





'''simple docstring'''







import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType
class 			_lowercase						:







     def __init__( self    ,   A__=2    ,   A__=3    ,   A__=64    ,   A__=None    ) ->	Dict:
      snake_case				=			np.random.default_rng(A__    )
      snake_case				=			length
      snake_case				=			rng.normal(size=(length,)    ).astype(np.floataa    )
      snake_case				=			a * self.x + b + rng.normal(scale=0.1    ,   size=(length,)    ).astype(np.floataa    )







     def __len__( self    ) ->	str:
      return self.length







     def __getitem__( self    ,   A__    ) ->	Optional[Any]:
      return {"x": self.x[i], "y": self.y[i]}




class 			_lowercase						(       torch.nn.Module					):







     def __init__( self    ,   A__=0    ,   A__=0    ,   A__=False    ) ->	str:
      super().__init__()
      snake_case				=			torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
      snake_case				=			torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
      snake_case				=			True







     def     UpperCamelCase			( self    ,   A__=None    ) ->	Tuple:
      if self.first_batch:
       print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""    )
       snake_case				=			False
      return x * self.a[0] + self.b[0]




class 			_lowercase						(       torch.nn.Module					):







     def __init__( self    ,   A__=0    ,   A__=0    ,   A__=False    ) ->	Optional[int]:
      super().__init__()
      snake_case				=			torch.nn.Parameter(torch.tensor(A__    ).float()    )
      snake_case				=			torch.nn.Parameter(torch.tensor(A__    ).float()    )
      snake_case				=			True







     def     UpperCamelCase			( self    ,   A__=None    ) ->	List[str]:
      if self.first_batch:
       print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""    )
       snake_case				=			False
      return x * self.a + self.b




def    __UpperCamelCase	(	a    : Tuple					,   a    : int = 16     ) ->Dict:
 from datasets import load_dataset
 from transformers import AutoTokenizer

 snake_case				=			AutoTokenizer.from_pretrained('''bert-base-cased'''     )
 snake_case				=			{'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
 snake_case				=			load_dataset('''csv'''					,   data_files=a     )
 snake_case				=			datasets['''train'''].unique('''label'''     )

 snake_case				=			{v: i for i, v in enumerate(a     )}

 def tokenize_function(a    : List[str]     ):
  # max_length=None => use the model max length (it's actually the default)
  snake_case				=			tokenizer(
      examples['''sentence1''']					,   examples['''sentence2''']					,   truncation=a					,   max_length=a					,   padding='''max_length'''     )
  if "label" in examples:
   snake_case				=			[label_to_id[l] for l in examples['''label''']]
  return outputs

 # Apply the method we just defined to all the examples in all the splits of the dataset
 snake_case				=			datasets.map(
     a					,   batched=a					,   remove_columns=['''sentence1''', '''sentence2''', '''label''']					,   )

 def collate_fn(a    : Union[str, Any]     ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  if accelerator.distributed_type == DistributedType.TPU:
   return tokenizer.pad(a					,   padding='''max_length'''					,   max_length=128					,   return_tensors='''pt'''     )
  return tokenizer.pad(a					,   padding='''longest'''					,   return_tensors='''pt'''     )

 # Instantiate dataloaders.
 snake_case				=			DataLoader(tokenized_datasets['''train''']					,   shuffle=a					,   collate_fn=a					,   batch_size=2     )
 snake_case				=			DataLoader(tokenized_datasets['''validation''']					,   shuffle=a					,   collate_fn=a					,   batch_size=1     )

 return train_dataloader, eval_dataloader








'''simple docstring'''







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()
_lowercase       						=			logging.get_logger(__name__)

def    __UpperCamelCase	(	a    : Dict					,   a    : Optional[int]					,   a    : Dict					,   a    : Dict     ) ->Union[str, Any]:
 snake_case				=			original_name.split('''.'''     )[0]
 snake_case				=			key.split('''.'''     )
 snake_case				=			int(key_list[key_list.index(a     ) - 2]     )
 snake_case				=			int(key_list[key_list.index(a     ) - 1]     )
 snake_case				=			orig_block_num - offset

 snake_case				=			key.replace(f"""{orig_block_num}.{layer_num}.{original_name}"""					,   f"""block.{new_block_num}.{layer_num}.{new_name}"""     )
 return key

def    __UpperCamelCase	(	a    : Tuple     ) ->Dict:
 snake_case				=			OrderedDict()
 snake_case				,		snake_case				=			0, 0
 for key, value in state_dict.items():
  if key.startswith('''network'''     ):
   snake_case				=			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				=			key[: key.find('''proj'''     )]
   snake_case				=			key.replace(a					,   f"""patch_embeddings.{total_embed_found}."""     )
   snake_case				=			key.replace('''proj'''					,   '''projection'''     )
   if key.endswith('''bias'''     ):
    total_embed_found += 1
  if "patch_embeddings" in key:
   snake_case				=			'''poolformer.encoder.''' + key
  if "mlp.fc1" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''mlp.fc1'''					,   '''output.conv1'''     )
  if "mlp.fc2" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''mlp.fc2'''					,   '''output.conv2'''     )
  if "norm1" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''norm1'''					,   '''before_norm'''     )
  if "norm2" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''norm2'''					,   '''after_norm'''     )
  if "layer_scale_1" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''layer_scale_1'''					,   '''layer_scale_1'''     )
  if "layer_scale_2" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''layer_scale_2'''					,   '''layer_scale_2'''     )
  if "head" in key:
   snake_case				=			key.replace('''head'''					,   '''classifier'''     )
  snake_case				=			value
 return new_state_dict

def    __UpperCamelCase	(	) ->Optional[int]:
 snake_case				=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
 snake_case				=			Image.open(requests.get(a					,   stream=a     ).raw     )

 return image

@torch.no_grad()
def    __UpperCamelCase	(	a    : Dict					,   a    : Optional[Any]					,   a    : Tuple     ) ->List[str]:
 snake_case				=			PoolFormerConfig()

 # set attributes based on model_name
 snake_case				=			'''huggingface/label-files'''
 snake_case				=			model_name[-3:]
 snake_case				=			1000
 snake_case				=			'''imagenet-1k-id2label.json'''
 snake_case				=			(1, 1000)

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

 # load image processor
 snake_case				=			PoolFormerImageProcessor(crop_pct=a     )

 # Prepare image
 snake_case				=			prepare_img()
 snake_case				=			image_processor(images=a					,   return_tensors='''pt'''     ).pixel_values

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

 # load original state dict
 snake_case				=			torch.load(a					,   map_location=torch.device('''cpu'''     )     )

 # rename keys
 snake_case				=			rename_keys(a     )

 # create HuggingFace model and load state dict
 snake_case				=			PoolFormerForImageClassification(a     )
 model.load_state_dict(a     )
 model.eval()

 # Define image processor
 snake_case				=			PoolFormerImageProcessor(crop_pct=a     )
 snake_case				=			image_processor(images=prepare_img()					,   return_tensors='''pt'''     ).pixel_values

 # forward pass
 snake_case				=			model(a     )
 snake_case				=			outputs.logits

 # define expected logit slices for different models
 if size == "s12":
  snake_case				=			torch.tensor([-0.3045, -0.6758, -0.4869]     )
 elif size == "s24":
  snake_case				=			torch.tensor([0.4402, -0.1374, -0.8045]     )
 elif size == "s36":
  snake_case				=			torch.tensor([-0.6080, -0.5133, -0.5898]     )
 elif size == "m36":
  snake_case				=			torch.tensor([0.3952, 0.2263, -1.2668]     )
 elif size == "m48":
  snake_case				=			torch.tensor([0.1167, -0.0656, -0.3423]     )
 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__":
   _lowercase       						=			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.'
   )
   _lowercase       						=			parser.parse_args()
   convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)

