






"""simple docstring"""
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 							snake_case (  A__      ,A__		):
			UpperCAmelCase_	:					Union[str, Any]     	=   old_name

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

						if layer == "0":
									UpperCAmelCase_	:					List[Any]     	=   old_name.replace("0"      ,"convolution1"		)
						elif layer == "1":
									UpperCAmelCase_	:					List[str]     	=   old_name.replace("1"      ,"batchnorm_before"		)
						elif layer == "3":
									UpperCAmelCase_	:					Union[str, Any]     	=   old_name.replace("3"      ,"convolution2"		)
						else:
									UpperCAmelCase_	:					Union[str, Any]     	=   old_name.replace("4"      ,"batchnorm_after"		)

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

									UpperCAmelCase_	:					str     	=   "last_stage." + trimmed_name

			elif "network" in old_name and re.search(r".\d."      ,A__		):
						UpperCAmelCase_	:					Any     	=   old_name.replace("network"      ,"intermediate_stages"		)

			if "fc" in new_name:
						UpperCAmelCase_	:					Dict     	=   new_name.replace("fc"      ,"convolution"		)
			elif ("norm1" in new_name) and ("layernorm1" not in new_name):
						UpperCAmelCase_	:					Optional[Any]     	=   new_name.replace("norm1"      ,"batchnorm_before"		)
			elif ("norm2" in new_name) and ("layernorm2" not in new_name):
						UpperCAmelCase_	:					List[str]     	=   new_name.replace("norm2"      ,"batchnorm_after"		)
			if "proj" in new_name:
						UpperCAmelCase_	:					Dict     	=   new_name.replace("proj"      ,"projection"		)
			if "dist_head" in new_name:
						UpperCAmelCase_	:					Tuple     	=   new_name.replace("dist_head"      ,"distillation_classifier"		)
			elif "head" in new_name:
						UpperCAmelCase_	:					Any     	=   new_name.replace("head"      ,"classifier"		)
			elif "patch_embed" in new_name:
						UpperCAmelCase_	:					int     	=   "efficientformer." + new_name
			elif new_name == "norm.weight" or new_name == "norm.bias":
						UpperCAmelCase_	:					Tuple     	=   new_name.replace("norm"      ,"layernorm"		)
						UpperCAmelCase_	:					Optional[int]     	=   "efficientformer." + new_name
			else:
						UpperCAmelCase_	:					Any     	=   "efficientformer.encoder." + new_name

			return new_name

def 							snake_case (  A__      ,A__		):
			for key in checkpoint.copy().keys():
						UpperCAmelCase_	:					int     	=   checkpoint.pop(A__		)
						UpperCAmelCase_	:					Tuple     	=   val

			return checkpoint

def 							snake_case (  ):
			UpperCAmelCase_	:					str     	=   "http://images.cocodataset.org/val2017/000000039769.jpg"
			UpperCAmelCase_	:					int     	=   Image.open(requests.get(A__      ,stream=A__		).raw		)

			return image

def 							snake_case (  A__      ,A__      ,A__      ,A__		):
			UpperCAmelCase_	:					List[Any]     	=   torch.load(A__      ,map_location="cpu"		)["model"]
			UpperCAmelCase_	:					Any     	=   EfficientFormerConfig.from_json_file(A__		)
			UpperCAmelCase_	:					Optional[Any]     	=   EfficientFormerForImageClassificationWithTeacher(A__		)
			UpperCAmelCase_	:					Optional[int]     	=   "_".join(checkpoint_path.split("/"		)[-1].split("."		)[0].split("_"		)[:-1]		)

			UpperCAmelCase_	:					Optional[Any]     	=   config.depths[-1] - config.num_metaad_blocks + 1
			UpperCAmelCase_	:					List[str]     	=   convert_torch_checkpoint(A__      ,A__		)

			model.load_state_dict(A__		)
			model.eval()

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

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

			# original processing pipeline
			UpperCAmelCase_	:					List[Any]     	=   Compose(
			    [
			        Resize(A__      ,interpolation=pillow_resamplings["bicubic"]		),
			        CenterCrop(A__		),
			        ToTensor(),
			        Normalize(A__      ,A__		),
			    ]		)
			UpperCAmelCase_	:					str     	=   image_transforms(A__		).unsqueeze(0		)

			assert torch.allclose(A__      ,A__		)

			UpperCAmelCase_	:					Optional[Any]     	=   model(A__		)
			UpperCAmelCase_	:					int     	=   outputs.logits

			UpperCAmelCase_	:					Tuple     	=   (1, 10_00)

			if "l1" in model_name:
						UpperCAmelCase_	:					Dict     	=   torch.Tensor(
						    [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]		)
						assert torch.allclose(logits[0, :10]      ,A__      ,atol=1e-3		)
						assert logits.shape == expected_shape
			elif "l3" in model_name:
						UpperCAmelCase_	:					Optional[Any]     	=   torch.Tensor(
						    [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]		)
						assert torch.allclose(logits[0, :10]      ,A__      ,atol=1e-3		)
						assert logits.shape == expected_shape
			elif "l7" in model_name:
						UpperCAmelCase_	:					int     	=   torch.Tensor(
						    [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]		)
						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(A__		).mkdir(exist_ok=A__		)
			model.save_pretrained(A__		)
			print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""		)
			processor.save_pretrained(A__		)
			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=A__      ,)
						processor.push_to_hub(
						    repo_id=F"""Bearnardd/{pytorch_dump_path}"""      ,commit_message="Add image processor"      ,use_temp_dir=A__      ,)


if __name__ == "__main__":
			lowerCamelCase_							      = 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)

			lowerCamelCase_							      = 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,
			)











"""simple docstring"""
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_			(__A							,		unittest.TestCase	):
	__magic_name__       =							BertTokenizer
	__magic_name__       =							BertTokenizerFast
	__magic_name__       =							True
	__magic_name__       =							True
	__magic_name__       =							filter_non_english





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

				UpperCAmelCase_	:					Optional[int]     	=   [
				    "[UNK]",
				    "[CLS]",
				    "[SEP]",
				    "[PAD]",
				    "[MASK]",
				    "want",
				    "##want",
				    "##ed",
				    "wa",
				    "un",
				    "runn",
				    "##ing",
				    ",",
				    "low",
				    "lowest",
				]
				UpperCAmelCase_	:					Optional[Any]     	=   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   :					Union[str, Any]      ,      lowerCAmelCase_   :					List[str]		)		->     List[str]:
				UpperCAmelCase_	:					str     	=   "UNwant\u00E9d,running"
				UpperCAmelCase_	:					Optional[int]     	=   "unwanted, running"
				return input_text, output_text





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Any:
				UpperCAmelCase_	:					Optional[int]     	=   self.tokenizer_class(self.vocab_file		)

				UpperCAmelCase_	:					int     	=   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   :					Union[str, Any]		)		->     List[Any]:
				if not self.test_rust_tokenizer:
							return

				UpperCAmelCase_	:					List[Any]     	=   self.get_tokenizer()
				UpperCAmelCase_	:					str     	=   self.get_rust_tokenizer()

				UpperCAmelCase_	:					str     	=   "UNwant\u00E9d,running"

				UpperCAmelCase_	:					Any     	=   tokenizer.tokenize(lowerCAmelCase_		)
				UpperCAmelCase_	:					List[Any]     	=   rust_tokenizer.tokenize(lowerCAmelCase_		)
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)

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

				UpperCAmelCase_	:					Dict     	=   self.get_rust_tokenizer()
				UpperCAmelCase_	:					int     	=   tokenizer.encode(lowerCAmelCase_		)
				UpperCAmelCase_	:					Optional[Any]     	=   rust_tokenizer.encode(lowerCAmelCase_		)
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)

				# With lower casing
				UpperCAmelCase_	:					int     	=   self.get_tokenizer(do_lower_case=lowerCAmelCase_		)
				UpperCAmelCase_	:					str     	=   self.get_rust_tokenizer(do_lower_case=lowerCAmelCase_		)

				UpperCAmelCase_	:					Any     	=   "UNwant\u00E9d,running"

				UpperCAmelCase_	:					Dict     	=   tokenizer.tokenize(lowerCAmelCase_		)
				UpperCAmelCase_	:					Union[str, Any]     	=   rust_tokenizer.tokenize(lowerCAmelCase_		)
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					Optional[int]     	=   tokenizer.encode(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_		)
				UpperCAmelCase_	:					Any     	=   rust_tokenizer.encode(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_		)
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					Union[str, Any]     	=   self.get_rust_tokenizer()
				UpperCAmelCase_	:					Dict     	=   tokenizer.encode(lowerCAmelCase_		)
				UpperCAmelCase_	:					Union[str, Any]     	=   rust_tokenizer.encode(lowerCAmelCase_		)
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     List[str]:
				UpperCAmelCase_	:					Union[str, Any]     	=   BasicTokenizer()

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





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Optional[int]:
				UpperCAmelCase_	:					Union[str, Any]     	=   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   :					List[str]		)		->     Optional[Any]:
				UpperCAmelCase_	:					Dict     	=   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   :					Any		)		->     Any:
				UpperCAmelCase_	:					str     	=   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[Any]		)		->     Optional[Any]:
				UpperCAmelCase_	:					List[str]     	=   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   :					List[Any]		)		->     Optional[Any]:
				UpperCAmelCase_	:					List[Any]     	=   BasicTokenizer(do_lower_case=lowerCAmelCase_		)

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





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     Union[str, Any]:
				UpperCAmelCase_	:					str     	=   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   :					Optional[Any]		)		->     str:
				UpperCAmelCase_	:					Any     	=   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   :					Union[str, Any]		)		->     Dict:
				UpperCAmelCase_	:					Dict     	=   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   :					Tuple		)		->     Union[str, Any]:
				UpperCAmelCase_	:					Optional[int]     	=   BasicTokenizer()
				UpperCAmelCase_	:					int     	=   "a\n'll !!to?'d of, can't."
				UpperCAmelCase_	:					Union[str, Any]     	=   ["a", "'", "ll", "!", "!", "to", "?", "'", "d", "of", ",", "can", "'", "t", "."]
				self.assertListEqual(tokenizer.tokenize(lowerCAmelCase_		)      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     Dict:
				UpperCAmelCase_	:					List[str]     	=   ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

				UpperCAmelCase_	:					int     	=   {}
				for i, token in enumerate(lowerCAmelCase_		):
							UpperCAmelCase_	:					Tuple     	=   i
				UpperCAmelCase_	:					Dict     	=   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   :					Dict		)		->     Any:
				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   :					List[str]		)		->     Any:
				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   :					List[Any]		)		->     int:
				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   :					List[str]		)		->     str:
				UpperCAmelCase_	:					List[Any]     	=   self.get_tokenizer()
				UpperCAmelCase_	:					List[Any]     	=   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   :					int		)		->     List[Any]:
				UpperCAmelCase_	:					str     	=   self.tokenizer_class.from_pretrained("bert-base-uncased"		)

				UpperCAmelCase_	:					Optional[Any]     	=   tokenizer.encode("sequence builders"      ,      add_special_tokens=lowerCAmelCase_		)
				UpperCAmelCase_	:					str     	=   tokenizer.encode("multi-sequence build"      ,      add_special_tokens=lowerCAmelCase_		)

				UpperCAmelCase_	:					str     	=   tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_		)
				UpperCAmelCase_	:					Tuple     	=   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]		)		->     List[str]:
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
										UpperCAmelCase_	:					Optional[int]     	=   self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_      ,      **lowerCAmelCase_		)

										UpperCAmelCase_	:					int     	=   f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
										UpperCAmelCase_	:					Dict     	=   tokenizer_r.encode_plus(
										    lowerCAmelCase_      ,      return_attention_mask=lowerCAmelCase_      ,      return_token_type_ids=lowerCAmelCase_      ,      return_offsets_mapping=lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_      ,      )

										UpperCAmelCase_	:					int     	=   tokenizer_r.do_lower_case if hasattr(lowerCAmelCase_      ,      "do_lower_case"		) else False
										UpperCAmelCase_	:					Dict     	=   (
										    [
										        ((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   :					List[str]		)		->     str:
				UpperCAmelCase_	:					Dict     	=   ["的", "人", "有"]
				UpperCAmelCase_	:					Union[str, Any]     	=   "".join(lowerCAmelCase_		)
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
										UpperCAmelCase_	:					Tuple     	=   True
										UpperCAmelCase_	:					Optional[Any]     	=   self.tokenizer_class.from_pretrained(lowerCAmelCase_      ,      **lowerCAmelCase_		)
										UpperCAmelCase_	:					Dict     	=   self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_      ,      **lowerCAmelCase_		)

										UpperCAmelCase_	:					Union[str, Any]     	=   tokenizer_p.encode(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_		)
										UpperCAmelCase_	:					Union[str, Any]     	=   tokenizer_r.encode(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_		)

										UpperCAmelCase_	:					str     	=   tokenizer_r.convert_ids_to_tokens(lowerCAmelCase_		)
										UpperCAmelCase_	:					Optional[Any]     	=   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_		)

										UpperCAmelCase_	:					int     	=   False
										UpperCAmelCase_	:					str     	=   self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_      ,      **lowerCAmelCase_		)
										UpperCAmelCase_	:					List[str]     	=   self.tokenizer_class.from_pretrained(lowerCAmelCase_      ,      **lowerCAmelCase_		)

										UpperCAmelCase_	:					int     	=   tokenizer_r.encode(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_		)
										UpperCAmelCase_	:					int     	=   tokenizer_p.encode(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_		)

										UpperCAmelCase_	:					Optional[Any]     	=   tokenizer_r.convert_ids_to_tokens(lowerCAmelCase_		)
										UpperCAmelCase_	:					List[str]     	=   tokenizer_p.convert_ids_to_tokens(lowerCAmelCase_		)

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



