





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							:			Dict         =			logging.get_logger(__name__)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Dict:




    """simple docstring"""




    _A     =							MobileViTConfig()

    # size of the architecture
    if "mobilevit_s" in mobilevit_name:
        _A     =							[144, 192, 240]
        _A     =							[16, 32, 64, 96, 128, 160, 640]
    elif "mobilevit_xs" in mobilevit_name:
        _A     =							[96, 120, 144]
        _A     =							[16, 32, 48, 64, 80, 96, 384]
    elif "mobilevit_xxs" in mobilevit_name:
        _A     =							[64, 80, 96]
        _A     =							[16, 16, 24, 48, 64, 80, 320]
        _A     =							0.05
        _A     =							2.0

    if mobilevit_name.startswith('deeplabv3_'				):
        _A     =							512
        _A     =							16
        _A     =							21
        _A     =							'pascal-voc-id2label.json'
    else:
        _A     =							1_000
        _A     =							'imagenet-1k-id2label.json'

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

    return config





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=False				)							-> Tuple:




    """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 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=False				)							-> int:




    """simple docstring"""




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

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

        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 				__lowerCAmelCase(       )							-> Tuple:




    """simple docstring"""




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





@torch.no_grad()
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=False				)							-> str:




    """simple docstring"""




    _A     =							get_mobilevit_config(_SCREAMING_SNAKE_CASE				)

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

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

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

    # 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(**_SCREAMING_SNAKE_CASE				)
    _A     =							outputs.logits

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

        if mobilevit_name == "deeplabv3_mobilevit_s":
            _A     =							torch.tensor(
                [
                    [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
                    [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
                    [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
                ]				)
        elif mobilevit_name == "deeplabv3_mobilevit_xs":
            _A     =							torch.tensor(
                [
                    [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
                    [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
                    [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
                ]				)
        elif mobilevit_name == "deeplabv3_mobilevit_xxs":
            _A     =							torch.tensor(
                [
                    [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
                    [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
                    [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
                ]				)
        else:
            raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}"				)

        assert torch.allclose(logits[0, :3, :3, :3]							,     _SCREAMING_SNAKE_CASE							,     atol=1e-4				)
    else:
        assert logits.shape == (1, 1_000)

        if mobilevit_name == "mobilevit_s":
            _A     =							torch.tensor([-0.9866, 0.2392, -1.1241]				)
        elif mobilevit_name == "mobilevit_xs":
            _A     =							torch.tensor([-2.4761, -0.9399, -1.9587]				)
        elif mobilevit_name == "mobilevit_xxs":
            _A     =							torch.tensor([-1.9364, -1.2327, -0.4653]				)
        else:
            raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}"				)

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

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

    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(_SCREAMING_SNAKE_CASE							,     organization='apple'				)
        model.push_to_hub(_SCREAMING_SNAKE_CASE							,     organization='apple'				)


if __name__ == "__main__":
 __A							:			List[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."
 )

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












def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    print('\nThe shortest path matrix using Floyd Warshall algorithm\n'				)
    for i in range(_SCREAMING_SNAKE_CASE				):
        for j in range(_SCREAMING_SNAKE_CASE				):
            if dist[i][j] != float('inf'				):
                print(int(dist[i][j]				)							,     end='\t'				)
            else:
                print('INF'							,     end='\t'				)
        print()





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> str:




    """simple docstring"""




    _A     =							[[float('inf'				) for _ in range(_SCREAMING_SNAKE_CASE				)] for _ in range(_SCREAMING_SNAKE_CASE				)]

    for i in range(_SCREAMING_SNAKE_CASE				):
        for j in range(_SCREAMING_SNAKE_CASE				):
            _A     =							graph[i][j]

            # check vertex k against all other vertices (i, j)
    for k in range(_SCREAMING_SNAKE_CASE				):
        # looping through rows of graph array
        for i in range(_SCREAMING_SNAKE_CASE				):
            # looping through columns of graph array
            for j in range(_SCREAMING_SNAKE_CASE				):
                if (
                    dist[i][k] != float('inf'				)
                    and dist[k][j] != float('inf'				)
                    and dist[i][k] + dist[k][j] < dist[i][j]
                ):
                    _A     =							dist[i][k] + dist[k][j]

    _print_dist(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
    return dist, v


if __name__ == "__main__":
 __A							:			Dict         =			int(input("Enter number of vertices: "))
 __A							:			Union[str, Any]         =			int(input("Enter number of edges: "))

 __A							:			List[str]         =			[[float("inf") for i in range(v)] for j in range(v)]

 for i in range(v):
  __A							:			List[Any]         =			0.0

  # src and dst are indices that must be within the array size graph[e][v]
  # failure to follow this will result in an error
 for i in range(e):
  print("\nEdge ", i + 1)
  __A							:			Union[str, Any]         =			int(input("Enter source:"))
  __A							:			List[str]         =			int(input("Enter destination:"))
  __A							:			Union[str, Any]         =			float(input("Enter weight:"))
  __A							:			Any         =			weight

 floyd_warshall(graph, v)

 # Example Input
 # Enter number of vertices: 3
 # Enter number of edges: 2

 # # generated graph from vertex and edge inputs
 # [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
 # [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]

 # specify source, destination and weight for edge #1
 # Edge  1
 # Enter source:1
 # Enter destination:2
 # Enter weight:2

 # specify source, destination and weight for edge #2
 # Edge  2
 # Enter source:2
 # Enter destination:1
 # Enter weight:1

 # # Expected Output from the vertice, edge and src, dst, weight inputs!!
 # 0		INF	INF
 # INF	0	2
 # INF	1	0





