


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      :    str		=					logging.get_logger(__name__)

def       lowercase_		(		_A			:    List[Any]						):

      """simple docstring"""




      lowerCamelCase__		:				Optional[Any]    =  MobileViTConfig()

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

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

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

      return config

def       lowercase_		(		_A			:    List[str]    ,					_A			:    Tuple=False						):

      """simple docstring"""




      for i in range(1    ,					6						):
            if F"layer_{i}." in name:
                  lowerCamelCase__		:				Any    =  name.replace(F"layer_{i}."    ,					F"encoder.layer.{i - 1}."						)

      if "conv_1." in name:
            lowerCamelCase__		:				Tuple    =  name.replace("conv_1."    ,					"conv_stem."						)
      if ".block." in name:
            lowerCamelCase__		:				List[str]    =  name.replace(".block."    ,					"."						)

      if "exp_1x1" in name:
            lowerCamelCase__		:				List[Any]    =  name.replace("exp_1x1"    ,					"expand_1x1"						)
      if "red_1x1" in name:
            lowerCamelCase__		:				int    =  name.replace("red_1x1"    ,					"reduce_1x1"						)
      if ".local_rep.conv_3x3." in name:
            lowerCamelCase__		:				Dict    =  name.replace(".local_rep.conv_3x3."    ,					".conv_kxk."						)
      if ".local_rep.conv_1x1." in name:
            lowerCamelCase__		:				Union[str, Any]    =  name.replace(".local_rep.conv_1x1."    ,					".conv_1x1."						)
      if ".norm." in name:
            lowerCamelCase__		:				Union[str, Any]    =  name.replace(".norm."    ,					".normalization."						)
      if ".conv." in name:
            lowerCamelCase__		:				Optional[Any]    =  name.replace(".conv."    ,					".convolution."						)
      if ".conv_proj." in name:
            lowerCamelCase__		:				Optional[int]    =  name.replace(".conv_proj."    ,					".conv_projection."						)

      for i in range(0    ,					2						):
            for j in range(0    ,					4						):
                  if F".{i}.{j}." in name:
                        lowerCamelCase__		:				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:
                        lowerCamelCase__		:				Optional[Any]    =  name.replace(F".{i}.{j}."    ,					F".{i}."						)
                        if "expand_1x1" in name:
                              lowerCamelCase__		:				Tuple    =  name.replace("expand_1x1"    ,					"downsampling_layer.expand_1x1"						)
                        if "conv_3x3" in name:
                              lowerCamelCase__		:				List[Any]    =  name.replace("conv_3x3"    ,					"downsampling_layer.conv_3x3"						)
                        if "reduce_1x1" in name:
                              lowerCamelCase__		:				Optional[Any]    =  name.replace("reduce_1x1"    ,					"downsampling_layer.reduce_1x1"						)

      for i in range(2    ,					5						):
            if F".global_rep.{i}.weight" in name:
                  lowerCamelCase__		:				List[Any]    =  name.replace(F".global_rep.{i}.weight"    ,					".layernorm.weight"						)
            if F".global_rep.{i}.bias" in name:
                  lowerCamelCase__		:				int    =  name.replace(F".global_rep.{i}.bias"    ,					".layernorm.bias"						)

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

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

      if "classifier.fc." in name:
            lowerCamelCase__		:				Optional[Any]    =  name.replace("classifier.fc."    ,					"classifier."						)
      elif (not base_model) and ("segmentation_head." not in name):
            lowerCamelCase__		:				Dict    =  "mobilevit." + name

      return name

def       lowercase_		(		_A			:    Tuple    ,					_A			:    str    ,					_A			:    List[Any]=False						):

      """simple docstring"""




      if base_model:
            lowerCamelCase__		:				Optional[int]    =  ""
      else:
            lowerCamelCase__		:				Optional[Any]    =  "mobilevit."

      for key in orig_state_dict.copy().keys():
            lowerCamelCase__		:				Tuple    =  orig_state_dict.pop(__snake_case						)

            if key[:8] == "encoder.":
                  lowerCamelCase__		:				Union[str, Any]    =  key[8:]

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

      return orig_state_dict

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Tuple    =  "http://images.cocodataset.org/val2017/000000039769.jpg"
      lowerCamelCase__		:				int    =  Image.open(requests.get(__snake_case    ,					stream=__snake_case						).raw						)
      return im

@torch.no_grad()
def       lowercase_		(		_A			:    Dict    ,					_A			:    Dict    ,					_A			:    Optional[int]    ,					_A			:    Optional[int]=False						):

      """simple docstring"""




      lowerCamelCase__		:				List[Any]    =  get_mobilevit_config(__snake_case						)

      # load original state_dict
      lowerCamelCase__		:				str    =  torch.load(__snake_case    ,					map_location="cpu"						)

      # load 🤗 model
      if mobilevit_name.startswith("deeplabv3_"						):
            lowerCamelCase__		:				Union[str, Any]    =  MobileViTForSemanticSegmentation(__snake_case						).eval()
      else:
            lowerCamelCase__		:				List[Any]    =  MobileViTForImageClassification(__snake_case						).eval()

      lowerCamelCase__		:				Tuple    =  convert_state_dict(__snake_case    ,					__snake_case						)
      model.load_state_dict(__snake_case						)

      # Check outputs on an image, prepared by MobileViTImageProcessor
      lowerCamelCase__		:				Any    =  MobileViTImageProcessor(crop_size=config.image_size    ,					size=config.image_size + 32						)
      lowerCamelCase__		:				Any    =  image_processor(images=prepare_img()    ,					return_tensors="pt"						)
      lowerCamelCase__		:				Tuple    =  model(**__snake_case						)
      lowerCamelCase__		:				int    =  outputs.logits

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

            if mobilevit_name == "deeplabv3_mobilevit_s":
                  lowerCamelCase__		:				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":
                  lowerCamelCase__		:				List[str]    =  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":
                  lowerCamelCase__		:				Union[str, Any]    =  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]    ,					__snake_case    ,					atol=1E-4						)
      else:
            assert logits.shape == (1, 1000)

            if mobilevit_name == "mobilevit_s":
                  lowerCamelCase__		:				int    =  torch.tensor([-0.9_866, 0.2_392, -1.1_241]						)
            elif mobilevit_name == "mobilevit_xs":
                  lowerCamelCase__		:				Optional[Any]    =  torch.tensor([-2.4_761, -0.9_399, -1.9_587]						)
            elif mobilevit_name == "mobilevit_xxs":
                  lowerCamelCase__		:				Optional[Any]    =  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]    ,					__snake_case    ,					atol=1E-4						)

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

      if push_to_hub:
            lowerCamelCase__		:				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..."						)
            lowerCamelCase__		:				Union[str, Any]    =  model_mapping[mobilevit_name]
            image_processor.push_to_hub(__snake_case    ,					organization="apple"						)
            model.push_to_hub(__snake_case    ,					organization="apple"						)


if __name__ == "__main__":
    A      :    Optional[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."
    )

    A      :    int		=					parser.parse_args()
    convert_movilevit_checkpoint(
        args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__   	=							{
    '''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TimesformerModel''',
      '''TimesformerForVideoClassification''',
      '''TimesformerPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_timesformer import (
        TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimesformerForVideoClassification,
        TimesformerModel,
        TimesformerPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase__   	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

