






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()
__snake_case												=  logging.get_logger(__name__)

__snake_case												=  torch.device("""cpu""")


def   _A   (				):
    UpperCamelCase       :List[str]								=   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCamelCase       :Any								=   Image.open(requests.get(A_     ,  stream=A_       ).raw       )
    return im


def   _A   (				SCREAMING_SNAKE_CASE__  :	List[str]       ):
    if swiftformer_name == "swiftformer_xs":
        return torch.tensor([-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0, 8.8_6_8_5e-0_1, 2.4_3_6_0e-0_1]       )

    elif swiftformer_name == "swiftformer_s":
        return torch.tensor([3.9_6_3_6e-0_1, 2.3_4_7_8e-0_1, -1.6_9_6_3e0_0, -1.7_3_8_1e0_0, -8.6_3_3_7e-0_1]       )

    elif swiftformer_name == "swiftformer_l1":
        return torch.tensor([-4.2_7_6_8e-0_1, -4.7_4_2_9e-0_1, -1.0_8_9_7e0_0, -1.0_2_4_8e0_0, 3.5_5_2_3e-0_2]       )

    elif swiftformer_name == "swiftformer_l3":
        return torch.tensor([-2.5_3_3_0e-0_1, 2.4_2_1_1e-0_1, -6.0_1_8_5e-0_1, -8.2_7_8_9e-0_1, -6.0_4_4_6e-0_2]       )


def   _A   (				SCREAMING_SNAKE_CASE__  :	List[str]     ,  SCREAMING_SNAKE_CASE__  :	str     ,  SCREAMING_SNAKE_CASE__  :	Union[str, Any]       ):
    UpperCamelCase       :Union[str, Any]								=   dct.pop(A_       )
    UpperCamelCase       :Optional[Any]								=   val


def   _A   (				SCREAMING_SNAKE_CASE__  :	Any       ):
    UpperCamelCase       :Optional[Any]								=   []
    for k in state_dict.keys():
        UpperCamelCase       :int								=   k
        if ".pwconv" in k:
            UpperCamelCase       :Dict								=   k_new.replace('''.pwconv'''     ,  '''.point_wise_conv'''       )
        if ".dwconv" in k:
            UpperCamelCase       :Dict								=   k_new.replace('''.dwconv'''     ,  '''.depth_wise_conv'''       )
        if ".Proj." in k:
            UpperCamelCase       :Any								=   k_new.replace('''.Proj.'''     ,  '''.proj.'''       )
        if "patch_embed" in k_new:
            UpperCamelCase       :Union[str, Any]								=   k_new.replace('''patch_embed'''     ,  '''swiftformer.patch_embed.patch_embedding'''       )
        if "network" in k_new:
            UpperCamelCase       :Dict								=   k_new.split('''.'''       )
            if ls[2].isdigit():
                UpperCamelCase       :List[str]								=   '''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:]       )
            else:
                UpperCamelCase       :List[str]								=   k_new.replace('''network'''     ,  '''swiftformer.encoder.network'''       )
        rename_keys.append((k, k_new)       )
    return rename_keys


@torch.no_grad()
def   _A   (				SCREAMING_SNAKE_CASE__  :	int     ,  SCREAMING_SNAKE_CASE__  :	Tuple     ,  SCREAMING_SNAKE_CASE__  :	Any       ):
    UpperCamelCase       :int								=   SwiftFormerConfig()

    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    UpperCamelCase       :Any								=   1000
    UpperCamelCase       :Union[str, Any]								=   '''huggingface/label-files'''
    UpperCamelCase       :int								=   '''imagenet-1k-id2label.json'''
    UpperCamelCase       :int								=   json.load(open(hf_hub_download(A_     ,  A_     ,  repo_type='''dataset'''       )     ,  '''r'''       )       )
    UpperCamelCase       :Optional[int]								=   {int(A_       ): v for k, v in idalabel.items()}
    UpperCamelCase       :int								=   idalabel
    UpperCamelCase       :int								=   {v: k for k, v in idalabel.items()}

    # size of the architecture
    if swiftformer_name == "swiftformer_xs":
        UpperCamelCase       :Optional[int]								=   [3, 3, 6, 4]
        UpperCamelCase       :Dict								=   [48, 56, 112, 220]

    elif swiftformer_name == "swiftformer_s":
        UpperCamelCase       :Any								=   [3, 3, 9, 6]
        UpperCamelCase       :Optional[int]								=   [48, 64, 168, 224]

    elif swiftformer_name == "swiftformer_l1":
        UpperCamelCase       :Any								=   [4, 3, 10, 5]
        UpperCamelCase       :List[Any]								=   [48, 96, 192, 384]

    elif swiftformer_name == "swiftformer_l3":
        UpperCamelCase       :str								=   [4, 4, 12, 6]
        UpperCamelCase       :List[str]								=   [64, 128, 320, 512]

    # load state_dict of original model, remove and rename some keys
    if original_ckpt:
        if original_ckpt.startswith('''https'''       ):
            UpperCamelCase       :Tuple								=   torch.hub.load_state_dict_from_url(A_     ,  map_location='''cpu'''     ,  check_hash=A_       )
        else:
            UpperCamelCase       :str								=   torch.load(A_     ,  map_location='''cpu'''       )
    UpperCamelCase       :Optional[int]								=   checkpoint

    UpperCamelCase       :Dict								=   create_rename_keys(A_       )
    for rename_key_src, rename_key_dest in rename_keys:
        rename_key(A_     ,  A_     ,  A_       )

    # load HuggingFace model
    UpperCamelCase       :Tuple								=   SwiftFormerForImageClassification(A_       ).eval()
    hf_model.load_state_dict(A_       )

    # prepare test inputs
    UpperCamelCase       :Optional[int]								=   prepare_img()
    UpperCamelCase       :List[Any]								=   ViTImageProcessor.from_pretrained('''preprocessor_config'''       )
    UpperCamelCase       :int								=   processor(images=A_     ,  return_tensors='''pt'''       )

    # compare outputs from both models
    UpperCamelCase       :str								=   get_expected_output(A_       )
    UpperCamelCase       :int								=   hf_model(inputs['''pixel_values''']       ).logits

    assert hf_logits.shape == torch.Size([1, 1000]       )
    assert torch.allclose(hf_logits[0, 0:5]     ,  A_     ,  atol=1e-3       )

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


if __name__ == "__main__":
 __snake_case												=  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.""")

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








"""simple docstring"""





import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


__UpperCamelCase  :      Optional[Any]							      =		'''scheduler_config.json'''







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       1
					lowercase__				 =       2
					lowercase__				 =       3
					lowercase__				 =       4
					lowercase__				 =       5







@dataclass
class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       42







class 				SCREAMING_SNAKE_CASE					:






					"""simple docstring"""



					lowercase__				 =       SCHEDULER_CONFIG_NAME
					lowercase__				 =       ["dtype"]
					lowercase__				 =       []
					lowercase__				 =       True







					@classmethod
					def        __lowerCAmelCase (       cls			:				List[Any]   ,lowercase_			:				Dict[str, Any] = None   ,lowercase_			:				Optional[str] = None   ,lowercase_			:				Optional[int]=False   ,**lowercase_			:				Any   ,):
						lowerCAmelCase__				,lowerCAmelCase__	:						Dict  				=	cls.load_config(
						    pretrained_model_name_or_path=lowercase_   ,subfolder=lowercase_   ,return_unused_kwargs=lowercase_   ,**lowercase_   ,)
						lowerCAmelCase__				,lowerCAmelCase__	:						Union[str, Any]  				=	cls.from_config(lowercase_   ,return_unused_kwargs=lowercase_   ,**lowercase_		)

						if hasattr(lowercase_   ,'''create_state'''		) and getattr(lowercase_   ,'''has_state'''   ,lowercase_		):
							lowerCAmelCase__	:						List[Any]  				=	scheduler.create_state()

						if return_unused_kwargs:
							return scheduler, state, unused_kwargs

						return scheduler, state







					def        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				Union[str, os.PathLike]   ,lowercase_			:				bool = False   ,**lowercase_			:				str		):
						self.save_config(save_directory=lowercase_   ,push_to_hub=lowercase_   ,**lowercase_		)







					@property
					def        __lowerCAmelCase (       self			:				List[str]		):
						return self._get_compatibles()







					@classmethod
					def        __lowerCAmelCase (       cls			:				List[Any]		):
						lowerCAmelCase__	:						Tuple  				=	list(set([cls.__name__] + cls._compatibles		)		)
						lowerCAmelCase__	:						Tuple  				=	importlib.import_module(__name__.split('''.'''		)[0]		)
						lowerCAmelCase__	:						Union[str, Any]  				=	[
						    getattr(lowercase_   ,lowercase_		) for c in compatible_classes_str if hasattr(lowercase_   ,lowercase_		)
						]
						return compatible_classes

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	assert len(A_ ) >= x.ndim
	return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(A_ ) - x.ndim) )						,     A_ )

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=0.999						,     A_=jnp.floataa ):

	def alpha_bar(A_ ):
		return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2

	lowerCAmelCase__	:						Optional[Any]  				=	[]
	for i in range(A_ ):
		lowerCAmelCase__	:						str  				=	i / num_diffusion_timesteps
		lowerCAmelCase__	:						Union[str, Any]  				=	(i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar(A_ ) / alpha_bar(A_ )						,     A_ ) )
	return jnp.array(A_						,     dtype=A_ )







@flax.struct.dataclass
class 				SCREAMING_SNAKE_CASE					:






					"""simple docstring"""



					lowercase__				 =       42
					lowercase__				 =       42
					lowercase__				 =       42







					@classmethod
					def        __lowerCAmelCase (       cls			:				Union[str, Any]   ,lowercase_			:				List[Any]		):
						lowerCAmelCase__	:						Optional[int]  				=	scheduler.config

						if config.trained_betas is not None:
							lowerCAmelCase__	:						Any  				=	jnp.asarray(config.trained_betas   ,dtype=scheduler.dtype		)
						elif config.beta_schedule == "linear":
							lowerCAmelCase__	:						Union[str, Any]  				=	jnp.linspace(config.beta_start   ,config.beta_end   ,config.num_train_timesteps   ,dtype=scheduler.dtype		)
						elif config.beta_schedule == "scaled_linear":
							# this schedule is very specific to the latent diffusion model.
							lowerCAmelCase__	:						int  				=	(
							    jnp.linspace(
							        config.beta_start**0.5   ,config.beta_end**0.5   ,config.num_train_timesteps   ,dtype=scheduler.dtype		)
							    ** 2
							)
						elif config.beta_schedule == "squaredcos_cap_v2":
							# Glide cosine schedule
							lowerCAmelCase__	:						List[Any]  				=	betas_for_alpha_bar(config.num_train_timesteps   ,dtype=scheduler.dtype		)
						else:
							raise NotImplementedError(
							    F'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'		)

						lowerCAmelCase__	:						str  				=	1.0 - betas

						lowerCAmelCase__	:						Union[str, Any]  				=	jnp.cumprod(lowercase_   ,axis=0		)

						return cls(
						    alphas=lowercase_   ,betas=lowercase_   ,alphas_cumprod=lowercase_   ,)

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Any  				=	state.alphas_cumprod

	lowerCAmelCase__	:						Optional[Any]  				=	alphas_cumprod[timesteps] ** 0.5
	lowerCAmelCase__	:						Tuple  				=	sqrt_alpha_prod.flatten()
	lowerCAmelCase__	:						str  				=	broadcast_to_shape_from_left(A_						,     original_samples.shape )

	lowerCAmelCase__	:						Optional[Any]  				=	(1 - alphas_cumprod[timesteps]) ** 0.5
	lowerCAmelCase__	:						Optional[Any]  				=	sqrt_one_minus_alpha_prod.flatten()
	lowerCAmelCase__	:						Optional[int]  				=	broadcast_to_shape_from_left(A_						,     original_samples.shape )

	return sqrt_alpha_prod, sqrt_one_minus_alpha_prod

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_ ):
	lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	get_sqrt_alpha_prod(A_						,     A_						,     A_						,     A_ )
	lowerCAmelCase__	:						str  				=	sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
	return noisy_samples





def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_ ):
	lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	get_sqrt_alpha_prod(A_						,     A_						,     A_						,     A_ )
	lowerCAmelCase__	:						Union[str, Any]  				=	sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
	return velocity




