






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()
UpperCamelCase							       =    logging.get_logger(__name__)

UpperCamelCase							       =    {
    """b0""": efficientnet.EfficientNetBa,
    """b1""": efficientnet.EfficientNetBa,
    """b2""": efficientnet.EfficientNetBa,
    """b3""": efficientnet.EfficientNetBa,
    """b4""": efficientnet.EfficientNetBa,
    """b5""": efficientnet.EfficientNetBa,
    """b6""": efficientnet.EfficientNetBa,
    """b7""": efficientnet.EfficientNetBa,
}

UpperCamelCase							       =    {
    """b0""": {
        """hidden_dim""": 1_280,
        """width_coef""": 1.0,
        """depth_coef""": 1.0,
        """image_size""": 224,
        """dropout_rate""": 0.2,
        """dw_padding""": [],
    },
    """b1""": {
        """hidden_dim""": 1_280,
        """width_coef""": 1.0,
        """depth_coef""": 1.1,
        """image_size""": 240,
        """dropout_rate""": 0.2,
        """dw_padding""": [16],
    },
    """b2""": {
        """hidden_dim""": 1_408,
        """width_coef""": 1.1,
        """depth_coef""": 1.2,
        """image_size""": 260,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 8, 16],
    },
    """b3""": {
        """hidden_dim""": 1_536,
        """width_coef""": 1.2,
        """depth_coef""": 1.4,
        """image_size""": 300,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 18],
    },
    """b4""": {
        """hidden_dim""": 1_792,
        """width_coef""": 1.4,
        """depth_coef""": 1.8,
        """image_size""": 380,
        """dropout_rate""": 0.4,
        """dw_padding""": [6],
    },
    """b5""": {
        """hidden_dim""": 2_048,
        """width_coef""": 1.6,
        """depth_coef""": 2.2,
        """image_size""": 456,
        """dropout_rate""": 0.4,
        """dw_padding""": [13, 27],
    },
    """b6""": {
        """hidden_dim""": 2_304,
        """width_coef""": 1.8,
        """depth_coef""": 2.6,
        """image_size""": 528,
        """dropout_rate""": 0.5,
        """dw_padding""": [31],
    },
    """b7""": {
        """hidden_dim""": 2_560,
        """width_coef""": 2.0,
        """depth_coef""": 3.1,
        """image_size""": 600,
        """dropout_rate""": 0.5,
        """dw_padding""": [18],
    },
}






def        lowerCAmelCase (      UpperCamelCase_:							Union[str, Any]					)       ->							List[Any]:







  '''simple docstring'''







  _a							=	EfficientNetConfig()
  _a							=	CONFIG_MAP[model_name]["hidden_dim"]
  _a							=	CONFIG_MAP[model_name]["width_coef"]
  _a							=	CONFIG_MAP[model_name]["depth_coef"]
  _a							=	CONFIG_MAP[model_name]["image_size"]
  _a							=	CONFIG_MAP[model_name]["dropout_rate"]
  _a							=	CONFIG_MAP[model_name]["dw_padding"]

  _a							=	"huggingface/label-files"
  _a							=	"imagenet-1k-id2label.json"
  _a							=	1000
  _a							=	json.load(open(hf_hub_download(UpperCamelCase_     , UpperCamelCase_     , repo_type="dataset"					)     , "r"					)					)
  _a							=	{int(UpperCamelCase_					): v for k, v in idalabel.items()}

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






def        lowerCAmelCase (      )       ->							List[str]:







  '''simple docstring'''







  _a							=	"http://images.cocodataset.org/val2017/000000039769.jpg"
  _a							=	Image.open(requests.get(UpperCamelCase_     , stream=UpperCamelCase_					).raw					)
  return im






def        lowerCAmelCase (      UpperCamelCase_:							int					)       ->							List[str]:







  '''simple docstring'''







  _a							=	CONFIG_MAP[model_name]["image_size"]
  _a							=	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        lowerCAmelCase (      UpperCamelCase_:							Dict					)       ->							Dict:







  '''simple docstring'''







  _a							=	[v.split("_"					)[0].split("block"					)[1] for v in original_param_names if v.startswith("block"					)]
  _a							=	sorted(set(UpperCamelCase_					)					)
  _a							=	len(UpperCamelCase_					)
  _a							=	{b: str(UpperCamelCase_					) for b, i in zip(UpperCamelCase_     , range(UpperCamelCase_					)					)}

  _a							=	[]
  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:
    _a							=	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")					)

  _a							=	{}
  for item in rename_keys:
    if item[0] in original_param_names:
      _a							=	"efficientnet." + item[1]

  _a							=	"classifier.weight"
  _a							=	"classifier.bias"
  return key_mapping






def        lowerCAmelCase (      UpperCamelCase_:							int     , UpperCamelCase_:							Tuple     , UpperCamelCase_:							Any					)       ->							int:







  '''simple docstring'''







  for key, value in tf_params.items():
    if "normalization" in key:
      continue

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







  '''simple docstring'''







  _a							=	model_classes[model_name](
      include_top=UpperCamelCase_     , weights="imagenet"     , input_tensor=UpperCamelCase_     , input_shape=UpperCamelCase_     , pooling=UpperCamelCase_     , classes=1000     , classifier_activation="softmax"     , )

  _a							=	original_model.trainable_variables
  _a							=	original_model.non_trainable_variables
  _a							=	{param.name: param.numpy() for param in tf_params}
  for param in tf_non_train_params:
    _a							=	param.numpy()
  _a							=	list(tf_params.keys()					)

  # Load HuggingFace model
  _a							=	get_efficientnet_config(UpperCamelCase_					)
  _a							=	EfficientNetForImageClassification(UpperCamelCase_					).eval()
  _a							=	hf_model.state_dict()

  # Create src-to-dst parameter name mapping dictionary
  print("Converting parameters..."					)
  _a							=	rename_keys(UpperCamelCase_					)
  replace_params(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					)

  # Initialize preprocessor and preprocess input image
  _a							=	convert_image_processor(UpperCamelCase_					)
  _a							=	preprocessor(images=prepare_img()     , return_tensors="pt"					)

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

  # Original model inference
  _a							=	False
  _a							=	CONFIG_MAP[model_name]["image_size"]
  _a							=	prepare_img().resize((image_size, image_size)     , resample=PIL.Image.NEAREST					)
  _a							=	image.img_to_array(UpperCamelCase_					)
  _a							=	np.expand_dims(UpperCamelCase_     , axis=0					)
  _a							=	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...'''					)
    _a							=	f'''efficientnet-{model_name}'''
    preprocessor.push_to_hub(UpperCamelCase_					)
    hf_model.push_to_hub(UpperCamelCase_					)


if __name__ == "__main__":
     UpperCamelCase							       =    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""")

     UpperCamelCase							       =    parser.parse_args()
     convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)










"""simple docstring"""




import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
     UpperCamelCase							       =    """pt"""
elif is_tf_available():
     UpperCamelCase							       =    """tf"""
else:
     UpperCamelCase							       =    """jax"""




class    lowercase_    (_UpperCAmelCase,    unittest.TestCase     ):
      A__	:		List[str]       			=				ByTaTokenizer
      A__	:		List[Any]       			=				False






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        super().setUp()
        _a							=	ByTaTokenizer()
        tokenizer.save_pretrained(self.tmpdirname	)






      @cached_property
      def 			lowerCamelCase__    (    self	)	->List[str]:







        '''simple docstring'''


        return ByTaTokenizer.from_pretrained("google/byt5-small"	)






      def 			lowerCamelCase__    (    self     ,     **a_	)	->ByTaTokenizer:







        '''simple docstring'''


        return self.tokenizer_class.from_pretrained(self.tmpdirname     ,     **a_	)






      def 			lowerCamelCase__    (    self     ,     a_     ,     a_=False     ,     a_=2_0     ,     a_=5	)	->Tuple[str, list]:







        '''simple docstring'''


        _a							=	[]
        for i in range(len(a_	)	):
          try:
            _a							=	tokenizer.decode([i]     ,     clean_up_tokenization_spaces=a_	)
          except UnicodeDecodeError:
            pass
          toks.append((i, tok)	)

        _a							=	list(filter(lambda a_	: re.match(R"^[ a-zA-Z]+$"     ,     t[1]	)     ,     a_	)	)
        _a							=	list(filter(lambda a_	: [t[0]] == tokenizer.encode(t[1]     ,     add_special_tokens=a_	)     ,     a_	)	)
        if max_length is not None and len(a_	) > max_length:
          _a							=	toks[:max_length]
        if min_length is not None and len(a_	) < min_length and len(a_	) > 0:
          while len(a_	) < min_length:
            _a							=	toks + toks
        # toks_str = [t[1] for t in toks]
        _a							=	[t[0] for t in toks]

        # Ensure consistency
        _a							=	tokenizer.decode(a_     ,     clean_up_tokenization_spaces=a_	)
        if " " not in output_txt and len(a_	) > 1:
          _a							=	(
              tokenizer.decode([toks_ids[0]]     ,     clean_up_tokenization_spaces=a_	)
              + " "
              + tokenizer.decode(toks_ids[1:]     ,     clean_up_tokenization_spaces=a_	)
          )
        if with_prefix_space:
          _a							=	" " + output_txt
        _a							=	tokenizer.encode(a_     ,     add_special_tokens=a_	)
        return output_txt, output_ids






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







        '''simple docstring'''


        _a							=	self.ta_base_tokenizer
        _a							=	tokenizer(["hi</s>", "I went to the gym</s>", "</s>"]	)
        _a							=	tokenizer(["hi", "I went to the gym", ""]	)
        self.assertListEqual(batch_with_eos_added["input_ids"]     ,     batch_without_eos_added["input_ids"]	)






      def 			lowerCamelCase__    (    self	)	->Tuple:







        '''simple docstring'''


        _a							=	self.ta_base_tokenizer
        _a							=	"Unicode €."
        _a							=	tokenizer(a_	)
        _a							=	[8_8, 1_1_3, 1_0_8, 1_0_2, 1_1_4, 1_0_3, 1_0_4, 3_5, 2_2_9, 1_3_3, 1_7_5, 4_9, 1]
        self.assertEqual(encoded["input_ids"]     ,     a_	)

        # decoding
        _a							=	tokenizer.decode(a_	)
        self.assertEqual(a_     ,     "Unicode €.</s>"	)

        _a							=	tokenizer("e è é ê ë"	)
        _a							=	[1_0_4, 3_5, 1_9_8, 1_7_1, 3_5, 1_9_8, 1_7_2, 3_5, 1_9_8, 1_7_3, 3_5, 1_9_8, 1_7_4, 1]
        self.assertEqual(encoded["input_ids"]     ,     a_	)
        # decoding
        _a							=	tokenizer.decode(a_	)
        self.assertEqual(a_     ,     "e è é ê ë</s>"	)

        # encode/decode, but with `encode` instead of `__call__`
        self.assertEqual(tokenizer.decode(tokenizer.encode("e è é ê ë"	)	)     ,     "e è é ê ë</s>"	)






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        _a							=	self.ta_base_tokenizer
        _a							=	["A long paragraph for summarization.", "Another paragraph for summarization."]
        # fmt: off
        _a							=	[6_8, 3_5, 1_1_1, 1_1_4, 1_1_3, 1_0_6, 3_5, 1_1_5, 1_0_0, 1_1_7, 1_0_0, 1_0_6, 1_1_7, 1_0_0, 1_1_5, 1_0_7, 3_5, 1_0_5, 1_1_4, 1_1_7, 3_5, 1_1_8, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_0_8, 1_2_5, 1_0_0, 1_1_9, 1_0_8, 1_1_4, 1_1_3, 4_9, 1, 0]
        # fmt: on
        _a							=	tokenizer(a_     ,     padding=a_     ,     return_tensors=a_	)
        self.assertIsInstance(a_     ,     a_	)

        if FRAMEWORK != "jax":
          _a							=	list(batch.input_ids.numpy()[0]	)
        else:
          _a							=	list(batch.input_ids.tolist()[0]	)

        self.assertListEqual(a_     ,     a_	)

        self.assertEqual((2, 3_7)     ,     batch.input_ids.shape	)
        self.assertEqual((2, 3_7)     ,     batch.attention_mask.shape	)






      def 			lowerCamelCase__    (    self	)	->Dict:







        '''simple docstring'''


        _a							=	self.ta_base_tokenizer
        _a							=	["A long paragraph for summarization.", "Another paragraph for summarization."]
        _a							=	tokenizer(a_     ,     padding=a_     ,     return_tensors=a_	)
        # check if input_ids are returned and no decoder_input_ids
        self.assertIn("input_ids"     ,     a_	)
        self.assertIn("attention_mask"     ,     a_	)
        self.assertNotIn("decoder_input_ids"     ,     a_	)
        self.assertNotIn("decoder_attention_mask"     ,     a_	)






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







        '''simple docstring'''


        _a							=	self.ta_base_tokenizer
        _a							=	[
            "Summary of the text.",
            "Another summary.",
        ]
        _a							=	tokenizer(
            text_target=a_     ,     max_length=3_2     ,     padding="max_length"     ,     truncation=a_     ,     return_tensors=a_	)
        self.assertEqual(3_2     ,     targets["input_ids"].shape[1]	)






      def 			lowerCamelCase__    (    self	)	->Dict:







        '''simple docstring'''


        _a							=	self.ta_base_tokenizer
        _a							=	["A long paragraph for summarization. </s>"]
        _a							=	["Summary of the text. </s>"]
        # fmt: off
        _a							=	[6_8, 3_5, 1_1_1, 1_1_4, 1_1_3, 1_0_6, 3_5, 1_1_5, 1_0_0, 1_1_7, 1_0_0, 1_0_6, 1_1_7, 1_0_0, 1_1_5, 1_0_7, 3_5, 1_0_5, 1_1_4, 1_1_7, 3_5, 1_1_8, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_0_8, 1_2_5, 1_0_0, 1_1_9, 1_0_8, 1_1_4, 1_1_3, 4_9, 3_5, 1]
        _a							=	[8_6, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_2_4, 3_5, 1_1_4, 1_0_5, 3_5, 1_1_9, 1_0_7, 1_0_4, 3_5, 1_1_9, 1_0_4, 1_2_3, 1_1_9, 4_9, 3_5, 1]
        # fmt: on

        _a							=	tokenizer(a_     ,     text_target=a_	)

        self.assertEqual(a_     ,     batch["input_ids"][0]	)
        self.assertEqual(a_     ,     batch["labels"][0]	)






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        _a							=	self.get_tokenizers()
        for tokenizer in tokenizers:
          with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
            self.assertNotEqual(tokenizer.model_max_length     ,     4_2	)

        # Now let's start the test
        _a							=	self.get_tokenizers()
        for tokenizer in tokenizers:
          with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
            # Isolate this from the other tests because we save additional tokens/etc
            _a							=	tempfile.mkdtemp()

            _a							=	" He is very happy, UNwant\u00E9d,running"
            _a							=	tokenizer.encode(a_     ,     add_special_tokens=a_	)
            tokenizer.save_pretrained(a_	)

            _a							=	tokenizer.__class__.from_pretrained(a_	)
            _a							=	after_tokenizer.encode(a_     ,     add_special_tokens=a_	)
            self.assertListEqual(a_     ,     a_	)

            shutil.rmtree(a_	)

        _a							=	self.get_tokenizers(model_max_length=4_2	)
        for tokenizer in tokenizers:
          with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
            # Isolate this from the other tests because we save additional tokens/etc
            _a							=	tempfile.mkdtemp()

            _a							=	" He is very happy, UNwant\u00E9d,running"
            tokenizer.add_tokens(["bim", "bambam"]	)
            _a							=	tokenizer.additional_special_tokens
            additional_special_tokens.append("new_additional_special_token"	)
            tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}	)
            _a							=	tokenizer.encode(a_     ,     add_special_tokens=a_	)
            tokenizer.save_pretrained(a_	)

            _a							=	tokenizer.__class__.from_pretrained(a_	)
            _a							=	after_tokenizer.encode(a_     ,     add_special_tokens=a_	)
            self.assertListEqual(a_     ,     a_	)
            self.assertIn("new_additional_special_token"     ,     after_tokenizer.additional_special_tokens	)
            self.assertEqual(after_tokenizer.model_max_length     ,     4_2	)

            _a							=	tokenizer.__class__.from_pretrained(a_     ,     model_max_length=4_3	)
            self.assertEqual(tokenizer.model_max_length     ,     4_3	)

            shutil.rmtree(a_	)






      def 			lowerCamelCase__    (    self	)	->List[str]:







        '''simple docstring'''


        _a							=	[]
        if self.test_slow_tokenizer:
          tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())	)

        if self.test_rust_tokenizer:
          tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())	)

        for tokenizer_class, tokenizer_utils in tokenizer_list:
          with tempfile.TemporaryDirectory() as tmp_dir:
            tokenizer_utils.save_pretrained(a_	)

            with open(os.path.join(a_     ,     "special_tokens_map.json"	)     ,     encoding="utf-8"	) as json_file:
              _a							=	json.load(a_	)

            with open(os.path.join(a_     ,     "tokenizer_config.json"	)     ,     encoding="utf-8"	) as json_file:
              _a							=	json.load(a_	)

            _a							=	[f'''<extra_id_{i}>''' for i in range(1_2_5	)]

            _a							=	added_tokens_extra_ids + [
                "an_additional_special_token"
            ]
            _a							=	added_tokens_extra_ids + [
                "an_additional_special_token"
            ]

            with open(os.path.join(a_     ,     "special_tokens_map.json"	)     ,     "w"     ,     encoding="utf-8"	) as outfile:
              json.dump(a_     ,     a_	)
            with open(os.path.join(a_     ,     "tokenizer_config.json"	)     ,     "w"     ,     encoding="utf-8"	) as outfile:
              json.dump(a_     ,     a_	)

            # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
            # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
            # "special_tokens_map.json" files
            _a							=	tokenizer_class.from_pretrained(
                a_     ,     )
            self.assertIn(
                "an_additional_special_token"     ,     tokenizer_without_change_in_init.additional_special_tokens	)
            # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
            self.assertEqual(
                ["an_additional_special_token"]     ,     tokenizer_without_change_in_init.convert_ids_to_tokens(
                    tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"]	)	)     ,     )

            # Now we test that we can change the value of additional_special_tokens in the from_pretrained
            _a							=	added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"     ,     lstrip=a_	)]
            _a							=	tokenizer_class.from_pretrained(
                a_     ,     additional_special_tokens=a_     ,     )

            self.assertIn("a_new_additional_special_token"     ,     tokenizer.additional_special_tokens	)
            self.assertEqual(
                ["a_new_additional_special_token"]     ,     tokenizer.convert_ids_to_tokens(
                    tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"]	)	)     ,     )






      def 			lowerCamelCase__    (    self	)	->List[str]:







        '''simple docstring'''


        _a							=	[]
        if self.test_slow_tokenizer:
          tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())	)

        if self.test_rust_tokenizer:
          tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())	)

        for tokenizer_class, tokenizer_utils in tokenizer_list:
          with tempfile.TemporaryDirectory() as tmp_dir:
            tokenizer_utils.save_pretrained(a_	)

            _a							=	tokenizer_class.from_pretrained(a_	)

            self.assertTrue(tokenizer.decode([2_5_5]	) == ""	)






      def 			lowerCamelCase__    (    self	)	->Optional[int]:







        '''simple docstring'''


        pass






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







        '''simple docstring'''


        pass






      def 			lowerCamelCase__    (    self	)	->Tuple:







        '''simple docstring'''


        pass






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







        '''simple docstring'''


        pass






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







        '''simple docstring'''


        _a							=	self.get_tokenizers(fast=a_     ,     do_lower_case=a_	)
        for tokenizer in tokenizers:
          with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
            _a							=	["t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "x", "t", "</s>"]
            _a							=	tokenizer.convert_tokens_to_string(a_	)

            self.assertIsInstance(a_     ,     a_	)






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







        '''simple docstring'''


        _a							=	self.get_tokenizers()
        for tokenizer in tokenizers:
          with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
            _a							=	[
                "bos_token",
                "eos_token",
                "unk_token",
                "sep_token",
                "pad_token",
                "cls_token",
                "mask_token",
            ]

            _a							=	0
            _a							=	tokenizer.convert_ids_to_tokens(
                a_     ,     skip_special_tokens=a_	)

            for attr in attributes_list:
              setattr(a_     ,     attr + "_id"     ,     a_	)
              self.assertEqual(getattr(a_     ,     a_	)     ,     a_	)
              self.assertEqual(getattr(a_     ,     attr + "_id"	)     ,     a_	)

              setattr(a_     ,     attr + "_id"     ,     a_	)
              self.assertEqual(getattr(a_     ,     a_	)     ,     a_	)
              self.assertEqual(getattr(a_     ,     attr + "_id"	)     ,     a_	)

            setattr(a_     ,     "additional_special_tokens_ids"     ,     []	)
            self.assertListEqual(getattr(a_     ,     "additional_special_tokens"	)     ,     []	)
            self.assertListEqual(getattr(a_     ,     "additional_special_tokens_ids"	)     ,     []	)

            setattr(a_     ,     "additional_special_tokens_ids"     ,     [token_id_to_test_setters]	)
            self.assertListEqual(getattr(a_     ,     "additional_special_tokens"	)     ,     [token_to_test_setters]	)
            self.assertListEqual(getattr(a_     ,     "additional_special_tokens_ids"	)     ,     [token_id_to_test_setters]	)


