

'''simple docstring'''

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


A_							    =				{
    "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",
}

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





def  A_	(			snake_case   ,							snake_case=False			):
  SCREAMING_SNAKE_CASE:Optional[int]       =       create_model(
      "HTSAT-tiny"   ,							"roberta"   ,							lowerCAmelCase__   ,							precision="fp32"   ,							device="cuda:0" if torch.cuda.is_available() else "cpu"   ,							enable_fusion=lowerCAmelCase__   ,							fusion_type="aff_2d" if enable_fusion else None   ,							)
  return model, model_cfg





def  A_	(			snake_case			):
  SCREAMING_SNAKE_CASE:Any       =       {}

  SCREAMING_SNAKE_CASE:Tuple       =       R".*sequential.(\d+).*"
  SCREAMING_SNAKE_CASE:Tuple       =       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:str       =       key.replace(lowerCAmelCase__   ,							lowerCAmelCase__			)

    if re.match(lowerCAmelCase__   ,							lowerCAmelCase__			):
      # replace sequential layers with list
      SCREAMING_SNAKE_CASE:Optional[Any]       =       re.match(lowerCAmelCase__   ,							lowerCAmelCase__			).group(1			)

      SCREAMING_SNAKE_CASE:int       =       key.replace(F'''sequential.{sequential_layer}.'''   ,							F'''layers.{int(lowerCAmelCase__			)//3}.linear.'''			)
    elif re.match(lowerCAmelCase__   ,							lowerCAmelCase__			):
      SCREAMING_SNAKE_CASE:Tuple       =       int(re.match(lowerCAmelCase__   ,							lowerCAmelCase__			).group(1			)			)

      # Because in CLAP they use `nn.Sequential`...
      SCREAMING_SNAKE_CASE:List[Any]       =       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:List[Any]       =       value
      SCREAMING_SNAKE_CASE:List[Any]       =       mixed_qkv.size(0			) // 3

      SCREAMING_SNAKE_CASE:Optional[int]       =       mixed_qkv[:qkv_dim]
      SCREAMING_SNAKE_CASE:List[str]       =       mixed_qkv[qkv_dim : qkv_dim * 2]
      SCREAMING_SNAKE_CASE:Optional[Any]       =       mixed_qkv[qkv_dim * 2 :]

      SCREAMING_SNAKE_CASE:Tuple       =       query_layer
      SCREAMING_SNAKE_CASE:int       =       key_layer
      SCREAMING_SNAKE_CASE:Optional[int]       =       value_layer
    else:
      SCREAMING_SNAKE_CASE:List[str]       =       value

  return model_state_dict





def  A_	(			snake_case   ,							snake_case   ,							snake_case   ,							snake_case=False			):
  SCREAMING_SNAKE_CASE:Tuple       =       init_clap(lowerCAmelCase__   ,							enable_fusion=lowerCAmelCase__			)

  clap_model.eval()
  SCREAMING_SNAKE_CASE:Optional[int]       =       clap_model.state_dict()
  SCREAMING_SNAKE_CASE:Optional[Any]       =       rename_state_dict(lowerCAmelCase__			)

  SCREAMING_SNAKE_CASE:Union[str, Any]       =       ClapConfig()
  SCREAMING_SNAKE_CASE:Dict       =       enable_fusion
  SCREAMING_SNAKE_CASE:Any       =       ClapModel(lowerCAmelCase__			)

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

  model.save_pretrained(lowerCAmelCase__			)
  transformers_config.save_pretrained(lowerCAmelCase__			)


if __name__ == "__main__":
    A_							    =				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")
    A_							    =				parser.parse_args()

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













"""simple docstring"""






import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np



class 					__UpperCAmelCase							:
				def       UpperCAmelCase      (		self    :			List[str]    )	->	List[str]:



							'''simple docstring'''



							torch.manual_seed(0    )
							a__  :	List[str]		 =			TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"    )

							torch.manual_seed(0    )
							a__  :	int		 =			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"    )

							torch.manual_seed(0    )
							a__  :	Any		 =			UNetaDConditionModel(
							    sample_size=32	, layers_per_block=1	, block_out_channels=[32, 64]	, down_block_types=[
							        "ResnetDownsampleBlock2D",
							        "SimpleCrossAttnDownBlock2D",
							    ]	, mid_block_type="UNetMidBlock2DSimpleCrossAttn"	, up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]	, in_channels=3	, out_channels=6	, cross_attention_dim=32	, encoder_hid_dim=32	, attention_head_dim=8	, addition_embed_type="text"	, addition_embed_type_num_heads=2	, cross_attention_norm="group_norm"	, resnet_time_scale_shift="scale_shift"	, act_fn="gelu"	, )
							unet.set_attn_processor(AttnAddedKVProcessor()    )  # For reproducibility tests

							torch.manual_seed(0    )
							a__  :	str		 =			DDPMScheduler(
							    num_train_timesteps=10_00	, beta_schedule="squaredcos_cap_v2"	, beta_start=0.0001	, beta_end=0.02	, thresholding=a_	, dynamic_thresholding_ratio=0.95	, sample_max_value=1.0	, prediction_type="epsilon"	, variance_type="learned_range"	, )

							torch.manual_seed(0    )
							a__  :	Optional[int]		 =			IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	Union[str, Any]:



							'''simple docstring'''



							torch.manual_seed(0    )
							a__  :	List[Any]		 =			TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"    )

							torch.manual_seed(0    )
							a__  :	List[str]		 =			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"    )

							torch.manual_seed(0    )
							a__  :	Dict		 =			UNetaDConditionModel(
							    sample_size=32	, layers_per_block=[1, 2]	, block_out_channels=[32, 64]	, down_block_types=[
							        "ResnetDownsampleBlock2D",
							        "SimpleCrossAttnDownBlock2D",
							    ]	, mid_block_type="UNetMidBlock2DSimpleCrossAttn"	, up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]	, in_channels=6	, out_channels=6	, cross_attention_dim=32	, encoder_hid_dim=32	, attention_head_dim=8	, addition_embed_type="text"	, addition_embed_type_num_heads=2	, cross_attention_norm="group_norm"	, resnet_time_scale_shift="scale_shift"	, act_fn="gelu"	, class_embed_type="timestep"	, mid_block_scale_factor=1.414	, time_embedding_act_fn="gelu"	, time_embedding_dim=32	, )
							unet.set_attn_processor(AttnAddedKVProcessor()    )  # For reproducibility tests

							torch.manual_seed(0    )
							a__  :	Optional[int]		 =			DDPMScheduler(
							    num_train_timesteps=10_00	, beta_schedule="squaredcos_cap_v2"	, beta_start=0.0001	, beta_end=0.02	, thresholding=a_	, dynamic_thresholding_ratio=0.95	, sample_max_value=1.0	, prediction_type="epsilon"	, variance_type="learned_range"	, )

							torch.manual_seed(0    )
							a__  :	Tuple		 =			DDPMScheduler(
							    num_train_timesteps=10_00	, beta_schedule="squaredcos_cap_v2"	, beta_start=0.0001	, beta_end=0.02	, )

							torch.manual_seed(0    )
							a__  :	Optional[Any]		 =			IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "image_noising_scheduler": image_noising_scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	Tuple:



							'''simple docstring'''



							a__  :	Dict		 =			self.get_dummy_components()
							a__  :	Any		 =			self.pipeline_class(**a_    )
							pipe.to(a_    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Any		 =			self.get_dummy_inputs(a_    )

							a__  :	Optional[int]		 =			inputs["prompt"]
							a__  :	List[Any]		 =			inputs["generator"]
							a__  :	Optional[int]		 =			inputs["num_inference_steps"]
							a__  :	Any		 =			inputs["output_type"]

							if "image" in inputs:
										a__  :	Any		 =			inputs["image"]
							else:
										a__  :	Dict		 =			None

							if "mask_image" in inputs:
										a__  :	Optional[int]		 =			inputs["mask_image"]
							else:
										a__  :	Any		 =			None

							if "original_image" in inputs:
										a__  :	List[Any]		 =			inputs["original_image"]
							else:
										a__  :	str		 =			None

							a__ ,	a__  :	Optional[int]		 =			pipe.encode_prompt(a_    )

							# inputs with prompt converted to embeddings
							a__  :	Union[str, Any]		 =			{
							    "prompt_embeds": prompt_embeds,
							    "negative_prompt_embeds": negative_prompt_embeds,
							    "generator": generator,
							    "num_inference_steps": num_inference_steps,
							    "output_type": output_type,
							}

							if image is not None:
										a__  :	Dict		 =			image

							if mask_image is not None:
										a__  :	Any		 =			mask_image

							if original_image is not None:
										a__  :	Optional[int]		 =			original_image

							# set all optional components to None
							for optional_component in pipe._optional_components:
										setattr(a_	, a_	, a_    )

							a__  :	int		 =			pipe(**a_    )[0]

							with tempfile.TemporaryDirectory() as tmpdir:
										pipe.save_pretrained(a_    )
										a__  :	List[str]		 =			self.pipeline_class.from_pretrained(a_    )
										pipe_loaded.to(a_    )
										pipe_loaded.set_progress_bar_config(disable=a_    )

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()    )  # For reproducibility tests

							for optional_component in pipe._optional_components:
										self.assertTrue(
										    getattr(a_	, a_    ) is None	, F"`{optional_component}` did not stay set to None after loading."	, )

							a__  :	Union[str, Any]		 =			self.get_dummy_inputs(a_    )

							a__  :	str		 =			inputs["generator"]
							a__  :	Dict		 =			inputs["num_inference_steps"]
							a__  :	Optional[int]		 =			inputs["output_type"]

							# inputs with prompt converted to embeddings
							a__  :	List[Any]		 =			{
							    "prompt_embeds": prompt_embeds,
							    "negative_prompt_embeds": negative_prompt_embeds,
							    "generator": generator,
							    "num_inference_steps": num_inference_steps,
							    "output_type": output_type,
							}

							if image is not None:
										a__  :	Dict		 =			image

							if mask_image is not None:
										a__  :	Any		 =			mask_image

							if original_image is not None:
										a__  :	Dict		 =			original_image

							a__  :	Optional[Any]		 =			pipe_loaded(**a_    )[0]

							a__  :	int		 =			np.abs(to_np(a_    ) - to_np(a_    )    ).max()
							self.assertLess(a_	, 1E-4    )






				def       UpperCAmelCase      (		self    :			int    )	->	Any:



							'''simple docstring'''



							a__  :	Dict		 =			self.get_dummy_components()
							a__  :	Dict		 =			self.pipeline_class(**a_    )
							pipe.to(a_    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	List[str]		 =			self.get_dummy_inputs(a_    )
							a__  :	Dict		 =			pipe(**a_    )[0]

							with tempfile.TemporaryDirectory() as tmpdir:
										pipe.save_pretrained(a_    )
										a__  :	str		 =			self.pipeline_class.from_pretrained(a_    )
										pipe_loaded.to(a_    )
										pipe_loaded.set_progress_bar_config(disable=a_    )

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()    )  # For reproducibility tests

							a__  :	Optional[int]		 =			self.get_dummy_inputs(a_    )
							a__  :	Optional[int]		 =			pipe_loaded(**a_    )[0]

							a__  :	List[Any]		 =			np.abs(to_np(a_    ) - to_np(a_    )    ).max()
							self.assertLess(a_	, 1E-4    )