





import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						old_name

      if "patch_embed" in old_name:
            SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						old_name.split("""."""     )

            if layer == "0":
                  SCREAMING_SNAKE_CASE		=						old_name.replace("""0"""			,						"""convolution1"""     )
            elif layer == "1":
                  SCREAMING_SNAKE_CASE		=						old_name.replace("""1"""			,						"""batchnorm_before"""     )
            elif layer == "3":
                  SCREAMING_SNAKE_CASE		=						old_name.replace("""3"""			,						"""convolution2"""     )
            else:
                  SCREAMING_SNAKE_CASE		=						old_name.replace("""4"""			,						"""batchnorm_after"""     )

      if "network" in old_name and re.search(r"""\d\.\d"""			,						_SCREAMING_SNAKE_CASE     ):
            SCREAMING_SNAKE_CASE		=						r"""\b\d{2}\b"""
            if bool(re.search(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )     ):
                  SCREAMING_SNAKE_CASE		=						re.search(r"""\d\.\d\d."""			,						_SCREAMING_SNAKE_CASE     ).group()
            else:
                  SCREAMING_SNAKE_CASE		=						re.search(r"""\d\.\d."""			,						_SCREAMING_SNAKE_CASE     ).group()
            if int(match[0]     ) < 6:
                  SCREAMING_SNAKE_CASE		=						old_name.replace(_SCREAMING_SNAKE_CASE			,						""""""     )
                  SCREAMING_SNAKE_CASE		=						trimmed_name.replace("""network"""			,						match[0] + """.meta4D_layers.blocks.""" + match[2:-1]     )
                  SCREAMING_SNAKE_CASE		=						"""intermediate_stages.""" + trimmed_name
            else:
                  SCREAMING_SNAKE_CASE		=						old_name.replace(_SCREAMING_SNAKE_CASE			,						""""""     )
                  if int(match[2]     ) < num_meta4D_last_stage:
                        SCREAMING_SNAKE_CASE		=						trimmed_name.replace("""network"""			,						"""meta4D_layers.blocks.""" + match[2]     )
                  else:
                        SCREAMING_SNAKE_CASE		=						str(int(match[2]     ) - num_meta4D_last_stage     )
                        SCREAMING_SNAKE_CASE		=						trimmed_name.replace("""network"""			,						"""meta3D_layers.blocks.""" + layer_index     )
                        if "norm1" in old_name:
                              SCREAMING_SNAKE_CASE		=						trimmed_name.replace("""norm1"""			,						"""layernorm1"""     )
                        elif "norm2" in old_name:
                              SCREAMING_SNAKE_CASE		=						trimmed_name.replace("""norm2"""			,						"""layernorm2"""     )
                        elif "fc1" in old_name:
                              SCREAMING_SNAKE_CASE		=						trimmed_name.replace("""fc1"""			,						"""linear_in"""     )
                        elif "fc2" in old_name:
                              SCREAMING_SNAKE_CASE		=						trimmed_name.replace("""fc2"""			,						"""linear_out"""     )

                  SCREAMING_SNAKE_CASE		=						"""last_stage.""" + trimmed_name

      elif "network" in old_name and re.search(r""".\d."""			,						_SCREAMING_SNAKE_CASE     ):
            SCREAMING_SNAKE_CASE		=						old_name.replace("""network"""			,						"""intermediate_stages"""     )

      if "fc" in new_name:
            SCREAMING_SNAKE_CASE		=						new_name.replace("""fc"""			,						"""convolution"""     )
      elif ("norm1" in new_name) and ("layernorm1" not in new_name):
            SCREAMING_SNAKE_CASE		=						new_name.replace("""norm1"""			,						"""batchnorm_before"""     )
      elif ("norm2" in new_name) and ("layernorm2" not in new_name):
            SCREAMING_SNAKE_CASE		=						new_name.replace("""norm2"""			,						"""batchnorm_after"""     )
      if "proj" in new_name:
            SCREAMING_SNAKE_CASE		=						new_name.replace("""proj"""			,						"""projection"""     )
      if "dist_head" in new_name:
            SCREAMING_SNAKE_CASE		=						new_name.replace("""dist_head"""			,						"""distillation_classifier"""     )
      elif "head" in new_name:
            SCREAMING_SNAKE_CASE		=						new_name.replace("""head"""			,						"""classifier"""     )
      elif "patch_embed" in new_name:
            SCREAMING_SNAKE_CASE		=						"""efficientformer.""" + new_name
      elif new_name == "norm.weight" or new_name == "norm.bias":
            SCREAMING_SNAKE_CASE		=						new_name.replace("""norm"""			,						"""layernorm"""     )
            SCREAMING_SNAKE_CASE		=						"""efficientformer.""" + new_name
      else:
            SCREAMING_SNAKE_CASE		=						"""efficientformer.encoder.""" + new_name

      return new_name



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Optional[Any]:


      '''simple docstring'''


      for key in checkpoint.copy().keys():
            SCREAMING_SNAKE_CASE		=						checkpoint.pop(_SCREAMING_SNAKE_CASE     )
            SCREAMING_SNAKE_CASE		=						val

      return checkpoint



def        __lowercase       (				)			->       Tuple:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      SCREAMING_SNAKE_CASE		=						Image.open(requests.get(_SCREAMING_SNAKE_CASE			,						stream=_SCREAMING_SNAKE_CASE     ).raw     )

      return image



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Union[str, Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						torch.load(_SCREAMING_SNAKE_CASE			,						map_location="""cpu"""     )["""model"""]
      SCREAMING_SNAKE_CASE		=						EfficientFormerConfig.from_json_file(_SCREAMING_SNAKE_CASE     )
      SCREAMING_SNAKE_CASE		=						EfficientFormerForImageClassificationWithTeacher(_SCREAMING_SNAKE_CASE     )
      SCREAMING_SNAKE_CASE		=						"""_""".join(checkpoint_path.split("""/"""     )[-1].split("""."""     )[0].split("""_"""     )[:-1]     )

      SCREAMING_SNAKE_CASE		=						config.depths[-1] - config.num_metaad_blocks + 1
      SCREAMING_SNAKE_CASE		=						convert_torch_checkpoint(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )

      model.load_state_dict(_SCREAMING_SNAKE_CASE     )
      model.eval()

      SCREAMING_SNAKE_CASE		=						{
          """bilinear""": PILImageResampling.BILINEAR,
          """bicubic""": PILImageResampling.BICUBIC,
          """nearest""": PILImageResampling.NEAREST,
      }

      # prepare image
      SCREAMING_SNAKE_CASE		=						prepare_img()
      SCREAMING_SNAKE_CASE		=						2_56
      SCREAMING_SNAKE_CASE		=						2_24
      SCREAMING_SNAKE_CASE		=						EfficientFormerImageProcessor(
          size={"""shortest_edge""": image_size}			,						crop_size={"""height""": crop_size, """width""": crop_size}			,						resample=pillow_resamplings["""bicubic"""]			,						)
      SCREAMING_SNAKE_CASE		=						processor(images=_SCREAMING_SNAKE_CASE			,						return_tensors="""pt"""     ).pixel_values

      # original processing pipeline
      SCREAMING_SNAKE_CASE		=						Compose(
          [
              Resize(_SCREAMING_SNAKE_CASE			,						interpolation=pillow_resamplings["""bicubic"""]     ),
              CenterCrop(_SCREAMING_SNAKE_CASE     ),
              ToTensor(),
              Normalize(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     ),
          ]     )
      SCREAMING_SNAKE_CASE		=						image_transforms(_SCREAMING_SNAKE_CASE     ).unsqueeze(0     )

      assert torch.allclose(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )

      SCREAMING_SNAKE_CASE		=						model(_SCREAMING_SNAKE_CASE     )
      SCREAMING_SNAKE_CASE		=						outputs.logits

      SCREAMING_SNAKE_CASE		=						(1, 10_00)

      if "l1" in model_name:
            SCREAMING_SNAKE_CASE		=						torch.Tensor(
                [-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328]     )
            assert torch.allclose(logits[0, :10]			,						_SCREAMING_SNAKE_CASE			,						atol=1E-3     )
            assert logits.shape == expected_shape
      elif "l3" in model_name:
            SCREAMING_SNAKE_CASE		=						torch.Tensor(
                [-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127]     )
            assert torch.allclose(logits[0, :10]			,						_SCREAMING_SNAKE_CASE			,						atol=1E-3     )
            assert logits.shape == expected_shape
      elif "l7" in model_name:
            SCREAMING_SNAKE_CASE		=						torch.Tensor(
                [-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878]     )
            assert logits.shape == expected_shape
      else:
            raise ValueError(
                F"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""     )

      # Save Checkpoints
      Path(_SCREAMING_SNAKE_CASE     ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE     )
      model.save_pretrained(_SCREAMING_SNAKE_CASE     )
      print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""     )
      processor.save_pretrained(_SCREAMING_SNAKE_CASE     )
      print(F"""Processor successfuly saved at {pytorch_dump_path}"""     )

      if push_to_hub:
            print("""Pushing model to the hub..."""     )

            model.push_to_hub(
                repo_id=F"""Bearnardd/{pytorch_dump_path}"""			,						commit_message="""Add model"""			,						use_temp_dir=_SCREAMING_SNAKE_CASE			,						)
            processor.push_to_hub(
                repo_id=F"""Bearnardd/{pytorch_dump_path}"""			,						commit_message="""Add image processor"""			,						use_temp_dir=_SCREAMING_SNAKE_CASE			,						)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_						       =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--pytorch_model_path""",
          default=None,
          type=str,
          required=True,
          help="""Path to EfficientFormer pytorch checkpoint.""",
      )
      parser.add_argument(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help="""The json file for EfficientFormer model config.""",
      )
      parser.add_argument(
          """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )

      parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
      parser.add_argument(
          """--no-push_to_hub""",
          dest="""push_to_hub""",
          action="""store_false""",
          help="""Do not push model and image processor to the hub""",
      )
      parser.set_defaults(push_to_hub=True)

      SCREAMING_SNAKE_CASE_						       =	parser.parse_args()
      convert_efficientformer_checkpoint(
          checkpoint_path=args.pytorch_model_path,
          efficientformer_config_file=args.config_file,
          pytorch_dump_path=args.pytorch_dump_path,
          push_to_hub=args.push_to_hub,
      )









import os
import unittest

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    BertTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english

@require_tokenizers
class 		UpperCamelCase__			(				lowerCAmelCase_							,				unittest.TestCase       ):



     '''simple docstring'''

     __snake_case   :     Any		     =      BertTokenizer
     __snake_case   :     Dict		     =      BertTokenizerFast
     __snake_case   :     Tuple		     =      True
     __snake_case   :     List[Any]		     =      True
     __snake_case   :     Optional[Any]		     =      filter_non_english






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		Any:




           '''simple docstring'''


           super().setUp()

           SCREAMING_SNAKE_CASE		=						[
               """[UNK]""",
               """[CLS]""",
               """[SEP]""",
               """[PAD]""",
               """[MASK]""",
               """want""",
               """##want""",
               """##ed""",
               """wa""",
               """un""",
               """runn""",
               """##ing""",
               """,""",
               """low""",
               """lowest""",
           ]
           SCREAMING_SNAKE_CASE		=						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     SCREAMING_SNAKE_CASE__  (							self						:   str						,lowerCamelCase__						:   List[Any]	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""UNwant\u00E9d,running"""
           SCREAMING_SNAKE_CASE		=						"""unwanted, running"""
           return input_text, output_text






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.tokenizer_class(self.vocab_file	)

           SCREAMING_SNAKE_CASE		=						tokenizer.tokenize("""UNwant\u00E9d,running"""	)
           self.assertListEqual(lowerCamelCase__						,["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]	)
           self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__	)						,[9, 6, 7, 12, 10, 11]	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		List[Any]:




           '''simple docstring'''


           if not self.test_rust_tokenizer:
                 return

           SCREAMING_SNAKE_CASE		=						self.get_tokenizer()
           SCREAMING_SNAKE_CASE		=						self.get_rust_tokenizer()

           SCREAMING_SNAKE_CASE		=						"""UNwant\u00E9d,running"""

           SCREAMING_SNAKE_CASE		=						tokenizer.tokenize(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						rust_tokenizer.tokenize(lowerCamelCase__	)
           self.assertListEqual(lowerCamelCase__						,lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						tokenizer.encode(lowerCamelCase__						,add_special_tokens=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						rust_tokenizer.encode(lowerCamelCase__						,add_special_tokens=lowerCamelCase__	)
           self.assertListEqual(lowerCamelCase__						,lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						self.get_rust_tokenizer()
           SCREAMING_SNAKE_CASE		=						tokenizer.encode(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						rust_tokenizer.encode(lowerCamelCase__	)
           self.assertListEqual(lowerCamelCase__						,lowerCamelCase__	)

           # With lower casing
           SCREAMING_SNAKE_CASE		=						self.get_tokenizer(do_lower_case=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.get_rust_tokenizer(do_lower_case=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						"""UNwant\u00E9d,running"""

           SCREAMING_SNAKE_CASE		=						tokenizer.tokenize(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						rust_tokenizer.tokenize(lowerCamelCase__	)
           self.assertListEqual(lowerCamelCase__						,lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						tokenizer.encode(lowerCamelCase__						,add_special_tokens=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						rust_tokenizer.encode(lowerCamelCase__						,add_special_tokens=lowerCamelCase__	)
           self.assertListEqual(lowerCamelCase__						,lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						self.get_rust_tokenizer()
           SCREAMING_SNAKE_CASE		=						tokenizer.encode(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						rust_tokenizer.encode(lowerCamelCase__	)
           self.assertListEqual(lowerCamelCase__						,lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						BasicTokenizer()

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						BasicTokenizer(do_lower_case=lowerCamelCase__	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						BasicTokenizer(do_lower_case=lowerCamelCase__						,strip_accents=lowerCamelCase__	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						BasicTokenizer(do_lower_case=lowerCamelCase__						,strip_accents=lowerCamelCase__	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						BasicTokenizer(do_lower_case=lowerCamelCase__	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						BasicTokenizer(do_lower_case=lowerCamelCase__	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						BasicTokenizer(do_lower_case=lowerCamelCase__						,strip_accents=lowerCamelCase__	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						BasicTokenizer(do_lower_case=lowerCamelCase__						,strip_accents=lowerCamelCase__	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						BasicTokenizer(do_lower_case=lowerCamelCase__						,never_split=["""[UNK]"""]	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						BasicTokenizer()
           SCREAMING_SNAKE_CASE		=						"""a\n'll !!to?'d of, can't."""
           SCREAMING_SNAKE_CASE		=						["""a""", """'""", """ll""", """!""", """!""", """to""", """?""", """'""", """d""", """of""", """,""", """can""", """'""", """t""", """."""]
           self.assertListEqual(tokenizer.tokenize(lowerCamelCase__	)						,lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

           SCREAMING_SNAKE_CASE		=						{}
           for i, token in enumerate(lowerCamelCase__	):
                 SCREAMING_SNAKE_CASE		=						i
           SCREAMING_SNAKE_CASE		=						WordpieceTokenizer(vocab=lowerCamelCase__						,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     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		Dict:




           '''simple docstring'''


           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     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		Optional[int]:




           '''simple docstring'''


           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     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		List[str]:




           '''simple docstring'''


           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     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.get_tokenizer()
           SCREAMING_SNAKE_CASE		=						self.get_rust_tokenizer()

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

           self.assertListEqual(
               [rust_tokenizer.tokenize(lowerCamelCase__	) for t in ["""Test""", """\xad""", """test"""]]						,[["""[UNK]"""], [], ["""[UNK]"""]]	)






     @slow
     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.tokenizer_class.from_pretrained("""bert-base-uncased"""	)

           SCREAMING_SNAKE_CASE		=						tokenizer.encode("""sequence builders"""						,add_special_tokens=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						tokenizer.encode("""multi-sequence build"""						,add_special_tokens=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						tokenizer.build_inputs_with_special_tokens(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						tokenizer.build_inputs_with_special_tokens(lowerCamelCase__						,lowerCamelCase__	)

           assert encoded_sentence == [101] + text + [102]
           assert encoded_pair == [101] + text + [102] + text_a + [102]






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		str:




           '''simple docstring'''


           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""	):
                       SCREAMING_SNAKE_CASE		=						self.rust_tokenizer_class.from_pretrained(lowerCamelCase__						,**lowerCamelCase__	)

                       SCREAMING_SNAKE_CASE		=						F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
                       SCREAMING_SNAKE_CASE		=						tokenizer_r.encode_plus(
                           lowerCamelCase__						,return_attention_mask=lowerCamelCase__						,return_token_type_ids=lowerCamelCase__						,return_offsets_mapping=lowerCamelCase__						,add_special_tokens=lowerCamelCase__						,)

                       SCREAMING_SNAKE_CASE		=						tokenizer_r.do_lower_case if hasattr(lowerCamelCase__						,"""do_lower_case"""	) else False
                       SCREAMING_SNAKE_CASE		=						(
                           [
                               ((0, 0), tokenizer_r.cls_token),
                               ((0, 1), """A"""),
                               ((1, 2), ""","""),
                               ((3, 5), """na"""),
                               ((5, 6), """##ï"""),
                               ((6, 8), """##ve"""),
                               ((9, 15), tokenizer_r.mask_token),
                               ((16, 21), """Allen"""),
                               ((21, 23), """##NL"""),
                               ((23, 24), """##P"""),
                               ((25, 33), """sentence"""),
                               ((33, 34), """."""),
                               ((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, 15), tokenizer_r.mask_token),
                               ((16, 21), """allen"""),
                               ((21, 23), """##nl"""),
                               ((23, 24), """##p"""),
                               ((25, 33), """sentence"""),
                               ((33, 34), """."""),
                               ((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"""]	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						["""的""", """人""", """有"""]
           SCREAMING_SNAKE_CASE		=						"""""".join(lowerCamelCase__	)
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""	):
                       SCREAMING_SNAKE_CASE		=						True
                       SCREAMING_SNAKE_CASE		=						self.tokenizer_class.from_pretrained(lowerCamelCase__						,**lowerCamelCase__	)
                       SCREAMING_SNAKE_CASE		=						self.rust_tokenizer_class.from_pretrained(lowerCamelCase__						,**lowerCamelCase__	)

                       SCREAMING_SNAKE_CASE		=						tokenizer_p.encode(lowerCamelCase__						,add_special_tokens=lowerCamelCase__	)
                       SCREAMING_SNAKE_CASE		=						tokenizer_r.encode(lowerCamelCase__						,add_special_tokens=lowerCamelCase__	)

                       SCREAMING_SNAKE_CASE		=						tokenizer_r.convert_ids_to_tokens(lowerCamelCase__	)
                       SCREAMING_SNAKE_CASE		=						tokenizer_p.convert_ids_to_tokens(lowerCamelCase__	)

                       # it is expected that each Chinese character is not preceded by "##"
                       self.assertListEqual(lowerCamelCase__						,lowerCamelCase__	)
                       self.assertListEqual(lowerCamelCase__						,lowerCamelCase__	)

                       SCREAMING_SNAKE_CASE		=						False
                       SCREAMING_SNAKE_CASE		=						self.rust_tokenizer_class.from_pretrained(lowerCamelCase__						,**lowerCamelCase__	)
                       SCREAMING_SNAKE_CASE		=						self.tokenizer_class.from_pretrained(lowerCamelCase__						,**lowerCamelCase__	)

                       SCREAMING_SNAKE_CASE		=						tokenizer_r.encode(lowerCamelCase__						,add_special_tokens=lowerCamelCase__	)
                       SCREAMING_SNAKE_CASE		=						tokenizer_p.encode(lowerCamelCase__						,add_special_tokens=lowerCamelCase__	)

                       SCREAMING_SNAKE_CASE		=						tokenizer_r.convert_ids_to_tokens(lowerCamelCase__	)
                       SCREAMING_SNAKE_CASE		=						tokenizer_p.convert_ids_to_tokens(lowerCamelCase__	)

                       # it is expected that only the first Chinese character is not preceded by "##".
                       SCREAMING_SNAKE_CASE		=						[
                           F"""##{token}""" if idx != 0 else token for idx, token in enumerate(lowerCamelCase__	)
                       ]
                       self.assertListEqual(lowerCamelCase__						,lowerCamelCase__	)
                       self.assertListEqual(lowerCamelCase__						,lowerCamelCase__	)


