


'''simple docstring'''




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


_lowerCAmelCase  :		List[str]		     =	logging.get_logger(__name__)

_lowerCAmelCase  :		List[Any]		     =	{
    'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json',
}



class  lowerCAmelCase				(				a							):
	_lowerCamelCase   :					Optional[Any]			      = """lxmert"""
	_lowerCamelCase   :					List[Any]			      = {}



	def __init__(					self ,					snake_case__=3_0522 ,					snake_case__=768 ,					snake_case__=12 ,					snake_case__=9500 ,					snake_case__=1600 ,					snake_case__=400 ,					snake_case__=3072 ,					snake_case__="gelu" ,					snake_case__=0.1 ,					snake_case__=0.1 ,					snake_case__=512 ,					snake_case__=2 ,					snake_case__=0.0_2 ,					snake_case__=1e-1_2 ,					snake_case__=9 ,					snake_case__=5 ,					snake_case__=5 ,					snake_case__=2048 ,					snake_case__=4 ,					snake_case__=6.6_7 ,					snake_case__=True ,					snake_case__=True ,					snake_case__=True ,					snake_case__=True ,					snake_case__=True ,					snake_case__=True ,					snake_case__=True ,					**snake_case__ ,					):
		lowerCAmelCase					:    Tuple      							= vocab_size
		lowerCAmelCase					:    str      							= hidden_size
		lowerCAmelCase					:    Optional[Any]      							= num_attention_heads
		lowerCAmelCase					:    Dict      							= hidden_act
		lowerCAmelCase					:    List[str]      							= intermediate_size
		lowerCAmelCase					:    List[Any]      							= hidden_dropout_prob
		lowerCAmelCase					:    List[Any]      							= attention_probs_dropout_prob
		lowerCAmelCase					:    str      							= max_position_embeddings
		lowerCAmelCase					:    Union[str, Any]      							= type_vocab_size
		lowerCAmelCase					:    List[str]      							= initializer_range
		lowerCAmelCase					:    Dict      							= layer_norm_eps
		lowerCAmelCase					:    List[Any]      							= num_qa_labels
		lowerCAmelCase					:    Dict      							= num_object_labels
		lowerCAmelCase					:    str      							= num_attr_labels
		lowerCAmelCase					:    Union[str, Any]      							= l_layers
		lowerCAmelCase					:    int      							= x_layers
		lowerCAmelCase					:    Dict      							= r_layers
		lowerCAmelCase					:    int      							= visual_feat_dim
		lowerCAmelCase					:    Union[str, Any]      							= visual_pos_dim
		lowerCAmelCase					:    List[Any]      							= visual_loss_normalizer
		lowerCAmelCase					:    Dict      							= task_matched
		lowerCAmelCase					:    List[str]      							= task_mask_lm
		lowerCAmelCase					:    str      							= task_obj_predict
		lowerCAmelCase					:    Optional[int]      							= task_qa
		lowerCAmelCase					:    int      							= visual_obj_loss
		lowerCAmelCase					:    Dict      							= visual_attr_loss
		lowerCAmelCase					:    str      							= visual_feat_loss
		lowerCAmelCase					:    Optional[Any]      							= {'vision': r_layers, 'cross_encoder': x_layers, 'language': l_layers}
		super().__init__(**snake_case__    )




'''simple docstring'''




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 			__UpperCamelCase    (    _A    :				Dict				)    ->      int:




	"""simple docstring"""





	lowerCAmelCase					:    Tuple      							= []
	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 			__UpperCamelCase    (    _A    :				List[Any]					,   _A    :				Dict				)    ->      Any:




	"""simple docstring"""





	lowerCAmelCase					:    str      							= []
	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 			__UpperCamelCase    (    _A    :				Optional[int]				)    ->      Optional[int]:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[int]      							= []
	token.append((F"cvt.encoder.stages.{idx}.cls_token", 'stage2.cls_token')				)
	return token

def 			__UpperCamelCase    (    )    ->      int:




	"""simple docstring"""





	lowerCAmelCase					:    List[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 			__UpperCamelCase    (    _A    :				str					,   _A    :				Optional[Any]					,   _A    :				Dict					,   _A    :				str				)    ->      Optional[int]:




	"""simple docstring"""





	lowerCAmelCase					:    List[str]      							= 'imagenet-1k-id2label.json'
	lowerCAmelCase					:    Tuple      							= 10_00

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

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

	lowerCAmelCase					:    int      							= CvtConfig(num_labels=_A					,   idalabel=_A					,   labelaid=_A				)

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

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

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

	lowerCAmelCase					:    Union[str, Any]      							= CvtForImageClassification(_A				)
	lowerCAmelCase					:    str      							= AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'				)
	lowerCAmelCase					:    Optional[Any]      							= image_size
	lowerCAmelCase					:    List[Any]      							= torch.load(_A					,   map_location=torch.device('cpu'				)				)

	lowerCAmelCase					:    str      							= OrderedDict()
	lowerCAmelCase					:    int      							= []

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

	lowerCAmelCase					:    List[str]      							= list_of_state_dict + final()
	for gg in list_of_state_dict:
		print(_A				)
	for i in range(len(_A				)				):
		lowerCAmelCase					:    Tuple      							= original_weights[list_of_state_dict[i][1]]

	model.load_state_dict(_A				)
	model.save_pretrained(_A				)
	image_processor.save_pretrained(_A				)


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

if __name__ == "__main__":
				_lowerCAmelCase  :		Optional[Any]		     =	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=384,
				    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.'
				)

				_lowerCAmelCase  :		str		     =	parser.parse_args()
				convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
