"""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		(    __snake_case ):







				"""simple docstring"""






				a			=						'longformer'





				def __init__(      self							:							Optional[int]      ,    _A							:							Union[List[int], int] = 5_1_2      ,    _A							:							int = 2      ,    _A							:							int = 1      ,    _A							:							int = 0      ,    _A							:							int = 2      ,    _A							:							int = 3_0_5_2_2      ,    _A							:							int = 7_6_8      ,    _A							:							int = 1_2      ,    _A							:							int = 1_2      ,    _A							:							int = 3_0_7_2      ,    _A							:							str = "gelu"      ,    _A							:							float = 0.1      ,    _A							:							float = 0.1      ,    _A							:							int = 5_1_2      ,    _A							:							int = 2      ,    _A							:							float = 0.02      ,    _A							:							float = 1e-12      ,    _A							:							bool = False      ,    **_A							:							Optional[int]      ,    ):



											"""simple docstring"""







											super().__init__(pad_token_id=_A      ,    **_A)

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




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				def __init__(      self							:							Tuple      ,    _A							:							"PretrainedConfig"      ,    _A							:							str = "default"      ,    _A							:							"List[PatchingSpec]" = None):



											"""simple docstring"""







											super().__init__(_A      ,    _A      ,    _A)
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    True





				@property
				def 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







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





				@property
				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    super().outputs
											if self.task == "default":
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    {0: """batch"""}
											return outputs





				@property
				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											return 1e-4





				@property
				def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



											"""simple docstring"""







											return max(super().default_onnx_opset      ,    1_4)





				def 		_lowerCAmelCase		(      self							:							Any      ,    _A							:							"PreTrainedTokenizerBase"      ,    _A							:							int = -1      ,    _A							:							int = -1      ,    _A							:							bool = False      ,    _A							:							Optional[TensorType] = None      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    super().generate_dummy_inputs(
											    preprocessor=_A      ,    batch_size=_A      ,    seq_length=_A      ,    is_pair=_A      ,    framework=_A)
											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
											_SCREAMING_SNAKE_CASE		:						str			  =    torch.zeros_like(inputs["""input_ids"""])
											# make every second token global
											_SCREAMING_SNAKE_CASE		:						str			  =    1

											return inputs



"""simple docstring"""





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						["image_processor", "tokenizer"]
				a			=						"ChineseCLIPImageProcessor"
				a			=						("BertTokenizer", "BertTokenizerFast")





				def __init__(      self							:							Dict      ,    _A							:							Tuple=None      ,    _A							:							List[Any]=None      ,    **_A							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    None
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
																		    """ instead."""      ,    _A      ,    )
																		_SCREAMING_SNAKE_CASE		:						str			  =    kwargs.pop("""feature_extractor""")

											_SCREAMING_SNAKE_CASE		:						int			  =    image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																		raise ValueError("""You need to specify an `image_processor`.""")
											if tokenizer is None:
																		raise ValueError("""You need to specify a `tokenizer`.""")

											super().__init__(_A      ,    _A)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.image_processor





				def __call__(      self							:							Optional[int]      ,    _A							:							Optional[Any]=None      ,    _A							:							Any=None      ,    _A							:							Tuple=None      ,    **_A							:							int):



											"""simple docstring"""







											if text is None and images is None:
																		raise ValueError("""You have to specify either text or images. Both cannot be none.""")

											if text is not None:
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.tokenizer(_A      ,    return_tensors=_A      ,    **_A)

											if images is not None:
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self.image_processor(_A      ,    return_tensors=_A      ,    **_A)

											if text is not None and images is not None:
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    image_features.pixel_values
																		return encoding
											elif text is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**_A)      ,    tensor_type=_A)





				def 		_lowerCAmelCase		(      self							:							str      ,    *_A							:							Any      ,    **_A							:							Any):



											"""simple docstring"""







											return self.tokenizer.batch_decode(*_A      ,    **_A)





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    *_A							:							List[Any]      ,    **_A							:							Any):



											"""simple docstring"""







											return self.tokenizer.decode(*_A      ,    **_A)





				@property
				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.tokenizer.model_input_names
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





				@property
				def 		_lowerCAmelCase		(      self							:							List[str]):



											"""simple docstring"""







											warnings.warn(
											    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""      ,    _A      ,    )
											return self.image_processor_class


