


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      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




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

		lowercase__				= 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	):


		"""simple docstring"""




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


		"""simple docstring"""




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

		return image
@torch.no_grad()
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__				= PoolFormerConfig()

		# set attributes based on model_name
		lowercase__				= '''huggingface/label-files'''
		lowercase__				= model_name[-3:]
		lowercase__				= 10_00
		lowercase__				= '''imagenet-1k-id2label.json'''
		lowercase__				= (1, 10_00)

		# set config attributes
		lowercase__				= json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							repo_type='''dataset'''	)		,							'''r'''	)	)
		lowercase__				= {int(SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}
		lowercase__				= idalabel
		lowercase__				= {v: k for k, v in idalabel.items()}
		if size == "s12":
				lowercase__				= [2, 2, 6, 2]
				lowercase__				= [64, 1_28, 3_20, 5_12]
				lowercase__				= 4.0
				lowercase__				= 0.9
		elif size == "s24":
				lowercase__				= [4, 4, 12, 4]
				lowercase__				= [64, 1_28, 3_20, 5_12]
				lowercase__				= 4.0
				lowercase__				= 0.9
		elif size == "s36":
				lowercase__				= [6, 6, 18, 6]
				lowercase__				= [64, 1_28, 3_20, 5_12]
				lowercase__				= 4.0
				lowercase__				= 1E-6
				lowercase__				= 0.9
		elif size == "m36":
				lowercase__				= [6, 6, 18, 6]
				lowercase__				= [96, 1_92, 3_84, 7_68]
				lowercase__				= 4.0
				lowercase__				= 1E-6
				lowercase__				= 0.95
		elif size == "m48":
				lowercase__				= [8, 8, 24, 8]
				lowercase__				= [96, 1_92, 3_84, 7_68]
				lowercase__				= 4.0
				lowercase__				= 1E-6
				lowercase__				= 0.95
		else:
				raise ValueError(f'Size {size} not supported'	)

		# load image processor
		lowercase__				= PoolFormerImageProcessor(crop_pct=SCREAMING_SNAKE_CASE	)

		# Prepare image
		lowercase__				= prepare_img()
		lowercase__				= image_processor(images=SCREAMING_SNAKE_CASE		,							return_tensors='''pt'''	).pixel_values

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

		# load original state dict
		lowercase__				= torch.load(SCREAMING_SNAKE_CASE		,							map_location=torch.device('''cpu'''	)	)

		# rename keys
		lowercase__				= rename_keys(SCREAMING_SNAKE_CASE	)

		# create HuggingFace model and load state dict
		lowercase__				= PoolFormerForImageClassification(SCREAMING_SNAKE_CASE	)
		model.load_state_dict(SCREAMING_SNAKE_CASE	)
		model.eval()

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

		# forward pass
		lowercase__				= model(SCREAMING_SNAKE_CASE	)
		lowercase__				= outputs.logits

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

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


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)







'''simple docstring'''





import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


__a  			=						[
    {"dataset": "wikipedia", "config_name": "20220301.de"},
    {"dataset": "wikipedia", "config_name": "20220301.en"},
    {"dataset": "wikipedia", "config_name": "20220301.fr"},
    {"dataset": "wikipedia", "config_name": "20220301.frr"},
    {"dataset": "wikipedia", "config_name": "20220301.it"},
    {"dataset": "wikipedia", "config_name": "20220301.simple"},
    {"dataset": "snli", "config_name": "plain_text"},
    {"dataset": "eli5", "config_name": "LFQA_reddit"},
    {"dataset": "wiki40b", "config_name": "en"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.compressed"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.no_index"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.multiset.no_index"},
    {"dataset": "natural_questions", "config_name": "default"},
]





def 		__snake_case(							_lowerCAmelCase=True						)						->					Dict:
      if with_config:
            return [
                {
                    "testcase_name": d["dataset"] + "/" + d["config_name"],
                    "dataset": d["dataset"],
                    "config_name": d["config_name"],
                }
                for d in DATASETS_ON_HF_GCP
            ]
      else:
            return [
                {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
            ]







@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_a      )      )
class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						None
    lowercase   =						None




    def lowerCamelCase			(       self   :	List[Any]					,	snake_case_   :	Union[str, Any]					,	snake_case_   :	Optional[int]		):
          with TemporaryDirectory() as tmp_dir:
                snake_case__    :       Dict       		= dataset_module_factory(snake_case_					,	cache_dir=snake_case_		)

                snake_case__    :       Optional[int]       		= import_main_class(dataset_module.module_path					,	dataset=snake_case_		)

                snake_case__    :       DatasetBuilder       		= builder_cls(
                    cache_dir=snake_case_					,	config_name=snake_case_					,	hash=dataset_module.hash					,	)

                snake_case__    :       Dict       		= """/""".join(
                    [
                        HF_GCP_BASE_URL,
                        builder_instance._relative_data_dir(with_hash=snake_case_		).replace(os.sep					,	"""/"""		),
                        config.DATASET_INFO_FILENAME,
                    ]		)
                snake_case__    :       List[str]       		= cached_path(snake_case_					,	cache_dir=snake_case_		)
                self.assertTrue(os.path.exists(snake_case_		)		)





@pytest.mark.integration
def 		__snake_case(							_lowerCAmelCase						)						->					Tuple:
      snake_case__    :       Optional[int]       		= tmp_path_factory.mktemp("""test_hf_gcp"""						) / """test_wikipedia_simple"""
      snake_case__    :       Dict       		= dataset_module_factory("""wikipedia"""	,  cache_dir=_lowerCAmelCase						)
      snake_case__    :       Dict       		= import_main_class(dataset_module.module_path						)
      snake_case__    :       DatasetBuilder       		= builder_cls(
          cache_dir=_lowerCAmelCase	,  config_name="""20220301.frr"""	,  hash=dataset_module.hash	,  )
      # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
      snake_case__    :       Any       		= None
      builder_instance.download_and_prepare()
      snake_case__    :       Union[str, Any]       		= builder_instance.as_dataset()
      assert ds





@pytest.mark.integration
def 		__snake_case(							_lowerCAmelCase						)						->					List[str]:
      snake_case__    :       Optional[int]       		= dataset_module_factory("""wikipedia"""	,  cache_dir=_lowerCAmelCase						)
      snake_case__    :       List[str]       		= import_main_class(dataset_module.module_path	,  dataset=_lowerCAmelCase						)
      snake_case__    :       DatasetBuilder       		= builder_cls(
          cache_dir=_lowerCAmelCase	,  config_name="""20220301.frr"""	,  hash=dataset_module.hash	,  )
      snake_case__    :       Any       		= builder_instance.as_streaming_dataset()
      assert ds
      assert isinstance(_lowerCAmelCase	,  _lowerCAmelCase						)
      assert "train" in ds
      assert isinstance(ds["""train"""]	,  _lowerCAmelCase						)
      assert next(iter(ds["""train"""]						)						)
