
"""simple docstring"""
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      :			Any					=							logging.get_logger(__name__)
def 				a__   (   a   :				Optional[Any]		):

  """simple docstring"""







  _snake_case     :						int						=   MobileViTConfig()

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

  if mobilevit_name.startswith("deeplabv3_"		):
    _snake_case     :						str						=   512
    _snake_case     :						Optional[int]						=   16
    _snake_case     :						Union[str, Any]						=   21
    _snake_case     :						str						=   "pascal-voc-id2label.json"
  else:
    _snake_case     :						Union[str, Any]						=   1_000
    _snake_case     :						int						=   "imagenet-1k-id2label.json"

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

  return config
def 				a__   (   a   :				int       ,     a   :				Tuple=False		):

  """simple docstring"""







  for i in range(1       ,     6		):
    if f'layer_{i}.' in name:
      _snake_case     :						Dict						=   name.replace(f'layer_{i}.'       ,     f'encoder.layer.{i - 1}.'		)

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

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

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

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

  if ".aspp_layer." in name:
    _snake_case     :						List[str]						=   name.replace(".aspp_layer."       ,     "."		)
  if ".aspp_pool." in name:
    _snake_case     :						Union[str, Any]						=   name.replace(".aspp_pool."       ,     "."		)
  if "seg_head." in name:
    _snake_case     :						List[str]						=   name.replace("seg_head."       ,     "segmentation_head."		)
  if "segmentation_head.classifier.classifier." in name:
    _snake_case     :						Tuple						=   name.replace("segmentation_head.classifier.classifier."       ,     "segmentation_head.classifier."		)

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

  return name
def 				a__   (   a   :				Optional[Any]       ,     a   :				Dict       ,     a   :				Optional[Any]=False		):

  """simple docstring"""







  if base_model:
    _snake_case     :						Tuple						=   ""
  else:
    _snake_case     :						str						=   "mobilevit."

  for key in orig_state_dict.copy().keys():
    _snake_case     :						int						=   orig_state_dict.pop(__UpperCAmelCase		)

    if key[:8] == "encoder.":
      _snake_case     :						Dict						=   key[8:]

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

  return orig_state_dict
def 				a__   (   ):

  """simple docstring"""







  _snake_case     :						Tuple						=   "http://images.cocodataset.org/val2017/000000039769.jpg"
  _snake_case     :						Dict						=   Image.open(requests.get(__UpperCAmelCase       ,     stream=__UpperCAmelCase		).raw		)
  return im
@torch.no_grad()
def 				a__   (   a   :				List[str]       ,     a   :				Tuple       ,     a   :				Union[str, Any]       ,     a   :				List[str]=False		):

  """simple docstring"""







  _snake_case     :						Dict						=   get_mobilevit_config(__UpperCAmelCase		)

  # load original state_dict
  _snake_case     :						str						=   torch.load(__UpperCAmelCase       ,     map_location="cpu"		)

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

  _snake_case     :						List[Any]						=   convert_state_dict(__UpperCAmelCase       ,     __UpperCAmelCase		)
  model.load_state_dict(__UpperCAmelCase		)

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

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

    if mobilevit_name == "deeplabv3_mobilevit_s":
      _snake_case     :						Dict						=   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":
      _snake_case     :						Optional[Any]						=   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":
      _snake_case     :						Union[str, Any]						=   torch.tensor(
          [
              [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
              [[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8624, -9.5964], [-10.8_840, -10.8_158, -10.6_659]],
              [[-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]       ,     __UpperCAmelCase       ,     atol=1e-4		)
  else:
    assert logits.shape == (1, 1_000)

    if mobilevit_name == "mobilevit_s":
      _snake_case     :						List[Any]						=   torch.tensor([-0.9866, 0.2392, -1.1241]		)
    elif mobilevit_name == "mobilevit_xs":
      _snake_case     :						str						=   torch.tensor([-2.4761, -0.9399, -1.9587]		)
    elif mobilevit_name == "mobilevit_xxs":
      _snake_case     :						Union[str, Any]						=   torch.tensor([-1.9364, -1.2327, -0.4653]		)
    else:
      raise ValueError(f'Unknown mobilevit_name: {mobilevit_name}'		)

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

  Path(__UpperCAmelCase		).mkdir(exist_ok=__UpperCAmelCase		)
  print(f'Saving model {mobilevit_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 push_to_hub:
    _snake_case     :						Tuple						=   {
        "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..."		)
    _snake_case     :						str						=   model_mapping[mobilevit_name]
    image_processor.push_to_hub(__UpperCAmelCase       ,     organization="apple"		)
    model.push_to_hub(__UpperCAmelCase       ,     organization="apple"		)


if __name__ == "__main__":
    _a      :			List[str]					=							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
    )



"""simple docstring"""
from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def 				a__   (   a   :				Namespace		):

  """simple docstring"""







  return ConvertCommand(
      args.model_type       ,     args.tf_checkpoint       ,     args.pytorch_dump_output       ,     args.config       ,     args.finetuning_task_name		)


_a      :			int					=							"""
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""






class      _UpperCAmelCase				(					_snake_case):
    @staticmethod
    def 					lowerCamelCase__	(    snake_case_     ):
      _snake_case     :						Dict						=   parser.add_parser(
          "convert"						,    help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."						,    )
      train_parser.add_argument("--model_type"						,    type=snake_case_						,    required=snake_case_						,    help="Model's type."     )
      train_parser.add_argument(
          "--tf_checkpoint"						,    type=snake_case_						,    required=snake_case_						,    help="TensorFlow checkpoint path or folder."     )
      train_parser.add_argument(
          "--pytorch_dump_output"						,    type=snake_case_						,    required=snake_case_						,    help="Path to the PyTorch saved model output."     )
      train_parser.add_argument("--config"						,    type=snake_case_						,    default=""						,    help="Configuration file path or folder."     )
      train_parser.add_argument(
          "--finetuning_task_name"						,    type=snake_case_						,    default=snake_case_						,    help="Optional fine-tuning task name if the TF model was a finetuned model."						,    )
      train_parser.set_defaults(func=snake_case_     )
    def __init__(    self						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    *snake_case_						,    ):
      _snake_case     :						str						=   logging.get_logger("transformers-cli/converting"     )

      self._logger.info(F'Loading model {model_type}'     )
      _snake_case     :						Optional[int]						=   model_type
      _snake_case     :						Any						=   tf_checkpoint
      _snake_case     :						Optional[int]						=   pytorch_dump_output
      _snake_case     :						Tuple						=   config
      _snake_case     :						Tuple						=   finetuning_task_name


    def 					lowerCamelCase__	(    self     ):
      if self._model_type == "albert":
        try:
          from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
              convert_tf_checkpoint_to_pytorch,
          )
        except ImportError:
          raise ImportError(snake_case_     )

        convert_tf_checkpoint_to_pytorch(self._tf_checkpoint						,    self._config						,    self._pytorch_dump_output     )
      elif self._model_type == "bert":
        try:
          from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
              convert_tf_checkpoint_to_pytorch,
          )
        except ImportError:
          raise ImportError(snake_case_     )

        convert_tf_checkpoint_to_pytorch(self._tf_checkpoint						,    self._config						,    self._pytorch_dump_output     )
      elif self._model_type == "funnel":
        try:
          from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
              convert_tf_checkpoint_to_pytorch,
          )
        except ImportError:
          raise ImportError(snake_case_     )

        convert_tf_checkpoint_to_pytorch(self._tf_checkpoint						,    self._config						,    self._pytorch_dump_output     )
      elif self._model_type == "t5":
        try:
          from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
        except ImportError:
          raise ImportError(snake_case_     )

        convert_tf_checkpoint_to_pytorch(self._tf_checkpoint						,    self._config						,    self._pytorch_dump_output     )
      elif self._model_type == "gpt":
        from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
            convert_openai_checkpoint_to_pytorch,
        )

        convert_openai_checkpoint_to_pytorch(self._tf_checkpoint						,    self._config						,    self._pytorch_dump_output     )
      elif self._model_type == "transfo_xl":
        try:
          from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
              convert_transfo_xl_checkpoint_to_pytorch,
          )
        except ImportError:
          raise ImportError(snake_case_     )

        if "ckpt" in self._tf_checkpoint.lower():
          _snake_case     :						int						=   self._tf_checkpoint
          _snake_case     :						Optional[Any]						=   ""
        else:
          _snake_case     :						Optional[int]						=   self._tf_checkpoint
          _snake_case     :						List[str]						=   ""
        convert_transfo_xl_checkpoint_to_pytorch(
            snake_case_						,    self._config						,    self._pytorch_dump_output						,    snake_case_     )
      elif self._model_type == "gpt2":
        try:
          from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
              convert_gpta_checkpoint_to_pytorch,
          )
        except ImportError:
          raise ImportError(snake_case_     )

        convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint						,    self._config						,    self._pytorch_dump_output     )
      elif self._model_type == "xlnet":
        try:
          from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
              convert_xlnet_checkpoint_to_pytorch,
          )
        except ImportError:
          raise ImportError(snake_case_     )

        convert_xlnet_checkpoint_to_pytorch(
            self._tf_checkpoint						,    self._config						,    self._pytorch_dump_output						,    self._finetuning_task_name     )
      elif self._model_type == "xlm":
        from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
            convert_xlm_checkpoint_to_pytorch,
        )

        convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint						,    self._pytorch_dump_output     )
      elif self._model_type == "lxmert":
        from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
            convert_lxmert_checkpoint_to_pytorch,
        )

        convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint						,    self._pytorch_dump_output     )
      elif self._model_type == "rembert":
        from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
            convert_rembert_tf_checkpoint_to_pytorch,
        )

        convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint						,    self._config						,    self._pytorch_dump_output     )
      else:
        raise ValueError(
            "--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"     )

