"""simple docstring"""





import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


lowerCAmelCase_     =	logging.get_logger(__name__)  # pylint: disable=invalid-name




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				def __init__(      self							:							List[str]      ,    _A							:							WhisperForConditionalGeneration      ,    _A							:							WhisperProcessor      ,    _A							:							AutoencoderKL      ,    _A							:							CLIPTextModel      ,    _A							:							CLIPTokenizer      ,    _A							:							UNetaDConditionModel      ,    _A							:							Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]      ,    _A							:							StableDiffusionSafetyChecker      ,    _A							:							CLIPImageProcessor      ,    ):



											"""simple docstring"""







											super().__init__()

											if safety_checker is None:
																		logger.warning(
																		    f"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
																		    """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
																		    """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
																		    """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
																		    """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
																		    """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 .""")

											self.register_modules(
											    speech_model=_A      ,    speech_processor=_A      ,    vae=_A      ,    text_encoder=_A      ,    tokenizer=_A      ,    unet=_A      ,    scheduler=_A      ,    feature_extractor=_A      ,    )





				def 		_lowerCAmelCase		(      self							:							Any      ,    _A							:							Optional[Union[str, int]] = "auto"):



											"""simple docstring"""







											if slice_size == "auto":
																		_SCREAMING_SNAKE_CASE		:						Any			  =    self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(_A)





				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											self.enable_attention_slicing(_A)





				@torch.no_grad()
				def __call__(      self							:							str      ,    _A							:							str      ,    _A							:							str=1_6_0_0_0      ,    _A							:							int = 5_1_2      ,    _A							:							int = 5_1_2      ,    _A							:							int = 5_0      ,    _A							:							float = 7.5      ,    _A							:							Optional[Union[str, List[str]]] = None      ,    _A							:							Optional[int] = 1      ,    _A							:							float = 0.0      ,    _A							:							Optional[torch.Generator] = None      ,    _A							:							Optional[torch.FloatTensor] = None      ,    _A							:							Optional[str] = "pil"      ,    _A							:							bool = True      ,    _A							:							Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,    _A							:							int = 1      ,    **_A							:							Optional[int]      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self.speech_processor.feature_extractor(
											    _A      ,    return_tensors="""pt"""      ,    sampling_rate=_A).input_features.to(self.device)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    self.speech_model.generate(_A      ,    max_length=4_8_0_0_0_0)

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.speech_processor.tokenizer.batch_decode(_A      ,    skip_special_tokens=_A      ,    normalize=_A)[
											    0
											]

											if isinstance(_A      ,    _A):
																		_SCREAMING_SNAKE_CASE		:						str			  =    1
											elif isinstance(_A      ,    _A):
																		_SCREAMING_SNAKE_CASE		:						str			  =    len(_A)
											else:
																		raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(_A)}""")

											if height % 8 != 0 or width % 8 != 0:
																		raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}.""")

											if (callback_steps is None) or (
											    callback_steps is not None and (not isinstance(_A      ,    _A) or callback_steps <= 0)
											):
																		raise ValueError(
																		    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
																		    f""" {type(_A)}.""")

											# get prompt text embeddings
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self.tokenizer(
											    _A      ,    padding="""max_length"""      ,    max_length=self.tokenizer.model_max_length      ,    return_tensors="""pt"""      ,    )
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    text_inputs.input_ids

											if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																		_SCREAMING_SNAKE_CASE		:						Tuple			  =    self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
																		logger.warning(
																		    """The following part of your input was truncated because CLIP can only handle sequences up to"""
																		    f""" {self.tokenizer.model_max_length} tokens: {removed_text}""")
																		_SCREAMING_SNAKE_CASE		:						int			  =    text_input_ids[:, : self.tokenizer.model_max_length]
											_SCREAMING_SNAKE_CASE		:						int			  =    self.text_encoder(text_input_ids.to(self.device))[0]

											# duplicate text embeddings for each generation per prompt, using mps friendly method
											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						str			  =    text_embeddings.shape
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    text_embeddings.repeat(1      ,    _A      ,    1)
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    text_embeddings.view(bs_embed * num_images_per_prompt      ,    _A      ,    -1)

											# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
											# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
											# corresponds to doing no classifier free guidance.
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    guidance_scale > 1.0
											# get unconditional embeddings for classifier free guidance
											if do_classifier_free_guidance:
																		_SCREAMING_SNAKE_CASE		:						List[str]
																		if negative_prompt is None:
																									_SCREAMING_SNAKE_CASE		:						Tuple			  =    [""""""] * batch_size
																		elif type(_A) is not type(_A):
																									raise TypeError(
																									    f"""`negative_prompt` should be the same type to `prompt`, but got {type(_A)} !="""
																									    f""" {type(_A)}.""")
																		elif isinstance(_A      ,    _A):
																									_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    [negative_prompt]
																		elif batch_size != len(_A):
																									raise ValueError(
																									    f"""`negative_prompt`: {negative_prompt} has batch size {len(_A)}, but `prompt`:"""
																									    f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
																									    """ the batch size of `prompt`.""")
																		else:
																									_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    negative_prompt

																		_SCREAMING_SNAKE_CASE		:						List[str]			  =    text_input_ids.shape[-1]
																		_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    self.tokenizer(
																		    _A      ,    padding="""max_length"""      ,    max_length=_A      ,    truncation=_A      ,    return_tensors="""pt"""      ,    )
																		_SCREAMING_SNAKE_CASE		:						Any			  =    self.text_encoder(uncond_input.input_ids.to(self.device))[0]

																		# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																		_SCREAMING_SNAKE_CASE		:						Dict			  =    uncond_embeddings.shape[1]
																		_SCREAMING_SNAKE_CASE		:						Dict			  =    uncond_embeddings.repeat(1      ,    _A      ,    1)
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    uncond_embeddings.view(batch_size * num_images_per_prompt      ,    _A      ,    -1)

																		# For classifier free guidance, we need to do two forward passes.
																		# Here we concatenate the unconditional and text embeddings into a single batch
																		# to avoid doing two forward passes
																		_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    torch.cat([uncond_embeddings, text_embeddings])

											# get the initial random noise unless the user supplied it

											# Unlike in other pipelines, latents need to be generated in the target device
											# for 1-to-1 results reproducibility with the CompVis implementation.
											# However this currently doesn't work in `mps`.
											_SCREAMING_SNAKE_CASE		:						str			  =    (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
											_SCREAMING_SNAKE_CASE		:						int			  =    text_embeddings.dtype
											if latents is None:
																		if self.device.type == "mps":
																									# randn does not exist on mps
																									_SCREAMING_SNAKE_CASE		:						Dict			  =    torch.randn(_A      ,    generator=_A      ,    device="""cpu"""      ,    dtype=_A).to(
																									    self.device)
																		else:
																									_SCREAMING_SNAKE_CASE		:						Dict			  =    torch.randn(_A      ,    generator=_A      ,    device=self.device      ,    dtype=_A)
											else:
																		if latents.shape != latents_shape:
																									raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""")
																		_SCREAMING_SNAKE_CASE		:						str			  =    latents.to(self.device)

											# set timesteps
											self.scheduler.set_timesteps(_A)

											# Some schedulers like PNDM have timesteps as arrays
											# It's more optimized to move all timesteps to correct device beforehand
											_SCREAMING_SNAKE_CASE		:						int			  =    self.scheduler.timesteps.to(self.device)

											# scale the initial noise by the standard deviation required by the scheduler
											_SCREAMING_SNAKE_CASE		:						int			  =    latents * self.scheduler.init_noise_sigma

											# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
											# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
											# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
											# and should be between [0, 1]
											_SCREAMING_SNAKE_CASE		:						Any			  =    """eta""" in set(inspect.signature(self.scheduler.step).parameters.keys())
											_SCREAMING_SNAKE_CASE		:						Dict			  =    {}
											if accepts_eta:
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    eta

											for i, t in enumerate(self.progress_bar(_A)):
																		# expand the latents if we are doing classifier free guidance
																		_SCREAMING_SNAKE_CASE		:						str			  =    torch.cat([latents] * 2) if do_classifier_free_guidance else latents
																		_SCREAMING_SNAKE_CASE		:						Dict			  =    self.scheduler.scale_model_input(_A      ,    _A)

																		# predict the noise residual
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.unet(_A      ,    _A      ,    encoder_hidden_states=_A).sample

																		# perform guidance
																		if do_classifier_free_guidance:
																									_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    noise_pred.chunk(2)
																									_SCREAMING_SNAKE_CASE		:						List[Any]			  =    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																		# compute the previous noisy sample x_t -> x_t-1
																		_SCREAMING_SNAKE_CASE		:						Any			  =    self.scheduler.step(_A      ,    _A      ,    _A      ,    **_A).prev_sample

																		# call the callback, if provided
																		if callback is not None and i % callback_steps == 0:
																									callback(_A      ,    _A      ,    _A)

											_SCREAMING_SNAKE_CASE		:						Tuple			  =    1 / 0.18_215 * latents
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.vae.decode(_A).sample

											_SCREAMING_SNAKE_CASE		:						int			  =    (image / 2 + 0.5).clamp(0      ,    1)

											# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
											_SCREAMING_SNAKE_CASE		:						int			  =    image.cpu().permute(0      ,    2      ,    3      ,    1).float().numpy()

											if output_type == "pil":
																		_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.numpy_to_pil(_A)

											if not return_dict:
																		return image

											return StableDiffusionPipelineOutput(images=_A      ,    nsfw_content_detected=_A)



"""simple docstring"""





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCAmelCase_     =	{
    '''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_     =	AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')



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



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Optional[Any]:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    {}

							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    R""".*sequential.(\d+).*"""
							_SCREAMING_SNAKE_CASE		:						Any			  =    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		:						Optional[Any]			  =    key.replace(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

														if re.match(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
																					# replace sequential layers with list
																					_SCREAMING_SNAKE_CASE		:						List[Any]			  =    re.match(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ).group(1  )

																					_SCREAMING_SNAKE_CASE		:						Dict			  =    key.replace(F"""sequential.{sequential_layer}."""     ,	F"""layers.{int(__SCREAMING_SNAKE_CASE  )//3}.linear."""  )
														elif re.match(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    int(re.match(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ).group(1  )  )

																					# Because in CLAP they use `nn.Sequential`...
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    1 if projecton_layer == 0 else 2

																					_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    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		:						Dict			  =    value
																					_SCREAMING_SNAKE_CASE		:						List[Any]			  =    mixed_qkv.size(0  ) // 3

																					_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    mixed_qkv[:qkv_dim]
																					_SCREAMING_SNAKE_CASE		:						str			  =    mixed_qkv[qkv_dim : qkv_dim * 2]
																					_SCREAMING_SNAKE_CASE		:						Any			  =    mixed_qkv[qkv_dim * 2 :]

																					_SCREAMING_SNAKE_CASE		:						Dict			  =    query_layer
																					_SCREAMING_SNAKE_CASE		:						List[Any]			  =    key_layer
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    value_layer
														else:
																					_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    value

							return model_state_dict



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False  )->    List[Any]:
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						int			  =    init_clap(__SCREAMING_SNAKE_CASE     ,	enable_fusion=__SCREAMING_SNAKE_CASE  )

							clap_model.eval()
							_SCREAMING_SNAKE_CASE		:						Dict			  =    clap_model.state_dict()
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    rename_state_dict(__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						int			  =    ClapConfig()
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    enable_fusion
							_SCREAMING_SNAKE_CASE		:						Dict			  =    ClapModel(__SCREAMING_SNAKE_CASE  )

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

							model.save_pretrained(__SCREAMING_SNAKE_CASE  )
							transformers_config.save_pretrained(__SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
			lowerCAmelCase_     =	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_     =	parser.parse_args()

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


