



"""simple docstring"""


import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel







def  lowercase	(      ):






				'''simple docstring'''



				_UpperCAmelCase					=    argparse.ArgumentParser()
				parser.add_argument(
				    '''-m'''					,     '''--pretrained_model_name_or_path'''					,     type=lowercase_					,     default=lowercase_					,     required=lowercase_					,     help='''Path to pretrained model or model identifier from huggingface.co/models.'''					,     )
				parser.add_argument(
				    '''-c'''					,     '''--caption'''					,     type=lowercase_					,     default='''robotic cat with wings'''					,     help='''Text used to generate images.'''					,     )
				parser.add_argument(
				    '''-n'''					,     '''--images_num'''					,     type=lowercase_					,     default=4					,     help='''How much images to generate.'''					,     )
				parser.add_argument(
				    '''-s'''					,     '''--seed'''					,     type=lowercase_					,     default=42					,     help='''Seed for random process.'''					,     )
				parser.add_argument(
				    '''-ci'''					,     '''--cuda_id'''					,     type=lowercase_					,     default=0					,     help='''cuda_id.'''					,     )
				_UpperCAmelCase					=    parser.parse_args()
				return args







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				if not len(lowercase_				) == rows * cols:
								raise ValueError('''The specified number of rows and columns are not correct.'''				)

				_UpperCAmelCase					=    imgs[0].size
				_UpperCAmelCase					=    Image.new('''RGB'''					,     size=(cols * w, rows * h)				)
				_UpperCAmelCase					=    grid.size

				for i, img in enumerate(lowercase_				):
								grid.paste(lowercase_					,     box=(i % cols * w, i // cols * h)				)
				return grid







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[int]					,     _SCREAMING_SNAKE_CASE   :						Dict="robotic cat with wings"					,     _SCREAMING_SNAKE_CASE   :						Optional[int]=7.5					,     _SCREAMING_SNAKE_CASE   :						Any=50					,     _SCREAMING_SNAKE_CASE   :						List[Any]=1					,     _SCREAMING_SNAKE_CASE   :						Dict=42					,     ):






				'''simple docstring'''



				_UpperCAmelCase					=    torch.Generator(pipeline.device				).manual_seed(lowercase_				)
				_UpperCAmelCase					=    pipeline(
				    lowercase_					,     guidance_scale=lowercase_					,     num_inference_steps=lowercase_					,     generator=lowercase_					,     num_images_per_prompt=lowercase_					,     ).images
				_UpperCAmelCase					=    int(math.sqrt(lowercase_				)				)
				_UpperCAmelCase					=    image_grid(lowercase_					,     rows=_rows					,     cols=num_images_per_prompt // _rows				)
				return grid, images


__A   :							Tuple										=   parse_args()
# Load models and create wrapper for stable diffusion
__A   :							Tuple										=   CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
__A   :							Union[str, Any]										=   CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
__A   :							Union[str, Any]										=   AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
__A   :							Dict										=   UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")

__A   :							List[Any]										=   StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__A   :							List[Any]										=   lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, "best_model.pt")):
					__A   :							Union[str, Any]										=   load(args.pretrained_model_name_or_path, model=unet)
					unet.eval()
					setattr(pipeline, "unet", unet)
else:
					__A   :							Optional[Any]										=   unet.to(torch.device("cuda", args.cuda_id))
__A   :							Tuple										=   pipeline.to(unet.device)
__A						,							__A   :							Optional[int]										=   generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, "{}.png".format("_".join(args.caption.split()))))
__A   :							Optional[Any]										=   os.path.join(args.pretrained_model_name_or_path, "_".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
					image.save(os.path.join(dirname, "{}.png".format(idx + 1)))








"""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)



