
'''simple docstring'''

import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
UpperCAmelCase__ :			int											=		logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")

UpperCAmelCase__ :			List[Any]											=		list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
UpperCAmelCase__ :			str											=		tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class  A  :
	snake_case__   :Optional[str]   =  field(
	    default='cifar10'	,   metadata={'help': 'Name of a dataset from the datasets package'}     )
	snake_case__   :Optional[str]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}     )
	snake_case__   :Optional[str]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'}	,   )
	snake_case__   :Optional[str]   =  field(default=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'A folder containing the training data.'}     )
	snake_case__   :Optional[str]   =  field(default=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'A folder containing the validation data.'}     )
	snake_case__   :Optional[float]   =  field(
	    default=0.15	,   metadata={'help': 'Percent to split off of train for validation.'}     )
	snake_case__   :int   =  field(default=32	,   metadata={'help': 'The size of the square patches to use for masking.'}     )
	snake_case__   :float   =  field(
	    default=0.6	,   metadata={'help': 'Percentage of patches to mask.'}	,   )
	snake_case__   :Optional[int]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={
	        'help': (
	            'For debugging purposes or quicker training, truncate the number of training examples to this '
	            'value if set.'
	        )
	    }	,   )
	snake_case__   :Optional[int]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={
	        'help': (
	            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
	            'value if set.'
	        )
	    }	,   )





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""simple docstring"""

				lowerCAmelCase__										=				{}
				if self.train_dir is not None:
							lowerCAmelCase__										=				self.train_dir
				if self.validation_dir is not None:
							lowerCAmelCase__										=				self.validation_dir
				lowerCAmelCase__										=				data_files if data_files else None






@dataclass
class  A  :
	snake_case__   :str   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={
	        'help': (
	            'The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '
	            'checkpoint identifier on the hub. '
	            'Don\'t set if you want to train a model from scratch.'
	        )
	    }	,   )
	snake_case__   :Optional[str]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(SCREAMING_SNAKE_CASE__     )}	,   )
	snake_case__   :Optional[str]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'Pretrained config name or path if not the same as model_name'}     )
	snake_case__   :Optional[str]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={
	        'help': (
	            'Override some existing default config settings when a model is trained from scratch. Example: '
	            'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
	        )
	    }	,   )
	snake_case__   :Optional[str]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'}	,   )
	snake_case__   :str   =  field(
	    default='main'	,   metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}	,   )
	snake_case__   :str   =  field(default=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'Name or path of preprocessor config.'}     )
	snake_case__   :bool   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={
	        'help': (
	            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
	            'with private models).'
	        )
	    }	,   )
	snake_case__   :Optional[int]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={
	        'help': (
	            'The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'
	        )
	    }	,   )
	snake_case__   :Optional[int]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={
	        'help': (
	            'The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'
	        )
	    }	,   )
	snake_case__   :Optional[int]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'Stride to use for the encoder.'}	,   )






class  A  :





	def __init__(       self      :							Any   ,				__magic_name__      :							List[Any]=192   ,				__magic_name__      :							int=32   ,				__magic_name__      :							Dict=4   ,				__magic_name__      :							List[Any]=0.6				):


				"""simple docstring"""

				lowerCAmelCase__										=				input_size
				lowerCAmelCase__										=				mask_patch_size
				lowerCAmelCase__										=				model_patch_size
				lowerCAmelCase__										=				mask_ratio

				if self.input_size % self.mask_patch_size != 0:
							raise ValueError("Input size must be divisible by mask patch size"				)
				if self.mask_patch_size % self.model_patch_size != 0:
							raise ValueError("Mask patch size must be divisible by model patch size"				)

				lowerCAmelCase__										=				self.input_size // self.mask_patch_size
				lowerCAmelCase__										=				self.mask_patch_size // self.model_patch_size

				lowerCAmelCase__										=				self.rand_size**2
				lowerCAmelCase__										=				int(np.ceil(self.token_count * self.mask_ratio				)				)







	def __call__(       self      :							Optional[int]				):


				"""simple docstring"""

				lowerCAmelCase__										=				np.random.permutation(self.token_count				)[: self.mask_count]
				lowerCAmelCase__										=				np.zeros(self.token_count   ,				dtype=__magic_name__				)
				lowerCAmelCase__										=				1

				lowerCAmelCase__										=				mask.reshape((self.rand_size, self.rand_size)				)
				lowerCAmelCase__										=				mask.repeat(self.scale   ,				axis=0				).repeat(self.scale   ,				axis=1				)

				return torch.tensor(mask.flatten()				)







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







			'''simple docstring'''



			lowerCAmelCase__										=				torch.stack([example["pixel_values"] for example in examples]   )
			lowerCAmelCase__										=				torch.stack([example["mask"] for example in examples]   )
			return {"pixel_values": pixel_values, "bool_masked_pos": mask}







def 	A  (   )       ->							Tuple:







			'''simple docstring'''



			lowerCAmelCase__										=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
			if len(sys.argv   ) == 2 and sys.argv[1].endswith(".json"   ):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						lowerCAmelCase__						,lowerCAmelCase__						,lowerCAmelCase__										=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
			else:
						lowerCAmelCase__						,lowerCAmelCase__						,lowerCAmelCase__										=				parser.parse_args_into_dataclasses()

			# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
			# information sent is the one passed as arguments along with your Python/PyTorch versions.
			send_example_telemetry("run_mim"    ,						UpperCamelCase_    ,						UpperCamelCase_   )

			# Setup logging
			logging.basicConfig(
			    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"    ,						datefmt="%m/%d/%Y %H:%M:%S"    ,						handlers=[logging.StreamHandler(sys.stdout   )]    ,						)

			if training_args.should_log:
						# The default of training_args.log_level is passive, so we set log level at info here to have that default.
						transformers.utils.logging.set_verbosity_info()

			lowerCAmelCase__										=				training_args.get_process_log_level()
			logger.setLevel(UpperCamelCase_   )
			transformers.utils.logging.set_verbosity(UpperCamelCase_   )
			transformers.utils.logging.enable_default_handler()
			transformers.utils.logging.enable_explicit_format()

			# Log on each process the small summary:
			logger.warning(
			    F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
			    + F"""distributed training: {bool(training_args.local_rank != -1   )}, 16-bits training: {training_args.fpaa}"""   )
			logger.info(F"""Training/evaluation parameters {training_args}"""   )

			# Detecting last checkpoint.
			lowerCAmelCase__										=				None
			if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
						lowerCAmelCase__										=				get_last_checkpoint(training_args.output_dir   )
						if last_checkpoint is None and len(os.listdir(training_args.output_dir   )   ) > 0:
									raise ValueError(
									    F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
									    "Use --overwrite_output_dir to overcome."   )
						elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
									logger.info(
									    F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
									    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."   )

    # Initialize our dataset.
			lowerCAmelCase__										=				load_dataset(
			    data_args.dataset_name    ,						data_args.dataset_config_name    ,						data_files=data_args.data_files    ,						cache_dir=model_args.cache_dir    ,						use_auth_token=True if model_args.use_auth_token else None    ,						)

			# If we don't have a validation split, split off a percentage of train as validation.
			lowerCAmelCase__										=				None if "validation" in ds.keys() else data_args.train_val_split
			if isinstance(data_args.train_val_split    ,						UpperCamelCase_   ) and data_args.train_val_split > 0.0:
						lowerCAmelCase__										=				ds["train"].train_test_split(data_args.train_val_split   )
						lowerCAmelCase__										=				split["train"]
						lowerCAmelCase__										=				split["test"]

			# Create config
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.
			lowerCAmelCase__										=				{
			    "cache_dir": model_args.cache_dir,
			    "revision": model_args.model_revision,
			    "use_auth_token": True if model_args.use_auth_token else None,
			}
			if model_args.config_name_or_path:
						lowerCAmelCase__										=				AutoConfig.from_pretrained(model_args.config_name_or_path    ,						**UpperCamelCase_   )
			elif model_args.model_name_or_path:
						lowerCAmelCase__										=				AutoConfig.from_pretrained(model_args.model_name_or_path    ,						**UpperCamelCase_   )
			else:
						lowerCAmelCase__										=				CONFIG_MAPPING[model_args.model_type]()
						logger.warning("You are instantiating a new config instance from scratch."   )
						if model_args.config_overrides is not None:
									logger.info(F"""Overriding config: {model_args.config_overrides}"""   )
									config.update_from_string(model_args.config_overrides   )
									logger.info(F"""New config: {config}"""   )

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
			if hasattr(UpperCamelCase_    ,						"decoder_type"   ):
						lowerCAmelCase__										=				"simmim"

			# adapt config
			lowerCAmelCase__										=				model_args.image_size if model_args.image_size is not None else config.image_size
			lowerCAmelCase__										=				model_args.patch_size if model_args.patch_size is not None else config.patch_size
			lowerCAmelCase__										=				(
			    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
			)

			config.update(
			    {
			        "image_size": model_args.image_size,
			        "patch_size": model_args.patch_size,
			        "encoder_stride": model_args.encoder_stride,
			    }   )

			# create image processor
			if model_args.image_processor_name:
						lowerCAmelCase__										=				AutoImageProcessor.from_pretrained(model_args.image_processor_name    ,						**UpperCamelCase_   )
			elif model_args.model_name_or_path:
						lowerCAmelCase__										=				AutoImageProcessor.from_pretrained(model_args.model_name_or_path    ,						**UpperCamelCase_   )
			else:
						lowerCAmelCase__										=				{
						    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
						}
						lowerCAmelCase__										=				IMAGE_PROCESSOR_TYPES[model_args.model_type]()

			# create model
			if model_args.model_name_or_path:
						lowerCAmelCase__										=				AutoModelForMaskedImageModeling.from_pretrained(
						    model_args.model_name_or_path    ,						from_tf=bool(".ckpt" in model_args.model_name_or_path   )    ,						config=UpperCamelCase_    ,						cache_dir=model_args.cache_dir    ,						revision=model_args.model_revision    ,						use_auth_token=True if model_args.use_auth_token else None    ,						)
			else:
						logger.info("Training new model from scratch"   )
						lowerCAmelCase__										=				AutoModelForMaskedImageModeling.from_config(UpperCamelCase_   )

			if training_args.do_train:
						lowerCAmelCase__										=				ds["train"].column_names
			else:
						lowerCAmelCase__										=				ds["validation"].column_names

			if data_args.image_column_name is not None:
						lowerCAmelCase__										=				data_args.image_column_name
			elif "image" in column_names:
						lowerCAmelCase__										=				"image"
			elif "img" in column_names:
						lowerCAmelCase__										=				"img"
			else:
						lowerCAmelCase__										=				column_names[0]

			# transformations as done in original SimMIM paper
			# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
			lowerCAmelCase__										=				Compose(
			    [
			        Lambda(lambda UpperCamelCase_   : img.convert("RGB"   ) if img.mode != "RGB" else img   ),
			        RandomResizedCrop(model_args.image_size    ,						scale=(0.67, 1.0)    ,						ratio=(3.0 / 4.0, 4.0 / 3.0)   ),
			        RandomHorizontalFlip(),
			        ToTensor(),
			        Normalize(mean=image_processor.image_mean    ,						std=image_processor.image_std   ),
			    ]   )

			# create mask generator
			lowerCAmelCase__										=				MaskGenerator(
			    input_size=model_args.image_size    ,						mask_patch_size=data_args.mask_patch_size    ,						model_patch_size=model_args.patch_size    ,						mask_ratio=data_args.mask_ratio    ,						)

			def preprocess_images(UpperCamelCase_       :  List[Any]   ):
						lowerCAmelCase__										=				[transforms(UpperCamelCase_   ) for image in examples[image_column_name]]
						lowerCAmelCase__										=				[mask_generator() for i in range(len(examples[image_column_name]   )   )]

						return examples

			if training_args.do_train:
						if "train" not in ds:
									raise ValueError("--do_train requires a train dataset"   )
						if data_args.max_train_samples is not None:
									lowerCAmelCase__										=				ds["train"].shuffle(seed=training_args.seed   ).select(range(data_args.max_train_samples   )   )
						# Set the training transforms
						ds["train"].set_transform(UpperCamelCase_   )

			if training_args.do_eval:
						if "validation" not in ds:
									raise ValueError("--do_eval requires a validation dataset"   )
						if data_args.max_eval_samples is not None:
									lowerCAmelCase__										=				(
									    ds["validation"].shuffle(seed=training_args.seed   ).select(range(data_args.max_eval_samples   )   )
									)
						# Set the validation transforms
						ds["validation"].set_transform(UpperCamelCase_   )

			# Initialize our trainer
			lowerCAmelCase__										=				Trainer(
			    model=UpperCamelCase_    ,						args=UpperCamelCase_    ,						train_dataset=ds["train"] if training_args.do_train else None    ,						eval_dataset=ds["validation"] if training_args.do_eval else None    ,						tokenizer=UpperCamelCase_    ,						data_collator=UpperCamelCase_    ,						)

			# Training
			if training_args.do_train:
						lowerCAmelCase__										=				None
						if training_args.resume_from_checkpoint is not None:
									lowerCAmelCase__										=				training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									lowerCAmelCase__										=				last_checkpoint
						lowerCAmelCase__										=				trainer.train(resume_from_checkpoint=UpperCamelCase_   )
						trainer.save_model()
						trainer.log_metrics("train"    ,						train_result.metrics   )
						trainer.save_metrics("train"    ,						train_result.metrics   )
						trainer.save_state()

			# Evaluation
			if training_args.do_eval:
						lowerCAmelCase__										=				trainer.evaluate()
						trainer.log_metrics("eval"    ,						UpperCamelCase_   )
						trainer.save_metrics("eval"    ,						UpperCamelCase_   )

			# Write model card and (optionally) push to hub
			lowerCAmelCase__										=				{
			    "finetuned_from": model_args.model_name_or_path,
			    "tasks": "masked-image-modeling",
			    "dataset": data_args.dataset_name,
			    "tags": ["masked-image-modeling"],
			}
			if training_args.push_to_hub:
						trainer.push_to_hub(**UpperCamelCase_   )
			else:
						trainer.create_model_card(**UpperCamelCase_   )


if __name__ == "__main__":
		main()













"""simple docstring"""







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      UpperCamelCase		(__snake_case						,						unittest.TestCase     ):
						_SCREAMING_SNAKE_CASE  : List[Any]												=		LongformerTokenizer
						_SCREAMING_SNAKE_CASE  : int												=		True
						_SCREAMING_SNAKE_CASE  : List[str]												=		LongformerTokenizerFast
						_SCREAMING_SNAKE_CASE  : Dict												=		True






						def      __snake_case      (		self  :Dict				)      ->Tuple:
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										lowercase    :      Dict       = [
										    """l""",
										    """o""",
										    """w""",
										    """e""",
										    """r""",
										    """s""",
										    """t""",
										    """i""",
										    """d""",
										    """n""",
										    """\u0120""",
										    """\u0120l""",
										    """\u0120n""",
										    """\u0120lo""",
										    """\u0120low""",
										    """er""",
										    """\u0120lowest""",
										    """\u0120newer""",
										    """\u0120wider""",
										    """<unk>""",
										]
										lowercase    :      Tuple       = dict(zip(__magic_name__			, range(len(__magic_name__				)				)				)				)
										lowercase    :      Tuple       = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
										lowercase    :      Dict       = {"""unk_token""": """<unk>"""}

										lowercase    :      Dict       = os.path.join(self.tmpdirname			, VOCAB_FILES_NAMES["""vocab_file"""]				)
										lowercase    :      Optional[int]       = 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(__magic_name__				) + """\n"""				)
										with open(self.merges_file			, """w"""			, encoding="""utf-8"""				) as fp:
														fp.write("""\n""".join(__magic_name__				)				)






						def      __snake_case      (		self  :List[Any]			, **__magic_name__  :Any				)      ->Tuple:
										kwargs.update(self.special_tokens_map				)
										return self.tokenizer_class.from_pretrained(self.tmpdirname			, **__magic_name__				)






						def      __snake_case      (		self  :Optional[Any]			, **__magic_name__  :Optional[Any]				)      ->Dict:
										kwargs.update(self.special_tokens_map				)
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			, **__magic_name__				)






						def      __snake_case      (		self  :Tuple			, __magic_name__  :Dict				)      ->str:
										lowercase    :      List[str]       = """lower newer"""
										lowercase    :      Any       = """lower newer"""
										return input_text, output_text






						def      __snake_case      (		self  :Tuple				)      ->Union[str, Any]:
										lowercase    :      Union[str, Any]       = self.tokenizer_class(self.vocab_file			, self.merges_file			, **self.special_tokens_map				)
										lowercase    :      List[str]       = """lower newer"""
										lowercase    :      Tuple       = ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
										lowercase    :      Optional[int]       = tokenizer.tokenize(__magic_name__				)  # , add_prefix_space=True)
										self.assertListEqual(__magic_name__			, __magic_name__				)

										lowercase    :      str       = tokens + [tokenizer.unk_token]
										lowercase    :      Any       = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__				)			, __magic_name__				)






						def      __snake_case      (		self  :Any				)      ->str:
										lowercase    :      int       = self.get_tokenizer()

										self.assertListEqual(tokenizer.encode("""Hello world!"""			, add_special_tokens=__magic_name__				)			, [0, 31_414, 232, 328, 2]				)
										self.assertListEqual(
										    tokenizer.encode("""Hello world! cécé herlolip 418"""			, add_special_tokens=__magic_name__				)			, [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]			, )






						@slow
						def      __snake_case      (		self  :Tuple				)      ->Union[str, Any]:
										lowercase    :      Any       = self.tokenizer_class.from_pretrained("""allenai/longformer-base-4096"""				)

										lowercase    :      Tuple       = tokenizer.encode("""sequence builders"""			, add_special_tokens=__magic_name__				)
										lowercase    :      List[Any]       = tokenizer.encode("""multi-sequence build"""			, add_special_tokens=__magic_name__				)

										lowercase    :      Union[str, Any]       = tokenizer.encode(
										    """sequence builders"""			, add_special_tokens=__magic_name__			, add_prefix_space=__magic_name__				)
										lowercase    :      List[Any]       = tokenizer.encode(
										    """sequence builders"""			, """multi-sequence build"""			, add_special_tokens=__magic_name__			, add_prefix_space=__magic_name__				)

										lowercase    :      int       = tokenizer.build_inputs_with_special_tokens(__magic_name__				)
										lowercase    :      List[Any]       = tokenizer.build_inputs_with_special_tokens(__magic_name__			, __magic_name__				)

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode






						def      __snake_case      (		self  :Optional[Any]				)      ->int:
										lowercase    :      Optional[int]       = self.get_tokenizer()

										lowercase    :      Tuple       = """Encode this sequence."""
										lowercase    :      Dict       = tokenizer.byte_encoder[""" """.encode("""utf-8"""				)[0]]

										# Testing encoder arguments
										lowercase    :      List[Any]       = tokenizer.encode(__magic_name__			, add_special_tokens=__magic_name__			, add_prefix_space=__magic_name__				)
										lowercase    :      int       = tokenizer.convert_ids_to_tokens(encoded[0]				)[0]
										self.assertNotEqual(__magic_name__			, __magic_name__				)

										lowercase    :      Optional[Any]       = tokenizer.encode(__magic_name__			, add_special_tokens=__magic_name__			, add_prefix_space=__magic_name__				)
										lowercase    :      Tuple       = tokenizer.convert_ids_to_tokens(encoded[0]				)[0]
										self.assertEqual(__magic_name__			, __magic_name__				)

										tokenizer.add_special_tokens({"""bos_token""": """<s>"""}				)
										lowercase    :      Union[str, Any]       = tokenizer.encode(__magic_name__			, add_special_tokens=__magic_name__				)
										lowercase    :      List[str]       = tokenizer.convert_ids_to_tokens(encoded[1]				)[0]
										self.assertNotEqual(__magic_name__			, __magic_name__				)

										# Testing spaces after special tokens
										lowercase    :      Any       = """<mask>"""
										tokenizer.add_special_tokens(
										    {"""mask_token""": AddedToken(__magic_name__			, lstrip=__magic_name__			, rstrip=__magic_name__				)}				)  # mask token has a left space
										lowercase    :      Any       = tokenizer.convert_tokens_to_ids(__magic_name__				)

										lowercase    :      Any       = """Encode <mask> sequence"""
										lowercase    :      str       = """Encode <mask>sequence"""

										lowercase    :      Optional[int]       = tokenizer.encode(__magic_name__				)
										lowercase    :      List[str]       = encoded.index(__magic_name__				)
										lowercase    :      Tuple       = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]				)[0]
										self.assertEqual(__magic_name__			, __magic_name__				)

										lowercase    :      Tuple       = tokenizer.encode(__magic_name__				)
										lowercase    :      List[str]       = encoded.index(__magic_name__				)
										lowercase    :      Optional[Any]       = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]				)[0]
										self.assertNotEqual(__magic_name__			, __magic_name__				)






						def      __snake_case      (		self  :Any				)      ->int:
										pass






						def      __snake_case      (		self  :List[Any]				)      ->str:
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
																		lowercase    :      int       = self.rust_tokenizer_class.from_pretrained(__magic_name__			, **__magic_name__				)
																		lowercase    :      List[str]       = self.tokenizer_class.from_pretrained(__magic_name__			, **__magic_name__				)
																		lowercase    :      Optional[int]       = """A, <mask> AllenNLP sentence."""
																		lowercase    :      Any       = tokenizer_r.encode_plus(__magic_name__			, add_special_tokens=__magic_name__			, return_token_type_ids=__magic_name__				)
																		lowercase    :      str       = tokenizer_p.encode_plus(__magic_name__			, add_special_tokens=__magic_name__			, return_token_type_ids=__magic_name__				)

																		# 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"""]				)			, )

																		lowercase    :      Union[str, Any]       = tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]				)
																		lowercase    :      Dict       = 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, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]				)
																		self.assertSequenceEqual(tokens_r["""input_ids"""]			, [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]				)

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






						def      __snake_case      (		self  :List[str]				)      ->Tuple:
										for trim_offsets, add_prefix_space in itertools.product([True, False]			, repeat=2				):
														lowercase    :      str       = self.rust_tokenizer_class.from_pretrained(
														    self.tmpdirname			, use_fast=__magic_name__			, add_prefix_space=__magic_name__			, trim_offsets=__magic_name__				)

														lowercase    :      str       = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()				)
														lowercase    :      Optional[Any]       = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()				)

														self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]			, __magic_name__				)

														self.assertEqual(post_processor_state["""add_prefix_space"""]			, __magic_name__				)
														self.assertEqual(post_processor_state["""trim_offsets"""]			, __magic_name__				)






						def      __snake_case      (		self  :Dict				)      ->List[str]:
										# 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})"""				):
																		lowercase    :      Optional[Any]       = """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
																		lowercase    :      Optional[Any]       = f"""{text_of_1_token} {text_of_1_token}"""

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

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

																		lowercase    :      Tuple       = self.rust_tokenizer_class.from_pretrained(
																		    __magic_name__			, use_fast=__magic_name__			, add_prefix_space=__magic_name__			, trim_offsets=__magic_name__				)
																		lowercase    :      List[str]       = tokenizer_r(__magic_name__			, return_offsets_mapping=__magic_name__			, add_special_tokens=__magic_name__				)
																		self.assertEqual(encoding.offset_mapping[0]			, (0, len(__magic_name__				))				)
																		self.assertEqual(
																		    encoding.offset_mapping[1]			, (len(__magic_name__				), len(__magic_name__				) + 1 + len(__magic_name__				))			, )

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

																		lowercase    :      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)),
																		# )

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

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

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