


"""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_       							=    logging.get_logger(__name__)






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase   )	->	Tuple:
				_A					  =			original_name.split("."   )[0]
				_A					  =			key.split("."   )
				_A					  =			int(key_list[key_list.index(UpperCamelCase__   ) - 2]   )
				_A					  =			int(key_list[key_list.index(UpperCamelCase__   ) - 1]   )
				_A					  =			orig_block_num - offset

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






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






def      a__ (				)	->	Optional[int]:
				_A					  =			"http://images.cocodataset.org/val2017/000000039769.jpg"
				_A					  =			Image.open(requests.get(UpperCamelCase__     ,			stream=UpperCamelCase__   ).raw   )

				return image






@torch.no_grad()
def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	Dict:
				_A					  =			PoolFormerConfig()

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

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

				# load image processor
				_A					  =			PoolFormerImageProcessor(crop_pct=UpperCamelCase__   )

				# Prepare image
				_A					  =			prepare_img()
				_A					  =			image_processor(images=UpperCamelCase__     ,			return_tensors="pt"   ).pixel_values

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

				# load original state dict
				_A					  =			torch.load(UpperCamelCase__     ,			map_location=torch.device("cpu"   )   )

				# rename keys
				_A					  =			rename_keys(UpperCamelCase__   )

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

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

				# forward pass
				_A					  =			model(UpperCamelCase__   )
				_A					  =			outputs.logits

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



"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_       							=    {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    ["RoFormerTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "RoFormerForCausalLM",
		    "RoFormerForMaskedLM",
		    "RoFormerForMultipleChoice",
		    "RoFormerForQuestionAnswering",
		    "RoFormerForSequenceClassification",
		    "RoFormerForTokenClassification",
		    "RoFormerLayer",
		    "RoFormerModel",
		    "RoFormerPreTrainedModel",
		    "load_tf_weights_in_roformer",
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFRoFormerForCausalLM",
		    "TFRoFormerForMaskedLM",
		    "TFRoFormerForMultipleChoice",
		    "TFRoFormerForQuestionAnswering",
		    "TFRoFormerForSequenceClassification",
		    "TFRoFormerForTokenClassification",
		    "TFRoFormerLayer",
		    "TFRoFormerModel",
		    "TFRoFormerPreTrainedModel",
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "FlaxRoFormerForMaskedLM",
		    "FlaxRoFormerForMultipleChoice",
		    "FlaxRoFormerForQuestionAnswering",
		    "FlaxRoFormerForSequenceClassification",
		    "FlaxRoFormerForTokenClassification",
		    "FlaxRoFormerModel",
		    "FlaxRoFormerPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
		from .tokenization_roformer import RoFormerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_roformer_fast import RoFormerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roformer import (
				    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoFormerForCausalLM,
				    RoFormerForMaskedLM,
				    RoFormerForMultipleChoice,
				    RoFormerForQuestionAnswering,
				    RoFormerForSequenceClassification,
				    RoFormerForTokenClassification,
				    RoFormerLayer,
				    RoFormerModel,
				    RoFormerPreTrainedModel,
				    load_tf_weights_in_roformer,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roformer import (
				    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRoFormerForCausalLM,
				    TFRoFormerForMaskedLM,
				    TFRoFormerForMultipleChoice,
				    TFRoFormerForQuestionAnswering,
				    TFRoFormerForSequenceClassification,
				    TFRoFormerForTokenClassification,
				    TFRoFormerLayer,
				    TFRoFormerModel,
				    TFRoFormerPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roformer import (
				    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FlaxRoFormerForMaskedLM,
				    FlaxRoFormerForMultipleChoice,
				    FlaxRoFormerForQuestionAnswering,
				    FlaxRoFormerForSequenceClassification,
				    FlaxRoFormerForTokenClassification,
				    FlaxRoFormerModel,
				    FlaxRoFormerPreTrainedModel,
				)


else:
		import sys

		a_       							=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)