
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 __snake_case		(      UpperCAmelCase_					,	UpperCAmelCase_					,	unittest.TestCase     ):
    _a		:						Tuple=				IFInpaintingSuperResolutionPipeline
    _a		:						List[Any]=				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
    _a		:						Optional[Any]=				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}     )
    _a		:						List[Any]=				PipelineTesterMixin.required_optional_params - {'''latents'''}
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return self._get_superresolution_dummy_components()
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=0      ):



           '''simple docstring'''







           if str(lowerCAmelCase__      ).startswith("""mps"""      ):
                  lowercase							:     Tuple       	=      torch.manual_seed(lowerCAmelCase__      )
           else:
                  lowercase							:     str       	=      torch.Generator(device=lowerCAmelCase__      ).manual_seed(lowerCAmelCase__      )

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

           lowercase							:     Any       	=      {
               "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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           self._test_save_load_optional_components()
    @unittest.skipIf(torch_device != """cuda"""		,reason="""float16 requires CUDA"""      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           super().test_save_load_floataa(expected_max_diff=1e-1      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







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



           '''simple docstring'''







           self._test_save_load_local()
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







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



import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCAmelCase							:  Union[str, Any]   	=						{
    """text_branch""": """text_model""",
    """audio_branch""": """audio_model.audio_encoder""",
    """attn""": """attention.self""",
    """self.proj""": """output.dense""",
    """attention.self_mask""": """attn_mask""",
    """mlp.fc1""": """intermediate.dense""",
    """mlp.fc2""": """output.dense""",
    """norm1""": """layernorm_before""",
    """norm2""": """layernorm_after""",
    """bn0""": """batch_norm""",
}

lowerCAmelCase							:  int   	=						AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       create_model(
						    "HTSAT-tiny"				,	"roberta"				,	_UpperCAmelCase				,	precision="fp32"				,	device="cuda:0" if torch.cuda.is_available() else "cpu"				,	enable_fusion=_UpperCAmelCase				,	fusion_type="aff_2d" if enable_fusion else None				,	)
						return model, model_cfg







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Any  		=       {}

						SCREAMING_SNAKE_CASE_: Tuple  		=       R".*sequential.(\d+).*"
						SCREAMING_SNAKE_CASE_: Dict  		=       R".*_projection.(\d+).*"

						for key, value in state_dict.items():
												# check if any key needs to be modified
												for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
																		if key_to_modify in key:
																								SCREAMING_SNAKE_CASE_: Any  		=       key.replace(_UpperCAmelCase				,	_UpperCAmelCase    )

												if re.match(_UpperCAmelCase				,	_UpperCAmelCase    ):
																		# replace sequential layers with list
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       re.match(_UpperCAmelCase				,	_UpperCAmelCase    ).group(1    )

																		SCREAMING_SNAKE_CASE_: Dict  		=       key.replace(f"sequential.{sequential_layer}."				,	f"layers.{int(_UpperCAmelCase    )//3}.linear."    )
												elif re.match(_UpperCAmelCase				,	_UpperCAmelCase    ):
																		SCREAMING_SNAKE_CASE_: Any  		=       int(re.match(_UpperCAmelCase				,	_UpperCAmelCase    ).group(1    )    )

																		# Because in CLAP they use `nn.Sequential`...
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       1 if projecton_layer == 0 else 2

																		SCREAMING_SNAKE_CASE_: Dict  		=       key.replace(f"_projection.{projecton_layer}."				,	f"_projection.linear{transformers_projection_layer}."    )

												if "audio" and "qkv" in key:
																		# split qkv into query key and value
																		SCREAMING_SNAKE_CASE_: Tuple  		=       value
																		SCREAMING_SNAKE_CASE_: List[str]  		=       mixed_qkv.size(0    ) // 3

																		SCREAMING_SNAKE_CASE_: Any  		=       mixed_qkv[:qkv_dim]
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       mixed_qkv[qkv_dim : qkv_dim * 2]
																		SCREAMING_SNAKE_CASE_: Optional[Any]  		=       mixed_qkv[qkv_dim * 2 :]

																		SCREAMING_SNAKE_CASE_: str  		=       query_layer
																		SCREAMING_SNAKE_CASE_: int  		=       key_layer
																		SCREAMING_SNAKE_CASE_: List[Any]  		=       value_layer
												else:
																		SCREAMING_SNAKE_CASE_: int  		=       value

						return model_state_dict







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       init_clap(_UpperCAmelCase				,	enable_fusion=_UpperCAmelCase    )

						clap_model.eval()
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       clap_model.state_dict()
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       rename_state_dict(_UpperCAmelCase    )

						SCREAMING_SNAKE_CASE_: List[str]  		=       ClapConfig()
						SCREAMING_SNAKE_CASE_: Tuple  		=       enable_fusion
						SCREAMING_SNAKE_CASE_: Tuple  		=       ClapModel(_UpperCAmelCase    )

						# ignore the spectrogram embedding layer
						model.load_state_dict(_UpperCAmelCase				,	strict=_UpperCAmelCase    )

						model.save_pretrained(_UpperCAmelCase    )
						transformers_config.save_pretrained(_UpperCAmelCase    )


if __name__ == "__main__":
						lowerCAmelCase							:  Tuple   	=						argparse.ArgumentParser()
						parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
						parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
						parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
						parser.add_argument("""--enable_fusion""", action="""store_true""", help="""Whether to enable fusion or not""")
						lowerCAmelCase							:  int   	=						parser.parse_args()

						convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)






