

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()
lowercase_       		=  logging.get_logger(__name__)
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[Any]      )							->      Any:





    '''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__          =   1000
        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 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	int=False      )							->      Optional[Any]:





    '''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 						_snake_case(    SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	List[Any]    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]=False      )							->      List[str]:





    '''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 						_snake_case(    )							->      Union[str, Any]:





    '''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 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Tuple=False      )							->      List[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, 1000)

        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__":
     lowercase_       		=  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."
     )

     lowercase_       		=  parser.parse_args()
     convert_movilevit_checkpoint(
         args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
     )




import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      Union[str, Any]:





    '''simple docstring'''






    return EnvironmentCommand()


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    @staticmethod
    def 						snake_case__     (      lowercase_ :       ArgumentParser		)->    Dict:





        '''simple docstring'''







        A__          =   parser.add_parser('env'		)
        download_parser.set_defaults(func=lowercase_		)


    def 						snake_case__     (      self :       List[Any]		)->    List[str]:





        '''simple docstring'''







        A__          =   huggingface_hub.__version__

        A__          =   'not installed'
        A__          =   'NA'
        if is_torch_available():
            import torch

            A__          =   torch.__version__
            A__          =   torch.cuda.is_available()

        A__          =   'not installed'
        if is_transformers_available():
            import transformers

            A__          =   transformers.__version__

        A__          =   'not installed'
        if is_accelerate_available():
            import accelerate

            A__          =   accelerate.__version__

        A__          =   'not installed'
        if is_xformers_available():
            import xformers

            A__          =   xformers.__version__

        A__          =   {
            '`diffusers` version': version,
            'Platform': platform.platform(),
            'Python version': platform.python_version(),
            'PyTorch version (GPU?)': F'{pt_version} ({pt_cuda_available})',
            'Huggingface_hub version': hub_version,
            'Transformers version': transformers_version,
            'Accelerate version': accelerate_version,
            'xFormers version': xformers_version,
            'Using GPU in script?': '<fill in>',
            'Using distributed or parallel set-up in script?': '<fill in>',
        }

        print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'		)
        print(self.format_dict(lowercase_		)		)

        return info


    @staticmethod
    def 						snake_case__     (      lowercase_ :       int		)->    Optional[Any]:





        '''simple docstring'''







        return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]		) + "\n"

