
import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__A				=logging.get_logger(__name__)

__A				={'''vocab_file''': '''vocab.json'''}

__A				={
    '''vocab_file''': {
        '''mgp-str''': '''https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json''',
    }
}

__A				={'''mgp-str''': 2_7}




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = VOCAB_FILES_NAMES
	lowerCAmelCase__         = PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__         = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



	def __init__(    self						,  lowercase						,  lowercase="[GO]"						,  lowercase="[GO]"						,  lowercase="[s]"						,  lowercase="[GO]"						,  **lowercase )				->       List[str]:
					super().__init__(
					    unk_token=lowercase						,  bos_token=lowercase						,  eos_token=lowercase						,  pad_token=lowercase						,  **lowercase						,  )

					with open(lowercase						,  encoding="utf-8" ) as vocab_handle:
									lowerCamelCase_						=			json.load(lowercase )
					lowerCamelCase_						=			{v: k for k, v in self.vocab.items()}



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					return len(self.vocab )



	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					return dict(self.vocab						,  **self.added_tokens_encoder )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       Optional[Any]:
					lowerCamelCase_						=			[]
					for s in text:
									char_tokens.extend(lowercase )
					return char_tokens



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       Union[str, Any]:
					return self.vocab.get(lowercase						,  self.vocab.get(self.unk_token ) )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       Tuple:
					return self.decoder.get(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None )				->       Tuple[str]:
					if not os.path.isdir(lowercase ):
									logger.error("Vocabulary path ({}) should be a directory".format(lowercase ) )
									return
					lowerCamelCase_						=			os.path.join(
					    lowercase						,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

					with open(lowercase						,  "w"						,  encoding="utf-8" ) as f:
									f.write(json.dumps(self.vocab						,  indent=2						,  sort_keys=lowercase						,  ensure_ascii=lowercase ) + "\n" )

					return (vocab_file,)




from typing import TYPE_CHECKING

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


__A				={
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''BloomTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''BloomForCausalLM''',
			    '''BloomModel''',
			    '''BloomPreTrainedModel''',
			    '''BloomForSequenceClassification''',
			    '''BloomForTokenClassification''',
			    '''BloomForQuestionAnswering''',
			]

if TYPE_CHECKING:
			from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bloom_fast import BloomTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bloom import (
						    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BloomForCausalLM,
						    BloomForQuestionAnswering,
						    BloomForSequenceClassification,
						    BloomForTokenClassification,
						    BloomModel,
						    BloomPreTrainedModel,
						)

else:
			import sys

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


