCamelCase					,do_normalize=__UpperCamelCase			)

		if hf_congfig.use_weighted_layer_sum:
				lowerCAmelCase_      :					str     =			checkpoint['''Featurizer''']['''weights''']

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

		hf_feature_extractor.save_pretrained(__UpperCamelCase			)
		hf_model.save_pretrained(__UpperCamelCase			)


if __name__ == "__main__":
						A__  :  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.''')
						A__  :  Tuple       = 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 IFImgaImgPipeline
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     __snake_case						(					UpperCamelCase_						,UpperCamelCase_						,unittest.TestCase						):
							_a      				=			IFImgaImgPipeline
							_a      				=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
							_a      				=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							_a      				=			PipelineTesterMixin.required_optional_params - {'''latents'''}




							def        UpperCAmelCase__      (	self					:	Any):
									return self._get_dummy_components()




							def        UpperCAmelCase__      (	self					:	str    ,    A_					:	Optional[int]    ,    A_					:	List[Any]=0):
									if str(A_).startswith('''mps'''):
											lowerCAmelCase_      :					Dict     =			torch.manual_seed(A_)
									else:
											lowerCAmelCase_      :					List[Any]     =			torch.Generator(device=A_).manual_seed(A_)

									lowerCAmelCase_      :					List[Any]     =			floats_tensor((1, 3, 3_2, 3_2)    ,    rng=random.Random(A_)).to(A_)

									lowerCAmelCase_      :					Any     =			{
									    '''prompt''': '''A painting of a squirrel eating a burger''',
									    '''image''': image,
									    '''generator''': generator,
									    '''num_inference_steps''': 2,
									    '''output_type''': '''numpy''',
									}

									return inputs




							def        UpperCAmelCase__      (	self					:	Union[str, Any]):
									self._test_save_load_optional_components()




							@unittest.skipIf(
							    torch_device != '''cuda''' or not is_xformers_available()    ,    reason='''XFormers attention is only available with CUDA and `xformers` installed'''    ,    )
							def        UpperCAmelCase__      (	self					:	List[str]):
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)




							@unittest.skipIf(torch_device != '''cuda'''    ,    reason='''float16 requires CUDA''')
							def        UpperCAmelCase__      (	self					:	Dict):
									# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
									super().test_save_load_floataa(expected_max_diff=1e-1)




							@unittest.skipIf(torch_device != '''cuda'''    ,    reason='''float16 requires CUDA''')
							def        UpperCAmelCase__      (	self					:	Tuple):
									super().test_floataa_inference(expected_max_diff=1e-1)




							def        UpperCAmelCase__      (	self					:	str):
									self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)




							def        UpperCAmelCase__      (	self					:	int):
									self._test_save_load_local()







							def        UpperCAmelCase__      (	self					:	Union[str, Any]):
									self._test_inference_batch_single_identical(
									    expected_max_diff=1e-2    ,    )





