





'''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()
__UpperCAmelCase            =       logging.get_logger(__name__)



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Optional[int]		=			original_name.split("""."""	)[0]
		SCREAMING_SNAKE_CASE :				Union[str, Any]		=			key.split("""."""	)
		SCREAMING_SNAKE_CASE :				List[Any]		=			int(key_list[key_list.index(lowerCamelCase_	) - 2]	)
		SCREAMING_SNAKE_CASE :				str		=			int(key_list[key_list.index(lowerCamelCase_	) - 1]	)
		SCREAMING_SNAKE_CASE :				Optional[Any]		=			orig_block_num - offset

		SCREAMING_SNAKE_CASE :				List[Any]		=			key.replace(f'''{orig_block_num}.{layer_num}.{original_name}'''			,			f'''block.{new_block_num}.{layer_num}.{new_name}'''	)
		return key



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



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



def 		__A		(  ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Union[str, Any]		=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
		SCREAMING_SNAKE_CASE :				Optional[int]		=			Image.open(requests.get(lowerCamelCase_			,			stream=lowerCamelCase_	).raw	)

		return image



@torch.no_grad()
def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Optional[Any]		=			PoolFormerConfig()

		# set attributes based on model_name
		SCREAMING_SNAKE_CASE :				int		=			"""huggingface/label-files"""
		SCREAMING_SNAKE_CASE :				Optional[Any]		=			model_name[-3:]
		SCREAMING_SNAKE_CASE :				Optional[int]		=			10_00
		SCREAMING_SNAKE_CASE :				Tuple		=			"""imagenet-1k-id2label.json"""
		SCREAMING_SNAKE_CASE :				Union[str, Any]		=			(1, 10_00)

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

		# load image processor
		SCREAMING_SNAKE_CASE :				List[str]		=			PoolFormerImageProcessor(crop_pct=lowerCamelCase_	)

		# Prepare image
		SCREAMING_SNAKE_CASE :				int		=			prepare_img()
		SCREAMING_SNAKE_CASE :				str		=			image_processor(images=lowerCamelCase_			,			return_tensors="""pt"""	).pixel_values

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

		# load original state dict
		SCREAMING_SNAKE_CASE :				Any		=			torch.load(lowerCamelCase_			,			map_location=torch.device("""cpu"""	)	)

		# rename keys
		SCREAMING_SNAKE_CASE :				List[Any]		=			rename_keys(lowerCamelCase_	)

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

		# Define image processor
		SCREAMING_SNAKE_CASE :				int		=			PoolFormerImageProcessor(crop_pct=lowerCamelCase_	)
		SCREAMING_SNAKE_CASE :				List[str]		=			image_processor(images=prepare_img()			,			return_tensors="""pt"""	).pixel_values

		# forward pass
		SCREAMING_SNAKE_CASE :				Any		=			model(lowerCamelCase_	)
		SCREAMING_SNAKE_CASE :				Optional[int]		=			outputs.logits

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










'''simple docstring'''




import os
import sys
import unittest


__UpperCAmelCase            =       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCAmelCase            =       os.path.join(git_repo_path, """src""", """transformers""")

__UpperCAmelCase            =       """
{0} = None
"""

__UpperCAmelCase            =       """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


__UpperCAmelCase            =       """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""




class      UpperCamelCase__					(					unittest.TestCase			):




					"""simple docstring"""


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			find_backend("""    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"""						)
							self.assertIsNone(lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				str		=			find_backend("""    if not is_tokenizers_available():"""						)
							self.assertEqual(lowerCamelCase_      ,						"""tokenizers"""						)

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			find_backend("""    if not is_tensorflow_text_available():"""						)
							self.assertEqual(lowerCamelCase_      ,						"""tensorflow_text"""						)

							SCREAMING_SNAKE_CASE :				List[str]		=			find_backend("""    if not (is_sentencepiece_available() and is_tokenizers_available()):"""						)
							self.assertEqual(lowerCamelCase_      ,						"""sentencepiece_and_tokenizers"""						)

							SCREAMING_SNAKE_CASE :				Tuple		=			find_backend(
							    """    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"""						)
							self.assertEqual(lowerCamelCase_      ,						"""sentencepiece_and_tensorflow_text"""						)

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			find_backend(
							    """    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"""						)
							self.assertEqual(lowerCamelCase_      ,						"""sentencepiece_and_tokenizers_and_vision"""						)


					def lowerCamelCase_			(						self				:			Any						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[Any]		=			read_init()
							# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
							self.assertIn("""torch"""      ,						lowerCamelCase_						)
							self.assertIn("""tensorflow_text"""      ,						lowerCamelCase_						)
							self.assertIn("""sentencepiece_and_tokenizers"""      ,						lowerCamelCase_						)

							# Likewise, we can't assert on the exact content of a key
							self.assertIn("""BertModel"""      ,						objects["""torch"""]						)
							self.assertIn("""TFBertModel"""      ,						objects["""tf"""]						)
							self.assertIn("""FlaxBertModel"""      ,						objects["""flax"""]						)
							self.assertIn("""BertModel"""      ,						objects["""torch"""]						)
							self.assertIn("""TFBertTokenizer"""      ,						objects["""tensorflow_text"""]						)
							self.assertIn("""convert_slow_tokenizer"""      ,						objects["""sentencepiece_and_tokenizers"""]						)


					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			create_dummy_object("""CONSTANT"""      ,						"""'torch'"""						)
							self.assertEqual(lowerCamelCase_      ,						"""\nCONSTANT = None\n"""						)

							SCREAMING_SNAKE_CASE :				List[Any]		=			create_dummy_object("""function"""      ,						"""'torch'"""						)
							self.assertEqual(
							    lowerCamelCase_      ,						"""\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""						)

							SCREAMING_SNAKE_CASE :				str		=			"""
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')
"""
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			create_dummy_object("""FakeClass"""      ,						"""'torch'"""						)
							self.assertEqual(lowerCamelCase_      ,						lowerCamelCase_						)



					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			"""# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])
"""
							SCREAMING_SNAKE_CASE :				List[Any]		=			create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}						)
							self.assertEqual(dummy_files["""torch"""]      ,						lowerCamelCase_						)



