


"""simple docstring"""







import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
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


lowerCAmelCase__      				=   '''▁'''

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


@require_sentencepiece
@require_tokenizers
class       SCREAMING_SNAKE_CASE__				(		lowercase   ,    unittest.TestCase				):
       """simple docstring"""


       a  :				str   =BigBirdTokenizer
       a  :				Union[str, Any]   =BigBirdTokenizerFast
       a  :				Tuple   =True
       a  :				Any   =True

       def        lowercase__							(						self						):






         """simple docstring"""





         super().setUp()

         lowerCAmelCase     :     str								=							self.tokenizer_class(snake_case__		,     keep_accents=snake_case__						)
         tokenizer.save_pretrained(self.tmpdirname						)

       def        lowercase__							(						self						):






         """simple docstring"""





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

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

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							list(self.get_tokenizer().get_vocab().keys()						)

         self.assertEqual(vocab_keys[0]		,     "<unk>"						)
         self.assertEqual(vocab_keys[1]		,     "<s>"						)
         self.assertEqual(vocab_keys[-1]		,     "[MASK]"						)
         self.assertEqual(len(snake_case__						)		,     1_004						)

       def        lowercase__							(						self						):






         """simple docstring"""





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

       def        lowercase__							(						self						):






         """simple docstring"""





         if not self.test_rust_tokenizer:
           return

         lowerCAmelCase     :     Tuple								=							self.get_tokenizer()
         lowerCAmelCase     :     Optional[int]								=							self.get_rust_tokenizer()

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

         lowerCAmelCase     :     Optional[int]								=							tokenizer.tokenize(snake_case__						)
         lowerCAmelCase     :     int								=							rust_tokenizer.tokenize(snake_case__						)
         self.assertListEqual(snake_case__		,     snake_case__						)

         lowerCAmelCase     :     Tuple								=							tokenizer.encode(snake_case__		,     add_special_tokens=snake_case__						)
         lowerCAmelCase     :     int								=							rust_tokenizer.encode(snake_case__		,     add_special_tokens=snake_case__						)
         self.assertListEqual(snake_case__		,     snake_case__						)

         lowerCAmelCase     :     List[Any]								=							self.get_rust_tokenizer()
         lowerCAmelCase     :     Tuple								=							tokenizer.encode(snake_case__						)
         lowerCAmelCase     :     List[Any]								=							rust_tokenizer.encode(snake_case__						)
         self.assertListEqual(snake_case__		,     snake_case__						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							BigBirdTokenizer(snake_case__		,     keep_accents=snake_case__						)

         lowerCAmelCase     :     Union[str, Any]								=							tokenizer.tokenize("This is a test"						)
         self.assertListEqual(snake_case__		,     ["▁This", "▁is", "▁a", "▁t", "est"]						)

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

         lowerCAmelCase     :     Any								=							tokenizer.tokenize("I was born in 92000, and this is falsé."						)
         self.assertListEqual(
             snake_case__		,     [
                 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",
                 ".",
             ]		,     )
         lowerCAmelCase     :     str								=							tokenizer.convert_tokens_to_ids(snake_case__						)
         self.assertListEqual(
             snake_case__		,     [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]		,     )

         lowerCAmelCase     :     List[str]								=							tokenizer.convert_ids_to_tokens(snake_case__						)
         self.assertListEqual(
             snake_case__		,     [
                 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        lowercase__							(						self						):






         """simple docstring"""





         return BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base"						)

       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							"Hello World!"
         lowerCAmelCase     :     Any								=							[65, 18_536, 2_260, 101, 66]

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

       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							(
             "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"
         )
         # fmt: off
         lowerCAmelCase     :     List[str]								=							[65, 871, 419, 358, 946, 991, 2_521, 452, 358, 1_357, 387, 7_751, 3_536, 112, 985, 456, 126, 865, 938, 5_400, 5_734, 458, 1_368, 467, 786, 2_462, 5_246, 1_159, 633, 865, 4_519, 457, 582, 852, 2_557, 427, 916, 508, 405, 34_324, 497, 391, 408, 11_342, 1_244, 385, 100, 938, 985, 456, 574, 362, 12_597, 3_200, 3_129, 1_172, 66]  # noqa: E231
         # fmt: on
         self.assertListEqual(snake_case__		,     self.big_tokenizer.encode(snake_case__						)						)

       @require_torch
       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         import torch

         from transformers import BigBirdConfig, BigBirdModel

         # Build sequence
         lowerCAmelCase     :     Dict								=							list(self.big_tokenizer.get_vocab().keys()						)[:10]
         lowerCAmelCase     :     int								=							" ".join(snake_case__						)
         lowerCAmelCase     :     Dict								=							self.big_tokenizer.encode_plus(snake_case__		,     return_tensors="pt"		,     return_token_type_ids=snake_case__						)
         lowerCAmelCase     :     Any								=							self.big_tokenizer.batch_encode_plus(
             [sequence + " " + sequence]		,     return_tensors="pt"		,     return_token_type_ids=snake_case__						)

         lowerCAmelCase     :     str								=							BigBirdConfig(attention_type="original_full"						)
         lowerCAmelCase     :     Any								=							BigBirdModel(snake_case__						)

         assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

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

       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base"						)
         lowerCAmelCase     :     Union[str, Any]								=							tokenizer.decode(tokenizer("Paris is the [MASK]."						).input_ids						)

         self.assertTrue(decoded_text == "[CLS] Paris is the[MASK].[SEP]"						)







       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							{"input_ids": [[65, 39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114, 66], [65, 448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "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, 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, 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, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=snake_case__		,     model_name="google/bigbird-roberta-base"		,     revision="215c99f1600e06f83acce68422f2035b2b5c3510"		,     )











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 				lowerCAmelCase_	(     snake_case_     ):
      print("""Loading config file..."""     )

      def flatten_yaml_as_dict(snake_case_,snake_case_="",snake_case_="."     ):
            _A							:     Union[str, Any]			=						[]
            for k, v in d.items():
                  _A							:     Optional[int]			=						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_     )

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

                  _A							:     Optional[int]			=						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 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _A							:     Optional[Any]			=						MobileViTVaConfig()

      _A							:     Tuple			=						False

      # dataset
      if task_name.startswith("""imagenet1k_"""     ):
            _A							:     Dict			=						1000
            if int(task_name.strip().split("""_"""     )[-1]     ) == 384:
                  _A							:     int			=						384
            else:
                  _A							:     int			=						256
            _A							:     List[str]			=						"""imagenet-1k-id2label.json"""
      elif task_name.startswith("""imagenet21k_to_1k_"""     ):
            _A							:     Union[str, Any]			=						21000
            if int(task_name.strip().split("""_"""     )[-1]     ) == 384:
                  _A							:     str			=						384
            else:
                  _A							:     List[Any]			=						256
            _A							:     List[str]			=						"""imagenet-22k-id2label.json"""
      elif task_name.startswith("""ade20k_"""     ):
            _A							:     int			=						151
            _A							:     int			=						512
            _A							:     Optional[int]			=						"""ade20k-id2label.json"""
            _A							:     Any			=						True
      elif task_name.startswith("""voc_"""     ):
            _A							:     List[Any]			=						21
            _A							:     Dict			=						512
            _A							:     Dict			=						"""pascal-voc-id2label.json"""
            _A							:     int			=						True

      # orig_config
      _A							:     Any			=						load_orig_config_file(snake_case_     )
      assert getattr(snake_case_,"""model.classification.name""",-1     ) == "mobilevit_v2", "Invalid model"
      _A							:     List[Any]			=						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"
      _A							:     str			=						getattr(snake_case_,"""model.classification.activation.name""","""swish"""     )
      # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

      if is_segmentation_model:
            _A							:     Optional[int]			=						getattr(snake_case_,"""model.segmentation.output_stride""",16     )
            if "_deeplabv3" in task_name:
                  _A							:     int			=						getattr(snake_case_,"""model.segmentation.deeplabv3.aspp_rates""",[12, 24, 36]     )
                  _A							:     int			=						getattr(snake_case_,"""model.segmentation.deeplabv3.aspp_out_channels""",512     )
                  _A							:     str			=						getattr(snake_case_,"""model.segmentation.deeplabv3.aspp_dropout""",0.1     )

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

      return config







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     Any			=						dct.pop(snake_case_     )
      _A							:     Union[str, Any]			=						val







def 				lowerCAmelCase_	(     snake_case_,snake_case_=False     ):
      if base_model:
            _A							:     Optional[int]			=						""""""
      else:
            _A							:     Dict			=						"""mobilevitv2."""

      _A							:     int			=						[]
      for k in state_dict.keys():
            if k[:8] == "encoder.":
                  _A							:     Any			=						k[8:]
            else:
                  _A							:     List[str]			=						k

            if ".block." in k:
                  _A							:     Any			=						k_new.replace(""".block.""","""."""     )
            if ".conv." in k:
                  _A							:     List[Any]			=						k_new.replace(""".conv.""",""".convolution."""     )
            if ".norm." in k:
                  _A							:     Any			=						k_new.replace(""".norm.""",""".normalization."""     )

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

            for i in [3, 4, 5]:
                  if f'''layer_{i}.0.''' in k:
                        _A							:     Optional[int]			=						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:
                        _A							:     Union[str, 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:
                        _A							:     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:
                        _A							:     Optional[int]			=						[0, 1]
                  elif i == 4:
                        _A							:     Union[str, Any]			=						[0, 1, 2, 3]
                  elif i == 5:
                        _A							:     Optional[Any]			=						[0, 1, 2]

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

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

            if "seg_head." in k:
                  _A							:     List[Any]			=						k_new.replace("""seg_head.""","""segmentation_head."""     )
            if ".aspp_layer." in k:
                  _A							:     List[Any]			=						k_new.replace(""".aspp_layer.""","""."""     )
            if ".aspp_pool." in k:
                  _A							:     Optional[Any]			=						k_new.replace(""".aspp_pool.""","""."""     )

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







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     Tuple			=						[]
      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 				lowerCAmelCase_	(     ):
      _A							:     Dict			=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
      _A							:     List[Any]			=						Image.open(requests.get(snake_case_,stream=snake_case_     ).raw     )
      return im







@torch.no_grad()
def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_,snake_case_     ):
      _A							:     List[Any]			=						get_mobilevitva_config(snake_case_,snake_case_     )

      # load original state_dict
      _A							:     Tuple			=						torch.load(snake_case_,map_location="""cpu"""     )

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

      # remove and rename some keys of load the original model
      _A							:     List[Any]			=						checkpoint
      remove_unused_keys(snake_case_     )
      _A							:     Optional[Any]			=						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
      _A							:     str			=						MobileViTImageProcessor(crop_size=config.image_size,size=config.image_size + 32     )
      _A							:     List[Any]			=						image_processor(images=prepare_img(),return_tensors="""pt"""     )
      _A							:     Optional[Any]			=						model(**snake_case_     )

      # verify classification model
      if task_name.startswith("""imagenet"""     ):
            _A							:     List[Any]			=						outputs.logits
            _A							:     Optional[int]			=						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
                  _A							:     int			=						torch.tensor([-1.63_36e00, -7.32_04e-02, -5.18_83e-01]     )
                  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__":
  _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
  )

