




import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


A_      			=       logging.get_logger(__name__)






class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      ["input_features", "is_longer"]


 def __init__(       self:	List[Any],   a_:	List[Any]=64,   a_:	int=48_000,   a_:	Union[str, Any]=480,   a_:	Any=10,   a_:	Optional[int]=1_024,   a_:	Optional[int]=0.0,   a_:	Tuple=False,   a_:	float = 0,   a_:	float = 14_000,   a_:	int = None,   a_:	str = "fusion",   a_:	str = "repeatpad",   **a_:	Optional[Any],   ):




   '''simple docstring'''



   super().__init__(
       feature_size=UpperCamelCase_,   sampling_rate=UpperCamelCase_,   padding_value=UpperCamelCase_,   return_attention_mask=UpperCamelCase_,   **UpperCamelCase_,   )
   _snake_case							:	Tuple			=						top_db
   _snake_case							:	Any			=						truncation
   _snake_case							:	str			=						padding
   _snake_case							:	int			=						fft_window_size
   _snake_case							:	Any			=						(fft_window_size >> 1) + 1
   _snake_case							:	int			=						hop_length
   _snake_case							:	Any			=						max_length_s
   _snake_case							:	str			=						max_length_s * sampling_rate
   _snake_case							:	Any			=						sampling_rate
   _snake_case							:	List[Any]			=						frequency_min
   _snake_case							:	Tuple			=						frequency_max
   _snake_case							:	Tuple			=						mel_filter_bank(
       num_frequency_bins=self.nb_frequency_bins,   num_mel_filters=UpperCamelCase_,   min_frequency=UpperCamelCase_,   max_frequency=UpperCamelCase_,   sampling_rate=UpperCamelCase_,   norm=UpperCamelCase_,   mel_scale="""htk""",   )
   _snake_case							:	Any			=						mel_filter_bank(
       num_frequency_bins=self.nb_frequency_bins,   num_mel_filters=UpperCamelCase_,   min_frequency=UpperCamelCase_,   max_frequency=UpperCamelCase_,   sampling_rate=UpperCamelCase_,   norm="""slaney""",   mel_scale="""slaney""",   )


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						copy.deepcopy(self.__dict__	)
   _snake_case							:	int			=						self.__class__.__name__
   if "mel_filters" in output:
     del output["mel_filters"]
   if "mel_filters_slaney" in output:
     del output["mel_filters_slaney"]
   return output


 def 				UpperCamelCase_     (       self:	Dict,   a_:	np.array,   a_:	Optional[np.array] = None	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						spectrogram(
       UpperCamelCase_,   window_function(self.fft_window_size,   """hann"""	),   frame_length=self.fft_window_size,   hop_length=self.hop_length,   power=2.0,   mel_filters=UpperCamelCase_,   log_mel="""dB""",   )
   return log_mel_spectrogram.T


 def 				UpperCamelCase_     (       self:	List[Any],   a_:	Any,   a_:	List[Any],   a_:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						np.array_split(list(range(0,   total_frames - chunk_frames + 1	)	),   3	)
   if len(ranges[1]	) == 0:
     # if the audio is too short, we just use the first chunk
     _snake_case							:	int			=						[0]
   if len(ranges[2]	) == 0:
     # if the audio is too short, we just use the first chunk
     _snake_case							:	Union[str, Any]			=						[0]
   # randomly choose index for each part
   _snake_case							:	Tuple			=						np.random.choice(ranges[0]	)
   _snake_case							:	int			=						np.random.choice(ranges[1]	)
   _snake_case							:	Any			=						np.random.choice(ranges[2]	)

   _snake_case							:	int			=						mel[idx_front : idx_front + chunk_frames, :]
   _snake_case							:	int			=						mel[idx_middle : idx_middle + chunk_frames, :]
   _snake_case							:	Tuple			=						mel[idx_back : idx_back + chunk_frames, :]

   _snake_case							:	List[Any]			=						torch.tensor(mel[None, None, :]	)
   _snake_case							:	Optional[int]			=						torch.nn.functional.interpolate(
       UpperCamelCase_,   size=[chunk_frames, 64],   mode="""bilinear""",   align_corners=UpperCamelCase_	)
   _snake_case							:	str			=						mel_shrink[0][0].numpy()
   _snake_case							:	int			=						np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back],   axis=0	)
   return mel_fusion


 def 				UpperCamelCase_     (       self:	List[str],   a_:	np.array,   a_:	List[Any],   a_:	int,   a_:	Optional[int]	):




   '''simple docstring'''



   if waveform.shape[0] > max_length:
     if truncation == "rand_trunc":
       _snake_case							:	Tuple			=						True
       # random crop to max_length (for compatibility) -> this should be handled by self.pad
       _snake_case							:	Any			=						len(UpperCamelCase_	) - max_length
       _snake_case							:	Dict			=						np.random.randint(0,   overflow + 1	)
       _snake_case							:	Optional[int]			=						waveform[idx : idx + max_length]
       _snake_case							:	Dict			=						self._np_extract_fbank_features(UpperCamelCase_,   self.mel_filters_slaney	)[None, :]
     elif truncation == "fusion":
       _snake_case							:	List[Any]			=						self._np_extract_fbank_features(UpperCamelCase_,   self.mel_filters	)
       _snake_case							:	List[Any]			=						max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
       _snake_case							:	Optional[int]			=						mel.shape[0]
       if chunk_frames == total_frames:
         # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
         # In this case, we just use the whole audio.
         _snake_case							:	Optional[Any]			=						np.stack([mel, mel, mel, mel],   axis=0	)
         _snake_case							:	List[Any]			=						False
       else:
         _snake_case							:	Union[str, Any]			=						self._random_mel_fusion(UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_	)
         _snake_case							:	int			=						True
     else:
       raise NotImplementedError(f"data_truncating {truncation} not implemented"	)

   else:
     _snake_case							:	Any			=						False
     # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
     if waveform.shape[0] < max_length:
       if padding == "repeat":
         _snake_case							:	List[Any]			=						int(max_length / len(UpperCamelCase_	)	)
         _snake_case							:	List[str]			=						np.stack(np.tile(UpperCamelCase_,   n_repeat + 1	)	)[:max_length]
       if padding == "repeatpad":
         _snake_case							:	Union[str, Any]			=						int(max_length / len(UpperCamelCase_	)	)
         _snake_case							:	Union[str, Any]			=						np.stack(np.tile(UpperCamelCase_,   UpperCamelCase_	)	)
       _snake_case							:	Dict			=						np.pad(UpperCamelCase_,   (0, max_length - waveform.shape[0]),   mode="""constant""",   constant_values=0	)

     if truncation == "fusion":
       _snake_case							:	str			=						self._np_extract_fbank_features(UpperCamelCase_,   self.mel_filters	)
       _snake_case							:	Dict			=						np.stack([input_mel, input_mel, input_mel, input_mel],   axis=0	)
     else:
       _snake_case							:	List[Any]			=						self._np_extract_fbank_features(UpperCamelCase_,   self.mel_filters_slaney	)[None, :]

   return input_mel, longer


 def __call__(       self:	Union[str, Any],   a_:	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],   a_:	str = None,   a_:	Optional[str] = None,   a_:	Optional[int] = None,   a_:	Optional[int] = None,   a_:	Optional[Union[str, TensorType]] = None,   **a_:	Dict,   ):




   '''simple docstring'''



   _snake_case							:	Dict			=						truncation if truncation is not None else self.truncation
   _snake_case							:	int			=						padding if padding else self.padding

   if sampling_rate is not None:
     if sampling_rate != self.sampling_rate:
       raise ValueError(
           f"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
           f" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
           f" was sampled with {self.sampling_rate} and not {sampling_rate}."	)
   else:
     logger.warning(
         """It is strongly recommended to pass the `sampling_rate` argument to this function. """
         """Failing to do so can result in silent errors that might be hard to debug."""	)

   _snake_case							:	Optional[Any]			=						isinstance(UpperCamelCase_,   np.ndarray	) and len(raw_speech.shape	) > 1
   if is_batched_numpy and len(raw_speech.shape	) > 2:
     raise ValueError(f"Only mono-channel audio is supported for input to {self}"	)
   _snake_case							:	List[str]			=						is_batched_numpy or (
       isinstance(UpperCamelCase_,   (list, tuple)	) and (isinstance(raw_speech[0],   (np.ndarray, tuple, list)	))
   )

   if is_batched:
     _snake_case							:	Dict			=						[np.asarray(UpperCamelCase_,   dtype=np.floataa	) for speech in raw_speech]
   elif not is_batched and not isinstance(UpperCamelCase_,   np.ndarray	):
     _snake_case							:	Any			=						np.asarray(UpperCamelCase_,   dtype=np.floataa	)
   elif isinstance(UpperCamelCase_,   np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
     _snake_case							:	Tuple			=						raw_speech.astype(np.floataa	)

   # always return batch
   if not is_batched:
     _snake_case							:	int			=						[np.asarray(UpperCamelCase_	)]

   # convert to mel spectrogram, truncate and pad if needed.
   _snake_case							:	Optional[Any]			=						[
       self._get_input_mel(UpperCamelCase_,   max_length if max_length else self.nb_max_samples,   UpperCamelCase_,   UpperCamelCase_	)
       for waveform in raw_speech
   ]

   _snake_case							:	List[Any]			=						[]
   _snake_case							:	Dict			=						[]
   for mel, longer in padded_inputs:
     input_mel.append(UpperCamelCase_	)
     is_longer.append(UpperCamelCase_	)

   if truncation == "fusion" and sum(UpperCamelCase_	) == 0:
     # if no audio is longer than 10s, then randomly select one audio to be longer
     _snake_case							:	Optional[Any]			=						np.random.randint(0,   len(UpperCamelCase_	)	)
     _snake_case							:	str			=						True

   if isinstance(input_mel[0],   UpperCamelCase_	):
     _snake_case							:	str			=						[np.asarray(UpperCamelCase_,   dtype=np.floataa	) for feature in input_mel]

   # is_longer is a list of bool
   _snake_case							:	Tuple			=						[[longer] for longer in is_longer]

   _snake_case							:	Optional[Any]			=						{'input_features': input_mel, 'is_longer': is_longer}
   _snake_case							:	Optional[int]			=						BatchFeature(UpperCamelCase_	)

   if return_tensors is not None:
     _snake_case							:	List[Any]			=						input_features.convert_to_tensors(UpperCamelCase_	)

   return input_features








"""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()
A_      			=       logging.get_logger(__name__)


def       UpperCAmelCase__ (snake_case__							:      Optional[int]					):
  """simple docstring"""

  print("""Loading config file..."""					)

  def flatten_yaml_as_dict(snake_case__							:      List[Any]	,     snake_case__							:      Optional[Any]=""	,     snake_case__							:      Tuple="."					):
    _snake_case							:	Union[str, Any]			=						[]
    for k, v in d.items():
      _snake_case							:	List[str]			=						parent_key + sep + k if parent_key else k
      if isinstance(snake_case__	,     collections.abc.MutableMapping					):
        items.extend(flatten_yaml_as_dict(snake_case__	,     snake_case__	,     sep=snake_case__					).items()					)
      else:
        items.append((new_key, v)					)
    return dict(snake_case__					)

  _snake_case							:	Dict			=						argparse.Namespace()
  with open(snake_case__	,     """r"""					) as yaml_file:
    try:
      _snake_case							:	List[Any]			=						yaml.load(snake_case__	,     Loader=yaml.FullLoader					)

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


def       UpperCAmelCase__ (snake_case__							:      int	,     snake_case__							:      int					):
  """simple docstring"""

  _snake_case							:	Dict			=						MobileViTVaConfig()

  _snake_case							:	Optional[int]			=						False

  # dataset
  if task_name.startswith("""imagenet1k_"""					):
    _snake_case							:	Dict			=						10_00
    if int(task_name.strip().split("""_"""					)[-1]					) == 3_84:
      _snake_case							:	Union[str, Any]			=						3_84
    else:
      _snake_case							:	Optional[Any]			=						2_56
    _snake_case							:	str			=						"""imagenet-1k-id2label.json"""
  elif task_name.startswith("""imagenet21k_to_1k_"""					):
    _snake_case							:	str			=						2_10_00
    if int(task_name.strip().split("""_"""					)[-1]					) == 3_84:
      _snake_case							:	Dict			=						3_84
    else:
      _snake_case							:	Union[str, Any]			=						2_56
    _snake_case							:	Tuple			=						"""imagenet-22k-id2label.json"""
  elif task_name.startswith("""ade20k_"""					):
    _snake_case							:	Tuple			=						1_51
    _snake_case							:	str			=						5_12
    _snake_case							:	List[Any]			=						"""ade20k-id2label.json"""
    _snake_case							:	Union[str, Any]			=						True
  elif task_name.startswith("""voc_"""					):
    _snake_case							:	List[Any]			=						21
    _snake_case							:	List[str]			=						5_12
    _snake_case							:	int			=						"""pascal-voc-id2label.json"""
    _snake_case							:	int			=						True

  # orig_config
  _snake_case							:	int			=						load_orig_config_file(snake_case__					)
  assert getattr(snake_case__	,     """model.classification.name"""	,     -1					) == "mobilevit_v2", "Invalid model"
  _snake_case							:	str			=						getattr(snake_case__	,     """model.classification.mitv2.width_multiplier"""	,     1.0					)
  assert (
      getattr(snake_case__	,     """model.classification.mitv2.attn_norm_layer"""	,     -1					) == "layer_norm_2d"
  ), "Norm layers other than layer_norm_2d is not supported"
  _snake_case							:	int			=						getattr(snake_case__	,     """model.classification.activation.name"""	,     """swish"""					)
  # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

  if is_segmentation_model:
    _snake_case							:	Tuple			=						getattr(snake_case__	,     """model.segmentation.output_stride"""	,     16					)
    if "_deeplabv3" in task_name:
      _snake_case							:	Any			=						getattr(snake_case__	,     """model.segmentation.deeplabv3.aspp_rates"""	,     [12, 24, 36]					)
      _snake_case							:	Tuple			=						getattr(snake_case__	,     """model.segmentation.deeplabv3.aspp_out_channels"""	,     5_12					)
      _snake_case							:	Any			=						getattr(snake_case__	,     """model.segmentation.deeplabv3.aspp_dropout"""	,     0.1					)

    # id2label
  _snake_case							:	Union[str, Any]			=						"""huggingface/label-files"""
  _snake_case							:	Any			=						json.load(open(hf_hub_download(snake_case__	,     snake_case__	,     repo_type="""dataset"""					)	,     """r"""					)					)
  _snake_case							:	List[Any]			=						{int(snake_case__					): v for k, v in idalabel.items()}
  _snake_case							:	Tuple			=						idalabel
  _snake_case							:	Any			=						{v: k for k, v in idalabel.items()}

  return config


def       UpperCAmelCase__ (snake_case__							:      Optional[Any]	,     snake_case__							:      Tuple	,     snake_case__							:      List[Any]					):
  """simple docstring"""

  _snake_case							:	List[str]			=						dct.pop(snake_case__					)
  _snake_case							:	List[Any]			=						val


def       UpperCAmelCase__ (snake_case__							:      Optional[Any]	,     snake_case__							:      int=False					):
  """simple docstring"""

  if base_model:
    _snake_case							:	Any			=						""""""
  else:
    _snake_case							:	Union[str, Any]			=						"""mobilevitv2."""

  _snake_case							:	Dict			=						[]
  for k in state_dict.keys():
    if k[:8] == "encoder.":
      _snake_case							:	List[str]			=						k[8:]
    else:
      _snake_case							:	str			=						k

    if ".block." in k:
      _snake_case							:	Optional[int]			=						k_new.replace(""".block."""	,     """."""					)
    if ".conv." in k:
      _snake_case							:	Union[str, Any]			=						k_new.replace(""".conv."""	,     """.convolution."""					)
    if ".norm." in k:
      _snake_case							:	str			=						k_new.replace(""".norm."""	,     """.normalization."""					)

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

    for i in [3, 4, 5]:
      if F"layer_{i}.0." in k:
        _snake_case							:	Tuple			=						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							:	Any			=						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							:	str			=						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							:	Optional[Any]			=						[0, 1]
      elif i == 4:
        _snake_case							:	Any			=						[0, 1, 2, 3]
      elif i == 5:
        _snake_case							:	List[Any]			=						[0, 1, 2]

      for j in j_in:
        if F"layer_{i}.1.global_rep.{j}." in k:
          _snake_case							:	Any			=						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							:	List[Any]			=						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							:	Union[str, Any]			=						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							:	List[Any]			=						k_new.replace("""pre_norm_attn.0."""	,     """layernorm_before."""					)
    if "pre_norm_attn.1." in k:
      _snake_case							:	Optional[int]			=						k_new.replace("""pre_norm_attn.1."""	,     """attention."""					)
    if "pre_norm_ffn.0." in k:
      _snake_case							:	List[Any]			=						k_new.replace("""pre_norm_ffn.0."""	,     """layernorm_after."""					)
    if "pre_norm_ffn.1." in k:
      _snake_case							:	Tuple			=						k_new.replace("""pre_norm_ffn.1."""	,     """ffn.conv1."""					)
    if "pre_norm_ffn.3." in k:
      _snake_case							:	Any			=						k_new.replace("""pre_norm_ffn.3."""	,     """ffn.conv2."""					)

    if "classifier.1." in k:
      _snake_case							:	List[str]			=						k_new.replace("""classifier.1."""	,     """classifier."""					)

    if "seg_head." in k:
      _snake_case							:	str			=						k_new.replace("""seg_head."""	,     """segmentation_head."""					)
    if ".aspp_layer." in k:
      _snake_case							:	Optional[int]			=						k_new.replace(""".aspp_layer."""	,     """."""					)
    if ".aspp_pool." in k:
      _snake_case							:	int			=						k_new.replace(""".aspp_pool."""	,     """."""					)

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


def       UpperCAmelCase__ (snake_case__							:      str					):
  """simple docstring"""

  _snake_case							:	List[str]			=						[]
  for k in state_dict.keys():
    if k.startswith("""seg_head.aux_head."""					):
      keys_to_ignore.append(snake_case__					)
  for k in keys_to_ignore:
    state_dict.pop(snake_case__	,     snake_case__					)


def       UpperCAmelCase__ ():
  """simple docstring"""

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


@torch.no_grad()
def       UpperCAmelCase__ (snake_case__							:      List[Any]	,     snake_case__							:      Optional[int]	,     snake_case__							:      List[str]	,     snake_case__							:      Tuple					):
  """simple docstring"""

  _snake_case							:	int			=						get_mobilevitva_config(snake_case__	,     snake_case__					)

  # load original state_dict
  _snake_case							:	Optional[int]			=						torch.load(snake_case__	,     map_location="""cpu"""					)

  # load huggingface model
  if task_name.startswith("""ade20k_"""					) or task_name.startswith("""voc_"""					):
    _snake_case							:	Any			=						MobileViTVaForSemanticSegmentation(snake_case__					).eval()
    _snake_case							:	List[Any]			=						False
  else:
    _snake_case							:	List[Any]			=						MobileViTVaForImageClassification(snake_case__					).eval()
    _snake_case							:	Optional[Any]			=						False

  # remove and rename some keys of load the original model
  _snake_case							:	Union[str, Any]			=						checkpoint
  remove_unused_keys(snake_case__					)
  _snake_case							:	List[str]			=						create_rename_keys(snake_case__	,     base_model=snake_case__					)
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(snake_case__	,     snake_case__	,     snake_case__					)

  # load modified state_dict
  model.load_state_dict(snake_case__					)

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

  # verify classification model
  if task_name.startswith("""imagenet"""					):
    _snake_case							:	List[str]			=						outputs.logits
    _snake_case							:	Any			=						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							:	List[str]			=						torch.tensor([-1.6_3_3_6e0_0, -7.3_2_0_4e-0_2, -5.1_8_8_3e-0_1]					)
      assert torch.allclose(logits[0, :3]	,     snake_case__	,     atol=1e-4					)

  Path(snake_case__					).mkdir(exist_ok=snake_case__					)
  print(F"Saving model {task_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 __name__ == "__main__":
     A_      			=       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 . '''
             '''
                Classification (ImageNet-1k)
                    - MobileViTV2 (256x256) : imagenet1k_256
                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
                      imagenet21k_to_1k_256
                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
                      ImageNet-1k 384x384) : imagenet21k_to_1k_384
                Segmentation
                    - ADE20K Dataset : ade20k_deeplabv3
                    - Pascal VOC 2012 Dataset: voc_deeplabv3
            '''
         ),
         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.'''
     )

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


