

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 			A_      (	__a     :				Union[str, Any]		):

				"""simple docstring"""
				a__				      =		SwinConfig()
				a__				      =		swin_name.split("""_"""		)

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

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

				if "in22k" in swin_name:
								a__				      =		21_841
				else:
								a__				      =		1_000
								a__				      =		"""huggingface/label-files"""
								a__				      =		"""imagenet-1k-id2label.json"""
								a__				      =		json.load(open(hf_hub_download(__a ,		__a ,		repo_type="""dataset"""		) ,		"""r"""		)		)
								a__				      =		{int(__a		): v for k, v in idalabel.items()}
								a__				      =		idalabel
								a__				      =		{v: k for k, v in idalabel.items()}

				a__				      =		img_size
				a__				      =		num_classes
				a__				      =		embed_dim
				a__				      =		depths
				a__				      =		num_heads
				a__				      =		window_size

				return config

def 			A_      (	__a     :				Tuple		):

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

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

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

				return name

def 			A_      (	__a     :				Any ,		__a     :				Union[str, Any]		):

				"""simple docstring"""
				for key in orig_state_dict.copy().keys():
								a__				      =		orig_state_dict.pop(__a		)

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

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

				return orig_state_dict

def 			A_      (	__a     :				List[Any] ,		__a     :				Dict		):

				"""simple docstring"""
				a__				      =		timm.create_model(__a ,		pretrained=__a		)
				timm_model.eval()

				a__				      =		get_swin_config(__a		)
				a__				      =		SwinForImageClassification(__a		)
				model.eval()

				a__				      =		convert_state_dict(timm_model.state_dict() ,		__a		)
				model.load_state_dict(__a		)

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

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

				a__				      =		timm_model(inputs["""pixel_values"""]		)
				a__				      =		model(**__a		).logits

				assert torch.allclose(__a ,		__a ,		atol=1e-3		)

				print(F'''Saving model {swin_name} to {pytorch_dump_folder_path}'''		)
				model.save_pretrained(__a		)

				print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
				image_processor.save_pretrained(__a		)


if __name__ == "__main__":
	UpperCAmelCase							 =					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							 =					parser.parse_args()
	convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase							 =					{
    """configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							 =					[
	    """FALCON_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """FalconForCausalLM""",
	    """FalconModel""",
	    """FalconPreTrainedModel""",
	    """FalconForSequenceClassification""",
	    """FalconForTokenClassification""",
	    """FalconForQuestionAnswering""",
	]


if TYPE_CHECKING:
	from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_falcon import (
		    FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FalconForCausalLM,
		    FalconForQuestionAnswering,
		    FalconForSequenceClassification,
		    FalconForTokenClassification,
		    FalconModel,
		    FalconPreTrainedModel,
		)


else:
	import sys

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

