


import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


__snake_case      = False
__snake_case      = True
__snake_case      = False


if __name__ == "__main__":
			__snake_case      = argparse.ArgumentParser()

			parser.add_argument(
			    "--repo_path",
			    default=None,
			    type=str,
			    required=True,
			    help="The config json file corresponding to the architecture.",
			)

			parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

			__snake_case      = parser.parse_args()

			__snake_case      = {
			    'image_size': 'sample_size',
			    'num_res_blocks': 'layers_per_block',
			    'block_channels': 'block_out_channels',
			    'down_blocks': 'down_block_types',
			    'up_blocks': 'up_block_types',
			    'downscale_freq_shift': 'freq_shift',
			    'resnet_num_groups': 'norm_num_groups',
			    'resnet_act_fn': 'act_fn',
			    'resnet_eps': 'norm_eps',
			    'num_head_channels': 'attention_head_dim',
			}

			__snake_case      = {
			    'time_steps': 'time_proj',
			    'mid': 'mid_block',
			    'downsample_blocks': 'down_blocks',
			    'upsample_blocks': 'up_blocks',
			}

			__snake_case      = '' if has_file(args.repo_path, "config.json") else 'unet'

			with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader:
						__snake_case      = reader.read()
						__snake_case      = json.loads(text)

			if do_only_config:
						for key in config_parameters_to_change.keys():
									config.pop(key, None)

			if has_file(args.repo_path, "config.json"):
						__snake_case      = UNetaDModel(**config)
			else:
						__snake_case      = UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
						__snake_case      = class_name(**config)

			if do_only_config:
						model.save_config(os.path.join(args.repo_path, subfolder))

			__snake_case      = dict(model.config)

			if do_only_renaming:
						for key, value in config_parameters_to_change.items():
									if key in config:
												__snake_case      = config[key]
												del config[key]

						__snake_case      = [k.replace("UNetRes", "") for k in config['down_block_types']]
						__snake_case      = [k.replace("UNetRes", "") for k in config['up_block_types']]

			if do_only_weights:
						__snake_case      = torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

						__snake_case      = {}
						for param_key, param_value in state_dict.items():
									if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"):
												continue
									__snake_case      = False
									for key, new_key in key_parameters_to_change.items():
												if not has_changed and param_key.split(".")[0] == key:
															__snake_case      = param_value
															__snake_case      = True
									if not has_changed:
												__snake_case      = param_value

						model.load_state_dict(new_state_dict)
						model.save_pretrained(os.path.join(args.repo_path, subfolder))











import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list





@require_torchaudio
@require_sentencepiece
class UpperCAmelCase_				( unittest.TestCase ):


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       '''laion/clap-htsat-unfused'''
       A_          =       tempfile.mkdtemp()


  def 						__UpperCAmelCase     (							self ,**__snake_case ):



       """simple docstring"""





       return RobertaTokenizer.from_pretrained(self.checkpoint ,**__snake_case )


  def 						__UpperCAmelCase     (							self ,**__snake_case ):



       """simple docstring"""





       return ClapFeatureExtractor.from_pretrained(self.checkpoint ,**__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       shutil.rmtree(self.tmpdirname )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_tokenizer()
       A_          =       self.get_feature_extractor()

       A_          =       ClapProcessor(tokenizer=__snake_case ,feature_extractor=__snake_case )

       processor.save_pretrained(self.tmpdirname )
       A_          =       ClapProcessor.from_pretrained(self.tmpdirname )

       self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer.get_vocab() )
       self.assertIsInstance(processor.tokenizer ,__snake_case )

       self.assertEqual(processor.feature_extractor.to_json_string() ,feature_extractor.to_json_string() )
       self.assertIsInstance(processor.feature_extractor ,__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       ClapProcessor(tokenizer=self.get_tokenizer() ,feature_extractor=self.get_feature_extractor() )
       processor.save_pretrained(self.tmpdirname )

       A_          =       self.get_tokenizer(bos_token='''(BOS)''' ,eos_token='''(EOS)''' )
       A_          =       self.get_feature_extractor(do_normalize=__snake_case ,padding_value=1.0 )

       A_          =       ClapProcessor.from_pretrained(
           self.tmpdirname ,bos_token='''(BOS)''' ,eos_token='''(EOS)''' ,do_normalize=__snake_case ,padding_value=1.0 )

       self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer_add_kwargs.get_vocab() )
       self.assertIsInstance(processor.tokenizer ,__snake_case )

       self.assertEqual(processor.feature_extractor.to_json_string() ,feature_extractor_add_kwargs.to_json_string() )
       self.assertIsInstance(processor.feature_extractor ,__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_feature_extractor()
       A_          =       self.get_tokenizer()

       A_          =       ClapProcessor(tokenizer=__snake_case ,feature_extractor=__snake_case )

       A_          =       floats_list((3, 1_0_0_0) )

       A_          =       feature_extractor(__snake_case ,return_tensors='''np''' )
       A_          =       processor(audios=__snake_case ,return_tensors='''np''' )

       for key in input_feat_extract.keys():
            self.assertAlmostEqual(input_feat_extract[key].sum() ,input_processor[key].sum() ,delta=1E-2 )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_feature_extractor()
       A_          =       self.get_tokenizer()

       A_          =       ClapProcessor(tokenizer=__snake_case ,feature_extractor=__snake_case )

       A_          =       '''This is a test string'''

       A_          =       processor(text=__snake_case )

       A_          =       tokenizer(__snake_case )

       for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key] ,encoded_processor[key] )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_feature_extractor()
       A_          =       self.get_tokenizer()

       A_          =       ClapProcessor(tokenizer=__snake_case ,feature_extractor=__snake_case )

       A_          =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       A_          =       processor.batch_decode(__snake_case )
       A_          =       tokenizer.batch_decode(__snake_case )

       self.assertListEqual(__snake_case ,__snake_case )




  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_feature_extractor()
       A_          =       self.get_tokenizer()

       A_          =       ClapProcessor(tokenizer=__snake_case ,feature_extractor=__snake_case )

       self.assertListEqual(
           processor.model_input_names[2:] ,feature_extractor.model_input_names ,msg='''`processor` and `feature_extractor` model input names do not match''' ,)



