






import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


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


def   _A   (				SCREAMING_SNAKE_CASE__  :	Optional[Any]     ,  SCREAMING_SNAKE_CASE__  :	Union[str, Any]     ,  SCREAMING_SNAKE_CASE__  :	List[str]     ,  SCREAMING_SNAKE_CASE__  :	Any       ):
    UpperCamelCase       :Union[str, Any]								=   original_name.split('''.'''       )[0]
    UpperCamelCase       :List[Any]								=   key.split('''.'''       )
    UpperCamelCase       :Union[str, Any]								=   int(key_list[key_list.index(A_       ) - 2]       )
    UpperCamelCase       :Any								=   int(key_list[key_list.index(A_       ) - 1]       )
    UpperCamelCase       :Any								=   orig_block_num - offset

    UpperCamelCase       :Optional[int]								=   key.replace(F'''{orig_block_num}.{layer_num}.{original_name}'''     ,  F'''block.{new_block_num}.{layer_num}.{new_name}'''       )
    return key


def   _A   (				SCREAMING_SNAKE_CASE__  :	Optional[Any]       ):
    UpperCamelCase       :Any								=   OrderedDict()
    UpperCamelCase       :Tuple								=   0, 0
    for key, value in state_dict.items():
        if key.startswith('''network'''       ):
            UpperCamelCase       :int								=   key.replace('''network'''     ,  '''poolformer.encoder'''       )
        if "proj" in key:
            # Works for the first embedding as well as the internal embedding layers
            if key.endswith('''bias'''       ) and "patch_embed" not in key:
                patch_emb_offset += 1
            UpperCamelCase       :Optional[int]								=   key[: key.find('''proj'''       )]
            UpperCamelCase       :Dict								=   key.replace(A_     ,  F'''patch_embeddings.{total_embed_found}.'''       )
            UpperCamelCase       :int								=   key.replace('''proj'''     ,  '''projection'''       )
            if key.endswith('''bias'''       ):
                total_embed_found += 1
        if "patch_embeddings" in key:
            UpperCamelCase       :Dict								=   '''poolformer.encoder.''' + key
        if "mlp.fc1" in key:
            UpperCamelCase       :Tuple								=   replace_key_with_offset(A_     ,  A_     ,  '''mlp.fc1'''     ,  '''output.conv1'''       )
        if "mlp.fc2" in key:
            UpperCamelCase       :str								=   replace_key_with_offset(A_     ,  A_     ,  '''mlp.fc2'''     ,  '''output.conv2'''       )
        if "norm1" in key:
            UpperCamelCase       :int								=   replace_key_with_offset(A_     ,  A_     ,  '''norm1'''     ,  '''before_norm'''       )
        if "norm2" in key:
            UpperCamelCase       :Dict								=   replace_key_with_offset(A_     ,  A_     ,  '''norm2'''     ,  '''after_norm'''       )
        if "layer_scale_1" in key:
            UpperCamelCase       :Union[str, Any]								=   replace_key_with_offset(A_     ,  A_     ,  '''layer_scale_1'''     ,  '''layer_scale_1'''       )
        if "layer_scale_2" in key:
            UpperCamelCase       :Any								=   replace_key_with_offset(A_     ,  A_     ,  '''layer_scale_2'''     ,  '''layer_scale_2'''       )
        if "head" in key:
            UpperCamelCase       :str								=   key.replace('''head'''     ,  '''classifier'''       )
        UpperCamelCase       :str								=   value
    return new_state_dict


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

    return image


@torch.no_grad()
def   _A   (				SCREAMING_SNAKE_CASE__  :	List[str]     ,  SCREAMING_SNAKE_CASE__  :	Optional[Any]     ,  SCREAMING_SNAKE_CASE__  :	str       ):
    UpperCamelCase       :Tuple								=   PoolFormerConfig()

    # set attributes based on model_name
    UpperCamelCase       :Union[str, Any]								=   '''huggingface/label-files'''
    UpperCamelCase       :Any								=   model_name[-3:]
    UpperCamelCase       :List[Any]								=   1000
    UpperCamelCase       :List[str]								=   '''imagenet-1k-id2label.json'''
    UpperCamelCase       :Optional[Any]								=   (1, 1000)

    # set config attributes
    UpperCamelCase       :List[Any]								=   json.load(open(hf_hub_download(A_     ,  A_     ,  repo_type='''dataset'''       )     ,  '''r'''       )       )
    UpperCamelCase       :Optional[Any]								=   {int(A_       ): v for k, v in idalabel.items()}
    UpperCamelCase       :Union[str, Any]								=   idalabel
    UpperCamelCase       :int								=   {v: k for k, v in idalabel.items()}
    if size == "s12":
        UpperCamelCase       :Dict								=   [2, 2, 6, 2]
        UpperCamelCase       :str								=   [64, 128, 320, 512]
        UpperCamelCase       :Tuple								=   4.0
        UpperCamelCase       :Any								=   0.9
    elif size == "s24":
        UpperCamelCase       :Optional[Any]								=   [4, 4, 12, 4]
        UpperCamelCase       :List[Any]								=   [64, 128, 320, 512]
        UpperCamelCase       :Any								=   4.0
        UpperCamelCase       :Tuple								=   0.9
    elif size == "s36":
        UpperCamelCase       :Optional[int]								=   [6, 6, 18, 6]
        UpperCamelCase       :Optional[Any]								=   [64, 128, 320, 512]
        UpperCamelCase       :Any								=   4.0
        UpperCamelCase       :int								=   1e-6
        UpperCamelCase       :List[Any]								=   0.9
    elif size == "m36":
        UpperCamelCase       :str								=   [6, 6, 18, 6]
        UpperCamelCase       :int								=   [96, 192, 384, 768]
        UpperCamelCase       :List[str]								=   4.0
        UpperCamelCase       :Union[str, Any]								=   1e-6
        UpperCamelCase       :Optional[int]								=   0.95
    elif size == "m48":
        UpperCamelCase       :Union[str, Any]								=   [8, 8, 24, 8]
        UpperCamelCase       :str								=   [96, 192, 384, 768]
        UpperCamelCase       :List[str]								=   4.0
        UpperCamelCase       :Optional[Any]								=   1e-6
        UpperCamelCase       :List[str]								=   0.95
    else:
        raise ValueError(F'''Size {size} not supported'''       )

    # load image processor
    UpperCamelCase       :Any								=   PoolFormerImageProcessor(crop_pct=A_       )

    # Prepare image
    UpperCamelCase       :Optional[Any]								=   prepare_img()
    UpperCamelCase       :List[str]								=   image_processor(images=A_     ,  return_tensors='''pt'''       ).pixel_values

    logger.info(F'''Converting model {model_name}...'''       )

    # load original state dict
    UpperCamelCase       :int								=   torch.load(A_     ,  map_location=torch.device('''cpu'''       )       )

    # rename keys
    UpperCamelCase       :Any								=   rename_keys(A_       )

    # create HuggingFace model and load state dict
    UpperCamelCase       :List[str]								=   PoolFormerForImageClassification(A_       )
    model.load_state_dict(A_       )
    model.eval()

    # Define image processor
    UpperCamelCase       :Union[str, Any]								=   PoolFormerImageProcessor(crop_pct=A_       )
    UpperCamelCase       :List[Any]								=   image_processor(images=prepare_img()     ,  return_tensors='''pt'''       ).pixel_values

    # forward pass
    UpperCamelCase       :Union[str, Any]								=   model(A_       )
    UpperCamelCase       :Dict								=   outputs.logits

    # define expected logit slices for different models
    if size == "s12":
        UpperCamelCase       :int								=   torch.tensor([-0.30_45, -0.67_58, -0.48_69]       )
    elif size == "s24":
        UpperCamelCase       :Optional[int]								=   torch.tensor([0.44_02, -0.13_74, -0.80_45]       )
    elif size == "s36":
        UpperCamelCase       :Tuple								=   torch.tensor([-0.60_80, -0.51_33, -0.58_98]       )
    elif size == "m36":
        UpperCamelCase       :Optional[int]								=   torch.tensor([0.39_52, 0.22_63, -1.26_68]       )
    elif size == "m48":
        UpperCamelCase       :int								=   torch.tensor([0.11_67, -0.06_56, -0.34_23]       )
    else:
        raise ValueError(F'''Size {size} not supported'''       )

    # verify logits
    assert logits.shape == expected_shape
    assert torch.allclose(logits[0, :3]     ,  A_     ,  atol=1e-2       )

    # finally, save model and image processor
    logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''       )
    Path(A_       ).mkdir(exist_ok=A_       )
    model.save_pretrained(A_       )
    print(F'''Saving image processor to {pytorch_dump_folder_path}'''       )
    image_processor.save_pretrained(A_       )


if __name__ == "__main__":
 __snake_case												=  argparse.ArgumentParser()

 parser.add_argument(
     """--model_name""",
     default="""poolformer_s12""",
     type=str,
     help="""Name of the model you\'d like to convert.""",
 )
 parser.add_argument(
     """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
 )
 __snake_case												=  parser.parse_args()
 convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)








"""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




