


import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification






def 	__lowerCamelCase						(							lowerCamelCase__		:						Tuple      ):





			'''simple docstring'''




			lowerCamelCase    		=  SwinConfig()
			lowerCamelCase    		=  swin_name.split("""_"""      )

			lowerCamelCase    		=  name_split[1]
			lowerCamelCase    		=  int(name_split[4]      )
			lowerCamelCase    		=  int(name_split[3][-1]      )

			if model_size == "tiny":
						lowerCamelCase    		=  96
						lowerCamelCase    		=  (2, 2, 6, 2)
						lowerCamelCase    		=  (3, 6, 12, 24)
			elif model_size == "small":
						lowerCamelCase    		=  96
						lowerCamelCase    		=  (2, 2, 18, 2)
						lowerCamelCase    		=  (3, 6, 12, 24)
			elif model_size == "base":
						lowerCamelCase    		=  128
						lowerCamelCase    		=  (2, 2, 18, 2)
						lowerCamelCase    		=  (4, 8, 16, 32)
			else:
						lowerCamelCase    		=  192
						lowerCamelCase    		=  (2, 2, 18, 2)
						lowerCamelCase    		=  (6, 12, 24, 48)

			if "in22k" in swin_name:
						lowerCamelCase    		=  21841
			else:
						lowerCamelCase    		=  1000
						lowerCamelCase    		=  """huggingface/label-files"""
						lowerCamelCase    		=  """imagenet-1k-id2label.json"""
						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()}

			lowerCamelCase    		=  img_size
			lowerCamelCase    		=  num_classes
			lowerCamelCase    		=  embed_dim
			lowerCamelCase    		=  depths
			lowerCamelCase    		=  num_heads
			lowerCamelCase    		=  window_size

			return config






def 	__lowerCamelCase						(							lowerCamelCase__		:						Dict      ):





			'''simple docstring'''




			if "patch_embed.proj" in name:
						lowerCamelCase    		=  name.replace("""patch_embed.proj"""  ,							"""embeddings.patch_embeddings.projection"""      )
			if "patch_embed.norm" in name:
						lowerCamelCase    		=  name.replace("""patch_embed.norm"""  ,							"""embeddings.norm"""      )
			if "layers" in name:
						lowerCamelCase    		=  """encoder.""" + name
			if "attn.proj" in name:
						lowerCamelCase    		=  name.replace("""attn.proj"""  ,							"""attention.output.dense"""      )
			if "attn" in name:
						lowerCamelCase    		=  name.replace("""attn"""  ,							"""attention.self"""      )
			if "norm1" in name:
						lowerCamelCase    		=  name.replace("""norm1"""  ,							"""layernorm_before"""      )
			if "norm2" in name:
						lowerCamelCase    		=  name.replace("""norm2"""  ,							"""layernorm_after"""      )
			if "mlp.fc1" in name:
						lowerCamelCase    		=  name.replace("""mlp.fc1"""  ,							"""intermediate.dense"""      )
			if "mlp.fc2" in name:
						lowerCamelCase    		=  name.replace("""mlp.fc2"""  ,							"""output.dense"""      )

			if name == "norm.weight":
						lowerCamelCase    		=  """layernorm.weight"""
			if name == "norm.bias":
						lowerCamelCase    		=  """layernorm.bias"""

			if "head" in name:
						lowerCamelCase    		=  name.replace("""head"""  ,							"""classifier"""      )
			else:
						lowerCamelCase    		=  """swin.""" + name

			return name






def 	__lowerCamelCase						(							lowerCamelCase__		:						Union[str, Any]  ,							lowerCamelCase__		:						Union[str, Any]      ):





			'''simple docstring'''




			for key in orig_state_dict.copy().keys():
						lowerCamelCase    		=  orig_state_dict.pop(lowerCamelCase__      )

						if "mask" in key:
									continue
						elif "qkv" in key:
									lowerCamelCase    		=  key.split("""."""      )
									lowerCamelCase    		=  int(key_split[1]      )
									lowerCamelCase    		=  int(key_split[3]      )
									lowerCamelCase    		=  model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

									if "weight" in key:
												lowerCamelCase    		=  val[:dim, :]
												lowerCamelCase    		=  val[
												    dim : dim * 2, :
												]
												lowerCamelCase    		=  val[-dim:, :]
									else:
												lowerCamelCase    		=  val[
												    :dim
												]
												lowerCamelCase    		=  val[
												    dim : dim * 2
												]
												lowerCamelCase    		=  val[
												    -dim:
												]
						else:
									lowerCamelCase    		=  val

			return orig_state_dict






def 	__lowerCamelCase						(							lowerCamelCase__		:						str  ,							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			lowerCamelCase    		=  timm.create_model(lowerCamelCase__  ,							pretrained=lowerCamelCase__      )
			timm_model.eval()

			lowerCamelCase    		=  get_swin_config(lowerCamelCase__      )
			lowerCamelCase    		=  SwinForImageClassification(lowerCamelCase__      )
			model.eval()

			lowerCamelCase    		=  convert_state_dict(timm_model.state_dict()  ,							lowerCamelCase__      )
			model.load_state_dict(lowerCamelCase__      )

			lowerCamelCase    		=  """http://images.cocodataset.org/val2017/000000039769.jpg"""

			lowerCamelCase    		=  AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_"""  ,							"""-"""      )      )      )
			lowerCamelCase    		=  Image.open(requests.get(lowerCamelCase__  ,							stream=lowerCamelCase__      ).raw      )
			lowerCamelCase    		=  image_processor(images=lowerCamelCase__  ,							return_tensors="""pt"""      )

			lowerCamelCase    		=  timm_model(inputs["""pixel_values"""]      )
			lowerCamelCase    		=  model(**lowerCamelCase__      ).logits

			assert torch.allclose(lowerCamelCase__  ,							lowerCamelCase__  ,							atol=1E-3      )

			print(f'Saving model {swin_name} to {pytorch_dump_folder_path}'      )
			model.save_pretrained(lowerCamelCase__      )

			print(f'Saving image processor to {pytorch_dump_folder_path}'      )
			image_processor.save_pretrained(lowerCamelCase__      )


if __name__ == "__main__":
		UpperCAmelCase   :    Any     =		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--swin_name",
		    default="swin_tiny_patch4_window7_224",
		    type=str,
		    help="Name of the Swin timm model you'd like to convert.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
		)

		UpperCAmelCase   :    int     =		parser.parse_args()
		convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)





import math

import tensorflow as tf
from packaging import version






def 	__lowerCamelCase						(							lowerCamelCase__		:						Any      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )
			lowerCamelCase    		=  0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0      )  ,							x.dtype      )      ))

			return x * cdf






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[str]      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )
			lowerCamelCase    		=  tf.cast(math.pi  ,							x.dtype      )
			lowerCamelCase    		=  tf.cast(0.0_4_4_7_1_5  ,							x.dtype      )
			lowerCamelCase    		=  0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi      ) * (x + coeff * tf.pow(lowerCamelCase__  ,							3      ))      ))

			return x * cdf






def 	__lowerCamelCase						(							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )

			return x * tf.tanh(tf.math.softplus(lowerCamelCase__      )      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						Union[str, Any]      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )
			lowerCamelCase    		=  tf.cast(0.0_4_4_7_1_5  ,							x.dtype      )
			lowerCamelCase    		=  tf.cast(0.7_9_7_8_8_4_5_6_0_8  ,							x.dtype      )

			return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)      ))






def 	__lowerCamelCase						(							lowerCamelCase__		:						Optional[int]      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )
			lowerCamelCase    		=  tf.cast(1.7_0_2  ,							x.dtype      )
			return x * tf.math.sigmoid(coeff * x      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						Dict      ):





			'''simple docstring'''




			return tf.clip_by_value(_gelu(lowerCamelCase__      )  ,							-10  ,							10      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						Dict=-1      ):





			'''simple docstring'''




			lowerCamelCase		,		lowerCamelCase    		=  tf.split(lowerCamelCase__  ,							2  ,							axis=lowerCamelCase__      )
			return a * tf.math.sigmoid(lowerCamelCase__      )


if version.parse(tf.version.VERSION) >= version.parse("2.4"):






		def 	__lowerCamelCase						(							lowerCamelCase__		:						Union[str, Any]      ):





					'''simple docstring'''




					return tf.keras.activations.gelu(lowerCamelCase__  ,							approximate=lowerCamelCase__      )

		UpperCAmelCase   :    Union[str, Any]     =		tf.keras.activations.gelu



		UpperCAmelCase   :    int     =		approximate_gelu_wrap
else:
		UpperCAmelCase   :    Dict     =		_gelu
		UpperCAmelCase   :    Dict     =		_gelu_new


UpperCAmelCase   :    List[str]     =		{
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}






def 	__lowerCamelCase						(							lowerCamelCase__		:						Tuple      ):





			'''simple docstring'''




			if activation_string in ACTaFN:
						return ACTaFN[activation_string]
			else:
						raise KeyError(f'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()      )}'      )

