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



def  __lowerCamelCase       (      _UpperCamelCase  :  List[str]							,    _UpperCamelCase  :  Any							,    _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  str					):


      '''simple docstring'''


      UpperCAmelCase_            =  original_name.split('''.'''					)[0]
      UpperCAmelCase_            =  key.split('''.'''					)
      UpperCAmelCase_            =  int(key_list[key_list.index(_UpperCamelCase					) - 2]					)
      UpperCAmelCase_            =  int(key_list[key_list.index(_UpperCamelCase					) - 1]					)
      UpperCAmelCase_            =  orig_block_num - offset

      UpperCAmelCase_            =  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'''


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



def  __lowerCamelCase       (      ):


      '''simple docstring'''


      UpperCAmelCase_            =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      UpperCAmelCase_            =  Image.open(requests.get(_UpperCamelCase							,    stream=_UpperCamelCase					).raw					)

      return image



@torch.no_grad()
def  __lowerCamelCase       (      _UpperCamelCase  :  Optional[Any]							,    _UpperCamelCase  :  Optional[int]							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      UpperCAmelCase_            =  PoolFormerConfig()

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

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

      # load image processor
      UpperCAmelCase_            =  PoolFormerImageProcessor(crop_pct=_UpperCamelCase					)

      # Prepare image
      UpperCAmelCase_            =  prepare_img()
      UpperCAmelCase_            =  image_processor(images=_UpperCamelCase							,    return_tensors='''pt'''					).pixel_values

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

      # load original state dict
      UpperCAmelCase_            =  torch.load(_UpperCamelCase							,    map_location=torch.device('''cpu'''					)					)

      # rename keys
      UpperCAmelCase_            =  rename_keys(_UpperCamelCase					)

      # create HuggingFace model and load state dict
      UpperCAmelCase_            =  PoolFormerForImageClassification(_UpperCamelCase					)
      model.load_state_dict(_UpperCamelCase					)
      model.eval()

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

      # forward pass
      UpperCAmelCase_            =  model(_UpperCamelCase					)
      UpperCAmelCase_            =  outputs.logits

      # define expected logit slices for different models
      if size == "s12":
            UpperCAmelCase_            =  torch.tensor([-0.3_045, -0.6_758, -0.4_869]					)
      elif size == "s24":
            UpperCAmelCase_            =  torch.tensor([0.4_402, -0.1_374, -0.8_045]					)
      elif size == "s36":
            UpperCAmelCase_            =  torch.tensor([-0.6_080, -0.5_133, -0.5_898]					)
      elif size == "m36":
            UpperCAmelCase_            =  torch.tensor([0.3_952, 0.2_263, -1.2_668]					)
      elif size == "m48":
            UpperCAmelCase_            =  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__":
      lowercase__  :      Any        =					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__  :      List[str]        =					parser.parse_args()
      convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)






'''simple docstring'''
import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class lowerCamelCase							(	lowerCamelCase     ,		unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	MvpTokenizer
  lowerCAmelCase__										=	MvpTokenizerFast
  lowerCAmelCase__										=	True
  lowerCAmelCase__										=	filter_roberta_detectors






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Tuple:
        super().setUp()
        UpperCAmelCase_            =  [
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''\u0120''',
            '''\u0120l''',
            '''\u0120n''',
            '''\u0120lo''',
            '''\u0120low''',
            '''er''',
            '''\u0120lowest''',
            '''\u0120newer''',
            '''\u0120wider''',
            '''<unk>''',
        ]
        UpperCAmelCase_            =  dict(zip(UpperCAmelCase__  ,							range(len(UpperCAmelCase__  )  )  )  )
        UpperCAmelCase_            =  ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
        UpperCAmelCase_            =  {'''unk_token''': '''<unk>'''}

        UpperCAmelCase_            =  os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['''vocab_file''']  )
        UpperCAmelCase_            =  os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['''merges_file''']  )
        with open(self.vocab_file  ,							'''w'''  ,							encoding='''utf-8'''  ) as fp:
              fp.write(json.dumps(UpperCAmelCase__  ) + '''\n'''  )
        with open(self.merges_file  ,							'''w'''  ,							encoding='''utf-8'''  ) as fp:
              fp.write('''\n'''.join(UpperCAmelCase__  )  )






  def  lowerCAmelCase__ (      self			:       Tuple  ,							**UpperCAmelCase__			:       List[str]  )				->Dict:
        kwargs.update(self.special_tokens_map  )
        return self.tokenizer_class.from_pretrained(self.tmpdirname  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Optional[int]  ,							**UpperCAmelCase__			:       int  )				->Tuple:
        kwargs.update(self.special_tokens_map  )
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  ,							UpperCAmelCase__			:       Optional[Any]  )				->Union[str, Any]:
        return "lower newer", "lower newer"






  @cached_property
  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Optional[int]:
        return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''  )






  @cached_property
  def  lowerCAmelCase__ (      self			:       Tuple  )				->Tuple:
        return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Any  )				->Dict:
        UpperCAmelCase_            =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        UpperCAmelCase_            =  [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							max_length=len(UpperCAmelCase__  )  ,							padding=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              self.assertIsInstance(UpperCAmelCase__  ,							UpperCAmelCase__  )

              self.assertEqual((2, 9)  ,							batch.input_ids.shape  )
              self.assertEqual((2, 9)  ,							batch.attention_mask.shape  )
              UpperCAmelCase_            =  batch.input_ids.tolist()[0]
              self.assertListEqual(UpperCAmelCase__  ,							UpperCAmelCase__  )
              # Test that special tokens are reset






  @require_torch
  def  lowerCAmelCase__ (      self			:       str  )				->int:
        UpperCAmelCase_            =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							padding=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              # check if input_ids are returned and no labels
              self.assertIn('''input_ids'''  ,							UpperCAmelCase__  )
              self.assertIn('''attention_mask'''  ,							UpperCAmelCase__  )
              self.assertNotIn('''labels'''  ,							UpperCAmelCase__  )
              self.assertNotIn('''decoder_attention_mask'''  ,							UpperCAmelCase__  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Tuple  )				->Optional[Any]:
        UpperCAmelCase_            =  [
            '''Summary of the text.''',
            '''Another summary.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(text_target=UpperCAmelCase__  ,							max_length=32  ,							padding='''max_length'''  ,							return_tensors='''pt'''  )
              self.assertEqual(32  ,							targets['''input_ids'''].shape[1]  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       List[str]  )				->int:
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(
                  ['''I am a small frog''' * 1024, '''I am a small frog''']  ,							padding=UpperCAmelCase__  ,							truncation=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              self.assertIsInstance(UpperCAmelCase__  ,							UpperCAmelCase__  )
              self.assertEqual(batch.input_ids.shape  ,							(2, 1024)  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Dict  )				->Optional[int]:
        UpperCAmelCase_            =  ['''A long paragraph for summarization.''']
        UpperCAmelCase_            =  [
            '''Summary of the text.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							text_target=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              UpperCAmelCase_            =  inputs['''input_ids''']
              UpperCAmelCase_            =  inputs['''labels''']
              self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
              self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
              self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
              self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )






  def  lowerCAmelCase__ (      self			:       str  )				->Optional[Any]:
        pass






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Tuple:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
                    UpperCAmelCase_            =  self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__  ,							**UpperCAmelCase__  )
                    UpperCAmelCase_            =  self.tokenizer_class.from_pretrained(UpperCAmelCase__  ,							**UpperCAmelCase__  )
                    UpperCAmelCase_            =  '''A, <mask> AllenNLP sentence.'''
                    UpperCAmelCase_            =  tokenizer_r.encode_plus(UpperCAmelCase__  ,							add_special_tokens=UpperCAmelCase__  ,							return_token_type_ids=UpperCAmelCase__  )
                    UpperCAmelCase_            =  tokenizer_p.encode_plus(UpperCAmelCase__  ,							add_special_tokens=UpperCAmelCase__  ,							return_token_type_ids=UpperCAmelCase__  )

                    # token_type_ids should put 0 everywhere
                    self.assertEqual(sum(tokens_r['''token_type_ids''']  )  ,							sum(tokens_p['''token_type_ids''']  )  )

                    # attention_mask should put 1 everywhere, so sum over length should be 1
                    self.assertEqual(
                        sum(tokens_r['''attention_mask''']  ) / len(tokens_r['''attention_mask''']  )  ,							sum(tokens_p['''attention_mask''']  ) / len(tokens_p['''attention_mask''']  )  ,							)

                    UpperCAmelCase_            =  tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']  )
                    UpperCAmelCase_            =  tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']  )

                    # Rust correctly handles the space before the mask while python doesnt
                    self.assertSequenceEqual(tokens_p['''input_ids''']  ,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )
                    self.assertSequenceEqual(tokens_r['''input_ids''']  ,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )

                    self.assertSequenceEqual(
                        UpperCAmelCase__  ,							['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )
                    self.assertSequenceEqual(
                        UpperCAmelCase__  ,							['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )





