



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__A       =						logging.get_logger(__name__)

__A       =						torch.device('''cpu''')


def 	__a					(   )				->			Optional[int]:






						'''simple docstring'''



						UpperCAmelCase_=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
						UpperCAmelCase_=       Image.open(requests.get(lowerCAmelCase_      ,stream=lowerCAmelCase_    ).raw    )
						return im


def 	__a					(   lowerCAmelCase_       : List[Any]    )				->			str:






						'''simple docstring'''



						if swiftformer_name == "swiftformer_xs":
												return torch.tensor([-2.1703E00, 2.1107E00, -2.0811E00, 8.8685E-01, 2.4360E-01]    )

						elif swiftformer_name == "swiftformer_s":
												return torch.tensor([3.9636E-01, 2.3478E-01, -1.6963E00, -1.7381E00, -8.6337E-01]    )

						elif swiftformer_name == "swiftformer_l1":
												return torch.tensor([-4.2768E-01, -4.7429E-01, -1.0897E00, -1.0248E00, 3.5523E-02]    )

						elif swiftformer_name == "swiftformer_l3":
												return torch.tensor([-2.5330E-01, 2.4211E-01, -6.0185E-01, -8.2789E-01, -6.0446E-02]    )


def 	__a					(   lowerCAmelCase_       : Optional[Any]      ,lowerCAmelCase_       : Any      ,lowerCAmelCase_       : Optional[int]    )				->			str:






						'''simple docstring'''



						UpperCAmelCase_=       dct.pop(lowerCAmelCase_    )
						UpperCAmelCase_=       val


def 	__a					(   lowerCAmelCase_       : str    )				->			Optional[int]:






						'''simple docstring'''



						UpperCAmelCase_=       []
						for k in state_dict.keys():
												UpperCAmelCase_=       k
												if ".pwconv" in k:
																		UpperCAmelCase_=       k_new.replace(""".pwconv"""      ,""".point_wise_conv"""    )
												if ".dwconv" in k:
																		UpperCAmelCase_=       k_new.replace(""".dwconv"""      ,""".depth_wise_conv"""    )
												if ".Proj." in k:
																		UpperCAmelCase_=       k_new.replace(""".Proj."""      ,""".proj."""    )
												if "patch_embed" in k_new:
																		UpperCAmelCase_=       k_new.replace("""patch_embed"""      ,"""swiftformer.patch_embed.patch_embedding"""    )
												if "network" in k_new:
																		UpperCAmelCase_=       k_new.split("""."""    )
																		if ls[2].isdigit():
																								UpperCAmelCase_=       """swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]    )
																		else:
																								UpperCAmelCase_=       k_new.replace("""network"""      ,"""swiftformer.encoder.network"""    )
												rename_keys.append((k, k_new)    )
						return rename_keys


@torch.no_grad()
def 	__a					(   lowerCAmelCase_       : Dict      ,lowerCAmelCase_       : List[Any]      ,lowerCAmelCase_       : Optional[Any]    )				->			Union[str, Any]:






						'''simple docstring'''



						UpperCAmelCase_=       SwiftFormerConfig()

						# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
						UpperCAmelCase_=       10_00
						UpperCAmelCase_=       """huggingface/label-files"""
						UpperCAmelCase_=       """imagenet-1k-id2label.json"""
						UpperCAmelCase_=       json.load(open(hf_hub_download(lowerCAmelCase_      ,lowerCAmelCase_      ,repo_type="""dataset"""    )      ,"""r"""    )    )
						UpperCAmelCase_=       {int(lowerCAmelCase_    ): v for k, v in idalabel.items()}
						UpperCAmelCase_=       idalabel
						UpperCAmelCase_=       {v: k for k, v in idalabel.items()}

						# size of the architecture
						if swiftformer_name == "swiftformer_xs":
												UpperCAmelCase_=       [3, 3, 6, 4]
												UpperCAmelCase_=       [48, 56, 1_12, 2_20]

						elif swiftformer_name == "swiftformer_s":
												UpperCAmelCase_=       [3, 3, 9, 6]
												UpperCAmelCase_=       [48, 64, 1_68, 2_24]

						elif swiftformer_name == "swiftformer_l1":
												UpperCAmelCase_=       [4, 3, 10, 5]
												UpperCAmelCase_=       [48, 96, 1_92, 3_84]

						elif swiftformer_name == "swiftformer_l3":
												UpperCAmelCase_=       [4, 4, 12, 6]
												UpperCAmelCase_=       [64, 1_28, 3_20, 5_12]

						# load state_dict of original model, remove and rename some keys
						if original_ckpt:
												if original_ckpt.startswith("""https"""    ):
																		UpperCAmelCase_=       torch.hub.load_state_dict_from_url(lowerCAmelCase_      ,map_location="""cpu"""      ,check_hash=lowerCAmelCase_    )
												else:
																		UpperCAmelCase_=       torch.load(lowerCAmelCase_      ,map_location="""cpu"""    )
						UpperCAmelCase_=       checkpoint

						UpperCAmelCase_=       create_rename_keys(lowerCAmelCase_    )
						for rename_key_src, rename_key_dest in rename_keys:
												rename_key(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )

						# load HuggingFace model
						UpperCAmelCase_=       SwiftFormerForImageClassification(lowerCAmelCase_    ).eval()
						hf_model.load_state_dict(lowerCAmelCase_    )

						# prepare test inputs
						UpperCAmelCase_=       prepare_img()
						UpperCAmelCase_=       ViTImageProcessor.from_pretrained("""preprocessor_config"""    )
						UpperCAmelCase_=       processor(images=lowerCAmelCase_      ,return_tensors="""pt"""    )

						# compare outputs from both models
						UpperCAmelCase_=       get_expected_output(lowerCAmelCase_    )
						UpperCAmelCase_=       hf_model(inputs["""pixel_values"""]    ).logits

						assert hf_logits.shape == torch.Size([1, 10_00]    )
						assert torch.allclose(hf_logits[0, 0:5]      ,lowerCAmelCase_      ,atol=1E-3    )

						Path(lowerCAmelCase_    ).mkdir(exist_ok=lowerCAmelCase_    )
						print(F"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""    )
						hf_model.save_pretrained(lowerCAmelCase_    )


if __name__ == "__main__":
	__A       =						argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--swiftformer_name''',
	    default='''swiftformer_xs''',
	    choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
	    type=str,
	    help='''Name of the SwiftFormer model you\'d like to convert.''',
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''',
	    default='''./converted_outputs/''',
	    type=str,
	    help='''Path to the output PyTorch model directory.''',
	)
	parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')

	__A       =						parser.parse_args()
	convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)









from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	a__				: str		       =							["vqvae"]




	def __init__(     self     :						List[Any]						,  __UpperCAmelCase     :						AutoencoderKL						,  __UpperCAmelCase     :						UNetaDConditionModel						,  __UpperCAmelCase     :						Mel						,  __UpperCAmelCase     :						Union[DDIMScheduler, DDPMScheduler]						,  )      ->			str:
							super().__init__()
							self.register_modules(unet=__UpperCAmelCase						,  scheduler=__UpperCAmelCase						,  mel=__UpperCAmelCase						,  vqvae=__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			int:
							return 50 if isinstance(self.scheduler						,  __UpperCAmelCase					) else 1_000




	@torch.no_grad()
	def __call__(     self     :						List[Any]						,  __UpperCAmelCase     :						int = 1						,  __UpperCAmelCase     :						str = None						,  __UpperCAmelCase     :						np.ndarray = None						,  __UpperCAmelCase     :						int = 0						,  __UpperCAmelCase     :						int = 0						,  __UpperCAmelCase     :						int = None						,  __UpperCAmelCase     :						torch.Generator = None						,  __UpperCAmelCase     :						float = 0						,  __UpperCAmelCase     :						float = 0						,  __UpperCAmelCase     :						torch.Generator = None						,  __UpperCAmelCase     :						float = 0						,  __UpperCAmelCase     :						torch.Tensor = None						,  __UpperCAmelCase     :						torch.Tensor = None						,  __UpperCAmelCase     :						Union[str, Any]=True						,  )      ->			Union[
	    Union[AudioPipelineOutput, ImagePipelineOutput],
	    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
	]:
							UpperCAmelCase_=       steps or self.get_default_steps()
							self.scheduler.set_timesteps(__UpperCAmelCase					)
							UpperCAmelCase_=       step_generator or generator
							# For backwards compatibility
							if type(self.unet.config.sample_size					) == int:
													UpperCAmelCase_=       (self.unet.config.sample_size, self.unet.config.sample_size)
							if noise is None:
													UpperCAmelCase_=       randn_tensor(
													    (
													        batch_size,
													        self.unet.config.in_channels,
													        self.unet.config.sample_size[0],
													        self.unet.config.sample_size[1],
													    )						,  generator=__UpperCAmelCase						,  device=self.device						,  )
							UpperCAmelCase_=       noise
							UpperCAmelCase_=       None

							if audio_file is not None or raw_audio is not None:
													self.mel.load_audio(__UpperCAmelCase						,  __UpperCAmelCase					)
													UpperCAmelCase_=       self.mel.audio_slice_to_image(__UpperCAmelCase					)
													UpperCAmelCase_=       np.frombuffer(input_image.tobytes()						,  dtype="""uint8"""					).reshape(
													    (input_image.height, input_image.width)					)
													UpperCAmelCase_=       (input_image / 255) * 2 - 1
													UpperCAmelCase_=       torch.tensor(input_image[np.newaxis, :, :]						,  dtype=torch.float					).to(self.device					)

													if self.vqvae is not None:
																			UpperCAmelCase_=       self.vqvae.encode(torch.unsqueeze(__UpperCAmelCase						,  0					)					).latent_dist.sample(
																			    generator=__UpperCAmelCase					)[0]
																			UpperCAmelCase_=       self.vqvae.config.scaling_factor * input_images

													if start_step > 0:
																			UpperCAmelCase_=       self.scheduler.add_noise(__UpperCAmelCase						,  __UpperCAmelCase						,  self.scheduler.timesteps[start_step - 1]					)

													UpperCAmelCase_=       (
													    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
													)
													UpperCAmelCase_=       int(mask_start_secs * pixels_per_second					)
													UpperCAmelCase_=       int(mask_end_secs * pixels_per_second					)
													UpperCAmelCase_=       self.scheduler.add_noise(__UpperCAmelCase						,  __UpperCAmelCase						,  torch.tensor(self.scheduler.timesteps[start_step:]					)					)

							for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]					)					):
													if isinstance(self.unet						,  __UpperCAmelCase					):
																			UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]
													else:
																			UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]

													if isinstance(self.scheduler						,  __UpperCAmelCase					):
																			UpperCAmelCase_=       self.scheduler.step(
																			    model_output=__UpperCAmelCase						,  timestep=__UpperCAmelCase						,  sample=__UpperCAmelCase						,  eta=__UpperCAmelCase						,  generator=__UpperCAmelCase						,  )["""prev_sample"""]
													else:
																			UpperCAmelCase_=       self.scheduler.step(
																			    model_output=__UpperCAmelCase						,  timestep=__UpperCAmelCase						,  sample=__UpperCAmelCase						,  generator=__UpperCAmelCase						,  )["""prev_sample"""]

													if mask is not None:
																			if mask_start > 0:
																									UpperCAmelCase_=       mask[:, step, :, :mask_start]
																			if mask_end > 0:
																									UpperCAmelCase_=       mask[:, step, :, -mask_end:]

							if self.vqvae is not None:
													# 0.18215 was scaling factor used in training to ensure unit variance
													UpperCAmelCase_=       1 / self.vqvae.config.scaling_factor * images
													UpperCAmelCase_=       self.vqvae.decode(__UpperCAmelCase					)["""sample"""]

							UpperCAmelCase_=       (images / 2 + 0.5).clamp(0						,  1					)
							UpperCAmelCase_=       images.cpu().permute(0						,  2						,  3						,  1					).numpy()
							UpperCAmelCase_=       (images * 255).round().astype("""uint8"""					)
							UpperCAmelCase_=       list(
							    (Image.fromarray(_[:, :, 0]					) for _ in images)
							    if images.shape[3] == 1
							    else (Image.fromarray(__UpperCAmelCase						,  mode="""RGB"""					).convert("""L"""					) for _ in images)					)

							UpperCAmelCase_=       [self.mel.image_to_audio(__UpperCAmelCase					) for _ in images]
							if not return_dict:
													return images, (self.mel.get_sample_rate(), audios)

							return BaseOutput(**AudioPipelineOutput(np.array(__UpperCAmelCase					)[:, np.newaxis, :]					)						,  **ImagePipelineOutput(__UpperCAmelCase					)					)




	@torch.no_grad()
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]						,  __UpperCAmelCase     :						List[Image.Image]						,  __UpperCAmelCase     :						int = 50					)      ->			np.ndarray:
							assert isinstance(self.scheduler						,  __UpperCAmelCase					)
							self.scheduler.set_timesteps(__UpperCAmelCase					)
							UpperCAmelCase_=       np.array(
							    [np.frombuffer(image.tobytes()						,  dtype="""uint8"""					).reshape((1, image.height, image.width)					) for image in images]					)
							UpperCAmelCase_=       (sample / 255) * 2 - 1
							UpperCAmelCase_=       torch.Tensor(__UpperCAmelCase					).to(self.device					)

							for t in self.progress_bar(torch.flip(self.scheduler.timesteps						,  (0,)					)					):
													UpperCAmelCase_=       t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
													UpperCAmelCase_=       self.scheduler.alphas_cumprod[t]
													UpperCAmelCase_=       (
													    self.scheduler.alphas_cumprod[prev_timestep]
													    if prev_timestep >= 0
													    else self.scheduler.final_alpha_cumprod
													)
													UpperCAmelCase_=       1 - alpha_prod_t
													UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]
													UpperCAmelCase_=       (1 - alpha_prod_t_prev) ** 0.5 * model_output
													UpperCAmelCase_=       (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
													UpperCAmelCase_=       sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

							return sample




	@staticmethod
	def   _SCREAMING_SNAKE_CASE		(     __UpperCAmelCase     :						torch.Tensor						,  __UpperCAmelCase     :						torch.Tensor						,  __UpperCAmelCase     :						float					)      ->			torch.Tensor:
							UpperCAmelCase_=       acos(torch.dot(torch.flatten(__UpperCAmelCase					)						,  torch.flatten(__UpperCAmelCase					)					) / torch.norm(__UpperCAmelCase					) / torch.norm(__UpperCAmelCase					)					)
							return sin((1 - alpha) * theta					) * xa / sin(__UpperCAmelCase					) + sin(alpha * theta					) * xa / sin(__UpperCAmelCase					)




