

'''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


_lowerCamelCase      					=     logging.get_logger(__name__)

_lowerCamelCase      					=     {
    """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 					_snake_case		(__SCREAMING_SNAKE_CASE):
			__A  :	Tuple        ="longformer"






			def __init__(  self			,_snake_case = 5_12			,_snake_case = 2			,_snake_case = 1			,_snake_case = 0			,_snake_case = 2			,_snake_case = 3_05_22			,_snake_case = 7_68			,_snake_case = 12			,_snake_case = 12			,_snake_case = 30_72			,_snake_case = "gelu"			,_snake_case = 0.1			,_snake_case = 0.1			,_snake_case = 5_12			,_snake_case = 2			,_snake_case = 0.02			,_snake_case = 1E-12			,_snake_case = False			,**_snake_case			,):
				super().__init__(pad_token_id=_snake_case			,**_snake_case     )

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




class 					_snake_case		(__SCREAMING_SNAKE_CASE):






			def __init__(  self			,_snake_case			,_snake_case = "default"			,_snake_case = None     ):
				super().__init__(_snake_case			,_snake_case			,_snake_case     )
				UpperCAmelCase_     :      int						=  True






			@property
			def 		UpperCamelCase__							(  self     ):
				if self.task == "multiple-choice":
					UpperCAmelCase_     :      Union[str, Any]						=  {0: "batch", 1: "choice", 2: "sequence"}
				else:
					UpperCAmelCase_     :      Union[str, Any]						=  {0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("input_ids", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				        ("global_attention_mask", dynamic_axis),
				    ]     )






			@property
			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Optional[Any]						=  super().outputs
				if self.task == "default":
					UpperCAmelCase_     :      str						=  {0: "batch"}
				return outputs






			@property
			def 		UpperCamelCase__							(  self     ):
				return 1E-4






			@property
			def 		UpperCamelCase__							(  self     ):
				# needs to be >= 14 to support tril operator
				return max(super().default_onnx_opset			,14     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case = -1			,_snake_case = -1			,_snake_case = False			,_snake_case = None			,):
				UpperCAmelCase_     :      Any						=  super().generate_dummy_inputs(
				    preprocessor=_snake_case			,batch_size=_snake_case			,seq_length=_snake_case			,is_pair=_snake_case			,framework=_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
				UpperCAmelCase_     :      Tuple						=  torch.zeros_like(inputs["input_ids"]     )
				# make every second token global
				UpperCAmelCase_     :      List[str]						=  1

				return inputs










'''simple docstring'''



from typing import TYPE_CHECKING

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


UpperCamelCase_    	=				{
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_    	=				[
          "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RoFormerForCausalLM",
          "RoFormerForMaskedLM",
          "RoFormerForMultipleChoice",
          "RoFormerForQuestionAnswering",
          "RoFormerForSequenceClassification",
          "RoFormerForTokenClassification",
          "RoFormerLayer",
          "RoFormerModel",
          "RoFormerPreTrainedModel",
          "load_tf_weights_in_roformer",
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_    	=				[
          "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRoFormerForCausalLM",
          "TFRoFormerForMaskedLM",
          "TFRoFormerForMultipleChoice",
          "TFRoFormerForQuestionAnswering",
          "TFRoFormerForSequenceClassification",
          "TFRoFormerForTokenClassification",
          "TFRoFormerLayer",
          "TFRoFormerModel",
          "TFRoFormerPreTrainedModel",
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_    	=				[
          "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "FlaxRoFormerForMaskedLM",
          "FlaxRoFormerForMultipleChoice",
          "FlaxRoFormerForQuestionAnswering",
          "FlaxRoFormerForSequenceClassification",
          "FlaxRoFormerForTokenClassification",
          "FlaxRoFormerModel",
          "FlaxRoFormerPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
      from .tokenization_roformer import RoFormerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roformer_fast import RoFormerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roformer import (
                ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoFormerForCausalLM,
                RoFormerForMaskedLM,
                RoFormerForMultipleChoice,
                RoFormerForQuestionAnswering,
                RoFormerForSequenceClassification,
                RoFormerForTokenClassification,
                RoFormerLayer,
                RoFormerModel,
                RoFormerPreTrainedModel,
                load_tf_weights_in_roformer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roformer import (
                TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRoFormerForCausalLM,
                TFRoFormerForMaskedLM,
                TFRoFormerForMultipleChoice,
                TFRoFormerForQuestionAnswering,
                TFRoFormerForSequenceClassification,
                TFRoFormerForTokenClassification,
                TFRoFormerLayer,
                TFRoFormerModel,
                TFRoFormerPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roformer import (
                FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxRoFormerForMaskedLM,
                FlaxRoFormerForMultipleChoice,
                FlaxRoFormerForQuestionAnswering,
                FlaxRoFormerForSequenceClassification,
                FlaxRoFormerForTokenClassification,
                FlaxRoFormerModel,
                FlaxRoFormerPreTrainedModel,
            )


else:
      import sys

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


