

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


a__				=		logging.get_logger(__name__)

a__				=		{
    '''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 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						int	    =    "longformer"


							def __init__(			self       ,							_a = 5_1_2       ,							_a = 2       ,							_a = 1       ,							_a = 0       ,							_a = 2       ,							_a = 3_0_5_2_2       ,							_a = 7_6_8       ,							_a = 1_2       ,							_a = 1_2       ,							_a = 3_0_7_2       ,							_a = "gelu"       ,							_a = 0.1       ,							_a = 0.1       ,							_a = 5_1_2       ,							_a = 2       ,							_a = 0.02       ,							_a = 1e-1_2       ,							_a = False       ,							**_a       ,							)    ->    Optional[Any]:
												super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE       ,							**_SCREAMING_SNAKE_CASE       )

												_a	:    int         =		attention_window
												_a	:    Dict         =		sep_token_id
												_a	:    List[str]         =		bos_token_id
												_a	:    Any         =		eos_token_id
												_a	:    Dict         =		vocab_size
												_a	:    Any         =		hidden_size
												_a	:    Tuple         =		num_hidden_layers
												_a	:    Dict         =		num_attention_heads
												_a	:    List[Any]         =		hidden_act
												_a	:    Optional[int]         =		intermediate_size
												_a	:    Union[str, Any]         =		hidden_dropout_prob
												_a	:    Tuple         =		attention_probs_dropout_prob
												_a	:    Union[str, Any]         =		max_position_embeddings
												_a	:    Optional[int]         =		type_vocab_size
												_a	:    str         =		initializer_range
												_a	:    List[str]         =		layer_norm_eps
												_a	:    Dict         =		onnx_export



class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							def __init__(			self       ,							_a       ,							_a = "default"       ,							_a = None       )    ->    Union[str, Any]:
												super().__init__(_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       )
												_a	:    int         =		True


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





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


							@property
							def 				__lowercase				(			self       )    ->    Mapping[str, Mapping[int, str]]:
												_a	:    str         =		super().outputs
												if self.task == "default":
																	_a	:    List[Any]         =		{0: '''batch'''}
												return outputs


							@property
							def 				__lowercase				(			self       )    ->    float:
												return 1e-4


							@property
							def 				__lowercase				(			self       )    ->    int:
												return max(super().default_onnx_opset       ,							1_4       )




							def 				__lowercase				(			self       ,							_a       ,							_a = -1       ,							_a = -1       ,							_a = False       ,							_a = None       ,							)    ->    Mapping[str, Any]:
												_a	:    Any         =		super().generate_dummy_inputs(
												    preprocessor=_SCREAMING_SNAKE_CASE       ,							batch_size=_SCREAMING_SNAKE_CASE       ,							seq_length=_SCREAMING_SNAKE_CASE       ,							is_pair=_SCREAMING_SNAKE_CASE       ,							framework=_SCREAMING_SNAKE_CASE       )
												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	:    List[str]         =		torch.zeros_like(inputs['''input_ids''']       )
												# make every second token global
												_a	:    Dict         =		1

												return inputs







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase								=	{
    """configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
    """tokenization_roberta""": ["""RobertaTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	["""RobertaTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """RobertaForCausalLM""",
         """RobertaForMaskedLM""",
         """RobertaForMultipleChoice""",
         """RobertaForQuestionAnswering""",
         """RobertaForSequenceClassification""",
         """RobertaForTokenClassification""",
         """RobertaModel""",
         """RobertaPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFRobertaForCausalLM""",
         """TFRobertaForMaskedLM""",
         """TFRobertaForMultipleChoice""",
         """TFRobertaForQuestionAnswering""",
         """TFRobertaForSequenceClassification""",
         """TFRobertaForTokenClassification""",
         """TFRobertaMainLayer""",
         """TFRobertaModel""",
         """TFRobertaPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """FlaxRobertaForCausalLM""",
         """FlaxRobertaForMaskedLM""",
         """FlaxRobertaForMultipleChoice""",
         """FlaxRobertaForQuestionAnswering""",
         """FlaxRobertaForSequenceClassification""",
         """FlaxRobertaForTokenClassification""",
         """FlaxRobertaModel""",
         """FlaxRobertaPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
     from .tokenization_roberta import RobertaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_roberta_fast import RobertaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_roberta import (
              ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              RobertaForCausalLM,
              RobertaForMaskedLM,
              RobertaForMultipleChoice,
              RobertaForQuestionAnswering,
              RobertaForSequenceClassification,
              RobertaForTokenClassification,
              RobertaModel,
              RobertaPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_roberta import (
              TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFRobertaForCausalLM,
              TFRobertaForMaskedLM,
              TFRobertaForMultipleChoice,
              TFRobertaForQuestionAnswering,
              TFRobertaForSequenceClassification,
              TFRobertaForTokenClassification,
              TFRobertaMainLayer,
              TFRobertaModel,
              TFRobertaPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_roberta import (
              FlaxRobertaForCausalLM,
              FlaxRobertaForMaskedLM,
              FlaxRobertaForMultipleChoice,
              FlaxRobertaForQuestionAnswering,
              FlaxRobertaForSequenceClassification,
              FlaxRobertaForTokenClassification,
              FlaxRobertaModel,
              FlaxRobertaPreTrainedModel,
          )

else:
     import sys

     UpperCamelCase								=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




