



import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		def __init__(self		,__lowerCamelCase = "▁"		,__lowerCamelCase = True		,__lowerCamelCase = "<unk>"		,__lowerCamelCase = "</s>"		,__lowerCamelCase = "<pad>"		,)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					{
						    '''pad''': {'''id''': 0, '''token''': pad_token},
						    '''eos''': {'''id''': 1, '''token''': eos_token},
						    '''unk''': {'''id''': 2, '''token''': unk_token},
						}

						lowerCAmelCase__					:    str												=					[None] * len(self.special_tokens			)
						for token_dict in self.special_tokens.values():
										lowerCAmelCase__					:    List[str]												=					token_dict['''token''']

						lowerCAmelCase__					:    str												=					Tokenizer(Unigram()			)

						lowerCAmelCase__					:    Any												=					normalizers.Sequence(
						    [
						        normalizers.Nmt(),
						        normalizers.NFKC(),
						        normalizers.Replace(Regex(''' {2,}'''			)		,''' '''			),
						        normalizers.Lowercase(),
						    ]			)
						lowerCAmelCase__					:    List[Any]												=					pre_tokenizers.Sequence(
						    [
						        pre_tokenizers.Metaspace(replacement=__lowerCamelCase		,add_prefix_space=__lowerCamelCase			),
						        pre_tokenizers.Digits(individual_digits=__lowerCamelCase			),
						        pre_tokenizers.Punctuation(),
						    ]			)
						lowerCAmelCase__					:    List[str]												=					decoders.Metaspace(replacement=__lowerCamelCase		,add_prefix_space=__lowerCamelCase			)

						lowerCAmelCase__					:    List[Any]												=					TemplateProcessing(
						    single=f"""$A {self.special_tokens['eos']['token']}"""		,special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])]		,)

						lowerCAmelCase__					:    Any												=					{
						    '''model''': '''SentencePieceUnigram''',
						    '''replacement''': replacement,
						    '''add_prefix_space''': add_prefix_space,
						}

						super().__init__(__lowerCamelCase		,__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase = 80_00		,__lowerCamelCase = True		,)					->      List[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					trainers.UnigramTrainer(
						    vocab_size=__lowerCamelCase		,special_tokens=self.special_tokens_list		,show_progress=__lowerCamelCase		,)

						if isinstance(__lowerCamelCase		,__lowerCamelCase			):
										lowerCAmelCase__					:    int												=					[files]
						self._tokenizer.train(__lowerCamelCase		,trainer=__lowerCamelCase			)

						self.add_unk_id()



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase = 80_00		,__lowerCamelCase = True		,)					->      Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					trainers.UnigramTrainer(
						    vocab_size=__lowerCamelCase		,special_tokens=self.special_tokens_list		,show_progress=__lowerCamelCase		,)

						self._tokenizer.train_from_iterator(__lowerCamelCase		,trainer=__lowerCamelCase			)

						self.add_unk_id()



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					json.loads(self._tokenizer.to_str()			)

						lowerCAmelCase__					:    int												=					self.special_tokens['''unk''']['''id''']

						lowerCAmelCase__					:    Optional[Any]												=					Tokenizer.from_str(json.dumps(__lowerCamelCase			)			)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__snake_case    :		str  ={
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		Tuple  =['ConditionalDetrFeatureExtractor']
							__snake_case    :		Union[str, Any]  =['ConditionalDetrImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		List[str]  =[
							    'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ConditionalDetrForObjectDetection',
							    'ConditionalDetrForSegmentation',
							    'ConditionalDetrModel',
							    'ConditionalDetrPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_conditional_detr import (
							    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    ConditionalDetrConfig,
							    ConditionalDetrOnnxConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
														from .image_processing_conditional_detr import ConditionalDetrImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_conditional_detr import (
														    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ConditionalDetrForObjectDetection,
														    ConditionalDetrForSegmentation,
														    ConditionalDetrModel,
														    ConditionalDetrPreTrainedModel,
														)

else:
							import sys

							__snake_case    :		str  =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

