
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     =  logging.get_logger(__name__)






def      lowerCamelCase__       (       __lowerCAmelCase						:						Dict					,							__lowerCAmelCase						:						List[Any]					,							__lowerCAmelCase						:						Union[str, Any]					,							__lowerCAmelCase						:						Tuple       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			original_name.split("."       )[0]
			lowerCAmelCase_      			=			key.split("."       )
			lowerCAmelCase_      			=			int(key_list[key_list.index(__lowerCAmelCase       ) - 2]       )
			lowerCAmelCase_      			=			int(key_list[key_list.index(__lowerCAmelCase       ) - 1]       )
			lowerCAmelCase_      			=			orig_block_num - offset

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






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[str]       ):




			"""simple docstring"""


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






def      lowerCamelCase__       (       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			"http://images.cocodataset.org/val2017/000000039769.jpg"
			lowerCAmelCase_      			=			Image.open(requests.get(__lowerCAmelCase					,							stream=__lowerCAmelCase       ).raw       )

			return image






@torch.no_grad()
def      lowerCamelCase__       (       __lowerCAmelCase						:						Union[str, Any]					,							__lowerCAmelCase						:						List[Any]					,							__lowerCAmelCase						:						Any       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			PoolFormerConfig()

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

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

			# load image processor
			lowerCAmelCase_      			=			PoolFormerImageProcessor(crop_pct=__lowerCAmelCase       )

			# Prepare image
			lowerCAmelCase_      			=			prepare_img()
			lowerCAmelCase_      			=			image_processor(images=__lowerCAmelCase					,							return_tensors="pt"       ).pixel_values

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

			# load original state dict
			lowerCAmelCase_      			=			torch.load(__lowerCAmelCase					,							map_location=torch.device("cpu"       )       )

			# rename keys
			lowerCAmelCase_      			=			rename_keys(__lowerCAmelCase       )

			# create HuggingFace model and load state dict
			lowerCAmelCase_      			=			PoolFormerForImageClassification(__lowerCAmelCase       )
			model.load_state_dict(__lowerCAmelCase       )
			model.eval()

			# Define image processor
			lowerCAmelCase_      			=			PoolFormerImageProcessor(crop_pct=__lowerCAmelCase       )
			lowerCAmelCase_      			=			image_processor(images=prepare_img()					,							return_tensors="pt"       ).pixel_values

			# forward pass
			lowerCAmelCase_      			=			model(__lowerCAmelCase       )
			lowerCAmelCase_      			=			outputs.logits

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





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A     =  logging.get_logger(__name__)

_A     =  {
    "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/config.json",
    "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/config.json",
    "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/config.json",
    "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/config.json",
    "bert-base-multilingual-uncased": "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json",
    "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json",
    "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/config.json",
    "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/config.json",
    "bert-large-uncased-whole-word-masking": (
        "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"
    ),
    "bert-large-cased-whole-word-masking": (
        "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"
    ),
    "bert-large-uncased-whole-word-masking-finetuned-squad": (
        "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"
    ),
    "bert-large-cased-whole-word-masking-finetuned-squad": (
        "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"
    ),
    "bert-base-cased-finetuned-mrpc": "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json",
    "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json",
    "bert-base-german-dbmdz-uncased": "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json",
    "cl-tohoku/bert-base-japanese": "https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json",
    "cl-tohoku/bert-base-japanese-whole-word-masking": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"
    ),
    "cl-tohoku/bert-base-japanese-char": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"
    ),
    "cl-tohoku/bert-base-japanese-char-whole-word-masking": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"
    ),
    "TurkuNLP/bert-base-finnish-cased-v1": (
        "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"
    ),
    "TurkuNLP/bert-base-finnish-uncased-v1": (
        "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"
    ),
    "wietsedv/bert-base-dutch-cased": "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json",
    # See all BERT models at https://huggingface.co/models?filter=bert
}





class 			_lowerCAmelCase      (			__a ):
				_lowercase    	='''bert'''




				def __init__(      self       ,       _UpperCamelCase=30_522       ,       _UpperCamelCase=768       ,       _UpperCamelCase=12       ,       _UpperCamelCase=12       ,       _UpperCamelCase=3_072       ,       _UpperCamelCase="gelu"       ,       _UpperCamelCase=0.1       ,       _UpperCamelCase=0.1       ,       _UpperCamelCase=512       ,       _UpperCamelCase=2       ,       _UpperCamelCase=0.02       ,       _UpperCamelCase=1e-1_2       ,       _UpperCamelCase=0       ,       _UpperCamelCase="absolute"       ,       _UpperCamelCase=True       ,       _UpperCamelCase=None       ,       **_UpperCamelCase       ,       )       ->   str:
							super().__init__(pad_token_id=_UpperCamelCase       ,       **_UpperCamelCase  )

							lowerCAmelCase_      			=			vocab_size
							lowerCAmelCase_      			=			hidden_size
							lowerCAmelCase_      			=			num_hidden_layers
							lowerCAmelCase_      			=			num_attention_heads
							lowerCAmelCase_      			=			hidden_act
							lowerCAmelCase_      			=			intermediate_size
							lowerCAmelCase_      			=			hidden_dropout_prob
							lowerCAmelCase_      			=			attention_probs_dropout_prob
							lowerCAmelCase_      			=			max_position_embeddings
							lowerCAmelCase_      			=			type_vocab_size
							lowerCAmelCase_      			=			initializer_range
							lowerCAmelCase_      			=			layer_norm_eps
							lowerCAmelCase_      			=			position_embedding_type
							lowerCAmelCase_      			=			use_cache
							lowerCAmelCase_      			=			classifier_dropout





class 			_lowerCAmelCase      (			__a ):




				@property
				def   __a		(      self  )       ->   Mapping[str, Mapping[int, str]]:
							if self.task == "multiple-choice":
										lowerCAmelCase_      			=			{0: "batch", 1: "choice", 2: "sequence"}
							else:
										lowerCAmelCase_      			=			{0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							        ("token_type_ids", dynamic_axis),
							    ]  )



