
"""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     __magic_name__		(					UpperCamelCase     :		Tuple							,		UpperCamelCase     :		Optional[int]     )     ->						Tuple:
    a__		=			old_name

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

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

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

            a__		=			'last_stage.' + trimmed_name

    elif "network" in old_name and re.search(r'.\d.'							,		UpperCamelCase     ):
        a__		=			old_name.replace('network'							,		'intermediate_stages'     )

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

    return new_name



def     __magic_name__		(					UpperCamelCase     :		Tuple							,		UpperCamelCase     :		List[Any]     )     ->						str:
    for key in checkpoint.copy().keys():
        a__		=			checkpoint.pop(UpperCamelCase     )
        a__		=			val

    return checkpoint



def     __magic_name__		(					)     ->						Union[str, Any]:
    a__		=			'http://images.cocodataset.org/val2017/000000039769.jpg'
    a__		=			Image.open(requests.get(UpperCamelCase							,		stream=UpperCamelCase     ).raw     )

    return image



def     __magic_name__		(					UpperCamelCase     :		Path							,		UpperCamelCase     :		Path							,		UpperCamelCase     :		Path							,		UpperCamelCase     :		bool     )     ->						Any:
    a__		=			torch.load(UpperCamelCase							,		map_location='cpu'     )['model']
    a__		=			EfficientFormerConfig.from_json_file(UpperCamelCase     )
    a__		=			EfficientFormerForImageClassificationWithTeacher(UpperCamelCase     )
    a__		=			'_'.join(checkpoint_path.split('/'     )[-1].split('.'     )[0].split('_'     )[:-1]     )

    a__		=			config.depths[-1] - config.num_metaad_blocks + 1
    a__		=			convert_torch_checkpoint(UpperCamelCase							,		UpperCamelCase     )

    model.load_state_dict(UpperCamelCase     )
    model.eval()

    a__		=			{
        'bilinear': PILImageResampling.BILINEAR,
        'bicubic': PILImageResampling.BICUBIC,
        'nearest': PILImageResampling.NEAREST,
    }

    # prepare image
    a__		=			prepare_img()
    a__		=			256
    a__		=			224
    a__		=			EfficientFormerImageProcessor(
        size={'shortest_edge': image_size}							,		crop_size={'height': crop_size, 'width': crop_size}							,		resample=pillow_resamplings['bicubic']							,		)
    a__		=			processor(images=UpperCamelCase							,		return_tensors='pt'     ).pixel_values

    # original processing pipeline
    a__		=			Compose(
        [
            Resize(UpperCamelCase							,		interpolation=pillow_resamplings['bicubic']     ),
            CenterCrop(UpperCamelCase     ),
            ToTensor(),
            Normalize(UpperCamelCase							,		UpperCamelCase     ),
        ]     )
    a__		=			image_transforms(UpperCamelCase     ).unsqueeze(0     )

    assert torch.allclose(UpperCamelCase							,		UpperCamelCase     )

    a__		=			model(UpperCamelCase     )
    a__		=			outputs.logits

    a__		=			(1, 1000)

    if "l1" in model_name:
        a__		=			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]							,		UpperCamelCase							,		atol=1E-3     )
        assert logits.shape == expected_shape
    elif "l3" in model_name:
        a__		=			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]							,		UpperCamelCase							,		atol=1E-3     )
        assert logits.shape == expected_shape
    elif "l7" in model_name:
        a__		=			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(UpperCamelCase     ).mkdir(exist_ok=UpperCamelCase     )
    model.save_pretrained(UpperCamelCase     )
    print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'     )
    processor.save_pretrained(UpperCamelCase     )
    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=UpperCamelCase							,		)
        processor.push_to_hub(
            repo_id=f'Bearnardd/{pytorch_dump_path}'							,		commit_message='Add image processor'							,		use_temp_dir=UpperCamelCase							,		)


if __name__ == "__main__":
    a		:       List[str]     						=       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)

    a		:       Optional[Any]     						=       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 json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

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

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
    a		:       str     						=       'pt'
elif is_tf_available():
    a		:       Dict     						=       'tf'
else:
    a		:       Optional[Any]     						=       'jax'







class     lowercase(_lowercase    ,						unittest.TestCase       ):
     __snake_case:	List[Any]						=     PerceiverTokenizer
     __snake_case:	Optional[Any]						=     False



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         super().setUp()
         a__		=			PerceiverTokenizer()
         tokenizer.save_pretrained(self.tmpdirname   )



     @cached_property
     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver'   )



     def 			lowercase__  (							self     ,				**__SCREAMING_SNAKE_CASE   )		->     PerceiverTokenizer:







         """simple docstring"""



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



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







         """simple docstring"""



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

         a__		=			list(filter(lambda __SCREAMING_SNAKE_CASE   : re.match(R'^[ a-zA-Z]+$'     ,				t[1]   )     ,				__SCREAMING_SNAKE_CASE   )   )
         a__		=			list(filter(lambda __SCREAMING_SNAKE_CASE   : [t[0]] == tokenizer.encode(t[1]     ,				add_special_tokens=__SCREAMING_SNAKE_CASE   )     ,				__SCREAMING_SNAKE_CASE   )   )
         if max_length is not None and len(__SCREAMING_SNAKE_CASE   ) > max_length:
             a__		=			toks[:max_length]
         if min_length is not None and len(__SCREAMING_SNAKE_CASE   ) < min_length and len(__SCREAMING_SNAKE_CASE   ) > 0:
             while len(__SCREAMING_SNAKE_CASE   ) < 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(__SCREAMING_SNAKE_CASE     ,				clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
         if " " not in output_txt and len(__SCREAMING_SNAKE_CASE   ) > 1:
             a__		=			(
                 tokenizer.decode([toks_ids[0]]     ,				clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
                 + ' '
                 + tokenizer.decode(toks_ids[1:]     ,				clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
             )
         if with_prefix_space:
             a__		=			' ' + output_txt
         a__		=			tokenizer.encode(__SCREAMING_SNAKE_CASE     ,				add_special_tokens=__SCREAMING_SNAKE_CASE   )
         return output_txt, output_ids



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







         """simple docstring"""



         a__		=			self.perceiver_tokenizer
         a__		=			'Unicode €.'
         a__		=			tokenizer(__SCREAMING_SNAKE_CASE   )
         a__		=			[4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
         self.assertEqual(encoded['input_ids']     ,				__SCREAMING_SNAKE_CASE   )

         # decoding
         a__		=			tokenizer.decode(__SCREAMING_SNAKE_CASE   )
         self.assertEqual(__SCREAMING_SNAKE_CASE     ,				'[CLS]Unicode €.[SEP]'   )

         a__		=			tokenizer('e è é ê ë'   )
         a__		=			[4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
         self.assertEqual(encoded['input_ids']     ,				__SCREAMING_SNAKE_CASE   )
         # decoding
         a__		=			tokenizer.decode(__SCREAMING_SNAKE_CASE   )
         self.assertEqual(__SCREAMING_SNAKE_CASE     ,				'[CLS]e è é ê ë[SEP]'   )

         # encode/decode, but with `encode` instead of `__call__`
         self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë'   )   )     ,				'[CLS]e è é ê ë[SEP]'   )



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







         """simple docstring"""



         a__		=			self.perceiver_tokenizer
         a__		=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
         # fmt: off
         a__		=			[4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
         # fmt: on
         a__		=			tokenizer(__SCREAMING_SNAKE_CASE     ,				padding=__SCREAMING_SNAKE_CASE     ,				return_tensors=__SCREAMING_SNAKE_CASE   )
         self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

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

         self.assertListEqual(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         self.assertEqual((2, 3_8)     ,				batch.input_ids.shape   )
         self.assertEqual((2, 3_8)     ,				batch.attention_mask.shape   )



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         a__		=			self.perceiver_tokenizer
         a__		=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
         a__		=			tokenizer(__SCREAMING_SNAKE_CASE     ,				padding=__SCREAMING_SNAKE_CASE     ,				return_tensors=__SCREAMING_SNAKE_CASE   )
         # check if input_ids are returned and no decoder_input_ids
         self.assertIn('input_ids'     ,				__SCREAMING_SNAKE_CASE   )
         self.assertIn('attention_mask'     ,				__SCREAMING_SNAKE_CASE   )
         self.assertNotIn('decoder_input_ids'     ,				__SCREAMING_SNAKE_CASE   )
         self.assertNotIn('decoder_attention_mask'     ,				__SCREAMING_SNAKE_CASE   )



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







         """simple docstring"""



         a__		=			self.perceiver_tokenizer
         a__		=			[
             'Summary of the text.',
             'Another summary.',
         ]
         a__		=			tokenizer(
             text_target=__SCREAMING_SNAKE_CASE     ,				max_length=3_2     ,				padding='max_length'     ,				truncation=__SCREAMING_SNAKE_CASE     ,				return_tensors=__SCREAMING_SNAKE_CASE   )
         self.assertEqual(3_2     ,				targets['input_ids'].shape[1]   )



     def 			lowercase__  (							self   )		->     Optional[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(__SCREAMING_SNAKE_CASE     ,				add_special_tokens=__SCREAMING_SNAKE_CASE   )
                 tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE   )

                 a__		=			tokenizer.__class__.from_pretrained(__SCREAMING_SNAKE_CASE   )
                 a__		=			after_tokenizer.encode(__SCREAMING_SNAKE_CASE     ,				add_special_tokens=__SCREAMING_SNAKE_CASE   )
                 self.assertListEqual(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

                 shutil.rmtree(__SCREAMING_SNAKE_CASE   )

         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(__SCREAMING_SNAKE_CASE     ,				add_special_tokens=__SCREAMING_SNAKE_CASE   )
                 tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE   )

                 a__		=			tokenizer.__class__.from_pretrained(__SCREAMING_SNAKE_CASE   )
                 a__		=			after_tokenizer.encode(__SCREAMING_SNAKE_CASE     ,				add_special_tokens=__SCREAMING_SNAKE_CASE   )
                 self.assertListEqual(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
                 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(__SCREAMING_SNAKE_CASE     ,				model_max_length=4_3   )
                 self.assertEqual(tokenizer.model_max_length     ,				4_3   )

                 shutil.rmtree(__SCREAMING_SNAKE_CASE   )



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







         """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(__SCREAMING_SNAKE_CASE   )

                 with open(os.path.join(__SCREAMING_SNAKE_CASE     ,				'special_tokens_map.json'   )     ,				encoding='utf-8'   ) as json_file:
                     a__		=			json.load(__SCREAMING_SNAKE_CASE   )

                 with open(os.path.join(__SCREAMING_SNAKE_CASE     ,				'tokenizer_config.json'   )     ,				encoding='utf-8'   ) as json_file:
                     a__		=			json.load(__SCREAMING_SNAKE_CASE   )

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

                 # 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(
                     __SCREAMING_SNAKE_CASE     ,				)
                 self.assertIn(
                     'an_additional_special_token'     ,				tokenizer_without_change_in_init.additional_special_tokens   )
                 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=__SCREAMING_SNAKE_CASE   )]
                 a__		=			tokenizer_class.from_pretrained(
                     __SCREAMING_SNAKE_CASE     ,				additional_special_tokens=__SCREAMING_SNAKE_CASE     ,				)

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







         """simple docstring"""



         a__		=			self.perceiver_tokenizer
         self.assertEqual(tokenizer.decode([1_7_8]   )     ,				'�'   )



     def 			lowercase__  (							self   )		->     str:







         """simple docstring"""



         pass



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







         """simple docstring"""



         pass



     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         pass



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







         """simple docstring"""



         pass




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







         """simple docstring"""



         a__		=			self.get_tokenizers(fast=__SCREAMING_SNAKE_CASE     ,				do_lower_case=__SCREAMING_SNAKE_CASE   )
         for tokenizer in tokenizers:
             with self.subTest(f'{tokenizer.__class__.__name__}'   ):
                 a__		=			['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
                 a__		=			tokenizer.convert_tokens_to_string(__SCREAMING_SNAKE_CASE   )

                 self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )


