

import argparse
import collections
import json
from pathlib import Path

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

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase						:  Optional[int]									=			logging.get_logger(__name__)





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							print('Loading config file...' )

							def flatten_yaml_as_dict(UpperCamelCase__  ,				UpperCamelCase__=""  ,				UpperCamelCase__="." ):
														snake_case_     							=       []
														for k, v in d.items():
																					snake_case_     							=       parent_key + sep + k if parent_key else k
																					if isinstance(UpperCamelCase__  ,				collections.abc.MutableMapping ):
																												items.extend(flatten_yaml_as_dict(UpperCamelCase__  ,				UpperCamelCase__  ,				sep=UpperCamelCase__ ).items() )
																					else:
																												items.append((new_key, v) )
														return dict(UpperCamelCase__ )

							snake_case_     							=       argparse.Namespace()
							with open(UpperCamelCase__  ,				'r' ) as yaml_file:
														try:
																					snake_case_     							=       yaml.load(UpperCamelCase__  ,				Loader=yaml.FullLoader )

																					snake_case_     							=       flatten_yaml_as_dict(UpperCamelCase__ )
																					for k, v in flat_cfg.items():
																												setattr(UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ )
														except yaml.YAMLError as exc:
																					logger.error('Error while loading config file: {}. Error message: {}'.format(UpperCamelCase__  ,				str(UpperCamelCase__ ) ) )
							return config





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       MobileViTVaConfig()

							snake_case_     							=       False

							# dataset
							if task_name.startswith('imagenet1k_' ):
														snake_case_     							=       1000
														if int(task_name.strip().split('_' )[-1] ) == 384:
																					snake_case_     							=       384
														else:
																					snake_case_     							=       256
														snake_case_     							=       'imagenet-1k-id2label.json'
							elif task_name.startswith('imagenet21k_to_1k_' ):
														snake_case_     							=       21000
														if int(task_name.strip().split('_' )[-1] ) == 384:
																					snake_case_     							=       384
														else:
																					snake_case_     							=       256
														snake_case_     							=       'imagenet-22k-id2label.json'
							elif task_name.startswith('ade20k_' ):
														snake_case_     							=       151
														snake_case_     							=       512
														snake_case_     							=       'ade20k-id2label.json'
														snake_case_     							=       True
							elif task_name.startswith('voc_' ):
														snake_case_     							=       21
														snake_case_     							=       512
														snake_case_     							=       'pascal-voc-id2label.json'
														snake_case_     							=       True

							# orig_config
							snake_case_     							=       load_orig_config_file(UpperCamelCase__ )
							assert getattr(UpperCamelCase__  ,				'model.classification.name'  ,				-1 ) == "mobilevit_v2", "Invalid model"
							snake_case_     							=       getattr(UpperCamelCase__  ,				'model.classification.mitv2.width_multiplier'  ,				1.0 )
							assert (
							    getattr(UpperCamelCase__  ,				'model.classification.mitv2.attn_norm_layer'  ,				-1 ) == "layer_norm_2d"
							), "Norm layers other than layer_norm_2d is not supported"
							snake_case_     							=       getattr(UpperCamelCase__  ,				'model.classification.activation.name'  ,				'swish' )
							# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

							if is_segmentation_model:
														snake_case_     							=       getattr(UpperCamelCase__  ,				'model.segmentation.output_stride'  ,				16 )
														if "_deeplabv3" in task_name:
																					snake_case_     							=       getattr(UpperCamelCase__  ,				'model.segmentation.deeplabv3.aspp_rates'  ,				[12, 24, 36] )
																					snake_case_     							=       getattr(UpperCamelCase__  ,				'model.segmentation.deeplabv3.aspp_out_channels'  ,				512 )
																					snake_case_     							=       getattr(UpperCamelCase__  ,				'model.segmentation.deeplabv3.aspp_dropout'  ,				0.1 )

    # id2label
							snake_case_     							=       'huggingface/label-files'
							snake_case_     							=       json.load(open(hf_hub_download(UpperCamelCase__  ,				UpperCamelCase__  ,				repo_type='dataset' )  ,				'r' ) )
							snake_case_     							=       {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
							snake_case_     							=       idalabel
							snake_case_     							=       {v: k for k, v in idalabel.items()}

							return config





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       dct.pop(UpperCamelCase__ )
							snake_case_     							=       val





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__=False ):







							'''simple docstring'''






							if base_model:
														snake_case_     							=       ''
							else:
														snake_case_     							=       'mobilevitv2.'

							snake_case_     							=       []
							for k in state_dict.keys():
														if k[:8] == "encoder.":
																					snake_case_     							=       k[8:]
														else:
																					snake_case_     							=       k

														if ".block." in k:
																					snake_case_     							=       k_new.replace('.block.'  ,				'.' )
														if ".conv." in k:
																					snake_case_     							=       k_new.replace('.conv.'  ,				'.convolution.' )
														if ".norm." in k:
																					snake_case_     							=       k_new.replace('.norm.'  ,				'.normalization.' )

														if "conv_1." in k:
																					snake_case_     							=       k_new.replace('conv_1.'  ,				F'''{model_prefix}conv_stem.''' )
														for i in [1, 2]:
																					if F'''layer_{i}.''' in k:
																												snake_case_     							=       k_new.replace(F'''layer_{i}.'''  ,				F'''{model_prefix}encoder.layer.{i-1}.layer.''' )
														if ".exp_1x1." in k:
																					snake_case_     							=       k_new.replace('.exp_1x1.'  ,				'.expand_1x1.' )
														if ".red_1x1." in k:
																					snake_case_     							=       k_new.replace('.red_1x1.'  ,				'.reduce_1x1.' )

														for i in [3, 4, 5]:
																					if F'''layer_{i}.0.''' in k:
																												snake_case_     							=       k_new.replace(F'''layer_{i}.0.'''  ,				F'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.''' )
																					if F'''layer_{i}.1.local_rep.0.''' in k:
																												snake_case_     							=       k_new.replace(F'''layer_{i}.1.local_rep.0.'''  ,				F'''{model_prefix}encoder.layer.{i-1}.conv_kxk.''' )
																					if F'''layer_{i}.1.local_rep.1.''' in k:
																												snake_case_     							=       k_new.replace(F'''layer_{i}.1.local_rep.1.'''  ,				F'''{model_prefix}encoder.layer.{i-1}.conv_1x1.''' )

														for i in [3, 4, 5]:
																					if i == 3:
																												snake_case_     							=       [0, 1]
																					elif i == 4:
																												snake_case_     							=       [0, 1, 2, 3]
																					elif i == 5:
																												snake_case_     							=       [0, 1, 2]

																					for j in j_in:
																												if F'''layer_{i}.1.global_rep.{j}.''' in k:
																																			snake_case_     							=       k_new.replace(
																																			    F'''layer_{i}.1.global_rep.{j}.'''  ,				F'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.''' )
																					if F'''layer_{i}.1.global_rep.{j+1}.''' in k:
																												snake_case_     							=       k_new.replace(
																												    F'''layer_{i}.1.global_rep.{j+1}.'''  ,				F'''{model_prefix}encoder.layer.{i-1}.layernorm.''' )

																					if F'''layer_{i}.1.conv_proj.''' in k:
																												snake_case_     							=       k_new.replace(F'''layer_{i}.1.conv_proj.'''  ,				F'''{model_prefix}encoder.layer.{i-1}.conv_projection.''' )

														if "pre_norm_attn.0." in k:
																					snake_case_     							=       k_new.replace('pre_norm_attn.0.'  ,				'layernorm_before.' )
														if "pre_norm_attn.1." in k:
																					snake_case_     							=       k_new.replace('pre_norm_attn.1.'  ,				'attention.' )
														if "pre_norm_ffn.0." in k:
																					snake_case_     							=       k_new.replace('pre_norm_ffn.0.'  ,				'layernorm_after.' )
														if "pre_norm_ffn.1." in k:
																					snake_case_     							=       k_new.replace('pre_norm_ffn.1.'  ,				'ffn.conv1.' )
														if "pre_norm_ffn.3." in k:
																					snake_case_     							=       k_new.replace('pre_norm_ffn.3.'  ,				'ffn.conv2.' )

														if "classifier.1." in k:
																					snake_case_     							=       k_new.replace('classifier.1.'  ,				'classifier.' )

														if "seg_head." in k:
																					snake_case_     							=       k_new.replace('seg_head.'  ,				'segmentation_head.' )
														if ".aspp_layer." in k:
																					snake_case_     							=       k_new.replace('.aspp_layer.'  ,				'.' )
														if ".aspp_pool." in k:
																					snake_case_     							=       k_new.replace('.aspp_pool.'  ,				'.' )

														rename_keys.append((k, k_new) )
							return rename_keys





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       []
							for k in state_dict.keys():
														if k.startswith('seg_head.aux_head.' ):
																					keys_to_ignore.append(UpperCamelCase__ )
							for k in keys_to_ignore:
														state_dict.pop(UpperCamelCase__  ,				UpperCamelCase__ )





def    __lowerCamelCase      (					):







							'''simple docstring'''






							snake_case_     							=       'http://images.cocodataset.org/val2017/000000039769.jpg'
							# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
							snake_case_     							=       Image.open(requests.get(UpperCamelCase__  ,				stream=UpperCamelCase__ ).raw )
							return im





@torch.no_grad()
def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       get_mobilevitva_config(UpperCamelCase__  ,				UpperCamelCase__ )

							# load original state_dict
							snake_case_     							=       torch.load(UpperCamelCase__  ,				map_location='cpu' )

							# load huggingface model
							if task_name.startswith('ade20k_' ) or task_name.startswith('voc_' ):
														snake_case_     							=       MobileViTVaForSemanticSegmentation(UpperCamelCase__ ).eval()
														snake_case_     							=       False
							else:
														snake_case_     							=       MobileViTVaForImageClassification(UpperCamelCase__ ).eval()
														snake_case_     							=       False

							# remove and rename some keys of load the original model
							snake_case_     							=       checkpoint
							remove_unused_keys(UpperCamelCase__ )
							snake_case_     							=       create_rename_keys(UpperCamelCase__  ,				base_model=UpperCamelCase__ )
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ )

							# load modified state_dict
							model.load_state_dict(UpperCamelCase__ )

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

							# verify classification model
							if task_name.startswith('imagenet' ):
														snake_case_     							=       outputs.logits
														snake_case_     							=       logits.argmax(-1 ).item()
														print('Predicted class:'  ,				model.config.idalabel[predicted_class_idx] )
														if task_name.startswith('imagenet1k_256' ) and config.width_multiplier == 1.0:
																					# expected_logits for base variant
																					snake_case_     							=       torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01] )
																					assert torch.allclose(logits[0, :3]  ,				UpperCamelCase__  ,				atol=1E-4 )

							Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
							print(F'''Saving model {task_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 __name__ == "__main__":
					_UpperCAmelCase						:  Optional[Any]									=			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--task""",
					    default="""imagenet1k_256""",
					    type=str,
					    help=(
					        """Name of the task for which the MobileViTV2 model you'd like to convert is trained on . """
					        """
                Classification (ImageNet-1k)
                    - MobileViTV2 (256x256) : imagenet1k_256
                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
                      imagenet21k_to_1k_256
                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
                      ImageNet-1k 384x384) : imagenet21k_to_1k_384
                Segmentation
                    - ADE20K Dataset : ade20k_deeplabv3
                    - Pascal VOC 2012 Dataset: voc_deeplabv3
            """
					    ),
					    choices=[
					        """imagenet1k_256""",
					        """imagenet1k_384""",
					        """imagenet21k_to_1k_256""",
					        """imagenet21k_to_1k_384""",
					        """ade20k_deeplabv3""",
					        """voc_deeplabv3""",
					    ],
					)

					parser.add_argument(
					    """--orig_checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
					)
					parser.add_argument("""--orig_config_path""", required=True, type=str, help="""Path to the original config file.""")
					parser.add_argument(
					    """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
					)

					_UpperCAmelCase						:  Tuple									=			parser.parse_args()
					convert_mobilevitva_checkpoint(
					    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
					)







_UpperCAmelCase						:  int									=			range(2, 20 + 1)
_UpperCAmelCase						:  Dict									=			[10**k for k in range(ks[-1] + 1)]
_UpperCAmelCase						:  dict[int, dict[int, list[list[int]]]]									=			{}





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       sum(a_i[j] for j in range(UpperCamelCase__  ,				len(UpperCamelCase__ ) ) )
							snake_case_     							=       sum(a_i[j] * base[j] for j in range(min(len(UpperCamelCase__ )  ,				UpperCamelCase__ ) ) )

							snake_case_							,       snake_case_     							=       0, 0
							snake_case_     							=       n - i

							snake_case_     							=       memo.get(UpperCamelCase__ )

							if sub_memo is not None:
														snake_case_     							=       sub_memo.get(UpperCamelCase__ )

														if jumps is not None and len(UpperCamelCase__ ) > 0:
																					# find and make the largest jump without going over
																					snake_case_     							=       -1
																					for _k in range(len(UpperCamelCase__ ) - 1  ,				-1  ,				-1 ):
																												if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
																																			snake_case_     							=       _k
																																			break

																					if max_jump >= 0:
																												snake_case_							,       snake_case_							,       snake_case_     							=       jumps[max_jump]
																												# since the difference between jumps is cached, add c
																												snake_case_     							=       diff + c
																												for j in range(min(UpperCamelCase__  ,				len(UpperCamelCase__ ) ) ):
																																			snake_case_							,       snake_case_     							=       divmod(UpperCamelCase__  ,				10 )
																												if new_c > 0:
																																			add(UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ )

														else:
																					snake_case_     							=       []
							else:
														snake_case_     							=       {c: []}
														snake_case_     							=       sub_memo

							if dn >= max_dn or c + diff >= base[k]:
														return diff, dn

							if k > ks[0]:
														while True:
																					# keep doing smaller jumps
																					snake_case_							,       snake_case_     							=       next_term(UpperCamelCase__  ,				k - 1  ,				i + dn  ,				UpperCamelCase__ )
																					diff += _diff
																					dn += terms_jumped

																					if dn >= max_dn or c + diff >= base[k]:
																												break
							else:
														# would be too small a jump, just compute sequential terms instead
														snake_case_							,       snake_case_     							=       compute(UpperCamelCase__  ,				UpperCamelCase__  ,				i + dn  ,				UpperCamelCase__ )
														diff += _diff
														dn += terms_jumped

							snake_case_     							=       sub_memo[c]

							# keep jumps sorted by # of terms skipped
							snake_case_     							=       0
							while j < len(UpperCamelCase__ ):
														if jumps[j][1] > dn:
																					break
														j += 1

							# cache the jump for this value digitsum(b) and c
							sub_memo[c].insert(UpperCamelCase__  ,				(diff, dn, k) )
							return (diff, dn)





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							if i >= n:
														return 0, i
							if k > len(UpperCamelCase__ ):
														a_i.extend([0 for _ in range(k - len(UpperCamelCase__ ) )] )

							# note: a_i -> b * 10^k + c
							# ds_b -> digitsum(b)
							# ds_c -> digitsum(c)
							snake_case_     							=       i
							snake_case_							,       snake_case_							,       snake_case_     							=       0, 0, 0
							for j in range(len(UpperCamelCase__ ) ):
														if j >= k:
																					ds_b += a_i[j]
														else:
																					ds_c += a_i[j]

							while i < n:
														i += 1
														snake_case_     							=       ds_c + ds_b
														diff += addend
														snake_case_     							=       0
														for j in range(UpperCamelCase__ ):
																					snake_case_     							=       a_i[j] + addend
																					snake_case_							,       snake_case_     							=       divmod(UpperCamelCase__  ,				10 )

																					ds_c += a_i[j]

														if addend > 0:
																					break

							if addend > 0:
														add(UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ )
							return diff, i - start_i





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							for j in range(UpperCamelCase__  ,				len(UpperCamelCase__ ) ):
														snake_case_     							=       digits[j] + addend
														if s >= 10:
																					snake_case_							,       snake_case_     							=       divmod(UpperCamelCase__  ,				10 )
																					snake_case_     							=       addend // 10 + quotient
														else:
																					snake_case_     							=       s
																					snake_case_     							=       addend // 10

														if addend == 0:
																					break

							while addend > 0:
														snake_case_							,       snake_case_     							=       divmod(UpperCamelCase__  ,				10 )
														digits.append(UpperCamelCase__ )





def    __lowerCamelCase      (					UpperCamelCase__ = 10**15 ):







							'''simple docstring'''






							snake_case_     							=       [1]
							snake_case_     							=       1
							snake_case_     							=       0
							while True:
														snake_case_							,       snake_case_     							=       next_term(UpperCamelCase__  ,				20  ,				i + dn  ,				UpperCamelCase__ )
														dn += terms_jumped
														if dn == n - i:
																					break

							snake_case_     							=       0
							for j in range(len(UpperCamelCase__ ) ):
														a_n += digits[j] * 10**j
							return a_n


if __name__ == "__main__":
					print(F'''{solution() = }''')




