



import os
import unittest

from transformers import RealmTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.realm.tokenization_realm import RealmTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english

@require_tokenizers
class    _SCREAMING_SNAKE_CASE						(UpperCamelCase      ,     unittest.TestCase			):
     lowerCAmelCase       =      RealmTokenizer
     lowerCAmelCase       =      RealmTokenizerFast
     lowerCAmelCase       =      True
     lowerCAmelCase       =      True
     lowerCAmelCase       =      filter_non_english





     def 						__snake_case     (							self			:				Union[str, Any]					)->List[Any]:
      super().setUp()

      __SCREAMING_SNAKE_CASE							:     Any							=						[
          "[UNK]",
          "[CLS]",
          "[SEP]",
          "[PAD]",
          "[MASK]",
          "want",
          "##want",
          "##ed",
          "wa",
          "un",
          "runn",
          "##ing",
          ",",
          "low",
          "lowest",
      ]
      __SCREAMING_SNAKE_CASE							:     int							=						os.path.join(self.tmpdirname  ,	VOCAB_FILES_NAMES["vocab_file"]					)
      with open(self.vocab_file  ,	"w"  ,	encoding="utf-8"					) as vocab_writer:
       vocab_writer.write("".join([x + "\n" for x in vocab_tokens]					)					)





     def 						__snake_case     (							self			:				List[str]  ,	UpperCamelCase			:				Any					)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						"UNwant\u00E9d,running"
      __SCREAMING_SNAKE_CASE							:     Any							=						"unwanted, running"
      return input_text, output_text





     def 						__snake_case     (							self			:				Tuple					)->Optional[int]:
      __SCREAMING_SNAKE_CASE							:     int							=						self.tokenizer_class(self.vocab_file					)

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						tokenizer.tokenize("UNwant\u00E9d,running"					)
      self.assertListEqual(UpperCamelCase  ,	["un", "##want", "##ed", ",", "runn", "##ing"]					)
      self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase					)  ,	[9, 6, 7, 1_2, 1_0, 1_1]					)





     def 						__snake_case     (							self			:				Optional[Any]					)->str:
      if not self.test_rust_tokenizer:
       return

      __SCREAMING_SNAKE_CASE							:     Any							=						self.get_tokenizer()
      __SCREAMING_SNAKE_CASE							:     Dict							=						self.get_rust_tokenizer()

      __SCREAMING_SNAKE_CASE							:     str							=						"UNwant\u00E9d,running"

      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						tokenizer.tokenize(UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Any							=						rust_tokenizer.tokenize(UpperCamelCase					)
      self.assertListEqual(UpperCamelCase  ,	UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						tokenizer.encode(UpperCamelCase  ,	add_special_tokens=UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     List[str]							=						rust_tokenizer.encode(UpperCamelCase  ,	add_special_tokens=UpperCamelCase					)
      self.assertListEqual(UpperCamelCase  ,	UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Tuple							=						self.get_rust_tokenizer()
      __SCREAMING_SNAKE_CASE							:     Dict							=						tokenizer.encode(UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						rust_tokenizer.encode(UpperCamelCase					)
      self.assertListEqual(UpperCamelCase  ,	UpperCamelCase					)

      # With lower casing
      __SCREAMING_SNAKE_CASE							:     Any							=						self.get_tokenizer(do_lower_case=UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Any							=						self.get_rust_tokenizer(do_lower_case=UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						"UNwant\u00E9d,running"

      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						tokenizer.tokenize(UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     str							=						rust_tokenizer.tokenize(UpperCamelCase					)
      self.assertListEqual(UpperCamelCase  ,	UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Dict							=						tokenizer.encode(UpperCamelCase  ,	add_special_tokens=UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Any							=						rust_tokenizer.encode(UpperCamelCase  ,	add_special_tokens=UpperCamelCase					)
      self.assertListEqual(UpperCamelCase  ,	UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     List[str]							=						self.get_rust_tokenizer()
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						tokenizer.encode(UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						rust_tokenizer.encode(UpperCamelCase					)
      self.assertListEqual(UpperCamelCase  ,	UpperCamelCase					)





     def 						__snake_case     (							self			:				Optional[int]					)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE							:     str							=						BasicTokenizer()

      self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"					)  ,	["ah", "\u535A", "\u63A8", "zz"]					)





     def 						__snake_case     (							self			:				Optional[int]					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     Tuple							=						BasicTokenizer(do_lower_case=UpperCamelCase					)

      self.assertListEqual(
          tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "					)  ,	["hello", "!", "how", "are", "you", "?"]					)
      self.assertListEqual(tokenizer.tokenize("H\u00E9llo"					)  ,	["hello"]					)





     def 						__snake_case     (							self			:				str					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						BasicTokenizer(do_lower_case=UpperCamelCase  ,	strip_accents=UpperCamelCase					)

      self.assertListEqual(
          tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "					)  ,	["hällo", "!", "how", "are", "you", "?"]					)
      self.assertListEqual(tokenizer.tokenize("H\u00E9llo"					)  ,	["h\u00E9llo"]					)





     def 						__snake_case     (							self			:				str					)->List[Any]:
      __SCREAMING_SNAKE_CASE							:     Dict							=						BasicTokenizer(do_lower_case=UpperCamelCase  ,	strip_accents=UpperCamelCase					)

      self.assertListEqual(
          tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "					)  ,	["hallo", "!", "how", "are", "you", "?"]					)
      self.assertListEqual(tokenizer.tokenize("H\u00E9llo"					)  ,	["hello"]					)





     def 						__snake_case     (							self			:				Union[str, Any]					)->Tuple:
      __SCREAMING_SNAKE_CASE							:     Tuple							=						BasicTokenizer(do_lower_case=UpperCamelCase					)

      self.assertListEqual(
          tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "					)  ,	["hallo", "!", "how", "are", "you", "?"]					)
      self.assertListEqual(tokenizer.tokenize("H\u00E9llo"					)  ,	["hello"]					)





     def 						__snake_case     (							self			:				Tuple					)->Tuple:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						BasicTokenizer(do_lower_case=UpperCamelCase					)

      self.assertListEqual(
          tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "					)  ,	["HeLLo", "!", "how", "Are", "yoU", "?"]					)





     def 						__snake_case     (							self			:				Union[str, Any]					)->Tuple:
      __SCREAMING_SNAKE_CASE							:     str							=						BasicTokenizer(do_lower_case=UpperCamelCase  ,	strip_accents=UpperCamelCase					)

      self.assertListEqual(
          tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "					)  ,	["HäLLo", "!", "how", "Are", "yoU", "?"]					)





     def 						__snake_case     (							self			:				str					)->Any:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						BasicTokenizer(do_lower_case=UpperCamelCase  ,	strip_accents=UpperCamelCase					)

      self.assertListEqual(
          tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "					)  ,	["HaLLo", "!", "how", "Are", "yoU", "?"]					)





     def 						__snake_case     (							self			:				Optional[int]					)->Any:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						BasicTokenizer(do_lower_case=UpperCamelCase  ,	never_split=["[UNK]"]					)

      self.assertListEqual(
          tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"					)  ,	["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]					)





     def 						__snake_case     (							self			:				Dict					)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

      __SCREAMING_SNAKE_CASE							:     int							=						{}
      for i, token in enumerate(UpperCamelCase					):
       __SCREAMING_SNAKE_CASE							:     int							=						i
      __SCREAMING_SNAKE_CASE							:     List[str]							=						WordpieceTokenizer(vocab=UpperCamelCase  ,	unk_token="[UNK]"					)

      self.assertListEqual(tokenizer.tokenize(""					)  ,	[]					)

      self.assertListEqual(tokenizer.tokenize("unwanted running"					)  ,	["un", "##want", "##ed", "runn", "##ing"]					)

      self.assertListEqual(tokenizer.tokenize("unwantedX running"					)  ,	["[UNK]", "runn", "##ing"]					)





     def 						__snake_case     (							self			:				Tuple					)->Optional[int]:
      self.assertTrue(_is_whitespace(" "					)					)
      self.assertTrue(_is_whitespace("\t"					)					)
      self.assertTrue(_is_whitespace("\r"					)					)
      self.assertTrue(_is_whitespace("\n"					)					)
      self.assertTrue(_is_whitespace("\u00A0"					)					)

      self.assertFalse(_is_whitespace("A"					)					)
      self.assertFalse(_is_whitespace("-"					)					)





     def 						__snake_case     (							self			:				List[Any]					)->List[str]:
      self.assertTrue(_is_control("\u0005"					)					)

      self.assertFalse(_is_control("A"					)					)
      self.assertFalse(_is_control(" "					)					)
      self.assertFalse(_is_control("\t"					)					)
      self.assertFalse(_is_control("\r"					)					)





     def 						__snake_case     (							self			:				List[Any]					)->List[str]:
      self.assertTrue(_is_punctuation("-"					)					)
      self.assertTrue(_is_punctuation("$"					)					)
      self.assertTrue(_is_punctuation("`"					)					)
      self.assertTrue(_is_punctuation("."					)					)

      self.assertFalse(_is_punctuation("A"					)					)
      self.assertFalse(_is_punctuation(" "					)					)





     def 						__snake_case     (							self			:				int					)->Dict:
      __SCREAMING_SNAKE_CASE							:     str							=						self.get_tokenizer()

      # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
      self.assertListEqual([tokenizer.tokenize(UpperCamelCase					) for t in ["Test", "\xad", "test"]]  ,	[["[UNK]"], [], ["[UNK]"]]					)

      if self.test_rust_tokenizer:
       __SCREAMING_SNAKE_CASE							:     Tuple							=						self.get_rust_tokenizer()
       self.assertListEqual(
           [rust_tokenizer.tokenize(UpperCamelCase					) for t in ["Test", "\xad", "test"]]  ,	[["[UNK]"], [], ["[UNK]"]]					)





     @slow
     def 						__snake_case     (							self			:				Optional[int]					)->Dict:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						self.tokenizer_class.from_pretrained("bert-base-uncased"					)

      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						tokenizer.encode("sequence builders"  ,	add_special_tokens=UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     str							=						tokenizer.encode("multi-sequence build"  ,	add_special_tokens=UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Dict							=						tokenizer.build_inputs_with_special_tokens(UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     List[str]							=						tokenizer.build_inputs_with_special_tokens(UpperCamelCase  ,	UpperCamelCase					)

      assert encoded_sentence == [1_0_1] + text + [1_0_2]
      assert encoded_pair == [1_0_1] + text + [1_0_2] + text_a + [1_0_2]





     def 						__snake_case     (							self			:				Dict					)->List[str]:
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""					):
        __SCREAMING_SNAKE_CASE							:     int							=						self.rust_tokenizer_class.from_pretrained(UpperCamelCase  ,	**UpperCamelCase					)

        __SCREAMING_SNAKE_CASE							:     List[Any]							=						F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
        __SCREAMING_SNAKE_CASE							:     Dict							=						tokenizer_r.encode_plus(
            UpperCamelCase  ,	return_attention_mask=UpperCamelCase  ,	return_token_type_ids=UpperCamelCase  ,	return_offsets_mapping=UpperCamelCase  ,	add_special_tokens=UpperCamelCase  ,	)

        __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						tokenizer_r.do_lower_case if hasattr(UpperCamelCase  ,	"do_lower_case"					) else False
        __SCREAMING_SNAKE_CASE							:     Optional[int]							=						(
            [
                ((0, 0), tokenizer_r.cls_token),
                ((0, 1), "A"),
                ((1, 2), ","),
                ((3, 5), "na"),
                ((5, 6), "##ï"),
                ((6, 8), "##ve"),
                ((9, 1_5), tokenizer_r.mask_token),
                ((1_6, 2_1), "Allen"),
                ((2_1, 2_3), "##NL"),
                ((2_3, 2_4), "##P"),
                ((2_5, 3_3), "sentence"),
                ((3_3, 3_4), "."),
                ((0, 0), tokenizer_r.sep_token),
            ]
            if not do_lower_case
            else [
                ((0, 0), tokenizer_r.cls_token),
                ((0, 1), "a"),
                ((1, 2), ","),
                ((3, 8), "naive"),
                ((9, 1_5), tokenizer_r.mask_token),
                ((1_6, 2_1), "allen"),
                ((2_1, 2_3), "##nl"),
                ((2_3, 2_4), "##p"),
                ((2_5, 3_3), "sentence"),
                ((3_3, 3_4), "."),
                ((0, 0), tokenizer_r.sep_token),
            ]
        )

        self.assertEqual(
            [e[1] for e in expected_results]  ,	tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]					)					)
        self.assertEqual([e[0] for e in expected_results]  ,	tokens["offset_mapping"]					)






     @slow
     def 						__snake_case     (							self			:				int					)->Dict:
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""					):
        __SCREAMING_SNAKE_CASE							:     int							=						self.rust_tokenizer_class.from_pretrained(UpperCamelCase  ,	**UpperCamelCase					)
        __SCREAMING_SNAKE_CASE							:     int							=						self.tokenizer_class.from_pretrained(UpperCamelCase  ,	**UpperCamelCase					)
        __SCREAMING_SNAKE_CASE							:     Dict							=						[["Hello world!", "Nice to meet you!"], ["The cute cat.", "The adorable dog."]]

        __SCREAMING_SNAKE_CASE							:     List[Any]							=						tokenizer_r.batch_encode_candidates(UpperCamelCase  ,	max_length=1_0  ,	return_tensors="np"					)
        __SCREAMING_SNAKE_CASE							:     List[str]							=						tokenizer_p.batch_encode_candidates(UpperCamelCase  ,	max_length=1_0  ,	return_tensors="np"					)

        __SCREAMING_SNAKE_CASE							:     List[str]							=						(2, 2, 1_0)

        self.assertEqual(encoded_sentence_r["input_ids"].shape  ,	UpperCamelCase					)
        self.assertEqual(encoded_sentence_r["attention_mask"].shape  ,	UpperCamelCase					)
        self.assertEqual(encoded_sentence_r["token_type_ids"].shape  ,	UpperCamelCase					)

        self.assertEqual(encoded_sentence_p["input_ids"].shape  ,	UpperCamelCase					)
        self.assertEqual(encoded_sentence_p["attention_mask"].shape  ,	UpperCamelCase					)
        self.assertEqual(encoded_sentence_p["token_type_ids"].shape  ,	UpperCamelCase					)











import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase         =	logging.get_logger(__name__)






def 						_lowerCAmelCase    (					__lowerCamelCase :  Union[str, Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Dict							=						MobileNetVaConfig(layer_norm_eps=0.001  )

 if "quant" in model_name:
  raise ValueError("Quantized models are not supported."  )

 __SCREAMING_SNAKE_CASE							:     Tuple							=						re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$"		,						__lowerCamelCase  )
 if matches:
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						float(matches[1]  )
  __SCREAMING_SNAKE_CASE							:     str							=						int(matches[2]  )

 if model_name.startswith("deeplabv3_"  ):
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						8
  __SCREAMING_SNAKE_CASE							:     Dict							=						21
  __SCREAMING_SNAKE_CASE							:     Tuple							=						"pascal-voc-id2label.json"
 else:
  # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
  # of the usual 1000. The first class (index 0) is "background".
  __SCREAMING_SNAKE_CASE							:     int							=						1001
  __SCREAMING_SNAKE_CASE							:     Dict							=						"imagenet-1k-id2label.json"

 __SCREAMING_SNAKE_CASE							:     List[Any]							=						"huggingface/label-files"
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						json.load(open(hf_hub_download(__lowerCamelCase		,						__lowerCamelCase		,						repo_type="dataset"  )		,						"r"  )  )

 if config.num_labels == 1001:
  __SCREAMING_SNAKE_CASE							:     int							=						{int(__lowerCamelCase  ) + 1: v for k, v in idalabel.items()}
  __SCREAMING_SNAKE_CASE							:     int							=						"background"
 else:
  __SCREAMING_SNAKE_CASE							:     Any							=						{int(__lowerCamelCase  ): v for k, v in idalabel.items()}

 __SCREAMING_SNAKE_CASE							:     str							=						idalabel
 __SCREAMING_SNAKE_CASE							:     Tuple							=						{v: k for k, v in idalabel.items()}

 return config






def 						_lowerCAmelCase    (					):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Dict							=						"http://images.cocodataset.org/val2017/000000039769.jpg"
 __SCREAMING_SNAKE_CASE							:     Any							=						Image.open(requests.get(__lowerCamelCase		,						stream=__lowerCamelCase  ).raw  )
 return im






@torch.no_grad()
def 						_lowerCAmelCase    (					__lowerCamelCase :  str		,						__lowerCamelCase :  Optional[Any]		,						__lowerCamelCase :  Optional[Any]		,						__lowerCamelCase :  Tuple=False  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Any							=						get_mobilenet_va_config(__lowerCamelCase  )

 # Load 🤗 model
 if model_name.startswith("deeplabv3_"  ):
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						MobileNetVaForSemanticSegmentation(__lowerCamelCase  ).eval()
 else:
  __SCREAMING_SNAKE_CASE							:     str							=						MobileNetVaForImageClassification(__lowerCamelCase  ).eval()

 # Load weights from TensorFlow checkpoint
 load_tf_weights_in_mobilenet_va(__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase  )

 # Check outputs on an image, prepared by MobileNetV2ImageProcessor
 __SCREAMING_SNAKE_CASE							:     Tuple							=						MobileNetVaImageProcessor(
     crop_size={"width": config.image_size, "height": config.image_size}		,						size={"shortest_edge": config.image_size + 32}		,						)
 __SCREAMING_SNAKE_CASE							:     Any							=						image_processor(images=prepare_img()		,						return_tensors="pt"  )
 __SCREAMING_SNAKE_CASE							:     List[str]							=						model(**__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Any							=						outputs.logits

 if model_name.startswith("deeplabv3_"  ):
  assert logits.shape == (1, 21, 65, 65)

  if model_name == "deeplabv3_mobilenet_v2_1.0_513":
   __SCREAMING_SNAKE_CASE							:     List[Any]							=						torch.tensor(
       [
           [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
           [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
           [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
       ]  )

  else:
   raise ValueError(F"""Unknown model name: {model_name}"""  )

  assert torch.allclose(logits[0, :3, :3, :3]		,						__lowerCamelCase		,						atol=1E-4  )
 else:
  assert logits.shape == (1, 1001)

  if model_name == "mobilenet_v2_1.4_224":
   __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						torch.tensor([0.0181, -1.0015, 0.4688]  )
  elif model_name == "mobilenet_v2_1.0_224":
   __SCREAMING_SNAKE_CASE							:     str							=						torch.tensor([0.2445, -1.1993, 0.1905]  )
  elif model_name == "mobilenet_v2_0.75_160":
   __SCREAMING_SNAKE_CASE							:     int							=						torch.tensor([0.2482, 0.4136, 0.6669]  )
  elif model_name == "mobilenet_v2_0.35_96":
   __SCREAMING_SNAKE_CASE							:     Any							=						torch.tensor([0.1451, -0.4624, 0.7192]  )
  else:
   __SCREAMING_SNAKE_CASE							:     Optional[int]							=						None

  if expected_logits is not None:
   assert torch.allclose(logits[0, :3]		,						__lowerCamelCase		,						atol=1E-4  )

 Path(__lowerCamelCase  ).mkdir(exist_ok=__lowerCamelCase  )
 print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""  )
 model.save_pretrained(__lowerCamelCase  )
 print(F"""Saving image processor to {pytorch_dump_folder_path}"""  )
 image_processor.save_pretrained(__lowerCamelCase  )

 if push_to_hub:
  print("Pushing to the hub..."  )
  __SCREAMING_SNAKE_CASE							:     Tuple							=						"google/" + model_name
  image_processor.push_to_hub(__lowerCamelCase  )
  model.push_to_hub(__lowerCamelCase  )


if __name__ == "__main__":
      _lowerCamelCase         =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default="""mobilenet_v2_1.0_224""",
          type=str,
          help="""Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<depth>_<size>'.""",
      )
      parser.add_argument(
          """--checkpoint_path""", required=True, type=str, help="""Path to the original TensorFlow checkpoint (.ckpt file)."""
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
      )
      parser.add_argument(
          """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
      )

      _lowerCamelCase         =	parser.parse_args()
      convert_movilevit_checkpoint(
          args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )






