





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()
lowerCAmelCase_    =    logging.get_logger(__name__)






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Optional[int]:






				"""simple docstring"""


				snake_case_						:   int           =  original_name.split('''.'''			)[0]
				snake_case_						:   List[str]           =  key.split('''.'''			)
				snake_case_						:   Union[str, Any]           =  int(key_list[key_list.index(_UpperCamelCase			) - 2]			)
				snake_case_						:   List[str]           =  int(key_list[key_list.index(_UpperCamelCase			) - 1]			)
				snake_case_						:   Tuple           =  orig_block_num - offset

				snake_case_						:   Tuple           =  key.replace(f'''{orig_block_num}.{layer_num}.{original_name}'''			,    f'''block.{new_block_num}.{layer_num}.{new_name}'''			)
				return key






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				snake_case_						:   Dict           =  OrderedDict()
				snake_case_      ,							snake_case_						:   List[Any]           =  0, 0
				for key, value in state_dict.items():
								if key.startswith('''network'''			):
												snake_case_						:   str           =  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
												snake_case_						:   Any           =  key[: key.find('''proj'''			)]
												snake_case_						:   List[Any]           =  key.replace(_UpperCamelCase			,    f'''patch_embeddings.{total_embed_found}.'''			)
												snake_case_						:   Optional[int]           =  key.replace('''proj'''			,    '''projection'''			)
												if key.endswith('''bias'''			):
																total_embed_found += 1
								if "patch_embeddings" in key:
												snake_case_						:   Optional[Any]           =  '''poolformer.encoder.''' + key
								if "mlp.fc1" in key:
												snake_case_						:   Union[str, Any]           =  replace_key_with_offset(_UpperCamelCase			,    _UpperCamelCase			,    '''mlp.fc1'''			,    '''output.conv1'''			)
								if "mlp.fc2" in key:
												snake_case_						:   Optional[Any]           =  replace_key_with_offset(_UpperCamelCase			,    _UpperCamelCase			,    '''mlp.fc2'''			,    '''output.conv2'''			)
								if "norm1" in key:
												snake_case_						:   Dict           =  replace_key_with_offset(_UpperCamelCase			,    _UpperCamelCase			,    '''norm1'''			,    '''before_norm'''			)
								if "norm2" in key:
												snake_case_						:   Optional[int]           =  replace_key_with_offset(_UpperCamelCase			,    _UpperCamelCase			,    '''norm2'''			,    '''after_norm'''			)
								if "layer_scale_1" in key:
												snake_case_						:   List[str]           =  replace_key_with_offset(_UpperCamelCase			,    _UpperCamelCase			,    '''layer_scale_1'''			,    '''layer_scale_1'''			)
								if "layer_scale_2" in key:
												snake_case_						:   List[str]           =  replace_key_with_offset(_UpperCamelCase			,    _UpperCamelCase			,    '''layer_scale_2'''			,    '''layer_scale_2'''			)
								if "head" in key:
												snake_case_						:   int           =  key.replace('''head'''			,    '''classifier'''			)
								snake_case_						:   int           =  value
				return new_state_dict






def 					lowerCamelCase_						(    )		->						Tuple:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				snake_case_						:   Dict           =  Image.open(requests.get(_UpperCamelCase			,    stream=_UpperCamelCase			).raw			)

				return image






@torch.no_grad()
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


				snake_case_						:   Dict           =  PoolFormerConfig()

				# set attributes based on model_name
				snake_case_						:   Optional[int]           =  '''huggingface/label-files'''
				snake_case_						:   Optional[int]           =  model_name[-3:]
				snake_case_						:   Tuple           =  1_000
				snake_case_						:   int           =  '''imagenet-1k-id2label.json'''
				snake_case_						:   List[str]           =  (1, 1_000)

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

				# load image processor
				snake_case_						:   Optional[Any]           =  PoolFormerImageProcessor(crop_pct=_UpperCamelCase			)

				# Prepare image
				snake_case_						:   Union[str, Any]           =  prepare_img()
				snake_case_						:   str           =  image_processor(images=_UpperCamelCase			,    return_tensors='''pt'''			).pixel_values

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

				# load original state dict
				snake_case_						:   Union[str, Any]           =  torch.load(_UpperCamelCase			,    map_location=torch.device('''cpu'''			)			)

				# rename keys
				snake_case_						:   Any           =  rename_keys(_UpperCamelCase			)

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

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

				# forward pass
				snake_case_						:   Union[str, Any]           =  model(_UpperCamelCase			)
				snake_case_						:   Optional[int]           =  outputs.logits

				# define expected logit slices for different models
				if size == "s12":
								snake_case_						:   str           =  torch.tensor([-0.3_045, -0.6_758, -0.4_869]			)
				elif size == "s24":
								snake_case_						:   Optional[Any]           =  torch.tensor([0.4_402, -0.1_374, -0.8_045]			)
				elif size == "s36":
								snake_case_						:   int           =  torch.tensor([-0.6_080, -0.5_133, -0.5_898]			)
				elif size == "m36":
								snake_case_						:   Optional[Any]           =  torch.tensor([0.3_952, 0.2_263, -1.2_668]			)
				elif size == "m48":
								snake_case_						:   Any           =  torch.tensor([0.1_167, -0.0_656, -0.3_423]			)
				else:
								raise ValueError(f'''Size {size} not supported'''			)

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

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


if __name__ == "__main__":
	lowerCAmelCase_    =    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.'''
	)
	lowerCAmelCase_    =    parser.parse_args()
	convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)







import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD



class  __lowerCAmelCase	(      nn.Module					):
			lowerCamelCase_					:					int
			lowerCamelCase_					:					int
			lowerCamelCase_					:					float	      =				0.0
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					bool	      =				True
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					jnp.dtype	      =				jnp.floataa






			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   Tuple           =  []
							snake_case_						:   List[str]           =  []

							for i in range(self.num_layers   ):
											snake_case_						:   Tuple           =  self.in_channels if i == 0 else self.out_channels

											snake_case_						:   Dict           =  FlaxResnetBlockaD(
											    in_channels=__magic_name__ ,							out_channels=self.out_channels ,							dropout_prob=self.dropout ,							dtype=self.dtype ,							)
											resnets.append(__magic_name__   )

											snake_case_						:   str           =  FlaxTransformeraDModel(
											    in_channels=self.out_channels ,							n_heads=self.num_attention_heads ,							d_head=self.out_channels // self.num_attention_heads ,							depth=1 ,							use_linear_projection=self.use_linear_projection ,							only_cross_attention=self.only_cross_attention ,							use_memory_efficient_attention=self.use_memory_efficient_attention ,							dtype=self.dtype ,							)
											attentions.append(__magic_name__   )

							snake_case_						:   Union[str, Any]           =  resnets
							snake_case_						:   Union[str, Any]           =  attentions

							if self.add_downsample:
											snake_case_						:   List[str]           =  FlaxDownsampleaD(self.out_channels ,							dtype=self.dtype   )






			def __call__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=True   )					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   str           =  ()

							for resnet, attn in zip(self.resnets ,							self.attentions   ):
											snake_case_						:   Optional[Any]           =  resnet(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )
											snake_case_						:   List[str]           =  attn(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )
											output_states += (hidden_states,)

							if self.add_downsample:
											snake_case_						:   Union[str, Any]           =  self.downsamplers_a(__magic_name__   )
											output_states += (hidden_states,)

							return hidden_states, output_states




class  __lowerCAmelCase	(      nn.Module					):
			lowerCamelCase_					:					int
			lowerCamelCase_					:					int
			lowerCamelCase_					:					float	      =				0.0
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					bool	      =				True
			lowerCamelCase_					:					jnp.dtype	      =				jnp.floataa






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  []

							for i in range(self.num_layers   ):
											snake_case_						:   List[Any]           =  self.in_channels if i == 0 else self.out_channels

											snake_case_						:   Tuple           =  FlaxResnetBlockaD(
											    in_channels=__magic_name__ ,							out_channels=self.out_channels ,							dropout_prob=self.dropout ,							dtype=self.dtype ,							)
											resnets.append(__magic_name__   )
							snake_case_						:   Dict           =  resnets

							if self.add_downsample:
											snake_case_						:   Optional[int]           =  FlaxDownsampleaD(self.out_channels ,							dtype=self.dtype   )






			def __call__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=True   )					->       Optional[Any]:


							'''simple docstring'''



							snake_case_						:   Any           =  ()

							for resnet in self.resnets:
											snake_case_						:   List[Any]           =  resnet(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )
											output_states += (hidden_states,)

							if self.add_downsample:
											snake_case_						:   str           =  self.downsamplers_a(__magic_name__   )
											output_states += (hidden_states,)

							return hidden_states, output_states




class  __lowerCAmelCase	(      nn.Module					):
			lowerCamelCase_					:					int
			lowerCamelCase_					:					int
			lowerCamelCase_					:					int
			lowerCamelCase_					:					float	      =				0.0
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					bool	      =				True
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					jnp.dtype	      =				jnp.floataa






			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  []
							snake_case_						:   Optional[Any]           =  []

							for i in range(self.num_layers   ):
											snake_case_						:   List[Any]           =  self.in_channels if (i == self.num_layers - 1) else self.out_channels
											snake_case_						:   Optional[int]           =  self.prev_output_channel if i == 0 else self.out_channels

											snake_case_						:   Dict           =  FlaxResnetBlockaD(
											    in_channels=resnet_in_channels + res_skip_channels ,							out_channels=self.out_channels ,							dropout_prob=self.dropout ,							dtype=self.dtype ,							)
											resnets.append(__magic_name__   )

											snake_case_						:   List[str]           =  FlaxTransformeraDModel(
											    in_channels=self.out_channels ,							n_heads=self.num_attention_heads ,							d_head=self.out_channels // self.num_attention_heads ,							depth=1 ,							use_linear_projection=self.use_linear_projection ,							only_cross_attention=self.only_cross_attention ,							use_memory_efficient_attention=self.use_memory_efficient_attention ,							dtype=self.dtype ,							)
											attentions.append(__magic_name__   )

							snake_case_						:   List[Any]           =  resnets
							snake_case_						:   Tuple           =  attentions

							if self.add_upsample:
											snake_case_						:   List[Any]           =  FlaxUpsampleaD(self.out_channels ,							dtype=self.dtype   )






			def __call__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=True   )					->       Union[str, Any]:


							'''simple docstring'''



							for resnet, attn in zip(self.resnets ,							self.attentions   ):
											# pop res hidden states
											snake_case_						:   Dict           =  res_hidden_states_tuple[-1]
											snake_case_						:   List[Any]           =  res_hidden_states_tuple[:-1]
											snake_case_						:   Optional[int]           =  jnp.concatenate((hidden_states, res_hidden_states) ,							axis=-1   )

											snake_case_						:   Tuple           =  resnet(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )
											snake_case_						:   Tuple           =  attn(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )

							if self.add_upsample:
											snake_case_						:   Optional[Any]           =  self.upsamplers_a(__magic_name__   )

							return hidden_states




class  __lowerCAmelCase	(      nn.Module					):
			lowerCamelCase_					:					int
			lowerCamelCase_					:					int
			lowerCamelCase_					:					int
			lowerCamelCase_					:					float	      =				0.0
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					bool	      =				True
			lowerCamelCase_					:					jnp.dtype	      =				jnp.floataa






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  []

							for i in range(self.num_layers   ):
											snake_case_						:   Tuple           =  self.in_channels if (i == self.num_layers - 1) else self.out_channels
											snake_case_						:   Optional[Any]           =  self.prev_output_channel if i == 0 else self.out_channels

											snake_case_						:   int           =  FlaxResnetBlockaD(
											    in_channels=resnet_in_channels + res_skip_channels ,							out_channels=self.out_channels ,							dropout_prob=self.dropout ,							dtype=self.dtype ,							)
											resnets.append(__magic_name__   )

							snake_case_						:   Tuple           =  resnets

							if self.add_upsample:
											snake_case_						:   List[str]           =  FlaxUpsampleaD(self.out_channels ,							dtype=self.dtype   )






			def __call__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=True   )					->       List[Any]:


							'''simple docstring'''



							for resnet in self.resnets:
											# pop res hidden states
											snake_case_						:   Tuple           =  res_hidden_states_tuple[-1]
											snake_case_						:   List[Any]           =  res_hidden_states_tuple[:-1]
											snake_case_						:   Dict           =  jnp.concatenate((hidden_states, res_hidden_states) ,							axis=-1   )

											snake_case_						:   Optional[Any]           =  resnet(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )

							if self.add_upsample:
											snake_case_						:   Optional[int]           =  self.upsamplers_a(__magic_name__   )

							return hidden_states




class  __lowerCAmelCase	(      nn.Module					):
			lowerCamelCase_					:					int
			lowerCamelCase_					:					float	      =				0.0
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					jnp.dtype	      =				jnp.floataa






			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							snake_case_						:   Dict           =  [
							    FlaxResnetBlockaD(
							        in_channels=self.in_channels ,							out_channels=self.in_channels ,							dropout_prob=self.dropout ,							dtype=self.dtype ,							)
							]

							snake_case_						:   int           =  []

							for _ in range(self.num_layers   ):
											snake_case_						:   str           =  FlaxTransformeraDModel(
											    in_channels=self.in_channels ,							n_heads=self.num_attention_heads ,							d_head=self.in_channels // self.num_attention_heads ,							depth=1 ,							use_linear_projection=self.use_linear_projection ,							use_memory_efficient_attention=self.use_memory_efficient_attention ,							dtype=self.dtype ,							)
											attentions.append(__magic_name__   )

											snake_case_						:   Dict           =  FlaxResnetBlockaD(
											    in_channels=self.in_channels ,							out_channels=self.in_channels ,							dropout_prob=self.dropout ,							dtype=self.dtype ,							)
											resnets.append(__magic_name__   )

							snake_case_						:   Optional[Any]           =  resnets
							snake_case_						:   Optional[int]           =  attentions






			def __call__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=True   )					->       Optional[int]:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  self.resnets[0](__magic_name__ ,							__magic_name__   )
							for attn, resnet in zip(self.attentions ,							self.resnets[1:]   ):
											snake_case_						:   Tuple           =  attn(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )
											snake_case_						:   Union[str, Any]           =  resnet(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )

							return hidden_states
