



"""simple docstring"""






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()
snake_case   					=				logging.get_logger(__name__)




def 			snake_case		(		lowerCAmelCase_	)						->				Union[str, Any]:
  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(_a  ,						collections.abc.MutableMapping	):
        items.extend(flatten_yaml_as_dict(_a  ,						_a  ,						sep=_a	).items()	)
      else:
        items.append((new_key, v)	)
    return dict(_a	)

  _snake_case   	=      argparse.Namespace()
  with open(_a  ,						'''r'''	) as yaml_file:
    try:
      _snake_case   	=      yaml.load(_a  ,						Loader=yaml.FullLoader	)

      _snake_case   	=      flatten_yaml_as_dict(_a	)
      for k, v in flat_cfg.items():
        setattr(_a  ,						_a  ,						_a	)
    except yaml.YAMLError as exc:
      logger.error('''Error while loading config file: {}. Error message: {}'''.format(_a  ,						str(_a	)	)	)
  return config




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				List[str]:
  _snake_case   	=      MobileViTVaConfig()

  _snake_case   	=      False

  # dataset
  if task_name.startswith('''imagenet1k_'''	):
    _snake_case   	=      1000
    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   	=      21000
    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(_a	)
  assert getattr(_a  ,						'''model.classification.name'''  ,						-1	) == "mobilevit_v2", "Invalid model"
  _snake_case   	=      getattr(_a  ,						'''model.classification.mitv2.width_multiplier'''  ,						1.0	)
  assert (
      getattr(_a  ,						'''model.classification.mitv2.attn_norm_layer'''  ,						-1	) == "layer_norm_2d"
  ), "Norm layers other than layer_norm_2d is not supported"
  _snake_case   	=      getattr(_a  ,						'''model.classification.activation.name'''  ,						'''swish'''	)
  # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

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

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

  return config




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)						->				int:
  _snake_case   	=      dct.pop(_a	)
  _snake_case   	=      val




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_=False	)						->				List[Any]:
  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 			snake_case		(		lowerCAmelCase_	)						->				str:
  _snake_case   	=      []
  for k in state_dict.keys():
    if k.startswith('''seg_head.aux_head.'''	):
      keys_to_ignore.append(_a	)
  for k in keys_to_ignore:
    state_dict.pop(_a  ,						_a	)




def 			snake_case		(		)						->				Optional[int]:
  _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(_a  ,						stream=_a	).raw	)
  return im




@torch.no_grad()
def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)						->				Optional[Any]:
  _snake_case   	=      get_mobilevitva_config(_a  ,						_a	)

  # load original state_dict
  _snake_case   	=      torch.load(_a  ,						map_location='''cpu'''	)

  # load huggingface model
  if task_name.startswith('''ade20k_'''	) or task_name.startswith('''voc_'''	):
    _snake_case   	=      MobileViTVaForSemanticSegmentation(_a	).eval()
    _snake_case   	=      False
  else:
    _snake_case   	=      MobileViTVaForImageClassification(_a	).eval()
    _snake_case   	=      False

  # remove and rename some keys of load the original model
  _snake_case   	=      checkpoint
  remove_unused_keys(_a	)
  _snake_case   	=      create_rename_keys(_a  ,						base_model=_a	)
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(_a  ,						_a  ,						_a	)

  # load modified state_dict
  model.load_state_dict(_a	)

  # 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(**_a	)

  # 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([-1.6_336E00, -7.3_204E-02, -5.1_883E-01]	)
      assert torch.allclose(logits[0, :3]  ,						_a  ,						atol=1E-4	)

  Path(_a	).mkdir(exist_ok=_a	)
  print(f"""Saving model {task_name} to {pytorch_dump_folder_path}"""	)
  model.save_pretrained(_a	)
  print(f"""Saving image processor to {pytorch_dump_folder_path}"""	)
  image_processor.save_pretrained(_a	)


if __name__ == "__main__":
     snake_case   					=				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.'''
     )

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








# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
			import jax
			import jaxlib

a_      =     get_logger()

a_      =     None






class _UpperCamelCase				(				TensorFormatter[Mapping, 'jax.Array', Mapping] ):
			'''simple docstring'''







			def __init__(      self				:			Optional[Any]   ,   a				:			str=None   ,   a				:			List[Any]=None   ,   **a				:			Any						)				->							Optional[Any]:

				"""simple docstring"""

				super().__init__(features=a						)
				import jax
				from jaxlib.xla_client import Device

				if isinstance(a   ,   a						):
					raise ValueError(
					    F"Expected {device} to be a `str` not {type(a						)}, as `jaxlib.xla_extension.Device` "
					    "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
					    "the device with `str()` to get its string identifier that will be internally mapped "
					    "to the actual `jaxlib.xla_extension.Device`."						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							device if isinstance(a   ,   a						) else str(jax.devices()[0]						)
				# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
				# with `pickle` nor with `dill`, so we need to use a global variable instead
				global DEVICE_MAPPING
				if DEVICE_MAPPING is None:
					SCREAMING_SNAKE_CASE :   str					  =							self._map_devices_to_str()
				if self.device not in list(DEVICE_MAPPING.keys()						):
					logger.warning(
					    F"Device with string identifier {self.device} not listed among the available "
					    F"devices: {list(DEVICE_MAPPING.keys()						)}, so falling back to the default "
					    F"device: {str(jax.devices()[0]						)}."						)
					SCREAMING_SNAKE_CASE :   Any					  =							str(jax.devices()[0]						)
				SCREAMING_SNAKE_CASE :   Any					  =							jnp_array_kwargs
			@staticmethod
			def 				__UpperCamelCase			(      )				->							Dict[str, "jaxlib.xla_extension.Device"]:

				"""simple docstring"""

				import jax

				return {str(a						): device for device in jax.devices()}
			def 				__UpperCamelCase			(      self				:			Dict   ,   a				:			Tuple						)				->							str:

				"""simple docstring"""

				import jax
				import jax.numpy as jnp

				if isinstance(a   ,   a						) and column:
					if all(
					    isinstance(a   ,   jax.Array						) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column						):
						return jnp.stack(a   ,   axis=0						)
				return column
			def 				__UpperCamelCase			(      self				:			Dict   ,   a				:			str						)				->							str:

				"""simple docstring"""

				import jax
				import jax.numpy as jnp

				if isinstance(a   ,   (str, bytes, type(a						))						):
					return value
				elif isinstance(a   ,   (np.character, np.ndarray)						) and np.issubdtype(value.dtype   ,   np.character						):
					return value.tolist()

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							{}

				if isinstance(a   ,   (np.number, np.ndarray)						) and np.issubdtype(value.dtype   ,   np.integer						):
					# the default int precision depends on the jax config
					# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
					if jax.config.jax_enable_xaa:
						SCREAMING_SNAKE_CASE :   Dict					  =							{"dtype": jnp.intaa}
					else:
						SCREAMING_SNAKE_CASE :   str					  =							{"dtype": jnp.intaa}
				elif isinstance(a   ,   (np.number, np.ndarray)						) and np.issubdtype(value.dtype   ,   np.floating						):
					SCREAMING_SNAKE_CASE :   int					  =							{"dtype": jnp.floataa}
				elif config.PIL_AVAILABLE and "PIL" in sys.modules:
					import PIL.Image

					if isinstance(a   ,   PIL.Image.Image						):
						SCREAMING_SNAKE_CASE :   Dict					  =							np.asarray(a						)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
				global DEVICE_MAPPING
				if DEVICE_MAPPING is None:
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self._map_devices_to_str()

				with jax.default_device(DEVICE_MAPPING[self.device]						):
					# calling jnp.array on a np.ndarray does copy the data
					# see https://github.com/google/jax/issues/4486
					return jnp.array(a   ,   **{**default_dtype, **self.jnp_array_kwargs}						)
			def 				__UpperCamelCase			(      self				:			Any   ,   a				:			List[str]						)				->							Dict:

				"""simple docstring"""

				import jax

				# support for torch, tf, jax etc.
				if config.TORCH_AVAILABLE and "torch" in sys.modules:
					import torch

					if isinstance(a   ,   torch.Tensor						):
						return self._tensorize(data_struct.detach().cpu().numpy()[()]						)
				if hasattr(a   ,   "__array__"						) and not isinstance(a   ,   jax.Array						):
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							data_struct.__array__()
				# support for nested types like struct of list of struct
				if isinstance(a   ,   np.ndarray						):
					if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
						return self._consolidate([self.recursive_tensorize(a						) for substruct in data_struct]						)
				elif isinstance(a   ,   (list, tuple)						):
					return self._consolidate([self.recursive_tensorize(a						) for substruct in data_struct]						)
				return self._tensorize(a						)
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			dict						)				->							Dict:

				"""simple docstring"""

				return map_nested(self._recursive_tensorize   ,   a   ,   map_list=a						)
			def 				__UpperCamelCase			(      self				:			Dict   ,   a				:			pa.Table						)				->							Mapping:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.numpy_arrow_extractor().extract_row(a						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.python_features_decoder.decode_row(a						)
				return self.recursive_tensorize(a						)
			def 				__UpperCamelCase			(      self				:			Optional[int]   ,   a				:			pa.Table						)				->							"jax.Array":

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.numpy_arrow_extractor().extract_column(a						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.python_features_decoder.decode_column(a   ,   pa_table.column_names[0]						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							self.recursive_tensorize(a						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							self._consolidate(a						)
				return column






			def 				__UpperCamelCase			(      self				:			List[Any]   ,   a				:			pa.Table						)				->							Mapping:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							self.numpy_arrow_extractor().extract_batch(a						)
				SCREAMING_SNAKE_CASE :   str					  =							self.python_features_decoder.decode_batch(a						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.recursive_tensorize(a						)
				for column_name in batch:
					SCREAMING_SNAKE_CASE :   List[Any]					  =							self._consolidate(batch[column_name]						)
				return batch