




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__				     = logging.get_logger(__name__)







def 		_A(       UpperCamelCase__							:			Union[str, Any]				)    -> int:
       '''simple docstring'''


       print('''Loading config file...'''				)

       def flatten_yaml_as_dict(UpperCamelCase__							:			Optional[int]				,       UpperCamelCase__							:			Union[str, Any]=""				,       UpperCamelCase__							:			Union[str, Any]="."				):
              __lowercase      =    []
              for k, v in d.items():
                     __lowercase      =    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__				)

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

                     __lowercase      =    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 		_A(       UpperCamelCase__							:			Any				,       UpperCamelCase__							:			Tuple				)    -> str:
       '''simple docstring'''


       __lowercase      =    MobileViTVaConfig()

       __lowercase      =    False

       # dataset
       if task_name.startswith('''imagenet1k_'''				):
              __lowercase      =    1000
              if int(task_name.strip().split('''_'''				)[-1]				) == 384:
                     __lowercase      =    384
              else:
                     __lowercase      =    256
              __lowercase      =    '''imagenet-1k-id2label.json'''
       elif task_name.startswith('''imagenet21k_to_1k_'''				):
              __lowercase      =    2_1000
              if int(task_name.strip().split('''_'''				)[-1]				) == 384:
                     __lowercase      =    384
              else:
                     __lowercase      =    256
              __lowercase      =    '''imagenet-22k-id2label.json'''
       elif task_name.startswith('''ade20k_'''				):
              __lowercase      =    151
              __lowercase      =    512
              __lowercase      =    '''ade20k-id2label.json'''
              __lowercase      =    True
       elif task_name.startswith('''voc_'''				):
              __lowercase      =    21
              __lowercase      =    512
              __lowercase      =    '''pascal-voc-id2label.json'''
              __lowercase      =    True

       # orig_config
       __lowercase      =    load_orig_config_file(UpperCamelCase__				)
       assert getattr(UpperCamelCase__				,       '''model.classification.name'''				,       -1				) == "mobilevit_v2", "Invalid model"
       __lowercase      =    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"
       __lowercase      =    getattr(UpperCamelCase__				,       '''model.classification.activation.name'''				,       '''swish'''				)
       # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

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

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

       return config







def 		_A(       UpperCamelCase__							:			Tuple				,       UpperCamelCase__							:			str				,       UpperCamelCase__							:			Optional[Any]				)    -> List[Any]:
       '''simple docstring'''


       __lowercase      =    dct.pop(UpperCamelCase__				)
       __lowercase      =    val







def 		_A(       UpperCamelCase__							:			Any				,       UpperCamelCase__							:			Optional[Any]=False				)    -> Union[str, Any]:
       '''simple docstring'''


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

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

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

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

              for i in [3, 4, 5]:
                     if F'layer_{i}.0.' in k:
                            __lowercase      =    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:
                            __lowercase      =    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:
                            __lowercase      =    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:
                            __lowercase      =    [0, 1]
                     elif i == 4:
                            __lowercase      =    [0, 1, 2, 3]
                     elif i == 5:
                            __lowercase      =    [0, 1, 2]

                     for j in j_in:
                            if F'layer_{i}.1.global_rep.{j}.' in k:
                                   __lowercase      =    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:
                            __lowercase      =    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:
                            __lowercase      =    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:
                     __lowercase      =    k_new.replace('''pre_norm_attn.0.'''				,       '''layernorm_before.'''				)
              if "pre_norm_attn.1." in k:
                     __lowercase      =    k_new.replace('''pre_norm_attn.1.'''				,       '''attention.'''				)
              if "pre_norm_ffn.0." in k:
                     __lowercase      =    k_new.replace('''pre_norm_ffn.0.'''				,       '''layernorm_after.'''				)
              if "pre_norm_ffn.1." in k:
                     __lowercase      =    k_new.replace('''pre_norm_ffn.1.'''				,       '''ffn.conv1.'''				)
              if "pre_norm_ffn.3." in k:
                     __lowercase      =    k_new.replace('''pre_norm_ffn.3.'''				,       '''ffn.conv2.'''				)

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

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

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







def 		_A(       UpperCamelCase__							:			str				)    -> List[Any]:
       '''simple docstring'''


       __lowercase      =    []
       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 		_A(       )    -> int:
       '''simple docstring'''


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







@torch.no_grad()
def 		_A(       UpperCamelCase__							:			List[str]				,       UpperCamelCase__							:			Any				,       UpperCamelCase__							:			List[str]				,       UpperCamelCase__							:			Optional[int]				)    -> List[Any]:
       '''simple docstring'''


       __lowercase      =    get_mobilevitva_config(UpperCamelCase__				,       UpperCamelCase__				)

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

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

       # remove and rename some keys of load the original model
       __lowercase      =    checkpoint
       remove_unused_keys(UpperCamelCase__				)
       __lowercase      =    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
       __lowercase      =    MobileViTImageProcessor(crop_size=config.image_size				,       size=config.image_size + 32				)
       __lowercase      =    image_processor(images=prepare_img()				,       return_tensors='''pt'''				)
       __lowercase      =    model(**UpperCamelCase__				)

       # verify classification model
       if task_name.startswith('''imagenet'''				):
              __lowercase      =    outputs.logits
              __lowercase      =    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
                     __lowercase      =    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__				     = 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 . "
           "\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            "
       ),
       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__				     = parser.parse_args()
   convert_mobilevitva_checkpoint(
       args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
   )












import os







def 		_A(       UpperCamelCase__							:			str = "matrix.txt"				)    -> int:
							'''simple docstring'''


							with open(os.path.join(os.path.dirname(UpperCamelCase__				)				,       UpperCamelCase__				)				) as in_file:
														__lowercase      =    in_file.read()

							__lowercase      =    [[int(UpperCamelCase__				) for cell in row.split(''','''				)] for row in data.strip().splitlines()]
							__lowercase      =    [[0 for cell in row] for row in grid]
							__lowercase      =    len(grid[0]				)

							__lowercase      =    [[0 for i in range(UpperCamelCase__				)] for j in range(UpperCamelCase__				)]
							__lowercase      =    grid[0][0]
							for i in range(1				,       UpperCamelCase__				):
														__lowercase      =    grid[0][i] + dp[0][i - 1]
							for i in range(1				,       UpperCamelCase__				):
														__lowercase      =    grid[i][0] + dp[i - 1][0]

							for i in range(1				,       UpperCamelCase__				):
														for j in range(1				,       UpperCamelCase__				):
																					__lowercase      =    grid[i][j] + min(dp[i - 1][j]				,       dp[i][j - 1]				)

							return dp[-1][-1]


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






