




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() = }""")






