



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()
SCREAMING_SNAKE_CASE_:List[Any] 			=		logging.get_logger(__name__)




def       __UpperCamelCase      (    _lowerCAmelCase						)			->    str:



    """simple docstring"""






    A					:			Any		=  MobileViTConfig()

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

    if mobilevit_name.startswith("""deeplabv3_"""						):
        A					:			List[str]		=  512
        A					:			Dict		=  16
        A					:			Union[str, Any]		=  21
        A					:			List[str]		=  'pascal-voc-id2label.json'
    else:
        A					:			List[Any]		=  1000
        A					:			List[str]		=  'imagenet-1k-id2label.json'

    A					:			Optional[int]		=  'huggingface/label-files'
    A					:			List[str]		=  json.load(open(hf_hub_download(__lowerCAmelCase ,	__lowerCAmelCase ,	repo_type="""dataset"""						) ,	"""r"""						)						)
    A					:			Optional[Any]		=  {int(__lowerCAmelCase						): v for k, v in idalabel.items()}
    A					:			List[str]		=  idalabel
    A					:			Tuple		=  {v: k for k, v in idalabel.items()}

    return config




def       __UpperCamelCase      (    _lowerCAmelCase ,	_lowerCAmelCase=False						)			->    int:



    """simple docstring"""






    for i in range(1 ,	6						):
        if f'''layer_{i}.''' in name:
            A					:			List[Any]		=  name.replace(f'''layer_{i}.''' ,	f'''encoder.layer.{i - 1}.'''						)

    if "conv_1." in name:
        A					:			Any		=  name.replace("""conv_1.""" ,	"""conv_stem."""						)
    if ".block." in name:
        A					:			List[Any]		=  name.replace(""".block.""" ,	"""."""						)

    if "exp_1x1" in name:
        A					:			Dict		=  name.replace("""exp_1x1""" ,	"""expand_1x1"""						)
    if "red_1x1" in name:
        A					:			str		=  name.replace("""red_1x1""" ,	"""reduce_1x1"""						)
    if ".local_rep.conv_3x3." in name:
        A					:			Optional[Any]		=  name.replace(""".local_rep.conv_3x3.""" ,	""".conv_kxk."""						)
    if ".local_rep.conv_1x1." in name:
        A					:			Tuple		=  name.replace(""".local_rep.conv_1x1.""" ,	""".conv_1x1."""						)
    if ".norm." in name:
        A					:			Optional[int]		=  name.replace(""".norm.""" ,	""".normalization."""						)
    if ".conv." in name:
        A					:			List[Any]		=  name.replace(""".conv.""" ,	""".convolution."""						)
    if ".conv_proj." in name:
        A					:			str		=  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					:			int		=  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					:			Optional[int]		=  name.replace(f'''.{i}.{j}.''' ,	f'''.{i}.'''						)
                if "expand_1x1" in name:
                    A					:			Union[str, Any]		=  name.replace("""expand_1x1""" ,	"""downsampling_layer.expand_1x1"""						)
                if "conv_3x3" in name:
                    A					:			str		=  name.replace("""conv_3x3""" ,	"""downsampling_layer.conv_3x3"""						)
                if "reduce_1x1" in name:
                    A					:			Dict		=  name.replace("""reduce_1x1""" ,	"""downsampling_layer.reduce_1x1"""						)

    for i in range(2 ,	5						):
        if f'''.global_rep.{i}.weight''' in name:
            A					:			List[str]		=  name.replace(f'''.global_rep.{i}.weight''' ,	""".layernorm.weight"""						)
        if f'''.global_rep.{i}.bias''' in name:
            A					:			Union[str, Any]		=  name.replace(f'''.global_rep.{i}.bias''' ,	""".layernorm.bias"""						)

    if ".global_rep." in name:
        A					:			Tuple		=  name.replace(""".global_rep.""" ,	""".transformer."""						)
    if ".pre_norm_mha.0." in name:
        A					:			Any		=  name.replace(""".pre_norm_mha.0.""" ,	""".layernorm_before."""						)
    if ".pre_norm_mha.1.out_proj." in name:
        A					:			Optional[Any]		=  name.replace(""".pre_norm_mha.1.out_proj.""" ,	""".attention.output.dense."""						)
    if ".pre_norm_ffn.0." in name:
        A					:			str		=  name.replace(""".pre_norm_ffn.0.""" ,	""".layernorm_after."""						)
    if ".pre_norm_ffn.1." in name:
        A					:			Any		=  name.replace(""".pre_norm_ffn.1.""" ,	""".intermediate.dense."""						)
    if ".pre_norm_ffn.4." in name:
        A					:			Union[str, Any]		=  name.replace(""".pre_norm_ffn.4.""" ,	""".output.dense."""						)
    if ".transformer." in name:
        A					:			Dict		=  name.replace(""".transformer.""" ,	""".transformer.layer."""						)

    if ".aspp_layer." in name:
        A					:			Optional[int]		=  name.replace(""".aspp_layer.""" ,	"""."""						)
    if ".aspp_pool." in name:
        A					:			Dict		=  name.replace(""".aspp_pool.""" ,	"""."""						)
    if "seg_head." in name:
        A					:			Optional[Any]		=  name.replace("""seg_head.""" ,	"""segmentation_head."""						)
    if "segmentation_head.classifier.classifier." in name:
        A					:			Union[str, Any]		=  name.replace("""segmentation_head.classifier.classifier.""" ,	"""segmentation_head.classifier."""						)

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

    return name




def       __UpperCamelCase      (    _lowerCAmelCase ,	_lowerCAmelCase ,	_lowerCAmelCase=False						)			->    Any:



    """simple docstring"""






    if base_model:
        A					:			Optional[int]		=  ''
    else:
        A					:			str		=  'mobilevit.'

    for key in orig_state_dict.copy().keys():
        A					:			List[Any]		=  orig_state_dict.pop(__lowerCAmelCase						)

        if key[:8] == "encoder.":
            A					:			Any		=  key[8:]

        if "qkv" in key:
            A					:			Any		=  key.split("""."""						)
            A					:			List[Any]		=  int(key_split[0][6:]						) - 1
            A					:			Union[str, Any]		=  int(key_split[3]						)
            A					:			Any		=  model.get_submodule(f'''{model_prefix}encoder.layer.{layer_num}'''						)
            A					:			Optional[Any]		=  layer.transformer.layer[transformer_num].attention.attention.all_head_size
            A					:			Union[str, Any]		=  (
                f'''{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'''
            )
            if "weight" in key:
                A					:			Tuple		=  val[:dim, :]
                A					:			Any		=  val[dim : dim * 2, :]
                A					:			Tuple		=  val[-dim:, :]
            else:
                A					:			Optional[Any]		=  val[:dim]
                A					:			List[Any]		=  val[dim : dim * 2]
                A					:			Union[str, Any]		=  val[-dim:]
        else:
            A					:			List[Any]		=  val

    return orig_state_dict




def       __UpperCamelCase      (    )			->    int:



    """simple docstring"""






    A					:			Tuple		=  'http://images.cocodataset.org/val2017/000000039769.jpg'
    A					:			Union[str, Any]		=  Image.open(requests.get(__lowerCAmelCase ,	stream=__lowerCAmelCase						).raw						)
    return im




@torch.no_grad()
def       __UpperCamelCase      (    _lowerCAmelCase ,	_lowerCAmelCase ,	_lowerCAmelCase ,	_lowerCAmelCase=False						)			->    Optional[int]:



    """simple docstring"""






    A					:			Optional[Any]		=  get_mobilevit_config(__lowerCAmelCase						)

    # load original state_dict
    A					:			Union[str, Any]		=  torch.load(__lowerCAmelCase ,	map_location="""cpu"""						)

    # load 🤗 model
    if mobilevit_name.startswith("""deeplabv3_"""						):
        A					:			str		=  MobileViTForSemanticSegmentation(__lowerCAmelCase						).eval()
    else:
        A					:			str		=  MobileViTForImageClassification(__lowerCAmelCase						).eval()

    A					:			List[str]		=  convert_state_dict(__lowerCAmelCase ,	__lowerCAmelCase						)
    model.load_state_dict(__lowerCAmelCase						)

    # Check outputs on an image, prepared by MobileViTImageProcessor
    A					:			List[Any]		=  MobileViTImageProcessor(crop_size=config.image_size ,	size=config.image_size + 32						)
    A					:			List[str]		=  image_processor(images=prepare_img() ,	return_tensors="""pt"""						)
    A					:			List[str]		=  model(**__lowerCAmelCase						)
    A					:			Optional[int]		=  outputs.logits

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

        if mobilevit_name == "deeplabv3_mobilevit_s":
            A					:			Union[str, Any]		=  torch.tensor(
                [
                    [[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
                    [[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
                    [[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
                ]						)
        elif mobilevit_name == "deeplabv3_mobilevit_xs":
            A					:			Tuple		=  torch.tensor(
                [
                    [[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
                    [[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
                    [[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
                ]						)
        elif mobilevit_name == "deeplabv3_mobilevit_xxs":
            A					:			Tuple		=  torch.tensor(
                [
                    [[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
                    [[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
                    [[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
                ]						)
        else:
            raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}'''						)

        assert torch.allclose(logits[0, :3, :3, :3] ,	__lowerCAmelCase ,	atol=1e-4						)
    else:
        assert logits.shape == (1, 1000)

        if mobilevit_name == "mobilevit_s":
            A					:			int		=  torch.tensor([-0.9_866, 0.2_392, -1.1_241]						)
        elif mobilevit_name == "mobilevit_xs":
            A					:			Optional[int]		=  torch.tensor([-2.4_761, -0.9_399, -1.9_587]						)
        elif mobilevit_name == "mobilevit_xxs":
            A					:			List[str]		=  torch.tensor([-1.9_364, -1.2_327, -0.4_653]						)
        else:
            raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}'''						)

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

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

    if push_to_hub:
        A					:			List[str]		=  {
            '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					:			Optional[Any]		=  model_mapping[mobilevit_name]
        image_processor.push_to_hub(__lowerCAmelCase ,	organization="""apple"""						)
        model.push_to_hub(__lowerCAmelCase ,	organization="""apple"""						)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:int 			=		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."""
     )

     SCREAMING_SNAKE_CASE_:Optional[Any] 			=		parser.parse_args()
     convert_movilevit_checkpoint(
         args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
     )







def        __a			(   __lowerCAmelCase   )     ->     list[list[float]]:
					SCREAMING_SNAKE_CASE							:     list[list[float]]						=				[]
					for data in source_data:
										for i, el in enumerate(__lowerCAmelCase   ):
															if len(__lowerCAmelCase   ) < i + 1:
																				data_lists.append([]   )
															data_lists[i].append(float(__lowerCAmelCase   )   )
					return data_lists

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     list[list[float]]:
					SCREAMING_SNAKE_CASE							:     list[list[float]]						=				[]
					for dlist, weight in zip(__lowerCAmelCase      ,	__lowerCAmelCase   ):
										SCREAMING_SNAKE_CASE							:     str						=				min(__lowerCAmelCase   )
										SCREAMING_SNAKE_CASE							:     int						=				max(__lowerCAmelCase   )

										SCREAMING_SNAKE_CASE							:     list[float]						=				[]
										# for weight 0 score is 1 - actual score
										if weight == 0:
															for item in dlist:
																				try:
																									score.append(1 - ((item - mind) / (maxd - mind))   )
																				except ZeroDivisionError:
																									score.append(1   )

										elif weight == 1:
															for item in dlist:
																				try:
																									score.append((item - mind) / (maxd - mind)   )
																				except ZeroDivisionError:
																									score.append(0   )

        # weight not 0 or 1
										else:
															SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				F'''Invalid weight of {weight:f} provided'''
															raise ValueError(__lowerCAmelCase   )

										score_lists.append(__lowerCAmelCase   )

					return score_lists

def        __a			(   __lowerCAmelCase   )     ->     list[float]:
					SCREAMING_SNAKE_CASE							:     list[float]						=				[0 for i in range(len(score_lists[0]   )   )]

					for slist in score_lists:
										for j, ele in enumerate(__lowerCAmelCase   ):
															SCREAMING_SNAKE_CASE							:     str						=				final_scores[j] + ele

					return final_scores



def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     list[list[float]]:
					SCREAMING_SNAKE_CASE							:     Tuple						=				get_data(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     List[str]						=				calculate_each_score(__lowerCAmelCase      ,	__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     str						=				generate_final_scores(__lowerCAmelCase   )

					# append scores to source data
					for i, ele in enumerate(__lowerCAmelCase   ):
										source_data[i].append(__lowerCAmelCase   )

					return source_data