




import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A_      			=       get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_sentencepiece
@require_tokenizers
class       lowercase(  __a ,    unittest.TestCase				):







 '''simple docstring'''





 lowercase__				      =      ReformerTokenizer
 lowercase__				      =      ReformerTokenizerFast
 lowercase__				      =      True
 lowercase__				      =      False
 lowercase__				      =      True


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   super().setUp()

   _snake_case							:	Any			=						ReformerTokenizer(a_,   keep_accents=a_	)
   tokenizer.save_pretrained(self.tmpdirname	)


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case							:	str			=						"""<s>"""
   _snake_case							:	Optional[int]			=						1

   self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_	),   a_	)
   self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_	),   a_	)


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




   '''simple docstring'''



   _snake_case							:	int			=						list(self.get_tokenizer().get_vocab().keys()	)

   self.assertEqual(vocab_keys[0],   """<unk>"""	)
   self.assertEqual(vocab_keys[1],   """<s>"""	)
   self.assertEqual(vocab_keys[-1],   """j"""	)
   self.assertEqual(len(a_	),   1_000	)


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   self.assertEqual(self.get_tokenizer().vocab_size,   1_000	)


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   if not self.test_rust_tokenizer:
     return

   _snake_case							:	Union[str, Any]			=						self.get_tokenizer()
   _snake_case							:	Union[str, Any]			=						self.get_rust_tokenizer()

   _snake_case							:	Tuple			=						"""I was born in 92000, and this is falsé."""

   _snake_case							:	List[Any]			=						tokenizer.tokenize(a_	)
   _snake_case							:	Optional[int]			=						rust_tokenizer.tokenize(a_	)
   self.assertListEqual(a_,   a_	)

   _snake_case							:	Optional[int]			=						tokenizer.encode(a_,   add_special_tokens=a_	)
   _snake_case							:	Dict			=						rust_tokenizer.encode(a_,   add_special_tokens=a_	)
   self.assertListEqual(a_,   a_	)

   _snake_case							:	Tuple			=						self.get_rust_tokenizer()
   _snake_case							:	List[str]			=						tokenizer.encode(a_	)
   _snake_case							:	List[str]			=						rust_tokenizer.encode(a_	)
   self.assertListEqual(a_,   a_	)


 def 				UpperCamelCase_     (       self:	Union[str, Any],   a_:	str=15	):




   '''simple docstring'''



   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"	):
       _snake_case							:	int			=						self.rust_tokenizer_class.from_pretrained(a_,   **a_	)

       # Simple input
       _snake_case							:	List[Any]			=						"""This is a simple input"""
       _snake_case							:	Union[str, Any]			=						["""This is a simple input 1""", """This is a simple input 2"""]
       _snake_case							:	Union[str, Any]			=						("""This is a simple input""", """This is a pair""")
       _snake_case							:	int			=						[
           ("""This is a simple input 1""", """This is a simple input 2"""),
           ("""This is a simple pair 1""", """This is a simple pair 2"""),
       ]

       # Simple input tests
       self.assertRaises(a_,   tokenizer_r.encode,   a_,   max_length=a_,   padding="""max_length"""	)

       # Simple input
       self.assertRaises(a_,   tokenizer_r.encode_plus,   a_,   max_length=a_,   padding="""max_length"""	)

       # Simple input
       self.assertRaises(
           a_,   tokenizer_r.batch_encode_plus,   a_,   max_length=a_,   padding="""max_length""",   )

       # Pair input
       self.assertRaises(a_,   tokenizer_r.encode,   a_,   max_length=a_,   padding="""max_length"""	)

       # Pair input
       self.assertRaises(a_,   tokenizer_r.encode_plus,   a_,   max_length=a_,   padding="""max_length"""	)

       # Pair input
       self.assertRaises(
           a_,   tokenizer_r.batch_encode_plus,   a_,   max_length=a_,   padding="""max_length""",   )


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   pass


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




   '''simple docstring'''



   _snake_case							:	Tuple			=						ReformerTokenizer(a_,   keep_accents=a_	)

   _snake_case							:	List[str]			=						tokenizer.tokenize("""This is a test"""	)
   self.assertListEqual(a_,   ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]	)

   self.assertListEqual(
       tokenizer.convert_tokens_to_ids(a_	),   [285, 46, 10, 170, 382],   )

   _snake_case							:	Union[str, Any]			=						tokenizer.tokenize("""I was born in 92000, and this is falsé."""	)
   self.assertListEqual(
       a_,   [
           SPIECE_UNDERLINE + """I""",
           SPIECE_UNDERLINE + """was""",
           SPIECE_UNDERLINE + """b""",
           """or""",
           """n""",
           SPIECE_UNDERLINE + """in""",
           SPIECE_UNDERLINE + """""",
           """9""",
           """2""",
           """0""",
           """0""",
           """0""",
           """,""",
           SPIECE_UNDERLINE + """and""",
           SPIECE_UNDERLINE + """this""",
           SPIECE_UNDERLINE + """is""",
           SPIECE_UNDERLINE + """f""",
           """al""",
           """s""",
           """é""",
           """.""",
       ],   )
   _snake_case							:	List[Any]			=						tokenizer.convert_tokens_to_ids(a_	)
   self.assertListEqual(
       a_,   [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4],   )

   _snake_case							:	List[str]			=						tokenizer.convert_ids_to_tokens(a_	)
   self.assertListEqual(
       a_,   [
           SPIECE_UNDERLINE + """I""",
           SPIECE_UNDERLINE + """was""",
           SPIECE_UNDERLINE + """b""",
           """or""",
           """n""",
           SPIECE_UNDERLINE + """in""",
           SPIECE_UNDERLINE + """""",
           """<unk>""",
           """2""",
           """0""",
           """0""",
           """0""",
           """,""",
           SPIECE_UNDERLINE + """and""",
           SPIECE_UNDERLINE + """this""",
           SPIECE_UNDERLINE + """is""",
           SPIECE_UNDERLINE + """f""",
           """al""",
           """s""",
           """<unk>""",
           """.""",
       ],   )


 @cached_property
 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment"""	)


 @slow
 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						"""Hello World!"""
   _snake_case							:	Tuple			=						[126, 32, 262, 152, 38, 72, 287]

   self.assertListEqual(a_,   self.big_tokenizer.encode(a_	)	)


 @slow
 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						(
       """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
       """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
   )
   _snake_case							:	List[Any]			=						[
       108,
       265,
       24,
       111,
       4,
       258,
       156,
       35,
       28,
       275,
       3,
       259,
       297,
       260,
       84,
       4,
       35,
       110,
       44,
       8,
       259,
       91,
       268,
       21,
       11,
       209,
       274,
       109,
       266,
       277,
       117,
       86,
       93,
       315,
       258,
       278,
       258,
       277,
       258,
       0,
       258,
       288,
       258,
       319,
       258,
       0,
       258,
       0,
       258,
       0,
       258,
       0,
       258,
       287,
       258,
       315,
       258,
       289,
       258,
       278,
       99,
       269,
       266,
       262,
       8,
       259,
       241,
       4,
       217,
       230,
       268,
       266,
       55,
       168,
       106,
       75,
       193,
       266,
       223,
       27,
       49,
       26,
       282,
       25,
       264,
       299,
       19,
       26,
       0,
       258,
       277,
       117,
       86,
       93,
       176,
       183,
       270,
       11,
       262,
       42,
       61,
       265,
   ]

   self.assertListEqual(a_,   self.big_tokenizer.encode(a_	)	)


 @require_torch
 @slow
 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   import torch

   from transformers import ReformerConfig, ReformerModel

   # Build sequence
   _snake_case							:	Optional[int]			=						list(self.big_tokenizer.get_vocab().keys()	)[:10]
   _snake_case							:	Tuple			=						""" """.join(a_	)
   _snake_case							:	Union[str, Any]			=						self.big_tokenizer.encode_plus(a_,   return_tensors="""pt"""	)
   _snake_case							:	List[Any]			=						self.big_tokenizer.batch_encode_plus([sequence, sequence],   return_tensors="""pt"""	)

   _snake_case							:	List[str]			=						ReformerConfig()
   # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
   _snake_case							:	Optional[Any]			=						encoded_sequence["""input_ids"""].shape
   _snake_case							:	Dict			=						ReformerModel(a_	)

   # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
   assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

   with torch.no_grad():
     model(**a_	)
     model(**a_	)


 @slow
 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   _snake_case							:	Dict			=						{"""input_ids""": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
   # fmt: on

   # This tokenizer does not know some characters like ")".
   # That is the reason why we use very simple texts here.
   # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
   _snake_case							:	Tuple			=						[
       """This is a very simple sentence.""",
       """The quick brown fox jumps over the lazy dog.""",
   ]

   self.tokenizer_integration_test_util(
       expected_encoding=a_,   model_name="""google/reformer-crime-and-punishment""",   revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a""",   padding=a_,   sequences=a_,   )








"""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
     )


