






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()
__A      =				logging.get_logger(__name__)




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= MobileViTConfig()

					# size of the architecture
					if "mobilevit_s" in mobilevit_name:
										_A								= [1_44, 1_92, 2_40]
										_A								= [16, 32, 64, 96, 1_28, 1_60, 6_40]
					elif "mobilevit_xs" in mobilevit_name:
										_A								= [96, 1_20, 1_44]
										_A								= [16, 32, 48, 64, 80, 96, 3_84]
					elif "mobilevit_xxs" in mobilevit_name:
										_A								= [64, 80, 96]
										_A								= [16, 16, 24, 48, 64, 80, 3_20]
										_A								= 0.05
										_A								= 2.0

					if mobilevit_name.startswith('''deeplabv3_''' ):
										_A								= 5_12
										_A								= 16
										_A								= 21
										_A								= '''pascal-voc-id2label.json'''
					else:
										_A								= 10_00
										_A								= '''imagenet-1k-id2label.json'''

					_A								= '''huggingface/label-files'''
					_A								= json.load(open(hf_hub_download(_lowercase					,			_lowercase					,			repo_type='''dataset''' )					,			'''r''' ) )
					_A								= {int(_lowercase ): v for k, v in idalabel.items()}
					_A								= idalabel
					_A								= {v: k for k, v in idalabel.items()}

					return config




def      __A					(     _lowercase					,			_lowercase=False ):

					'''simple docstring'''







					for i in range(1					,			6 ):
										if f"""layer_{i}.""" in name:
															_A								= name.replace(f"""layer_{i}."""					,			f"""encoder.layer.{i - 1}.""" )

					if "conv_1." in name:
										_A								= name.replace('''conv_1.'''					,			'''conv_stem.''' )
					if ".block." in name:
										_A								= name.replace('''.block.'''					,			'''.''' )

					if "exp_1x1" in name:
										_A								= name.replace('''exp_1x1'''					,			'''expand_1x1''' )
					if "red_1x1" in name:
										_A								= name.replace('''red_1x1'''					,			'''reduce_1x1''' )
					if ".local_rep.conv_3x3." in name:
										_A								= name.replace('''.local_rep.conv_3x3.'''					,			'''.conv_kxk.''' )
					if ".local_rep.conv_1x1." in name:
										_A								= name.replace('''.local_rep.conv_1x1.'''					,			'''.conv_1x1.''' )
					if ".norm." in name:
										_A								= name.replace('''.norm.'''					,			'''.normalization.''' )
					if ".conv." in name:
										_A								= name.replace('''.conv.'''					,			'''.convolution.''' )
					if ".conv_proj." in name:
										_A								= name.replace('''.conv_proj.'''					,			'''.conv_projection.''' )

					for i in range(0					,			2 ):
										for j in range(0					,			4 ):
															if f""".{i}.{j}.""" in name:
																				_A								= 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:
																				_A								= name.replace(f""".{i}.{j}."""					,			f""".{i}.""" )
																				if "expand_1x1" in name:
																									_A								= name.replace('''expand_1x1'''					,			'''downsampling_layer.expand_1x1''' )
																				if "conv_3x3" in name:
																									_A								= name.replace('''conv_3x3'''					,			'''downsampling_layer.conv_3x3''' )
																				if "reduce_1x1" in name:
																									_A								= name.replace('''reduce_1x1'''					,			'''downsampling_layer.reduce_1x1''' )

					for i in range(2					,			5 ):
										if f""".global_rep.{i}.weight""" in name:
															_A								= name.replace(f""".global_rep.{i}.weight"""					,			'''.layernorm.weight''' )
										if f""".global_rep.{i}.bias""" in name:
															_A								= name.replace(f""".global_rep.{i}.bias"""					,			'''.layernorm.bias''' )

					if ".global_rep." in name:
										_A								= name.replace('''.global_rep.'''					,			'''.transformer.''' )
					if ".pre_norm_mha.0." in name:
										_A								= name.replace('''.pre_norm_mha.0.'''					,			'''.layernorm_before.''' )
					if ".pre_norm_mha.1.out_proj." in name:
										_A								= name.replace('''.pre_norm_mha.1.out_proj.'''					,			'''.attention.output.dense.''' )
					if ".pre_norm_ffn.0." in name:
										_A								= name.replace('''.pre_norm_ffn.0.'''					,			'''.layernorm_after.''' )
					if ".pre_norm_ffn.1." in name:
										_A								= name.replace('''.pre_norm_ffn.1.'''					,			'''.intermediate.dense.''' )
					if ".pre_norm_ffn.4." in name:
										_A								= name.replace('''.pre_norm_ffn.4.'''					,			'''.output.dense.''' )
					if ".transformer." in name:
										_A								= name.replace('''.transformer.'''					,			'''.transformer.layer.''' )

					if ".aspp_layer." in name:
										_A								= name.replace('''.aspp_layer.'''					,			'''.''' )
					if ".aspp_pool." in name:
										_A								= name.replace('''.aspp_pool.'''					,			'''.''' )
					if "seg_head." in name:
										_A								= name.replace('''seg_head.'''					,			'''segmentation_head.''' )
					if "segmentation_head.classifier.classifier." in name:
										_A								= name.replace('''segmentation_head.classifier.classifier.'''					,			'''segmentation_head.classifier.''' )

					if "classifier.fc." in name:
										_A								= name.replace('''classifier.fc.'''					,			'''classifier.''' )
					elif (not base_model) and ("segmentation_head." not in name):
										_A								= '''mobilevit.''' + name

					return name




def      __A					(     _lowercase					,			_lowercase					,			_lowercase=False ):

					'''simple docstring'''







					if base_model:
										_A								= ''''''
					else:
										_A								= '''mobilevit.'''

					for key in orig_state_dict.copy().keys():
										_A								= orig_state_dict.pop(_lowercase )

										if key[:8] == "encoder.":
															_A								= key[8:]

										if "qkv" in key:
															_A								= key.split('''.''' )
															_A								= int(key_split[0][6:] ) - 1
															_A								= int(key_split[3] )
															_A								= model.get_submodule(f"""{model_prefix}encoder.layer.{layer_num}""" )
															_A								= layer.transformer.layer[transformer_num].attention.attention.all_head_size
															_A								= (
															    f"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
															)
															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					(     ):

					'''simple docstring'''







					_A								= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_A								= Image.open(requests.get(_lowercase					,			stream=_lowercase ).raw )
					return im




@torch.no_grad()
def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase=False ):

					'''simple docstring'''







					_A								= get_mobilevit_config(_lowercase )

					# load original state_dict
					_A								= torch.load(_lowercase					,			map_location='''cpu''' )

					# load 🤗 model
					if mobilevit_name.startswith('''deeplabv3_''' ):
										_A								= MobileViTForSemanticSegmentation(_lowercase ).eval()
					else:
										_A								= MobileViTForImageClassification(_lowercase ).eval()

					_A								= convert_state_dict(_lowercase					,			_lowercase )
					model.load_state_dict(_lowercase )

					# Check outputs on an image, prepared by MobileViTImageProcessor
					_A								= MobileViTImageProcessor(crop_size=config.image_size					,			size=config.image_size + 32 )
					_A								= image_processor(images=prepare_img()					,			return_tensors='''pt''' )
					_A								= model(**_lowercase )
					_A								= outputs.logits

					if mobilevit_name.startswith('''deeplabv3_''' ):
										assert logits.shape == (1, 21, 32, 32)

										if mobilevit_name == "deeplabv3_mobilevit_s":
															_A								= torch.tensor(
															    [
															        [[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
															        [[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
															        [[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
															    ] )
										elif mobilevit_name == "deeplabv3_mobilevit_xs":
															_A								= torch.tensor(
															    [
															        [[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
															        [[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
															        [[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
															    ] )
										elif mobilevit_name == "deeplabv3_mobilevit_xxs":
															_A								= torch.tensor(
															    [
															        [[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
															        [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
															        [[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
															    ] )
										else:
															raise ValueError(f"""Unknown mobilevit_name: {mobilevit_name}""" )

										assert torch.allclose(logits[0, :3, :3, :3]					,			_lowercase					,			atol=1e-4 )
					else:
										assert logits.shape == (1, 10_00)

										if mobilevit_name == "mobilevit_s":
															_A								= torch.tensor([-0.98_66, 0.23_92, -1.12_41] )
										elif mobilevit_name == "mobilevit_xs":
															_A								= torch.tensor([-2.47_61, -0.93_99, -1.95_87] )
										elif mobilevit_name == "mobilevit_xxs":
															_A								= torch.tensor([-1.93_64, -1.23_27, -0.46_53] )
										else:
															raise ValueError(f"""Unknown mobilevit_name: {mobilevit_name}""" )

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

					Path(_lowercase ).mkdir(exist_ok=_lowercase )
					print(f"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}""" )
					model.save_pretrained(_lowercase )
					print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
					image_processor.save_pretrained(_lowercase )

					if push_to_hub:
										_A								= {
										    '''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...''' )
										_A								= model_mapping[mobilevit_name]
										image_processor.push_to_hub(_lowercase					,			organization='''apple''' )
										model.push_to_hub(_lowercase					,			organization='''apple''' )


if __name__ == "__main__":
				__A      =				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.'
				)

				__A      =				parser.parse_args()
				convert_movilevit_checkpoint(
				    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)











__A      =				{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

__A      =				{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= True
					_A								= []

					for neighbour in graph[vert]:
										if not visited[neighbour]:
															order += topology_sort(_lowercase					,			_lowercase					,			_lowercase )

					order.append(_lowercase )

					return order




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= True
					_A								= [vert]

					for neighbour in reversed_graph[vert]:
										if not visited[neighbour]:
															component += find_components(_lowercase					,			_lowercase					,			_lowercase )

					return component




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= len(_lowercase ) * [False]
					_A								= {vert: [] for vert in range(len(_lowercase ) )}

					for vert, neighbours in graph.items():
										for neighbour in neighbours:
															reversed_graph[neighbour].append(_lowercase )

					_A								= []
					for i, was_visited in enumerate(_lowercase ):
										if not was_visited:
															order += topology_sort(_lowercase					,			_lowercase					,			_lowercase )

					_A								= []
					_A								= len(_lowercase ) * [False]

					for i in range(len(_lowercase ) ):
										_A								= order[len(_lowercase ) - i - 1]
										if not visited[vert]:
															_A								= find_components(_lowercase					,			_lowercase					,			_lowercase )
															components_list.append(_lowercase )

					return components_list



