



"""simple docstring"""





import argparse
import json
from pathlib import Path

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

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase       :					Dict      					=					logging.get_logger(__name__)
def 	__lowerCAmelCase						(		__UpperCamelCase      :			str    ):


						'''simple docstring'''






						snake_case_   : int						=			MobileViTConfig()

						# size of the architecture
						if "mobilevit_s" in mobilevit_name:
												snake_case_   : Any						=			[1_4_4, 1_9_2, 2_4_0]
												snake_case_   : Any						=			[1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]
						elif "mobilevit_xs" in mobilevit_name:
												snake_case_   : Optional[Any]						=			[9_6, 1_2_0, 1_4_4]
												snake_case_   : List[str]						=			[1_6, 3_2, 4_8, 6_4, 8_0, 9_6, 3_8_4]
						elif "mobilevit_xxs" in mobilevit_name:
												snake_case_   : Union[str, Any]						=			[6_4, 8_0, 9_6]
												snake_case_   : str						=			[1_6, 1_6, 2_4, 4_8, 6_4, 8_0, 3_2_0]
												snake_case_   : List[Any]						=			0.05
												snake_case_   : str						=			2.0

						if mobilevit_name.startswith("""deeplabv3_"""    ):
												snake_case_   : Optional[Any]						=			5_1_2
												snake_case_   : Dict						=			1_6
												snake_case_   : str						=			2_1
												snake_case_   : List[str]						=			"""pascal-voc-id2label.json"""
						else:
												snake_case_   : Union[str, Any]						=			1_0_0_0
												snake_case_   : Union[str, Any]						=			"""imagenet-1k-id2label.json"""

						snake_case_   : List[Any]						=			"""huggingface/label-files"""
						snake_case_   : Any						=			json.load(open(hf_hub_download(__UpperCamelCase					, __UpperCamelCase					, repo_type="""dataset"""    )					, """r"""    )    )
						snake_case_   : Optional[int]						=			{int(__UpperCamelCase    ): v for k, v in idalabel.items()}
						snake_case_   : Optional[int]						=			idalabel
						snake_case_   : int						=			{v: k for k, v in idalabel.items()}

						return config
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Dict					, __UpperCamelCase      :			List[Any]=False    ):


						'''simple docstring'''






						for i in range(1					, 6    ):
												if F'layer_{i}.' in name:
																		snake_case_   : str						=			name.replace(F'layer_{i}.'					, F'encoder.layer.{i - 1}.'    )

						if "conv_1." in name:
												snake_case_   : Any						=			name.replace("""conv_1."""					, """conv_stem."""    )
						if ".block." in name:
												snake_case_   : Any						=			name.replace(""".block."""					, """."""    )

						if "exp_1x1" in name:
												snake_case_   : List[str]						=			name.replace("""exp_1x1"""					, """expand_1x1"""    )
						if "red_1x1" in name:
												snake_case_   : Tuple						=			name.replace("""red_1x1"""					, """reduce_1x1"""    )
						if ".local_rep.conv_3x3." in name:
												snake_case_   : int						=			name.replace(""".local_rep.conv_3x3."""					, """.conv_kxk."""    )
						if ".local_rep.conv_1x1." in name:
												snake_case_   : List[Any]						=			name.replace(""".local_rep.conv_1x1."""					, """.conv_1x1."""    )
						if ".norm." in name:
												snake_case_   : Optional[int]						=			name.replace(""".norm."""					, """.normalization."""    )
						if ".conv." in name:
												snake_case_   : Dict						=			name.replace(""".conv."""					, """.convolution."""    )
						if ".conv_proj." in name:
												snake_case_   : List[str]						=			name.replace(""".conv_proj."""					, """.conv_projection."""    )

						for i in range(0					, 2    ):
												for j in range(0					, 4    ):
																		if F'.{i}.{j}.' in name:
																								snake_case_   : Union[str, Any]						=			name.replace(F'.{i}.{j}.'					, F'.{i}.layer.{j}.'    )

						for i in range(2					, 6    ):
												for j in range(0					, 4    ):
																		if F'.{i}.{j}.' in name:
																								snake_case_   : int						=			name.replace(F'.{i}.{j}.'					, F'.{i}.'    )
																								if "expand_1x1" in name:
																														snake_case_   : Tuple						=			name.replace("""expand_1x1"""					, """downsampling_layer.expand_1x1"""    )
																								if "conv_3x3" in name:
																														snake_case_   : Optional[Any]						=			name.replace("""conv_3x3"""					, """downsampling_layer.conv_3x3"""    )
																								if "reduce_1x1" in name:
																														snake_case_   : Optional[Any]						=			name.replace("""reduce_1x1"""					, """downsampling_layer.reduce_1x1"""    )

						for i in range(2					, 5    ):
												if F'.global_rep.{i}.weight' in name:
																		snake_case_   : Tuple						=			name.replace(F'.global_rep.{i}.weight'					, """.layernorm.weight"""    )
												if F'.global_rep.{i}.bias' in name:
																		snake_case_   : Optional[Any]						=			name.replace(F'.global_rep.{i}.bias'					, """.layernorm.bias"""    )

						if ".global_rep." in name:
												snake_case_   : List[str]						=			name.replace(""".global_rep."""					, """.transformer."""    )
						if ".pre_norm_mha.0." in name:
												snake_case_   : Optional[Any]						=			name.replace(""".pre_norm_mha.0."""					, """.layernorm_before."""    )
						if ".pre_norm_mha.1.out_proj." in name:
												snake_case_   : List[Any]						=			name.replace(""".pre_norm_mha.1.out_proj."""					, """.attention.output.dense."""    )
						if ".pre_norm_ffn.0." in name:
												snake_case_   : Optional[int]						=			name.replace(""".pre_norm_ffn.0."""					, """.layernorm_after."""    )
						if ".pre_norm_ffn.1." in name:
												snake_case_   : Tuple						=			name.replace(""".pre_norm_ffn.1."""					, """.intermediate.dense."""    )
						if ".pre_norm_ffn.4." in name:
												snake_case_   : Dict						=			name.replace(""".pre_norm_ffn.4."""					, """.output.dense."""    )
						if ".transformer." in name:
												snake_case_   : str						=			name.replace(""".transformer."""					, """.transformer.layer."""    )

						if ".aspp_layer." in name:
												snake_case_   : Any						=			name.replace(""".aspp_layer."""					, """."""    )
						if ".aspp_pool." in name:
												snake_case_   : Optional[Any]						=			name.replace(""".aspp_pool."""					, """."""    )
						if "seg_head." in name:
												snake_case_   : Union[str, Any]						=			name.replace("""seg_head."""					, """segmentation_head."""    )
						if "segmentation_head.classifier.classifier." in name:
												snake_case_   : Tuple						=			name.replace("""segmentation_head.classifier.classifier."""					, """segmentation_head.classifier."""    )

						if "classifier.fc." in name:
												snake_case_   : Tuple						=			name.replace("""classifier.fc."""					, """classifier."""    )
						elif (not base_model) and ("segmentation_head." not in name):
												snake_case_   : List[str]						=			"""mobilevit.""" + name

						return name
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[str]					, __UpperCamelCase      :			Optional[int]					, __UpperCamelCase      :			Any=False    ):


						'''simple docstring'''






						if base_model:
												snake_case_   : Any						=			""""""
						else:
												snake_case_   : List[Any]						=			"""mobilevit."""

						for key in orig_state_dict.copy().keys():
												snake_case_   : Union[str, Any]						=			orig_state_dict.pop(__UpperCamelCase    )

												if key[:8] == "encoder.":
																		snake_case_   : List[Any]						=			key[8:]

												if "qkv" in key:
																		snake_case_   : str						=			key.split("""."""    )
																		snake_case_   : Tuple						=			int(key_split[0][6:]    ) - 1
																		snake_case_   : Union[str, Any]						=			int(key_split[3]    )
																		snake_case_   : List[Any]						=			model.get_submodule(F'{model_prefix}encoder.layer.{layer_num}'    )
																		snake_case_   : Union[str, Any]						=			layer.transformer.layer[transformer_num].attention.attention.all_head_size
																		snake_case_   : Optional[int]						=			(
																		    F'{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'
																		)
																		if "weight" in key:
																								snake_case_   : Union[str, Any]						=			val[:dim, :]
																								snake_case_   : str						=			val[dim : dim * 2, :]
																								snake_case_   : str						=			val[-dim:, :]
																		else:
																								snake_case_   : Tuple						=			val[:dim]
																								snake_case_   : List[Any]						=			val[dim : dim * 2]
																								snake_case_   : int						=			val[-dim:]
												else:
																		snake_case_   : int						=			val

						return orig_state_dict
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						snake_case_   : str						=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						snake_case_   : Dict						=			Image.open(requests.get(__UpperCamelCase					, stream=__UpperCamelCase    ).raw    )
						return im
@torch.no_grad()
def 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			Any					, __UpperCamelCase      :			Dict=False    ):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			get_mobilevit_config(__UpperCamelCase    )

						# load original state_dict
						snake_case_   : Any						=			torch.load(__UpperCamelCase					, map_location="""cpu"""    )

						# load 🤗 model
						if mobilevit_name.startswith("""deeplabv3_"""    ):
												snake_case_   : Dict						=			MobileViTForSemanticSegmentation(__UpperCamelCase    ).eval()
						else:
												snake_case_   : Dict						=			MobileViTForImageClassification(__UpperCamelCase    ).eval()

						snake_case_   : Optional[int]						=			convert_state_dict(__UpperCamelCase					, __UpperCamelCase    )
						model.load_state_dict(__UpperCamelCase    )

						# Check outputs on an image, prepared by MobileViTImageProcessor
						snake_case_   : Dict						=			MobileViTImageProcessor(crop_size=config.image_size					, size=config.image_size + 3_2    )
						snake_case_   : Any						=			image_processor(images=prepare_img()					, return_tensors="""pt"""    )
						snake_case_   : str						=			model(**__UpperCamelCase    )
						snake_case_   : str						=			outputs.logits

						if mobilevit_name.startswith("""deeplabv3_"""    ):
												assert logits.shape == (1, 2_1, 3_2, 3_2)

												if mobilevit_name == "deeplabv3_mobilevit_s":
																		snake_case_   : Union[str, Any]						=			torch.tensor(
																		    [
																		        [[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
																		        [[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
																		        [[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
																		    ]    )
												elif mobilevit_name == "deeplabv3_mobilevit_xs":
																		snake_case_   : str						=			torch.tensor(
																		    [
																		        [[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
																		        [[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
																		        [[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
																		    ]    )
												elif mobilevit_name == "deeplabv3_mobilevit_xxs":
																		snake_case_   : Dict						=			torch.tensor(
																		    [
																		        [[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
																		        [[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
																		        [[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
																		    ]    )
												else:
																		raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}'    )

												assert torch.allclose(logits[0, :3, :3, :3]					, __UpperCamelCase					, atol=1E-4    )
						else:
												assert logits.shape == (1, 1_0_0_0)

												if mobilevit_name == "mobilevit_s":
																		snake_case_   : Tuple						=			torch.tensor([-0.9_866, 0.2_392, -1.1_241]    )
												elif mobilevit_name == "mobilevit_xs":
																		snake_case_   : int						=			torch.tensor([-2.4_761, -0.9_399, -1.9_587]    )
												elif mobilevit_name == "mobilevit_xxs":
																		snake_case_   : List[str]						=			torch.tensor([-1.9_364, -1.2_327, -0.4_653]    )
												else:
																		raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}'    )

												assert torch.allclose(logits[0, :3]					, __UpperCamelCase					, atol=1E-4    )

						Path(__UpperCamelCase    ).mkdir(exist_ok=__UpperCamelCase    )
						print(F'Saving model {mobilevit_name} to {pytorch_dump_folder_path}'    )
						model.save_pretrained(__UpperCamelCase    )
						print(F'Saving image processor to {pytorch_dump_folder_path}'    )
						image_processor.save_pretrained(__UpperCamelCase    )

						if push_to_hub:
												snake_case_   : Any						=			{
												    """mobilevit_s""": """mobilevit-small""",
												    """mobilevit_xs""": """mobilevit-x-small""",
												    """mobilevit_xxs""": """mobilevit-xx-small""",
												    """deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
												    """deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
												    """deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
												}

												print("""Pushing to the hub..."""    )
												snake_case_   : Any						=			model_mapping[mobilevit_name]
												image_processor.push_to_hub(__UpperCamelCase					, organization="""apple"""    )
												model.push_to_hub(__UpperCamelCase					, organization="""apple"""    )


if __name__ == "__main__":
					__lowerCAmelCase       :					Any      					=					argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--mobilevit_name''',
					    default='''mobilevit_s''',
					    type=str,
					    help=(
					        '''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
					        ''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
					    ),
					)
					parser.add_argument(
					    '''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
					)
					parser.add_argument(
					    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
					)
					parser.add_argument(
					    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
					)

					__lowerCAmelCase       :					str      					=					parser.parse_args()
					convert_movilevit_checkpoint(
					    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
					)






"""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 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						snake_case_   : List[str]						=			[]
						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 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			int    ):


						'''simple docstring'''






						snake_case_   : 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 	__lowerCAmelCase						(		__UpperCamelCase      :			Tuple    ):


						'''simple docstring'''






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


						'''simple docstring'''






						snake_case_   : Union[str, 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 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Optional[Any]					, __UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			"""imagenet-1k-id2label.json"""
						snake_case_   : Optional[Any]						=			1_0_0_0

						snake_case_   : Any						=			"""huggingface/label-files"""
						snake_case_   : Tuple						=			num_labels
						snake_case_   : Dict						=			json.load(open(cached_download(hf_hub_url(__UpperCamelCase					, __UpperCamelCase					, repo_type="""dataset"""    )    )					, """r"""    )    )
						snake_case_   : str						=			{int(__UpperCamelCase    ): v for k, v in idalabel.items()}

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

						snake_case_   : Dict						=			CvtConfig(num_labels=__UpperCamelCase					, idalabel=__UpperCamelCase					, labelaid=__UpperCamelCase    )

						# For depth size 13 (13 = 1+2+10)
						if cvt_model.rsplit("""/"""					, 1    )[-1][4:6] == "13":
												snake_case_   : Any						=			[1, 2, 1_0]

						# For depth size 21 (21 = 1+4+16)
						elif cvt_model.rsplit("""/"""					, 1    )[-1][4:6] == "21":
												snake_case_   : Any						=			[1, 4, 1_6]

						# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
						else:
												snake_case_   : Optional[int]						=			[2, 2, 2_0]
												snake_case_   : str						=			[3, 1_2, 1_6]
												snake_case_   : Any						=			[1_9_2, 7_6_8, 1_0_2_4]

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

						snake_case_   : Any						=			OrderedDict()
						snake_case_   : Tuple						=			[]

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

						snake_case_   : str						=			list_of_state_dict + final()
						for gg in list_of_state_dict:
												print(__UpperCamelCase    )
						for i in range(len(__UpperCamelCase    )    ):
												snake_case_   : Union[str, Any]						=			original_weights[list_of_state_dict[i][1]]

						model.load_state_dict(__UpperCamelCase    )
						model.save_pretrained(__UpperCamelCase    )
						image_processor.save_pretrained(__UpperCamelCase    )


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

if __name__ == "__main__":
					__lowerCAmelCase       :					Optional[int]      					=					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       :					Dict      					=					parser.parse_args()
					convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)

