





import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	snake_case_		:			Optional[Any]											=	[]
	embed.append(
	    (
	        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
	        f"""stage{idx}.patch_embed.proj.weight""",
	    )   )
	embed.append(
	    (
	        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
	        f"""stage{idx}.patch_embed.proj.bias""",
	    )   )
	embed.append(
	    (
	        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
	        f"""stage{idx}.patch_embed.norm.weight""",
	    )   )
	embed.append(
	    (
	        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
	        f"""stage{idx}.patch_embed.norm.bias""",
	    )   )
	return embed


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	snake_case_		:			Optional[Any]											=	[]
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
	    )   )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
	    )   )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
	    )   )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
	    )   )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
	    )   )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
	    )   )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
	    )   )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
	    )   )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
	    )   )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
	    )   )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
	    )   )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
	    )   )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
	    )   )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
	    )   )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
	    )   )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
	    )   )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
	    )   )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
	    )   )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
	    )   )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
	        f"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
	    )   )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
	    )   )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
	        f"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
	    )   )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
	    )   )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
	        f"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
	    )   )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
	    )   )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
	        f"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
	    )   )
	attention_weights.append(
	    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""")   )
	attention_weights.append(
	    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""")   )
	attention_weights.append(
	    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""")   )
	attention_weights.append(
	    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""")   )
	attention_weights.append(
	    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", f"""stage{idx}.blocks.{cnt}.norm1.weight""")   )
	attention_weights.append(
	    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", f"""stage{idx}.blocks.{cnt}.norm1.bias""")   )
	attention_weights.append(
	    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", f"""stage{idx}.blocks.{cnt}.norm2.weight""")   )
	attention_weights.append(
	    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", f"""stage{idx}.blocks.{cnt}.norm2.bias""")   )
	return attention_weights


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	snake_case_		:			int											=	[]
	token.append((f"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token')   )
	return token


def 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			Any											=	[]
	head.append(('layernorm.weight', 'norm.weight')   )
	head.append(('layernorm.bias', 'norm.bias')   )
	head.append(('classifier.weight', 'head.weight')   )
	head.append(('classifier.bias', 'head.bias')   )
	return head


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   ):
	snake_case_		:			List[str]											=	'''imagenet-1k-id2label.json'''
	snake_case_		:			str											=	10_00

	snake_case_		:			Optional[Any]											=	'''huggingface/label-files'''
	snake_case_		:			List[Any]											=	num_labels
	snake_case_		:			List[str]											=	json.load(open(cached_download(hf_hub_url(_lowercase   , _lowercase   , repo_type='dataset'   )   )   , 'r'   )   )
	snake_case_		:			Tuple											=	{int(_lowercase   ): v for k, v in idalabel.items()}

	snake_case_		:			List[Any]											=	idalabel
	snake_case_		:			Any											=	{v: k for k, v in idalabel.items()}

	snake_case_		:			int											=	CvtConfig(num_labels=_lowercase   , idalabel=_lowercase   , labelaid=_lowercase   )

	# For depth size 13 (13 = 1+2+10)
	if cvt_model.rsplit('/'   , 1   )[-1][4:6] == "13":
		snake_case_		:			int											=	[1, 2, 10]

	# For depth size 21 (21 = 1+4+16)
	elif cvt_model.rsplit('/'   , 1   )[-1][4:6] == "21":
		snake_case_		:			Dict											=	[1, 4, 16]

	# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
	else:
		snake_case_		:			Any											=	[2, 2, 20]
		snake_case_		:			List[str]											=	[3, 12, 16]
		snake_case_		:			int											=	[1_92, 7_68, 10_24]

	snake_case_		:			Any											=	CvtForImageClassification(_lowercase   )
	snake_case_		:			str											=	AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'   )
	snake_case_		:			Any											=	image_size
	snake_case_		:			Any											=	torch.load(_lowercase   , map_location=torch.device('cpu'   )   )

	snake_case_		:			str											=	OrderedDict()
	snake_case_		:			Union[str, Any]											=	[]

	for idx in range(len(config.depth   )   ):
		if config.cls_token[idx]:
			snake_case_		:			List[str]											=	list_of_state_dict + cls_token(_lowercase   )
		snake_case_		:			Optional[Any]											=	list_of_state_dict + embeddings(_lowercase   )
		for cnt in range(config.depth[idx]   ):
			snake_case_		:			List[Any]											=	list_of_state_dict + attention(_lowercase   , _lowercase   )

	snake_case_		:			Any											=	list_of_state_dict + final()
	for gg in list_of_state_dict:
		print(_lowercase   )
	for i in range(len(_lowercase   )   ):
		snake_case_		:			Tuple											=	original_weights[list_of_state_dict[i][1]]

	model.load_state_dict(_lowercase   )
	model.save_pretrained(_lowercase   )
	image_processor.save_pretrained(_lowercase   )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		argparse.ArgumentParser()
						parser.add_argument(
						    """--cvt_model""",
						    default="""cvt-w24""",
						    type=str,
						    help="""Name of the cvt model you\'d like to convert.""",
						)
						parser.add_argument(
						    """--image_size""",
						    default=3_84,
						    type=int,
						    help="""Input Image Size""",
						)
						parser.add_argument(
						    """--cvt_file_name""",
						    default=R"""cvtmodels\CvT-w24-384x384-IN-22k.pth""",
						    type=str,
						    help="""Input Image Size""",
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
						)

						_SCREAMING_SNAKE_CASE      	=		parser.parse_args()
						convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)










from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      	=		{
    """SCUT-DLVCLab/lilt-roberta-en-base""": (
        """https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"""
    ),
}
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			str       =       "lilt"






						def __init__(      self      :      Any		,   _A      :      Dict=30522		,   _A      :      List[Any]=768		,   _A      :      str=12		,   _A      :      int=12		,   _A      :      Optional[int]=3072		,   _A      :      Optional[Any]="gelu"		,   _A      :      Dict=0.1		,   _A      :      Tuple=0.1		,   _A      :      str=512		,   _A      :      Union[str, Any]=2		,   _A      :      Union[str, Any]=0.0_2		,   _A      :      Optional[Any]=1E-12		,   _A      :      Optional[int]=0		,   _A      :      Dict="absolute"		,   _A      :      Any=None		,   _A      :      Optional[Any]=4		,   _A      :      Any=1024		,   **_A      :      Union[str, Any]		,   )							->    List[str]:







							"""simple docstring"""


							super().__init__(pad_token_id=_A		,   **_A	)

							snake_case_		:			List[str]											=	vocab_size
							snake_case_		:			Dict											=	hidden_size
							snake_case_		:			Union[str, Any]											=	num_hidden_layers
							snake_case_		:			List[str]											=	num_attention_heads
							snake_case_		:			Tuple											=	hidden_act
							snake_case_		:			Tuple											=	intermediate_size
							snake_case_		:			int											=	hidden_dropout_prob
							snake_case_		:			Tuple											=	attention_probs_dropout_prob
							snake_case_		:			Dict											=	max_position_embeddings
							snake_case_		:			Any											=	type_vocab_size
							snake_case_		:			Union[str, Any]											=	initializer_range
							snake_case_		:			Optional[int]											=	layer_norm_eps
							snake_case_		:			List[str]											=	position_embedding_type
							snake_case_		:			Optional[int]											=	classifier_dropout
							snake_case_		:			Dict											=	channel_shrink_ratio
							snake_case_		:			Tuple											=	max_ad_position_embeddings

