



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()
__magic_name__            =  logging.get_logger(__name__)





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    print("Loading config file..."							)

    def flatten_yaml_as_dict(__lowerCAmelCase    ,		__lowerCAmelCase=""    ,		__lowerCAmelCase="."							):
        snake_case__						=				[]
        for k, v in d.items():
            snake_case__						=				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__							)

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

            snake_case__						=				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 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						=				MobileViTVaConfig()

    snake_case__						=				False

    # dataset
    if task_name.startswith("imagenet1k_"							):
        snake_case__						=				1_000
        if int(task_name.strip().split("_"							)[-1]							) == 384:
            snake_case__						=				384
        else:
            snake_case__						=				256
        snake_case__						=				"""imagenet-1k-id2label.json"""
    elif task_name.startswith("imagenet21k_to_1k_"							):
        snake_case__						=				21_000
        if int(task_name.strip().split("_"							)[-1]							) == 384:
            snake_case__						=				384
        else:
            snake_case__						=				256
        snake_case__						=				"""imagenet-22k-id2label.json"""
    elif task_name.startswith("ade20k_"							):
        snake_case__						=				151
        snake_case__						=				512
        snake_case__						=				"""ade20k-id2label.json"""
        snake_case__						=				True
    elif task_name.startswith("voc_"							):
        snake_case__						=				21
        snake_case__						=				512
        snake_case__						=				"""pascal-voc-id2label.json"""
        snake_case__						=				True

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

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

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

    return config





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						=				dct.pop(UpperCamelCase__							)
    snake_case__						=				val





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase=False							):
    if base_model:
        snake_case__						=				""""""
    else:
        snake_case__						=				"""mobilevitv2."""

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

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

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

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

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

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

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

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





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





@torch.no_grad()
def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						=				get_mobilevitva_config(UpperCamelCase__    ,		UpperCamelCase__							)

    # load original state_dict
    snake_case__						=				torch.load(UpperCamelCase__    ,		map_location="cpu"							)

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

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

    # verify classification model
    if task_name.startswith("imagenet"							):
        snake_case__						=				outputs.logits
        snake_case__						=				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
            snake_case__						=				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__":
     __magic_name__            =  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.'''
     )

     __magic_name__            =  parser.parse_args()
     convert_mobilevitva_checkpoint(
         args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
     )





import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    snake_case__						=				SwinConfig()
    snake_case__						=				swin_name.split("_"							)

    snake_case__						=				name_split[1]
    snake_case__						=				int(name_split[4]							)
    snake_case__						=				int(name_split[3][-1]							)

    if model_size == "tiny":
        snake_case__						=				96
        snake_case__						=				(2, 2, 6, 2)
        snake_case__						=				(3, 6, 12, 24)
    elif model_size == "small":
        snake_case__						=				96
        snake_case__						=				(2, 2, 18, 2)
        snake_case__						=				(3, 6, 12, 24)
    elif model_size == "base":
        snake_case__						=				128
        snake_case__						=				(2, 2, 18, 2)
        snake_case__						=				(4, 8, 16, 32)
    else:
        snake_case__						=				192
        snake_case__						=				(2, 2, 18, 2)
        snake_case__						=				(6, 12, 24, 48)

    if "in22k" in swin_name:
        snake_case__						=				21_841
    else:
        snake_case__						=				1_000
        snake_case__						=				"huggingface/label-files"
        snake_case__						=				"imagenet-1k-id2label.json"
        snake_case__						=				json.load(open(hf_hub_download(__lowerCAmelCase    ,		__lowerCAmelCase    ,		repo_type="dataset"							)    ,		"r"							)							)
        snake_case__						=				{int(__lowerCAmelCase							): v for k, v in idalabel.items()}
        snake_case__						=				idalabel
        snake_case__						=				{v: k for k, v in idalabel.items()}

    snake_case__						=				img_size
    snake_case__						=				num_classes
    snake_case__						=				embed_dim
    snake_case__						=				depths
    snake_case__						=				num_heads
    snake_case__						=				window_size

    return config





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    if "patch_embed.proj" in name:
        snake_case__						=				name.replace("patch_embed.proj"    ,		"embeddings.patch_embeddings.projection"							)
    if "patch_embed.norm" in name:
        snake_case__						=				name.replace("patch_embed.norm"    ,		"embeddings.norm"							)
    if "layers" in name:
        snake_case__						=				"encoder." + name
    if "attn.proj" in name:
        snake_case__						=				name.replace("attn.proj"    ,		"attention.output.dense"							)
    if "attn" in name:
        snake_case__						=				name.replace("attn"    ,		"attention.self"							)
    if "norm1" in name:
        snake_case__						=				name.replace("norm1"    ,		"layernorm_before"							)
    if "norm2" in name:
        snake_case__						=				name.replace("norm2"    ,		"layernorm_after"							)
    if "mlp.fc1" in name:
        snake_case__						=				name.replace("mlp.fc1"    ,		"intermediate.dense"							)
    if "mlp.fc2" in name:
        snake_case__						=				name.replace("mlp.fc2"    ,		"output.dense"							)

    if name == "norm.weight":
        snake_case__						=				"layernorm.weight"
    if name == "norm.bias":
        snake_case__						=				"layernorm.bias"

    if "head" in name:
        snake_case__						=				name.replace("head"    ,		"classifier"							)
    else:
        snake_case__						=				"swin." + name

    return name





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    for key in orig_state_dict.copy().keys():
        snake_case__						=				orig_state_dict.pop(__lowerCAmelCase							)

        if "mask" in key:
            continue
        elif "qkv" in key:
            snake_case__						=				key.split("."							)
            snake_case__						=				int(key_split[1]							)
            snake_case__						=				int(key_split[3]							)
            snake_case__						=				model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                snake_case__						=				val[:dim, :]
                snake_case__						=				val[
                    dim : dim * 2, :
                ]
                snake_case__						=				val[-dim:, :]
            else:
                snake_case__						=				val[
                    :dim
                ]
                snake_case__						=				val[
                    dim : dim * 2
                ]
                snake_case__						=				val[
                    -dim:
                ]
        else:
            snake_case__						=				val

    return orig_state_dict





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						=				timm.create_model(__lowerCAmelCase    ,		pretrained=__lowerCAmelCase							)
    timm_model.eval()

    snake_case__						=				get_swin_config(__lowerCAmelCase							)
    snake_case__						=				SwinForImageClassification(__lowerCAmelCase							)
    model.eval()

    snake_case__						=				convert_state_dict(timm_model.state_dict()    ,		__lowerCAmelCase							)
    model.load_state_dict(__lowerCAmelCase							)

    snake_case__						=				"http://images.cocodataset.org/val2017/000000039769.jpg"

    snake_case__						=				AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"    ,		"-"							)							)							)
    snake_case__						=				Image.open(requests.get(__lowerCAmelCase    ,		stream=__lowerCAmelCase							).raw							)
    snake_case__						=				image_processor(images=__lowerCAmelCase    ,		return_tensors="pt"							)

    snake_case__						=				timm_model(inputs["pixel_values"]							)
    snake_case__						=				model(**__lowerCAmelCase							).logits

    assert torch.allclose(__lowerCAmelCase    ,		__lowerCAmelCase    ,		atol=1E-3							)

    print(F"""Saving model {swin_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 __name__ == "__main__":
     __magic_name__            =  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--swin_name''',
         default='''swin_tiny_patch4_window7_224''',
         type=str,
         help='''Name of the Swin timm model you\'d like to convert.''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
     )

     __magic_name__            =  parser.parse_args()
     convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)



