
import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
					import jieba

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


_UpperCAmelCase					: List[Any]      =       logging.get_logger(__name__)

_UpperCAmelCase					: int      =       {"vocab_file": "vocab.txt"}

_UpperCAmelCase					: str      =       {
    "vocab_file": {
        "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt",
    },
}

_UpperCAmelCase					: Optional[Any]      =       {
    "openbmb/cpm-ant-10b": 1_024,
}


def       A       (					lowercase )   ->  Dict:
		'''simple docstring'''

		UpperCamelCase		=     collections.OrderedDict()
		with open(lowercase    ,       'r'    ,       encoding='utf-8' ) as reader:
				UpperCamelCase		=     reader.readlines()
		for index, token in enumerate(lowercase ):
				UpperCamelCase		=     token.rstrip('\n' )
				UpperCamelCase		=     index
		return vocab


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __init__( self				,      A_				,      A_="<unk>"				,      A_=200       )  ->					Dict:
				"""simple docstring"""
				UpperCamelCase		=     vocab
				UpperCamelCase		=     unk_token
				UpperCamelCase		=     max_input_chars_per_word



		def __UpperCamelCase ( self				,      A_       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     list(A_       )
				if len(A_       ) > self.max_input_chars_per_word:
						return [self.unk_token]

				UpperCamelCase		=     0
				UpperCamelCase		=     []
				while start < len(A_       ):
						UpperCamelCase		=     len(A_       )
						UpperCamelCase		=     None
						while start < end:
								UpperCamelCase		=     ''.join(chars[start:end]       )
								if substr in self.vocab:
										UpperCamelCase		=     substr
										break
								end -= 1
						if cur_substr is None:
								sub_tokens.append(self.unk_token       )
								start += 1
						else:
								sub_tokens.append(A_       )
								UpperCamelCase		=     end

				return sub_tokens






class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
		__lowercase       :       List[str]	     =				VOCAB_FILES_NAMES
		__lowercase       :       Dict	     =				PRETRAINED_VOCAB_FILES_MAP
		__lowercase       :       int	     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase       :       Any	     =				["input_ids", "attention_mask"]
		__lowercase       :       Tuple	     =				False



		def __init__( self				,      A_				,      A_="<d>"				,      A_="</d>"				,      A_="<s>"				,      A_="</s>"				,      A_="<pad>"				,      A_="<unk>"				,      A_="</n>"				,      A_="</_>"				,      A_="left"				,      **A_				,      )  ->					Tuple:
				"""simple docstring"""
				requires_backends(self				,      ['jieba']       )
				super().__init__(
				    bod_token=A_				,      eod_token=A_				,      bos_token=A_				,      eos_token=A_				,      pad_token=A_				,      unk_token=A_				,      line_token=A_				,      space_token=A_				,      padding_side=A_				,      **A_				,      )
				UpperCamelCase		=     bod_token
				UpperCamelCase		=     eod_token
				UpperCamelCase		=     load_vocab(A_       )
				UpperCamelCase		=     self.encoder[space_token]
				UpperCamelCase		=     self.encoder[line_token]

				del self.encoder[space_token]
				del self.encoder[line_token]

				UpperCamelCase		=     collections.OrderedDict(sorted(self.encoder.items()				,      key=lambda A_       : x[1]       )       )
				UpperCamelCase		=     {v: k for k, v in self.encoder.items()}

				UpperCamelCase		=     WordpieceTokenizer(vocab=self.encoder				,      unk_token=self.unk_token       )



		@property
		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				return self.encoder[self.bod_token]



		@property
		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				return self.encoder[self.eod_token]



		@property
		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				return self.encoder["\n"]



		@property
		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				return len(self.encoder       )



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				return dict(self.encoder				,      **self.added_tokens_encoder       )



		def __UpperCamelCase ( self				,      A_       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     []
				for x in jieba.cut(A_				,      cut_all=A_       ):
						output_tokens.extend(self.wordpiece_tokenizer.tokenize(A_       )       )
				return output_tokens



		def __UpperCamelCase ( self				,      A_				,      **A_       )  ->					Dict:
				"""simple docstring"""
				UpperCamelCase		=     [i for i in token_ids if i >= 0]
				UpperCamelCase		=     [
				    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
				]
				return super()._decode(A_				,      **A_       )



		def __UpperCamelCase ( self				,      A_       )  ->					int:
				"""simple docstring"""
				return token in self.encoder



		def __UpperCamelCase ( self				,      A_       )  ->					str:
				"""simple docstring"""
				return "".join(A_       )



		def __UpperCamelCase ( self				,      A_       )  ->					Tuple:
				"""simple docstring"""
				return self.encoder.get(A_				,      self.encoder.get(self.unk_token       )       )



		def __UpperCamelCase ( self				,      A_       )  ->					Optional[Any]:
				"""simple docstring"""
				return self.decoder.get(A_				,      self.unk_token       )



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					Tuple[str]:
				"""simple docstring"""
				if os.path.isdir(A_       ):
						UpperCamelCase		=     os.path.join(
						    A_				,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )
				else:
						UpperCamelCase		=     (filename_prefix + '-' if filename_prefix else '') + save_directory
				UpperCamelCase		=     0
				if " " in self.encoder:
						UpperCamelCase		=     self.encoder[' ']
						del self.encoder[" "]
				if "\n" in self.encoder:
						UpperCamelCase		=     self.encoder['\n']
						del self.encoder["\n"]
				UpperCamelCase		=     collections.OrderedDict(sorted(self.encoder.items()				,      key=lambda A_       : x[1]       )       )
				with open(A_				,      'w'				,      encoding='utf-8'       ) as writer:
						for token, token_index in self.encoder.items():
								if index != token_index:
										logger.warning(
										    F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
										    ' Please check that the vocabulary is not corrupted!'       )
										UpperCamelCase		=     token_index
								writer.write(token + '\n'       )
								index += 1
				return (vocab_file,)



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					List[int]:
				"""simple docstring"""
				if token_ids_a is None:
						return [self.bos_token_id] + token_ids_a
				return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a



		def __UpperCamelCase ( self				,      A_				,      A_ = None				,      A_ = False       )  ->					List[int]:
				"""simple docstring"""

				if already_has_special_tokens:
						return super().get_special_tokens_mask(
						    token_ids_a=A_				,      token_ids_a=A_				,      already_has_special_tokens=A_       )

				if token_ids_a is not None:
						return [1] + ([0] * len(A_       )) + [1] + ([0] * len(A_       ))
				return [1] + ([0] * len(A_       ))


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase					: Union[str, Any]      =       {
    "configuration_instructblip": [
        "INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "InstructBlipConfig",
        "InstructBlipQFormerConfig",
        "InstructBlipVisionConfig",
    ],
    "processing_instructblip": ["InstructBlipProcessor"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase					: List[Any]      =       [
					    "INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "InstructBlipQFormerModel",
					    "InstructBlipPreTrainedModel",
					    "InstructBlipForConditionalGeneration",
					    "InstructBlipVisionModel",
					]

if TYPE_CHECKING:
					from .configuration_instructblip import (
					    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    InstructBlipConfig,
					    InstructBlipQFormerConfig,
					    InstructBlipVisionConfig,
					)
					from .processing_instructblip import InstructBlipProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_instructblip import (
										    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    InstructBlipForConditionalGeneration,
										    InstructBlipPreTrainedModel,
										    InstructBlipQFormerModel,
										    InstructBlipVisionModel,
										)

else:
					import sys

					_UpperCAmelCase					: Union[str, Any]      =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
