


'''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()
A				:					Optional[Any]    						=			logging.get_logger(__name__)






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

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






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






def 				lowerCAmelCase__	(    ):
       _A					:     str     = 'http://images.cocodataset.org/val2017/000000039769.jpg'
       _A					:     Optional[Any]     = Image.open(requests.get(lowerCamelCase  ,stream=lowerCamelCase       ).raw       )

       return image






@torch.no_grad()
def 				lowerCAmelCase__	(    lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      List[str]  ,lowerCamelCase					:      str       ):
       _A					:     Optional[Any]     = PoolFormerConfig()

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

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

       # load image processor
       _A					:     str     = PoolFormerImageProcessor(crop_pct=lowerCamelCase       )

       # Prepare image
       _A					:     str     = prepare_img()
       _A					:     Dict     = image_processor(images=lowerCamelCase  ,return_tensors='pt'       ).pixel_values

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

       # load original state dict
       _A					:     str     = torch.load(lowerCamelCase  ,map_location=torch.device('cpu'       )       )

       # rename keys
       _A					:     List[str]     = rename_keys(lowerCamelCase       )

       # create HuggingFace model and load state dict
       _A					:     str     = PoolFormerForImageClassification(lowerCamelCase       )
       model.load_state_dict(lowerCamelCase       )
       model.eval()

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

       # forward pass
       _A					:     Dict     = model(lowerCamelCase       )
       _A					:     str     = outputs.logits

       # define expected logit slices for different models
       if size == "s12":
              _A					:     Tuple     = torch.tensor([-0.3_045, -0.6_758, -0.4_869]       )
       elif size == "s24":
              _A					:     Optional[int]     = torch.tensor([0.4_402, -0.1_374, -0.8_045]       )
       elif size == "s36":
              _A					:     Tuple     = torch.tensor([-0.6_080, -0.5_133, -0.5_898]       )
       elif size == "m36":
              _A					:     Tuple     = torch.tensor([0.3_952, 0.2_263, -1.2_668]       )
       elif size == "m48":
              _A					:     Tuple     = 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]  ,lowerCamelCase  ,atol=1E-2       )

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


if __name__ == "__main__":
   A				:					Optional[int]    						=			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.'''
   )
   A				:					Union[str, Any]    						=			parser.parse_args()
   convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)










'''simple docstring'''







# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py

import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      Tuple  ,lowerCamelCase					:      List[str]       ):
       _A					:     Dict     = multiprocessing.Manager()
       _A					:     List[Any]     = manager.list()

       _A					:     Dict     = multiprocessing.Process(target=lowerCamelCase  ,args=(check_program, result, timeout)       )
       p.start()
       p.join(timeout=timeout + 1       )
       if p.is_alive():
              p.kill()

       if not result:
              result.append('timed out'       )

       return {
           "task_id": task_id,
           "passed": result[0] == "passed",
           "result": result[0],
           "completion_id": completion_id,
       }






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      Optional[Any]       ):
       with create_tempdir():
              # These system calls are needed when cleaning up tempdir.
              import os
              import shutil

              _A					:     Any     = shutil.rmtree
              _A					:     Optional[int]     = os.rmdir
              _A					:     str     = os.chdir

              # Disable functionalities that can make destructive changes to the test.
              reliability_guard()

              # Run program.
              try:
                     _A					:     str     = {}
                     with swallow_io():
                            with time_limit(lowerCamelCase       ):
                                   exec(lowerCamelCase  ,lowerCamelCase       )
                     result.append('passed'       )
              except TimeoutException:
                     result.append('timed out'       )
              except BaseException as e:
                     result.append(F'failed: {e}'       )

              # Needed for cleaning up.
              _A					:     Optional[int]     = rmtree
              _A					:     Optional[Any]     = rmdir
              _A					:     Dict     = chdir






@contextlib.contextmanager
def 				lowerCAmelCase__	(    lowerCamelCase					:      int       ):
       def signal_handler(lowerCamelCase					:      str  ,lowerCamelCase					:      Any       ):
              raise TimeoutException('Timed out!'       )

       signal.setitimer(signal.ITIMER_REAL  ,lowerCamelCase       )
       signal.signal(signal.SIGALRM  ,lowerCamelCase       )
       try:
              yield
       finally:
              signal.setitimer(signal.ITIMER_REAL  ,0       )






@contextlib.contextmanager
def 				lowerCAmelCase__	(    ):
       _A					:     Any     = WriteOnlyStringIO()
       with contextlib.redirect_stdout(lowerCamelCase       ):
              with contextlib.redirect_stderr(lowerCamelCase       ):
                     with redirect_stdin(lowerCamelCase       ):
                            yield






@contextlib.contextmanager
def 				lowerCAmelCase__	(    ):
       with tempfile.TemporaryDirectory() as dirname:
              with chdir(lowerCamelCase       ):
                     yield dirname


class 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    pass




class 			__lowerCamelCase			(	io.StringIO	):
    """simple docstring"""


    def        A   (  self      :							Tuple				,							*SCREAMING_SNAKE_CASE      :							Any				,							**SCREAMING_SNAKE_CASE      :							Dict):
           raise OSError


    def        A   (  self      :							Optional[int]				,							*SCREAMING_SNAKE_CASE      :							List[str]				,							**SCREAMING_SNAKE_CASE      :							Optional[Any]):
           raise OSError


    def        A   (  self      :							Optional[int]				,							*SCREAMING_SNAKE_CASE      :							str				,							**SCREAMING_SNAKE_CASE      :							Optional[int]):
           raise OSError


    def        A   (  self      :							Union[str, Any]				,							*SCREAMING_SNAKE_CASE      :							Optional[int]				,							**SCREAMING_SNAKE_CASE      :							Tuple):
           return False




class 			__lowerCamelCase			(	contextlib._RedirectStream	):  # type: ignore
    """simple docstring"""
    a													=   "stdin"






@contextlib.contextmanager
def 				lowerCAmelCase__	(    lowerCamelCase					:      Tuple       ):
       if root == ".":
              yield
              return
       _A					:     Any     = os.getcwd()
       os.chdir(lowerCamelCase       )
       try:
              yield
       except BaseException as exc:
              raise exc
       finally:
              os.chdir(lowerCamelCase       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[Any]=None       ):

       if maximum_memory_bytes is not None:
              import resource

              resource.setrlimit(resource.RLIMIT_AS  ,(maximum_memory_bytes, maximum_memory_bytes)       )
              resource.setrlimit(resource.RLIMIT_DATA  ,(maximum_memory_bytes, maximum_memory_bytes)       )
              if not platform.uname().system == "Darwin":
                     resource.setrlimit(resource.RLIMIT_STACK  ,(maximum_memory_bytes, maximum_memory_bytes)       )

       faulthandler.disable()

       import builtins

       _A					:     List[Any]     = None
       _A					:     Dict     = None

       import os

       _A					:     Union[str, Any]     = '1'

       _A					:     int     = None
       _A					:     Optional[int]     = None
       _A					:     int     = None
       _A					:     Any     = None
       _A					:     Optional[int]     = None
       _A					:     Union[str, Any]     = None
       _A					:     List[Any]     = None
       _A					:     int     = None
       _A					:     List[Any]     = None
       _A					:     Tuple     = None
       _A					:     Any     = None
       _A					:     Tuple     = None
       _A					:     Optional[int]     = None
       _A					:     Optional[Any]     = None
       _A					:     str     = None
       _A					:     Dict     = None
       _A					:     List[str]     = None
       _A					:     Union[str, Any]     = None
       _A					:     Union[str, Any]     = None
       _A					:     str     = None
       _A					:     str     = None
       _A					:     str     = None
       _A					:     Any     = None
       _A					:     Union[str, Any]     = None
       _A					:     str     = None
       _A					:     List[str]     = None
       _A					:     Union[str, Any]     = None

       import shutil

       _A					:     int     = None
       _A					:     Any     = None
       _A					:     List[Any]     = None

       import subprocess

       _A					:     Optional[Any]     = None  # type: ignore

       _A					:     List[Any]     = None

       import sys

       _A					:     Any     = None
       _A					:     Tuple     = None
       _A					:     str     = None
       _A					:     Tuple     = None
       _A					:     List[str]     = None






