
"""simple docstring"""



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__				(						UpperCamelCase__	):







     def __init__(							self			:			Optional[Any]    ,      _lowerCamelCase			:			str = "▁"    ,      _lowerCamelCase			:			bool = True    ,      _lowerCamelCase			:			Union[str, AddedToken] = "<unk>"    ,      _lowerCamelCase			:			Union[str, AddedToken] = "</s>"    ,      _lowerCamelCase			:			Union[str, AddedToken] = "<pad>"    ,      ):
       _snake_case					     =       {
           '''pad''': {'''id''': 0, '''token''': pad_token},
           '''eos''': {'''id''': 1, '''token''': eos_token},
           '''unk''': {'''id''': 2, '''token''': unk_token},
       }

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

       _snake_case					     =       Tokenizer(Unigram()			)

       _snake_case					     =       normalizers.Sequence(
           [
               normalizers.Nmt(),
               normalizers.NFKC(),
               normalizers.Replace(Regex(''' {2,}'''			)    ,      ''' '''			),
               normalizers.Lowercase(),
           ]			)
       _snake_case					     =       pre_tokenizers.Sequence(
           [
               pre_tokenizers.Metaspace(replacement=UpperCamelCase_    ,      add_prefix_space=UpperCamelCase_			),
               pre_tokenizers.Digits(individual_digits=UpperCamelCase_			),
               pre_tokenizers.Punctuation(),
           ]			)
       _snake_case					     =       decoders.Metaspace(replacement=UpperCamelCase_    ,      add_prefix_space=UpperCamelCase_			)

       _snake_case					     =       TemplateProcessing(
           single=f'''$A {self.special_tokens['eos']['token']}'''    ,      special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])]    ,      )

       _snake_case					     =       {
           '''model''': '''SentencePieceUnigram''',
           '''replacement''': replacement,
           '''add_prefix_space''': add_prefix_space,
       }

       super().__init__(UpperCamelCase_    ,      UpperCamelCase_			)







     def      lowercase	(							self			:			Tuple    ,      _lowerCamelCase			:			Union[str, List[str]]    ,      _lowerCamelCase			:			int = 8000    ,      _lowerCamelCase			:			bool = True    ,      ):
       _snake_case					     =       trainers.UnigramTrainer(
           vocab_size=UpperCamelCase_    ,      special_tokens=self.special_tokens_list    ,      show_progress=UpperCamelCase_    ,      )

       if isinstance(UpperCamelCase_    ,      UpperCamelCase_			):
         _snake_case					     =       [files]
       self._tokenizer.train(UpperCamelCase_    ,      trainer=UpperCamelCase_			)

       self.add_unk_id()







     def      lowercase	(							self			:			List[Any]    ,      _lowerCamelCase			:			Union[Iterator[str], Iterator[Iterator[str]]]    ,      _lowerCamelCase			:			int = 8000    ,      _lowerCamelCase			:			bool = True    ,      ):
       _snake_case					     =       trainers.UnigramTrainer(
           vocab_size=UpperCamelCase_    ,      special_tokens=self.special_tokens_list    ,      show_progress=UpperCamelCase_    ,      )

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

       self.add_unk_id()







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       json.loads(self._tokenizer.to_str()			)

       _snake_case					     =       self.special_tokens['''unk''']['''id''']

       _snake_case					     =       Tokenizer.from_str(json.dumps(UpperCamelCase_			)			)







from typing import TYPE_CHECKING

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


lowerCAmelCase       =							{'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase       =							['DPTFeatureExtractor']
			lowerCAmelCase       =							['DPTImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase       =							[
			    'DPT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'DPTForDepthEstimation',
			    'DPTForSemanticSegmentation',
			    'DPTModel',
			    'DPTPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_dpt import DPTFeatureExtractor
						from .image_processing_dpt import DPTImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_dpt import (
						    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DPTForDepthEstimation,
						    DPTForSemanticSegmentation,
						    DPTModel,
						    DPTPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase       =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


