






import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
A_		:		int		     =			logging.get_logger(__name__)

A_		:		Tuple		     =			{
    'b0': efficientnet.EfficientNetBa,
    'b1': efficientnet.EfficientNetBa,
    'b2': efficientnet.EfficientNetBa,
    'b3': efficientnet.EfficientNetBa,
    'b4': efficientnet.EfficientNetBa,
    'b5': efficientnet.EfficientNetBa,
    'b6': efficientnet.EfficientNetBa,
    'b7': efficientnet.EfficientNetBa,
}

A_		:		Any		     =			{
    'b0': {
        'hidden_dim': 1280,
        'width_coef': 1.0,
        'depth_coef': 1.0,
        'image_size': 224,
        'dropout_rate': 0.2,
        'dw_padding': [],
    },
    'b1': {
        'hidden_dim': 1280,
        'width_coef': 1.0,
        'depth_coef': 1.1,
        'image_size': 240,
        'dropout_rate': 0.2,
        'dw_padding': [16],
    },
    'b2': {
        'hidden_dim': 1408,
        'width_coef': 1.1,
        'depth_coef': 1.2,
        'image_size': 260,
        'dropout_rate': 0.3,
        'dw_padding': [5, 8, 16],
    },
    'b3': {
        'hidden_dim': 1536,
        'width_coef': 1.2,
        'depth_coef': 1.4,
        'image_size': 300,
        'dropout_rate': 0.3,
        'dw_padding': [5, 18],
    },
    'b4': {
        'hidden_dim': 1792,
        'width_coef': 1.4,
        'depth_coef': 1.8,
        'image_size': 380,
        'dropout_rate': 0.4,
        'dw_padding': [6],
    },
    'b5': {
        'hidden_dim': 2048,
        'width_coef': 1.6,
        'depth_coef': 2.2,
        'image_size': 456,
        'dropout_rate': 0.4,
        'dw_padding': [13, 27],
    },
    'b6': {
        'hidden_dim': 2304,
        'width_coef': 1.8,
        'depth_coef': 2.6,
        'image_size': 528,
        'dropout_rate': 0.5,
        'dw_padding': [31],
    },
    'b7': {
        'hidden_dim': 2560,
        'width_coef': 2.0,
        'depth_coef': 3.1,
        'image_size': 600,
        'dropout_rate': 0.5,
        'dw_padding': [18],
    },
}

def   snake_case (UpperCAmelCase__  )	->    Any:
  UpperCamelCase_: Dict   				=    EfficientNetConfig()
  UpperCamelCase_: Dict   				=    CONFIG_MAP[model_name]['hidden_dim']
  UpperCamelCase_: Any   				=    CONFIG_MAP[model_name]['width_coef']
  UpperCamelCase_: int   				=    CONFIG_MAP[model_name]['depth_coef']
  UpperCamelCase_: List[Any]   				=    CONFIG_MAP[model_name]['image_size']
  UpperCamelCase_: Union[str, Any]   				=    CONFIG_MAP[model_name]['dropout_rate']
  UpperCamelCase_: Tuple   				=    CONFIG_MAP[model_name]['dw_padding']

  UpperCamelCase_: Dict   				=    'huggingface/label-files'
  UpperCamelCase_: Optional[Any]   				=    'imagenet-1k-id2label.json'
  UpperCamelCase_: Optional[int]   				=    1_0_0_0
  UpperCamelCase_: List[Any]   				=    json.load(open(hf_hub_download(UpperCAmelCase__     ,   UpperCAmelCase__     ,   repo_type='dataset'  )     ,   'r'  )  )
  UpperCamelCase_: List[Any]   				=    {int(UpperCAmelCase__  ): v for k, v in idalabel.items()}

  UpperCamelCase_: Tuple   				=    idalabel
  UpperCamelCase_: Any   				=    {v: k for k, v in idalabel.items()}
  return config

def   snake_case ()	->    List[str]:
  UpperCamelCase_: Tuple   				=    'http://images.cocodataset.org/val2017/000000039769.jpg'
  UpperCamelCase_: Dict   				=    Image.open(requests.get(UpperCAmelCase__     ,   stream=UpperCAmelCase__  ).raw  )
  return im

def   snake_case (UpperCAmelCase__  )	->    Any:
  UpperCamelCase_: Optional[int]   				=    CONFIG_MAP[model_name]['image_size']
  UpperCamelCase_: Tuple   				=    EfficientNetImageProcessor(
      size={'height': size, 'width': size}     ,   image_mean=[0.485, 0.456, 0.406]     ,   image_std=[0.4785_3944, 0.473_2864, 0.4743_4163]     ,   do_center_crop=UpperCAmelCase__     ,   )
  return preprocessor

def   snake_case (UpperCAmelCase__  )	->    Tuple:
  UpperCamelCase_: List[Any]   				=    [v.split('_'  )[0].split('block'  )[1] for v in original_param_names if v.startswith('block'  )]
  UpperCamelCase_: Tuple   				=    sorted(set(UpperCAmelCase__  )  )
  UpperCamelCase_: Any   				=    len(UpperCAmelCase__  )
  UpperCamelCase_: List[str]   				=    {b: str(UpperCAmelCase__  ) for b, i in zip(UpperCAmelCase__     ,   range(UpperCAmelCase__  )  )}

  UpperCamelCase_: Any   				=    []
  rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight')  )
  rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight')  )
  rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias')  )
  rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean')  )
  rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var')  )

  for b in block_names:
    UpperCamelCase_: Optional[int]   				=    block_name_mapping[b]
    rename_keys.append((F'''block{b}_expand_conv/kernel:0''', F'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''')  )
    rename_keys.append((F'''block{b}_expand_bn/gamma:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''')  )
    rename_keys.append((F'''block{b}_expand_bn/beta:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''')  )
    rename_keys.append(
        (F'''block{b}_expand_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''')  )
    rename_keys.append(
        (F'''block{b}_expand_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''')  )
    rename_keys.append(
        (F'''block{b}_dwconv/depthwise_kernel:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''')  )
    rename_keys.append((F'''block{b}_bn/gamma:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''')  )
    rename_keys.append((F'''block{b}_bn/beta:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''')  )
    rename_keys.append(
        (F'''block{b}_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''')  )
    rename_keys.append(
        (F'''block{b}_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''')  )

    rename_keys.append((F'''block{b}_se_reduce/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''')  )
    rename_keys.append((F'''block{b}_se_reduce/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''')  )
    rename_keys.append((F'''block{b}_se_expand/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''')  )
    rename_keys.append((F'''block{b}_se_expand/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''')  )
    rename_keys.append(
        (F'''block{b}_project_conv/kernel:0''', F'''encoder.blocks.{hf_b}.projection.project_conv.weight''')  )
    rename_keys.append((F'''block{b}_project_bn/gamma:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.weight''')  )
    rename_keys.append((F'''block{b}_project_bn/beta:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.bias''')  )
    rename_keys.append(
        (F'''block{b}_project_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''')  )
    rename_keys.append(
        (F'''block{b}_project_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_var''')  )

  rename_keys.append(('top_conv/kernel:0', 'encoder.top_conv.weight')  )
  rename_keys.append(('top_bn/gamma:0', 'encoder.top_bn.weight')  )
  rename_keys.append(('top_bn/beta:0', 'encoder.top_bn.bias')  )
  rename_keys.append(('top_bn/moving_mean:0', 'encoder.top_bn.running_mean')  )
  rename_keys.append(('top_bn/moving_variance:0', 'encoder.top_bn.running_var')  )

  UpperCamelCase_: int   				=    {}
  for item in rename_keys:
    if item[0] in original_param_names:
      UpperCamelCase_: Tuple   				=    'efficientnet.' + item[1]

  UpperCamelCase_: Optional[Any]   				=    'classifier.weight'
  UpperCamelCase_: Tuple   				=    'classifier.bias'
  return key_mapping

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Dict:
  for key, value in tf_params.items():
    if "normalization" in key:
      continue

    UpperCamelCase_: List[str]   				=    key_mapping[key]
    if "_conv" in key and "kernel" in key:
      UpperCamelCase_: int   				=    torch.from_numpy(UpperCAmelCase__  ).permute(3     ,   2     ,   0     ,   1  )
    elif "depthwise_kernel" in key:
      UpperCamelCase_: Optional[int]   				=    torch.from_numpy(UpperCAmelCase__  ).permute(2     ,   3     ,   0     ,   1  )
    elif "kernel" in key:
      UpperCamelCase_: Optional[Any]   				=    torch.from_numpy(np.transpose(UpperCAmelCase__  )  )
    else:
      UpperCamelCase_: Optional[Any]   				=    torch.from_numpy(UpperCAmelCase__  )

    # Replace HF parameters with original TF model parameters
    assert hf_params[hf_key].shape == new_hf_value.shape
    hf_params[hf_key].copy_(UpperCAmelCase__  )

@torch.no_grad()
def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    int:
  UpperCamelCase_: Union[str, Any]   				=    model_classes[model_name](
      include_top=UpperCAmelCase__     ,   weights='imagenet'     ,   input_tensor=UpperCAmelCase__     ,   input_shape=UpperCAmelCase__     ,   pooling=UpperCAmelCase__     ,   classes=1_0_0_0     ,   classifier_activation='softmax'     ,   )

  UpperCamelCase_: Union[str, Any]   				=    original_model.trainable_variables
  UpperCamelCase_: str   				=    original_model.non_trainable_variables
  UpperCamelCase_: Dict   				=    {param.name: param.numpy() for param in tf_params}
  for param in tf_non_train_params:
    UpperCamelCase_: Dict   				=    param.numpy()
  UpperCamelCase_: Optional[int]   				=    list(tf_params.keys()  )

  # Load HuggingFace model
  UpperCamelCase_: List[Any]   				=    get_efficientnet_config(UpperCAmelCase__  )
  UpperCamelCase_: Optional[Any]   				=    EfficientNetForImageClassification(UpperCAmelCase__  ).eval()
  UpperCamelCase_: Tuple   				=    hf_model.state_dict()

  # Create src-to-dst parameter name mapping dictionary
  print('Converting parameters...'  )
  UpperCamelCase_: Optional[Any]   				=    rename_keys(UpperCAmelCase__  )
  replace_params(UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )

  # Initialize preprocessor and preprocess input image
  UpperCamelCase_: List[Any]   				=    convert_image_processor(UpperCAmelCase__  )
  UpperCamelCase_: Dict   				=    preprocessor(images=prepare_img()     ,   return_tensors='pt'  )

  # HF model inference
  hf_model.eval()
  with torch.no_grad():
    UpperCamelCase_: Dict   				=    hf_model(**UpperCAmelCase__  )
  UpperCamelCase_: Any   				=    outputs.logits.detach().numpy()

  # Original model inference
  UpperCamelCase_: str   				=    False
  UpperCamelCase_: Tuple   				=    CONFIG_MAP[model_name]['image_size']
  UpperCamelCase_: Union[str, Any]   				=    prepare_img().resize((image_size, image_size)     ,   resample=PIL.Image.NEAREST  )
  UpperCamelCase_: Any   				=    image.img_to_array(UpperCAmelCase__  )
  UpperCamelCase_: int   				=    np.expand_dims(UpperCAmelCase__     ,   axis=0  )
  UpperCamelCase_: Dict   				=    original_model.predict(UpperCAmelCase__  )

  # Check whether original and HF model outputs match  -> np.allclose
  assert np.allclose(UpperCAmelCase__     ,   UpperCAmelCase__     ,   atol=1E-3  ), "The predicted logits are not the same."
  print('Model outputs match!'  )

  if save_model:
    # Create folder to save model
    if not os.path.isdir(UpperCAmelCase__  ):
      os.mkdir(UpperCAmelCase__  )
    # Save converted model and image processor
    hf_model.save_pretrained(UpperCAmelCase__  )
    preprocessor.save_pretrained(UpperCAmelCase__  )

  if push_to_hub:
    # Push model and image processor to hub
    print(F'''Pushing converted {model_name} to the hub...'''  )
    UpperCamelCase_: Tuple   				=    F'''efficientnet-{model_name}'''
    preprocessor.push_to_hub(UpperCAmelCase__  )
    hf_model.push_to_hub(UpperCAmelCase__  )


if __name__ == "__main__":
   A_		:		Tuple		     =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--model_name',
       default='b0',
       type=str,
       help='Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path',
       default='hf_model',
       type=str,
       help='Path to the output PyTorch model directory.',
   )
   parser.add_argument('--save_model', action='store_true', help='Save model to local')
   parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')

   A_		:		Optional[Any]		     =			parser.parse_args()
   convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)







import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class     _lowerCAmelCase(	UpperCAmelCase_							,     unittest.TestCase ):





       """simple docstring"""







       a    :		Optional[int]									=LongformerTokenizer
       a    :		Optional[int]									=True
       a    :		Tuple									=LongformerTokenizerFast
       a    :		Dict									=True





       def       _a   ( self							):
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         UpperCamelCase_: int   				=    [
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             '\u0120',
             '\u0120l',
             '\u0120n',
             '\u0120lo',
             '\u0120low',
             'er',
             '\u0120lowest',
             '\u0120newer',
             '\u0120wider',
             '<unk>',
         ]
         UpperCamelCase_: Optional[Any]   				=    dict(zip(_lowerCamelCase  ,      range(len(_lowerCamelCase							)							)							)							)
         UpperCamelCase_: Any   				=    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
         UpperCamelCase_: Tuple   				=    {'unk_token': '<unk>'}

         UpperCamelCase_: str   				=    os.path.join(self.tmpdirname  ,      VOCAB_FILES_NAMES['vocab_file']							)
         UpperCamelCase_: Tuple   				=    os.path.join(self.tmpdirname  ,      VOCAB_FILES_NAMES['merges_file']							)
         with open(self.vocab_file  ,      'w'  ,      encoding='utf-8'							) as fp:
           fp.write(json.dumps(_lowerCamelCase							) + '\n'							)
         with open(self.merges_file  ,      'w'  ,      encoding='utf-8'							) as fp:
           fp.write('\n'.join(_lowerCamelCase							)							)





       def       _a   ( self  ,      **_lowerCamelCase							):
         kwargs.update(self.special_tokens_map							)
         return self.tokenizer_class.from_pretrained(self.tmpdirname  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      **_lowerCamelCase							):
         kwargs.update(self.special_tokens_map							)
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase							):
         UpperCamelCase_: Union[str, Any]   				=    'lower newer'
         UpperCamelCase_: Optional[Any]   				=    'lower newer'
         return input_text, output_text





       def       _a   ( self							):
         UpperCamelCase_: int   				=    self.tokenizer_class(self.vocab_file  ,      self.merges_file  ,      **self.special_tokens_map							)
         UpperCamelCase_: Any   				=    'lower newer'
         UpperCamelCase_: Any   				=    ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
         UpperCamelCase_: str   				=    tokenizer.tokenize(_lowerCamelCase							)  # , add_prefix_space=True)
         self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)

         UpperCamelCase_: Dict   				=    tokens + [tokenizer.unk_token]
         UpperCamelCase_: int   				=    [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase							)  ,      _lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Tuple   				=    self.get_tokenizer()

         self.assertListEqual(tokenizer.encode('Hello world!'  ,      add_special_tokens=_lowerCamelCase							)  ,      [0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]							)
         self.assertListEqual(
             tokenizer.encode('Hello world! cécé herlolip 418'  ,      add_special_tokens=_lowerCamelCase							)  ,      [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]  ,      )





       @slow
       def       _a   ( self							):
         UpperCamelCase_: int   				=    self.tokenizer_class.from_pretrained('allenai/longformer-base-4096'							)

         UpperCamelCase_: Dict   				=    tokenizer.encode('sequence builders'  ,      add_special_tokens=_lowerCamelCase							)
         UpperCamelCase_: Any   				=    tokenizer.encode('multi-sequence build'  ,      add_special_tokens=_lowerCamelCase							)

         UpperCamelCase_: Union[str, Any]   				=    tokenizer.encode(
             'sequence builders'  ,      add_special_tokens=_lowerCamelCase  ,      add_prefix_space=_lowerCamelCase							)
         UpperCamelCase_: Any   				=    tokenizer.encode(
             'sequence builders'  ,      'multi-sequence build'  ,      add_special_tokens=_lowerCamelCase  ,      add_prefix_space=_lowerCamelCase							)

         UpperCamelCase_: int   				=    tokenizer.build_inputs_with_special_tokens(_lowerCamelCase							)
         UpperCamelCase_: Tuple   				=    tokenizer.build_inputs_with_special_tokens(_lowerCamelCase  ,      _lowerCamelCase							)

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode





       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    self.get_tokenizer()

         UpperCamelCase_: Optional[int]   				=    'Encode this sequence.'
         UpperCamelCase_: List[Any]   				=    tokenizer.byte_encoder[' '.encode('utf-8'							)[0]]

         # Testing encoder arguments
         UpperCamelCase_: Dict   				=    tokenizer.encode(_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase  ,      add_prefix_space=_lowerCamelCase							)
         UpperCamelCase_: List[str]   				=    tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
         self.assertNotEqual(_lowerCamelCase  ,      _lowerCamelCase							)

         UpperCamelCase_: List[Any]   				=    tokenizer.encode(_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase  ,      add_prefix_space=_lowerCamelCase							)
         UpperCamelCase_: int   				=    tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
         self.assertEqual(_lowerCamelCase  ,      _lowerCamelCase							)

         tokenizer.add_special_tokens({'bos_token': '<s>'}							)
         UpperCamelCase_: Optional[Any]   				=    tokenizer.encode(_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase							)
         UpperCamelCase_: Any   				=    tokenizer.convert_ids_to_tokens(encoded[1]							)[0]
         self.assertNotEqual(_lowerCamelCase  ,      _lowerCamelCase							)

         # Testing spaces after special tokens
         UpperCamelCase_: List[Any]   				=    '<mask>'
         tokenizer.add_special_tokens(
             {'mask_token': AddedToken(_lowerCamelCase  ,      lstrip=_lowerCamelCase  ,      rstrip=_lowerCamelCase							)}							)  # mask token has a left space
         UpperCamelCase_: List[Any]   				=    tokenizer.convert_tokens_to_ids(_lowerCamelCase							)

         UpperCamelCase_: Dict   				=    'Encode <mask> sequence'
         UpperCamelCase_: Dict   				=    'Encode <mask>sequence'

         UpperCamelCase_: Any   				=    tokenizer.encode(_lowerCamelCase							)
         UpperCamelCase_: Optional[Any]   				=    encoded.index(_lowerCamelCase							)
         UpperCamelCase_: Optional[Any]   				=    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
         self.assertEqual(_lowerCamelCase  ,      _lowerCamelCase							)

         UpperCamelCase_: List[str]   				=    tokenizer.encode(_lowerCamelCase							)
         UpperCamelCase_: List[Any]   				=    encoded.index(_lowerCamelCase							)
         UpperCamelCase_: Optional[int]   				=    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
         self.assertNotEqual(_lowerCamelCase  ,      _lowerCamelCase							)





       def       _a   ( self							):
         pass





       def       _a   ( self							):
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
             UpperCamelCase_: Any   				=    self.rust_tokenizer_class.from_pretrained(_lowerCamelCase  ,      **_lowerCamelCase							)
             UpperCamelCase_: Tuple   				=    self.tokenizer_class.from_pretrained(_lowerCamelCase  ,      **_lowerCamelCase							)
             UpperCamelCase_: List[Any]   				=    'A, <mask> AllenNLP sentence.'
             UpperCamelCase_: int   				=    tokenizer_r.encode_plus(_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase  ,      return_token_type_ids=_lowerCamelCase							)
             UpperCamelCase_: Any   				=    tokenizer_p.encode_plus(_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase  ,      return_token_type_ids=_lowerCamelCase							)

             # token_type_ids should put 0 everywhere
             self.assertEqual(sum(tokens_r['token_type_ids']							)  ,      sum(tokens_p['token_type_ids']							)							)

             # attention_mask should put 1 everywhere, so sum over length should be 1
             self.assertEqual(
                 sum(tokens_r['attention_mask']							) / len(tokens_r['attention_mask']							)  ,      sum(tokens_p['attention_mask']							) / len(tokens_p['attention_mask']							)  ,      )

             UpperCamelCase_: List[str]   				=    tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']							)
             UpperCamelCase_: str   				=    tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']							)

             # Rust correctly handles the space before the mask while python doesnt
             self.assertSequenceEqual(tokens_p['input_ids']  ,      [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]							)
             self.assertSequenceEqual(tokens_r['input_ids']  ,      [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]							)

             self.assertSequenceEqual(
                 _lowerCamelCase  ,      ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']							)
             self.assertSequenceEqual(
                 _lowerCamelCase  ,      ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']							)





       def       _a   ( self							):
         for trim_offsets, add_prefix_space in itertools.product([True, False]  ,      repeat=2							):
           UpperCamelCase_: Optional[Any]   				=    self.rust_tokenizer_class.from_pretrained(
               self.tmpdirname  ,      use_fast=_lowerCamelCase  ,      add_prefix_space=_lowerCamelCase  ,      trim_offsets=_lowerCamelCase							)

           UpperCamelCase_: Union[str, Any]   				=    json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()							)
           UpperCamelCase_: Optional[int]   				=    json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()							)

           self.assertEqual(pre_tokenizer_state['add_prefix_space']  ,      _lowerCamelCase							)

           self.assertEqual(post_processor_state['add_prefix_space']  ,      _lowerCamelCase							)
           self.assertEqual(post_processor_state['trim_offsets']  ,      _lowerCamelCase							)





       def       _a   ( self							):
         # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
         # `trim_offsets`
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
             UpperCamelCase_: Optional[int]   				=    'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
             UpperCamelCase_: Union[str, Any]   				=    f'''{text_of_1_token} {text_of_1_token}'''

             UpperCamelCase_: Optional[int]   				=    self.rust_tokenizer_class.from_pretrained(
                 _lowerCamelCase  ,      use_fast=_lowerCamelCase  ,      add_prefix_space=_lowerCamelCase  ,      trim_offsets=_lowerCamelCase							)
             UpperCamelCase_: str   				=    tokenizer_r(_lowerCamelCase  ,      return_offsets_mapping=_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase							)
             self.assertEqual(encoding.offset_mapping[0]  ,      (0, len(_lowerCamelCase							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]  ,      (len(_lowerCamelCase							) + 1, len(_lowerCamelCase							) + 1 + len(_lowerCamelCase							))  ,      )

             UpperCamelCase_: Optional[int]   				=    self.rust_tokenizer_class.from_pretrained(
                 _lowerCamelCase  ,      use_fast=_lowerCamelCase  ,      add_prefix_space=_lowerCamelCase  ,      trim_offsets=_lowerCamelCase							)
             UpperCamelCase_: str   				=    tokenizer_r(_lowerCamelCase  ,      return_offsets_mapping=_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase							)
             self.assertEqual(encoding.offset_mapping[0]  ,      (0, len(_lowerCamelCase							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]  ,      (len(_lowerCamelCase							) + 1, len(_lowerCamelCase							) + 1 + len(_lowerCamelCase							))  ,      )

             UpperCamelCase_: Optional[Any]   				=    self.rust_tokenizer_class.from_pretrained(
                 _lowerCamelCase  ,      use_fast=_lowerCamelCase  ,      add_prefix_space=_lowerCamelCase  ,      trim_offsets=_lowerCamelCase							)
             UpperCamelCase_: Dict   				=    tokenizer_r(_lowerCamelCase  ,      return_offsets_mapping=_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase							)
             self.assertEqual(encoding.offset_mapping[0]  ,      (0, len(_lowerCamelCase							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]  ,      (len(_lowerCamelCase							), len(_lowerCamelCase							) + 1 + len(_lowerCamelCase							))  ,      )

             UpperCamelCase_: List[str]   				=    self.rust_tokenizer_class.from_pretrained(
                 _lowerCamelCase  ,      use_fast=_lowerCamelCase  ,      add_prefix_space=_lowerCamelCase  ,      trim_offsets=_lowerCamelCase							)
             UpperCamelCase_: Optional[Any]   				=    tokenizer_r(_lowerCamelCase  ,      return_offsets_mapping=_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase							)
             self.assertEqual(encoding.offset_mapping[0]  ,      (0, len(_lowerCamelCase							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]  ,      (len(_lowerCamelCase							), len(_lowerCamelCase							) + 1 + len(_lowerCamelCase							))  ,      )

             UpperCamelCase_: Optional[int]   				=    f''' {text}'''

             # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
             #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
             # )
             # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
             # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
             # self.assertEqual(
             #     encoding.offset_mapping[1],
             #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
             # )

             UpperCamelCase_: Optional[int]   				=    self.rust_tokenizer_class.from_pretrained(
                 _lowerCamelCase  ,      use_fast=_lowerCamelCase  ,      add_prefix_space=_lowerCamelCase  ,      trim_offsets=_lowerCamelCase							)
             UpperCamelCase_: Tuple   				=    tokenizer_r(_lowerCamelCase  ,      return_offsets_mapping=_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase							)
             self.assertEqual(encoding.offset_mapping[0]  ,      (1, 1 + len(_lowerCamelCase							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]  ,      (1 + len(_lowerCamelCase							) + 1, 1 + len(_lowerCamelCase							) + 1 + len(_lowerCamelCase							))  ,      )

             UpperCamelCase_: Dict   				=    self.rust_tokenizer_class.from_pretrained(
                 _lowerCamelCase  ,      use_fast=_lowerCamelCase  ,      add_prefix_space=_lowerCamelCase  ,      trim_offsets=_lowerCamelCase							)
             UpperCamelCase_: Dict   				=    tokenizer_r(_lowerCamelCase  ,      return_offsets_mapping=_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase							)
             self.assertEqual(encoding.offset_mapping[0]  ,      (0, 1 + len(_lowerCamelCase							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]  ,      (1 + len(_lowerCamelCase							), 1 + len(_lowerCamelCase							) + 1 + len(_lowerCamelCase							))  ,      )

             UpperCamelCase_: Union[str, Any]   				=    self.rust_tokenizer_class.from_pretrained(
                 _lowerCamelCase  ,      use_fast=_lowerCamelCase  ,      add_prefix_space=_lowerCamelCase  ,      trim_offsets=_lowerCamelCase							)
             UpperCamelCase_: Union[str, Any]   				=    tokenizer_r(_lowerCamelCase  ,      return_offsets_mapping=_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase							)
             self.assertEqual(encoding.offset_mapping[0]  ,      (0, 1 + len(_lowerCamelCase							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]  ,      (1 + len(_lowerCamelCase							), 1 + len(_lowerCamelCase							) + 1 + len(_lowerCamelCase							))  ,      )