





'''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					    =	logging.get_logger(__name__)

__UpperCamelCase					    =	{
    "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  _A (					__lowercase		):
		lowercase__:   Union[str, Any]				     =   '''longformer'''

		def __init__(   self					:   List[str]  ,      __magic_name__					:   Union[List[int], int] = 5_12  ,      __magic_name__					:   int = 2  ,      __magic_name__					:   int = 1  ,      __magic_name__					:   int = 0  ,      __magic_name__					:   int = 2  ,      __magic_name__					:   int = 3_05_22  ,      __magic_name__					:   int = 7_68  ,      __magic_name__					:   int = 12  ,      __magic_name__					:   int = 12  ,      __magic_name__					:   int = 30_72  ,      __magic_name__					:   str = "gelu"  ,      __magic_name__					:   float = 0.1  ,      __magic_name__					:   float = 0.1  ,      __magic_name__					:   int = 5_12  ,      __magic_name__					:   int = 2  ,      __magic_name__					:   float = 0.02  ,      __magic_name__					:   float = 1E-12  ,      __magic_name__					:   bool = False  ,      **__magic_name__					:   str  ,      )					->    List[str]:


					"""simple docstring"""

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

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





class  _A (					__lowercase		):

		def __init__(   self					:   Dict  ,      __magic_name__					:   "PretrainedConfig"  ,      __magic_name__					:   str = "default"  ,      __magic_name__					:   "List[PatchingSpec]" = None			)					->    Any:


					"""simple docstring"""

					super().__init__(__magic_name__  ,      __magic_name__  ,      __magic_name__			)
					__snake_case					:      Optional[int]							      =     True

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


					"""simple docstring"""

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

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


					"""simple docstring"""

					__snake_case					:      int							      =     super().outputs
					if self.task == "default":
								__snake_case					:      Tuple							      =     {0: """batch"""}
					return outputs

		@property
		def 	lowercase__     (   self					:   int			)					->    float:


					"""simple docstring"""

					return 1E-4

		@property
		def 	lowercase__     (   self					:   Any			)					->    int:


					"""simple docstring"""

					return max(super().default_onnx_opset  ,      14			)

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   "PreTrainedTokenizerBase"  ,      __magic_name__					:   int = -1  ,      __magic_name__					:   int = -1  ,      __magic_name__					:   bool = False  ,      __magic_name__					:   Optional[TensorType] = None  ,      )					->    Mapping[str, Any]:


					"""simple docstring"""

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

					return inputs









'''simple docstring'''






import warnings

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




class  _A (					__lowercase		):
		lowercase__:   Any				     =   ['''image_processor''', '''tokenizer''']
		lowercase__:   Any				     =   '''CLIPImageProcessor'''
		lowercase__:   Optional[Any]				     =   ('''CLIPTokenizer''', '''CLIPTokenizerFast''')

		def __init__(   self					:   int  ,      __magic_name__					:   Dict=None  ,      __magic_name__					:   Dict=None  ,      **__magic_name__					:   Union[str, Any]			)					->    Any:


					"""simple docstring"""

					__snake_case					:      Optional[Any]							      =     None
					if "feature_extractor" in kwargs:
								warnings.warn(
								    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
								    """ instead."""  ,      __magic_name__  ,      )
								__snake_case					:      List[Any]							      =     kwargs.pop("""feature_extractor"""			)

					__snake_case					:      List[str]							      =     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__(__magic_name__  ,      __magic_name__			)

		def __call__(   self					:   int  ,      __magic_name__					:   List[str]=None  ,      __magic_name__					:   Tuple=None  ,      __magic_name__					:   Any=None  ,      **__magic_name__					:   Union[str, Any]			)					->    Optional[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:
								__snake_case					:      int							      =     self.tokenizer(__magic_name__  ,      return_tensors=__magic_name__  ,      **__magic_name__			)

					if images is not None:
								__snake_case					:      str							      =     self.image_processor(__magic_name__  ,      return_tensors=__magic_name__  ,      **__magic_name__			)

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

		def 	lowercase__     (   self					:   Optional[int]  ,      *__magic_name__					:   List[Any]  ,      **__magic_name__					:   Any			)					->    Optional[Any]:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   List[str]  ,      *__magic_name__					:   Tuple  ,      **__magic_name__					:   List[Any]			)					->    int:


					"""simple docstring"""

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

		@property
		def 	lowercase__     (   self					:   List[Any]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     self.tokenizer.model_input_names
					__snake_case					:      str							      =     self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)

		@property
		def 	lowercase__     (   self					:   int			)					->    List[str]:


					"""simple docstring"""

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

		@property
		def 	lowercase__     (   self					:   Union[str, Any]			)					->    Dict:


					"""simple docstring"""

					warnings.warn(
					    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""  ,      __magic_name__  ,      )
					return self.image_processor


