



"""simple docstring"""


from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

__A   :							Union[str, Any]										=   "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"A red cartoon frog, 4k\"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to(\"cuda\")\n\n        >>> init_image = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/frog.png\"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save(\"red_frog.png\")\n        ```\n"







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Any					,     _SCREAMING_SNAKE_CASE   :						Dict					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]=8				):






				'''simple docstring'''



				_UpperCAmelCase					=    height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				_UpperCAmelCase					=    width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						List[Any]=512					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]=512				):






				'''simple docstring'''



				_UpperCAmelCase					=    pil_image.resize((w, h)					,     resample=Image.BICUBIC					,     reducing_gap=1				)
				_UpperCAmelCase					=    np.array(pil_image.convert('''RGB'''				)				)
				_UpperCAmelCase					=    arr.astype(np.floataa				) / 127.5 - 1
				_UpperCAmelCase					=    np.transpose(_SCREAMING_SNAKE_CASE					,     [2, 0, 1]				)
				_UpperCAmelCase					=    torch.from_numpy(_SCREAMING_SNAKE_CASE				).unsqueeze(0				)
				return image
class 			_a (					A_):
			"""simple docstring"""



			def __init__(		self     :    Tuple				,   __UpperCamelCase     :    List[Any]				,   __UpperCamelCase     :    Any				,   __UpperCamelCase     :    str				,   )->Union[str, Any]:
							super().__init__()

							self.register_modules(
							    unet=__UpperCamelCase				,   scheduler=__UpperCamelCase				,   movq=__UpperCamelCase				,   )
							_UpperCAmelCase					=    2 ** (len(self.movq.config.block_out_channels   ) - 1)



			def  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    Tuple				,   __UpperCamelCase     :    Optional[int]				,   __UpperCamelCase     :    int   )->Tuple:
							# get the original timestep using init_timestep
							_UpperCAmelCase					=    min(int(num_inference_steps * strength   )				,   __UpperCamelCase   )

							_UpperCAmelCase					=    max(num_inference_steps - init_timestep				,   0   )
							_UpperCAmelCase					=    self.scheduler.timesteps[t_start:]

							return timesteps, num_inference_steps - t_start



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    Optional[Any]				,   __UpperCamelCase     :    Dict				,   __UpperCamelCase     :    List[Any]				,   __UpperCamelCase     :    List[Any]				,   __UpperCamelCase     :    List[Any]				,   __UpperCamelCase     :    Any				,   __UpperCamelCase     :    Tuple=None   )->int:
							if not isinstance(__UpperCamelCase				,   (torch.Tensor, PIL.Image.Image, list)   ):
											raise ValueError(
											    F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__UpperCamelCase   )}'   )

							_UpperCAmelCase					=    image.to(device=__UpperCamelCase				,   dtype=__UpperCamelCase   )

							_UpperCAmelCase					=    batch_size * num_images_per_prompt

							if image.shape[1] == 4:
											_UpperCAmelCase					=    image

							else:
											if isinstance(__UpperCamelCase				,   __UpperCamelCase   ) and len(__UpperCamelCase   ) != batch_size:
															raise ValueError(
															    F'You have passed a list of generators of length {len(__UpperCamelCase   )}, but requested an effective batch'
															    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'   )

											elif isinstance(__UpperCamelCase				,   __UpperCamelCase   ):
															_UpperCAmelCase					=    [
															    self.movq.encode(image[i : i + 1]   ).latent_dist.sample(generator[i]   ) for i in range(__UpperCamelCase   )
															]
															_UpperCAmelCase					=    torch.cat(__UpperCamelCase				,   dim=0   )
											else:
															_UpperCAmelCase					=    self.movq.encode(__UpperCamelCase   ).latent_dist.sample(__UpperCamelCase   )

											_UpperCAmelCase					=    self.movq.config.scaling_factor * init_latents

							_UpperCAmelCase					=    torch.cat([init_latents]				,   dim=0   )

							_UpperCAmelCase					=    init_latents.shape
							_UpperCAmelCase					=    randn_tensor(__UpperCamelCase				,   generator=__UpperCamelCase				,   device=__UpperCamelCase				,   dtype=__UpperCamelCase   )

							# get latents
							_UpperCAmelCase					=    self.scheduler.add_noise(__UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )

							_UpperCAmelCase					=    init_latents

							return latents



			def  lowercase__						(		self     :    str				,   __UpperCamelCase     :    Optional[int]=0   )->Union[str, Any]:
							if is_accelerate_available():
											from accelerate import cpu_offload
							else:
											raise ImportError('''Please install accelerate via `pip install accelerate`'''   )

							_UpperCAmelCase					=    torch.device(F'cuda:{gpu_id}'   )

							_UpperCAmelCase					=    [
							    self.unet,
							    self.movq,
							]
							for cpu_offloaded_model in models:
											if cpu_offloaded_model is not None:
															cpu_offload(__UpperCamelCase				,   __UpperCamelCase   )



			def  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    List[str]=0   )->List[Any]:
							if is_accelerate_available() and is_accelerate_version('''>='''				,   '''0.17.0.dev0'''   ):
											from accelerate import cpu_offload_with_hook
							else:
											raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''   )

							_UpperCAmelCase					=    torch.device(F'cuda:{gpu_id}'   )

							if self.device.type != "cpu":
											self.to('''cpu'''				,   silence_dtype_warnings=__UpperCamelCase   )
											torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

							_UpperCAmelCase					=    None
							for cpu_offloaded_model in [self.unet, self.movq]:
											_UpperCAmelCase					=    cpu_offload_with_hook(__UpperCamelCase				,   __UpperCamelCase				,   prev_module_hook=__UpperCamelCase   )

							# We'll offload the last model manually.
							_UpperCAmelCase					=    hook



			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def  lowercase__						(		self     :    int   )->List[Any]:
							if not hasattr(self.unet				,   '''_hf_hook'''   ):
											return self.device
							for module in self.unet.modules():
											if (
											    hasattr(__UpperCamelCase				,   '''_hf_hook'''   )
											    and hasattr(module._hf_hook				,   '''execution_device'''   )
											    and module._hf_hook.execution_device is not None
											):
															return torch.device(module._hf_hook.execution_device   )
							return self.device



			@torch.no_grad()
			@replace_example_docstring(__UpperCamelCase   )
			def __call__(		self     :    int				,   __UpperCamelCase     :    Union[str, Any]				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    Union[str, Any]				,   __UpperCamelCase     :    Tuple = 5_1_2				,   __UpperCamelCase     :    Any = 5_1_2				,   __UpperCamelCase     :    Dict = 1_0_0				,   __UpperCamelCase     :    List[str] = 4.0				,   __UpperCamelCase     :    Optional[Any] = 0.3				,   __UpperCamelCase     :    Tuple = 1				,   __UpperCamelCase     :    Tuple = None				,   __UpperCamelCase     :    Union[str, Any] = "pil"				,   __UpperCamelCase     :    str = True				,   )->str:
							_UpperCAmelCase					=    self._execution_device

							_UpperCAmelCase					=    guidance_scale > 1.0

							if isinstance(__UpperCamelCase				,   __UpperCamelCase   ):
											_UpperCAmelCase					=    torch.cat(__UpperCamelCase				,   dim=0   )
							_UpperCAmelCase					=    image_embeds.shape[0]
							if isinstance(__UpperCamelCase				,   __UpperCamelCase   ):
											_UpperCAmelCase					=    torch.cat(__UpperCamelCase				,   dim=0   )

							if do_classifier_free_guidance:
											_UpperCAmelCase					=    image_embeds.repeat_interleave(__UpperCamelCase				,   dim=0   )
											_UpperCAmelCase					=    negative_image_embeds.repeat_interleave(__UpperCamelCase				,   dim=0   )

							_UpperCAmelCase					=    torch.cat([negative_image_embeds, image_embeds]				,   dim=0   ).to(dtype=self.unet.dtype				,   device=__UpperCamelCase   )

							if not isinstance(__UpperCamelCase				,   __UpperCamelCase   ):
											_UpperCAmelCase					=    [image]
							if not all(isinstance(__UpperCamelCase				,   (PIL.Image.Image, torch.Tensor)   ) for i in image   ):
											raise ValueError(
											    F'Input is in incorrect format: {[type(__UpperCamelCase   ) for i in image]}. Currently, we only support  PIL image and pytorch tensor'   )

							_UpperCAmelCase					=    torch.cat([prepare_image(__UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   ) for i in image]				,   dim=0   )
							_UpperCAmelCase					=    image.to(dtype=image_embeds.dtype				,   device=__UpperCamelCase   )

							_UpperCAmelCase					=    self.movq.encode(__UpperCamelCase   )["""latents"""]
							_UpperCAmelCase					=    latents.repeat_interleave(__UpperCamelCase				,   dim=0   )
							self.scheduler.set_timesteps(__UpperCamelCase				,   device=__UpperCamelCase   )
							_UpperCAmelCase					=    self.get_timesteps(__UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )
							_UpperCAmelCase					=    timesteps[:1].repeat(batch_size * num_images_per_prompt   )
							_UpperCAmelCase					=    downscale_height_and_width(__UpperCamelCase				,   __UpperCamelCase				,   self.movq_scale_factor   )
							_UpperCAmelCase					=    self.prepare_latents(
							    __UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase				,   image_embeds.dtype				,   __UpperCamelCase				,   __UpperCamelCase   )
							for i, t in enumerate(self.progress_bar(__UpperCamelCase   )   ):
											# expand the latents if we are doing classifier free guidance
											_UpperCAmelCase					=    torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

											_UpperCAmelCase					=    {"""image_embeds""": image_embeds}
											_UpperCAmelCase					=    self.unet(
											    sample=__UpperCamelCase				,   timestep=__UpperCamelCase				,   encoder_hidden_states=__UpperCamelCase				,   added_cond_kwargs=__UpperCamelCase				,   return_dict=__UpperCamelCase				,   )[0]

											if do_classifier_free_guidance:
															_UpperCAmelCase					=    noise_pred.split(latents.shape[1]				,   dim=1   )
															_UpperCAmelCase					=    noise_pred.chunk(2   )
															_UpperCAmelCase					=    variance_pred.chunk(2   )
															_UpperCAmelCase					=    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
															_UpperCAmelCase					=    torch.cat([noise_pred, variance_pred_text]				,   dim=1   )

											if not (
											    hasattr(self.scheduler.config				,   '''variance_type'''   )
											    and self.scheduler.config.variance_type in ["learned", "learned_range"]
											):
															_UpperCAmelCase					=    noise_pred.split(latents.shape[1]				,   dim=1   )

											# compute the previous noisy sample x_t -> x_t-1
											_UpperCAmelCase					=    self.scheduler.step(
											    __UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase				,   generator=__UpperCamelCase				,   )[0]

							# post-processing
							_UpperCAmelCase					=    self.movq.decode(__UpperCamelCase				,   force_not_quantize=__UpperCamelCase   )["""sample"""]

							if output_type not in ["pt", "np", "pil"]:
											raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'   )

							if output_type in ["np", "pil"]:
											_UpperCAmelCase					=    image * 0.5 + 0.5
											_UpperCAmelCase					=    image.clamp(0				,   1   )
											_UpperCAmelCase					=    image.cpu().permute(0				,   2				,   3				,   1   ).float().numpy()

							if output_type == "pil":
											_UpperCAmelCase					=    self.numpy_to_pil(__UpperCamelCase   )

							if not return_dict:
											return (image,)

							return ImagePipelineOutput(images=__UpperCamelCase   )








"""simple docstring"""


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__A   :							List[str]										=   {
    "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   :							List[str]										=   AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]=False				):






				'''simple docstring'''



				_UpperCAmelCase		,						_UpperCAmelCase					=    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  lowercase	(      _SCREAMING_SNAKE_CASE   :						str				):






				'''simple docstring'''



				_UpperCAmelCase					=    {}

				_UpperCAmelCase					=    r'''.*sequential.(\d+).*'''
				_UpperCAmelCase					=    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:
																_UpperCAmelCase					=    key.replace(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)

								if re.match(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				):
												# replace sequential layers with list
												_UpperCAmelCase					=    re.match(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				).group(1				)

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

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

												_UpperCAmelCase					=    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
												_UpperCAmelCase					=    value
												_UpperCAmelCase					=    mixed_qkv.size(0				) // 3

												_UpperCAmelCase					=    mixed_qkv[:qkv_dim]
												_UpperCAmelCase					=    mixed_qkv[qkv_dim : qkv_dim * 2]
												_UpperCAmelCase					=    mixed_qkv[qkv_dim * 2 :]

												_UpperCAmelCase					=    query_layer
												_UpperCAmelCase					=    key_layer
												_UpperCAmelCase					=    value_layer
								else:
												_UpperCAmelCase					=    value

				return model_state_dict







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Union[str, Any]					,     _SCREAMING_SNAKE_CASE   :						Dict					,     _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						int=False				):






				'''simple docstring'''



				_UpperCAmelCase		,						_UpperCAmelCase					=    init_clap(_SCREAMING_SNAKE_CASE					,     enable_fusion=_SCREAMING_SNAKE_CASE				)

				clap_model.eval()
				_UpperCAmelCase					=    clap_model.state_dict()
				_UpperCAmelCase					=    rename_state_dict(_SCREAMING_SNAKE_CASE				)

				_UpperCAmelCase					=    ClapConfig()
				_UpperCAmelCase					=    enable_fusion
				_UpperCAmelCase					=    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__":
					__A   :							Any										=   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   :							int										=   parser.parse_args()

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



