
"""simple docstring"""





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
  from ...onnx.config import PatchingSpec
  from ...tokenization_utils_base import PreTrainedTokenizerBase


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

UpperCamelCase_					:						Tuple				= {
    '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096-finetuned-triviaqa''': (
        '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
    ),
    '''allenai/longformer-base-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
    '''allenai/longformer-large-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
}



class __lowerCAmelCase				(					SCREAMING_SNAKE_CASE__      ):




   """simple docstring"""







   snake_case	      =				"longformer"




   def __init__(				self						:					Optional[int]       ,   _snake_case						:					Any = 512       ,   _snake_case						:					Optional[Any] = 2       ,   _snake_case						:					int = 1       ,   _snake_case						:					Tuple = 0       ,   _snake_case						:					Dict = 2       ,   _snake_case						:					Any = 30_522       ,   _snake_case						:					List[str] = 768       ,   _snake_case						:					str = 12       ,   _snake_case						:					Optional[int] = 12       ,   _snake_case						:					Optional[Any] = 3_072       ,   _snake_case						:					int = "gelu"       ,   _snake_case						:					List[str] = 0.1       ,   _snake_case						:					List[Any] = 0.1       ,   _snake_case						:					str = 512       ,   _snake_case						:					Optional[int] = 2       ,   _snake_case						:					str = 0.0_2       ,   _snake_case						:					List[Any] = 1e-12       ,   _snake_case						:					List[Any] = False       ,   **_snake_case						:					str       ,   )      -> Optional[int]:







        """simple docstring"""
        super().__init__(pad_token_id=_lowercase       ,   **_lowercase	)

        A_				 =						attention_window
        A_				 =						sep_token_id
        A_				 =						bos_token_id
        A_				 =						eos_token_id
        A_				 =						vocab_size
        A_				 =						hidden_size
        A_				 =						num_hidden_layers
        A_				 =						num_attention_heads
        A_				 =						hidden_act
        A_				 =						intermediate_size
        A_				 =						hidden_dropout_prob
        A_				 =						attention_probs_dropout_prob
        A_				 =						max_position_embeddings
        A_				 =						type_vocab_size
        A_				 =						initializer_range
        A_				 =						layer_norm_eps
        A_				 =						onnx_export





class __lowerCAmelCase				(					SCREAMING_SNAKE_CASE__      ):




   """simple docstring"""







   def __init__(				self						:					str       ,   _snake_case						:					int       ,   _snake_case						:					List[str] = "default"       ,   _snake_case						:					Any = None	)      -> List[Any]:







        """simple docstring"""
        super().__init__(_lowercase       ,   _lowercase       ,   _lowercase	)
        A_				 =						True




   @property
   def      lowerCamelCase__     (				self						:					Dict	)      -> Mapping[str, Mapping[int, str]]:







        """simple docstring"""

        if self.task == "multiple-choice":
             A_				 =						{0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
             A_				 =						{0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("input_ids", dynamic_axis),
                ("attention_mask", dynamic_axis),
                ("global_attention_mask", dynamic_axis),
            ]	)




   @property
   def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> Mapping[str, Mapping[int, str]]:







        """simple docstring"""
        A_				 =						super().outputs
        if self.task == "default":
             A_				 =						{0: """batch"""}
        return outputs




   @property
   def      lowerCamelCase__     (				self						:					Any	)      -> float:







        """simple docstring"""
        return 1e-4




   @property
   def      lowerCamelCase__     (				self						:					Tuple	)      -> int:







        """simple docstring"""
        return max(super().default_onnx_opset       ,   14	)






   def      lowerCamelCase__     (				self						:					str       ,   _snake_case						:					int       ,   _snake_case						:					int = -1       ,   _snake_case						:					Dict = -1       ,   _snake_case						:					Optional[Any] = False       ,   _snake_case						:					List[Any] = None       ,   )      -> Mapping[str, Any]:







        """simple docstring"""
        A_				 =						super().generate_dummy_inputs(
            preprocessor=_lowercase       ,   batch_size=_lowercase       ,   seq_length=_lowercase       ,   is_pair=_lowercase       ,   framework=_lowercase	)
        import torch

        # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
        # makes the export fail randomly
        A_				 =						torch.zeros_like(inputs["input_ids"]	)
        # make every second token global
        A_				 =						1

        return inputs






"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


UpperCamelCase_					:						Any				= logging.get_logger(__name__)

UpperCamelCase_					:						Any				= {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase_					:						List[str]				= {
    '''vocab_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCamelCase_					:						Union[str, Any]				= {
    '''yjernite/retribert-base-uncased''': 512,
}


UpperCamelCase_					:						str				= {
    '''yjernite/retribert-base-uncased''': {'''do_lower_case''': True},
}



class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			snake_case	      =				VOCAB_FILES_NAMES
			snake_case	      =				PRETRAINED_VOCAB_FILES_MAP
			snake_case	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case	      =				PRETRAINED_INIT_CONFIGURATION
			snake_case	      =				RetriBertTokenizer
			snake_case	      =				["input_ids", "attention_mask"]




			def __init__(				self						:					Tuple       ,   _snake_case						:					int=None       ,   _snake_case						:					Any=None       ,   _snake_case						:					str=True       ,   _snake_case						:					List[Any]="[UNK]"       ,   _snake_case						:					Optional[Any]="[SEP]"       ,   _snake_case						:					str="[PAD]"       ,   _snake_case						:					List[str]="[CLS]"       ,   _snake_case						:					List[str]="[MASK]"       ,   _snake_case						:					Dict=True       ,   _snake_case						:					Optional[int]=None       ,   **_snake_case						:					Tuple       ,   )      -> Union[str, Any]:







								"""simple docstring"""
								super().__init__(
								    _snake_case       ,   tokenizer_file=_snake_case       ,   do_lower_case=_snake_case       ,   unk_token=_snake_case       ,   sep_token=_snake_case       ,   pad_token=_snake_case       ,   cls_token=_snake_case       ,   mask_token=_snake_case       ,   tokenize_chinese_chars=_snake_case       ,   strip_accents=_snake_case       ,   **_snake_case       ,   )

								A_				 =						json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
								if (
								    normalizer_state.get("lowercase"       ,   _snake_case	) != do_lower_case
								    or normalizer_state.get("strip_accents"       ,   _snake_case	) != strip_accents
								    or normalizer_state.get("handle_chinese_chars"       ,   _snake_case	) != tokenize_chinese_chars
								):
													A_				 =						getattr(_snake_case       ,   normalizer_state.pop("type"	)	)
													A_				 =						do_lower_case
													A_				 =						strip_accents
													A_				 =						tokenize_chinese_chars
													A_				 =						normalizer_class(**_snake_case	)

								A_				 =						do_lower_case




			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					List[Any]       ,   _snake_case						:					Union[str, Any]=None	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
													output += token_ids_a + [self.sep_token_id]

								return output




			def      lowerCamelCase__     (				self						:					Any       ,   _snake_case						:					List[int]       ,   _snake_case						:					Optional[List[int]] = None	)      -> List[int]:







								"""simple docstring"""
								A_				 =						[self.sep_token_id]
								A_				 =						[self.cls_token_id]
								if token_ids_a is None:
													return len(cls + token_ids_a + sep	) * [0]
								return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]






			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					str       ,   _snake_case						:					Optional[str] = None	)      -> Tuple[str]:







								"""simple docstring"""
								A_				 =						self._tokenizer.model.save(_snake_case       ,   name=_snake_case	)
								return tuple(_snake_case	)




